Detailed built-up area information is valuable for mapping complex urban environments. Although a large number of classification algorithms about built-up areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore we launched a unique investigation to provide a full test of the OLI imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we propose a method based on the ESA's 38-meter global built-up area data of 2014, Open Street Map and MOD13Q1-NDVI to achieve rapid and automatic generation of a large number of sample points. Our aim is to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape and texture as the input features, and SVM, random forest (RF) and AdaBoost as the classification algorithms. In feature learning, the convolution neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. Experimental results show that: (1) the approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers e.g., RF, is comparable to that of CNN. Two dimensional CNN and the 7 neighborhood RF have the highest classification accuracy of nearly 91%. (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (for example, PanTex and EMBI) can help improve classification accuracy.

This study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile statioThis study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4,525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this threat, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. Despite the important advances in UAV acquisition systems, automatic weeds detection remains a challenging problem because of its strong similarity with the crops. Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images. The results obtained are comparable to the traditional supervised training data labeling. The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.

Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative, is to resort to weather-insensitive synthetic aperture radar (SAR) images. However, in many real-world applications, critical decisions are made based on some informative spectral features, such as water, vegetation or soil indices, which cannot be extracted from SAR images. In the absence of optical sources, these data must be estimated. The current practice is to perform linear interpolation between data available at temporally close time instants. In this work, we propose to estimate missing spectral features through data fusion and deep-learning. Several sources of information are taken into account - optical sequences, SAR sequences, DEM - so as to exploit both temporal and cross-sensor dependencies. Based on these data, and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May to November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baselines methods according to all performance indicators.

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to various parameters, e.g. efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach. Thereby the Cyber-Physical Systems play an important role as sources of information to achieve context sensitivity. In this paper it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of Cyber-Physical System integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution is propos encompassing run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. This paper proposes a holistic solution to achieve context sensitivity for Flexible Manufacturing Systems, whereby the knowledge created by applying the context sensitivity approach can be used for (self-) optimization of manufacturing processes.

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. Predictive modeling of such microbiota count data for classification of human infection from parasitic worms such as helminths can help in detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously using different methods of feature reduction. To our knowledge, integrative methods such as transfer learning has not been explored in the microbiome domain yet, as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under ROC Curve (AUC) and Balanced Accuracy(Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.

Therapeutic measures aimed at optimising organ function prior to transplantation - whether by conditioning the donor after determination of brain death or by improving organ preservation after kidney removal - have the potential to enhance outcomes after transplantation. The particular advantage is that, unlike any optimised immunosuppressive therapy, a positive effect can be achieved without side effects for the organ recipient. In recent years, several such measures have been tested in controlled clinical trials on large patient cohorts following kidney transplantation. Hypothermic pulsatile machine perfusion in particular has become the focus of interest, but interventions in the donor prior to organ removal, such as the administration of low-dose dopamine until the start of cold perfusion as an example of conditioning antioxidant therapy, and therapeutic donor hypothermia in the intensive care unit after brain death confirmation have also significantly reduced the frequency of dialysis after transplantation with far less effort and cost. With regard to a favourable effect on graft survival in the long-term, the database for all procedures is less clear and controversial. The aim of this review article is to re-evaluate the available clinical evidence on the basis of the large multicentre controlled trials, which have also significantly influenced later meta-analyses, and to assess the significance for use in routine clinical practice.

Forests, integral to human civilization, hold immense value and play a vital role in maintaining ecological harmony. Despite India's goal of extensive forest coverage, significant progress is still needed. Uncontrolled forest fires pose a severe threat, particularly in Uttarakhand's Pauri Garhwal district. To address this challenge, a comprehensive study examined surface and subsurface hydrological factors influencing the forest fire occurrences, such as elevation, aspect, slope, vegetation, proximity to human settlements, proximity to waterbodies, Active faults and lineament density. A total of 15 such factors were integrated with advanced techniques of remote sensing and GIS and coupled with historical fire data to create a precise forest fire risk map using the support vector machine algorithm. Forest fire risk map was classified into 5 distinct risk zones, Very High Risk (47.38 Km2), High Risk (275.98 km2, Moderate Risk (985.49 km2), Low Risk (1741.17 km2) and Very Low Risk (2374.11 km2) aiding in proactive fire management. By embracing this innovative tool, decision-makers can protect forests, preserve biodiversity, and ensure a sustainable future for generations to come.

Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offers polynomial advantage, in contrast to an indispensable exhaustive enumeration in the corresponding classical case. The unstructured output data and the computational irreducibility of Turing machines make this algorithm impossible to approximate using heuristics. Thus, exploring the space of program-output relations is one of the most promising problems for demonstrating quantum supremacy using Grover search that cannot be dequantized. Experimental use cases for quantum acceleration are developed for self-replicating programs and algorithmic complexity of short strings. With quantum computing hardware rapidly attaining technological maturity, we discuss how this framework will have significant advantage for various genomics applications in meta-biology, phylogenetic tree analysis, protein-protein interaction mapping and synthetic biology. This is the first time experimental algorithmic information theory is implemented using quantum computation. Our implementation on the Qiskit quantum programming platform is copy-left and can be found on https://github.com/Advanced-Research-Centre/QPULBA

In recent years, numerous topologies of single phase and three phase Field Excited Flux-Switching Machine (FEFSM) have been developed for several applications. Comparative study of three types of single-phase low-priced Field Excited Flux-Switching Machine (FEFSM) is presented in this paper. Both the conventional 8S/4P sub-part rotor design and 6S/3P salient rotor design have an overlapped winding arrangements between armature coil and field excitation coil that depicts high copper losses as well as results in increased size of motor. Additionally, FEFSM with salient structure of the rotor have high flux strength in the stator-core that has much impact on high iron losses. Copper consumption and iron loss being a crucial proportion in total machine losses. Therefore a novel topology of single phase modular rotor field excited FSM with 8S/6P configuration is proposed, which enable non-overlap arrangement between armature coil and FEC winding that facilitates devaluation in the copper losses. The proposed modular rotor design acquires reduced iron losses as well as reduced active rotor mass comparatively to conventional rotor design. It is very persuasive to analyze the best range of speed for these rotors to avoid cracks and deformation, the maximum tensile strength (can be measured with principal stress in research) of the rotor analysis is conducted using JMAG. A deterministic optimization technique is used to enhance the performance of 8S/6P modular rotor design. The electromagnetic performance of conventional sub-part rotor design, F1-A3-3P design and proposed novel-modular rotor design are analyzed by 3D-Finite Element Analysis (3D-FEA), includes flux linkage, flux distribution, flux strength, back-EMF, cogging torque, torque characteristics, iron losses and efficiency.

The Mediterranean basin is a hot spot of climate change where the Posidonia oceanica (L.) Delile (PO) and other seagrass are under stress due to its effect on marine habitats and the rising influence of anthropogenic activities (tourism, fishery). The PO and seabed ecosystems, in the coastal environments of Pantelleria and Lampedusa, suffer additional growing impacts from tourism in synergy with specific stress factors due to increasing vessel traffic for supplying potable water, fossil fuels for electrical power generation. Earth Observation (EO) data, provided by high resolution (HR) multi/hyperspectral operative satellite sensors of the last generation (i.e. Sentinel 2 MSI and PRISMA) have been successfully tested, using innovative calibration and sea truth collecting methods, for monitoring and mapping of PO meadows under stress, in the coastal waters of these islands, located in the Sicily Channel, to better support the sustainable management of these vulnerable ecosystems. The area of interest in Pantelleria was where the first prototype of the Italian Inertial Sea Wave Energy Converter (ISWEC) for renewable energy production was installed in 2015, and sea truth campaigns on the PO meadows were conducted. The PO of Lampedusa coastal areas, impacted by ship traffic linked to the previous factors and tropicalization effects of Italy southernmost climate change transitional zone, was mapped through a multi/hyper spectral EO-based approach, using training/testing data provided by side scan sonar data, previously acquired. Some advanced machine learning algorithms (MLA) were successfully evaluated with different supervised regression/classification models to map seabed and PO meadow classes and related Leaf Area Index (LAI) distributions in the areas of interest, using multi/hyperspectral data atmospherically corrected via different advanced approaches.

Federated learning (FL) is a decentralized machine learning paradigm that allows multiple local clients to collaboratively train a global model by sharing their model parameters instead of private data, thereby mitigating privacy leakage. However, recent studies have shown that gradient-based data reconstruction attacks (DRA) can still expose private information by exploiting model parameters from local clients. Existing privacy-preserving FL strategies provide some defence against these attacks but at the cost of significantly reduced model accuracy. Moreover, the issue of client heterogeneity further exacerbates these FL methods, resulting in drifted global models, slower convergence, and decreased performance. This study aims to address the two main challenges of FL: data heterogeneity, particularly in Non-Identical and Independent Distributions (Non-IID) clients, and client privacy through DRA. By leveraging the Lagrange duality approach and employing a generator model to facilitate knowledge distillation (KD) between clients, thereby enhancing local model performance, this method aims to concurrently address the primary challenges encountered by FL in real-world applications.

Remote sensing image change detection is a key application technology in remote sensing. In recent years, the development of deep learning has brought new opportunities for remote sensing image change detection. However, to achieve high change detection accuracy, large-scale, good-quality labeled data is required to train the model, which is often difficult to obtain and costly. This paper proposes a semi-supervised remote sensing image change detection network based on consistent regularization for multi-scale feature fusion and reuse (SCMFM-CDNet), which can enhance change detection performance with limited labeled data. Specifically, the SCMFM-CDNet consists of two parts, the supervised stage multi-scale feature fusion and reuse network (MFM-CDNet) and the consistency regularization method of semi-supervised learning. The MFM-CDNet network uses channel attention mechanism and spatial attention mechanism to enhance the expression ability of features, and gradually completes the extraction and detection of change features through multi-scale feature fusion and reuse strategy. The consistency regularization method of semi-supervised learning adopts the teacher model and student model for alternate training, updating model parameters by minimizing the differences in their predictions, thus achieving semi-supervised learning. This paper conducted extensive experiments on three public datasets, LEVIR-CD, WHU-CD, and GoogleGZ-CD. Under the three labeling data ratios of 5%, 10%, and 20%, the F1 scores of the SCMFM-CDNet network on the LEVIR-CD dataset were 88.62, 90.78, and 90.63, respectively. On the WHU-CD dataset, the F1 scores were 88.40, 90.17, and 90.83, respectively. On the GoogleGZ-CD dataset, the F1 scores were 81.44, 82.36, and 85.40, respectively. The results indicate that the proposed network outperforms existing semi-supervised change detection methods in terms of comprehensive performance. In addition, this paper also conducted ablation experiments to evaluate the contribution of the global context module (GCM) and feature fusion reuse module (FFM) in the MFM-CDNet network. The experimental results show that these two modules are crucial for improving network performance.

Welding spot defect detection using deep learning methods provides an effective way of body-in-white quality monitoring. Based on the existing Faster R-CNN model, this paper proposed an improved faster R-CNN model for resistance welding spot surface defect inspection to improve inspection efficiency and accuracy. The model contains the following improvements. Firstly, the improved algorithm uses anchor box with higher confidence output by the RPN network to locate welding spots. When a defect is detected and the detection system is in a suspended state, the Fast R-CNN network is used to confirm the defect category and details. Secondly, a new pruning model is proposed to replace the entire backbone neural network, which unnecessary convolutional layers and connection layers are deleted, and some parameters of each hidden layer are further reduced. On the premise of ensuring detection accuracy, the parameter quantity is extremely reduced, and the speed is improved. Experiments show that the model proposed in this paper took about 15ms for one single image test, and both the detection accuracy and recall rate reached over 90% according to the test on our dataset. This deep learning model meets the requirements of welding spot defect detection.

Background and Objectives: Acute aortic dissection (AD) is a life-threatening condition and early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep-learning (DL)-based application for automated detection and prioritisation of AD on chest CT angiographies (CTAs), focusing on the reduction of scan-to-assessment (STAT) and interpretation times (IT). Materials and Methods: This retrospective Multi-Reader, Multi-Case (MRMC) study compared AD detection with and without artificial intelligence-(AI) assistance. Ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists participated as readers. All participants assessed the same CTAs without AI-assistance (pre-AI arm) and, after a 1-month washout period, with the help of the device outputs (post-AI arm). STAT and IT were compared between the two phases. Results: The study included 285 CTAs (95 per reader, per arm), with a mean patient age of 58.5 years ±14.7(SD), 52% men, 37% prevalence. AI assistance significantly reduced STAT for detecting 33 true positive AD cases, from 15.84 minutes (95% CI: 13.37–18.31min) without AI to 5.07 minutes (95% CI: 4.23–5.91min) with AI, a 68% reduction (p<0.01). IT also decreased significantly, from 21.22 seconds (95% CI: 19.87–22.58s) without AI to 14.17 seconds (95% CI: 13.39–14.95s) with AI (p<0.05). Conclusions: Integrating a DL-based algorithm for AD detection on chest CTAs significantly reduces both STAT and IT. By prioritising the most urgent cases, AI ensures faster diagnosis and improves the workflow efficiency in clinical radiology practice, compared to standard First-In First-Out (FIFO) workflow.

In the study of mountainous areas, mountain peaks play a vital role as geomorphic features that have significant influence on hydrological processes, vegetation distribution, and other important topographic characteristics. From the perspectives of geomorphology and hydrology, the river network water system is an interconnected entity that serves as a crucial component in describing regional topography, geomorphology, and hydrological features. The primary objective of this article is to extract water system information and mountain peak data in Chongli District, Hebei Province. To extract mountain peaks, coarse-resolution Digital Elevation Model (DEM) data is utilized to estimate their positions and initiate data sampling based on DEM elevations. Deep learning models are employed to avoid explicit feature extraction and instead implicitly learn from training data. By using weight sharing techniques, the training parameters of the network are reduced, achieving more accurate extraction and recognition of mountain peaks in Chongli District, along with their classification. For the extraction of the water system, the fast regional convolutional neural network algorithm from target detection is introduced to enhance the efficiency and accuracy of extracting water system information through image processing. This algorithm rapidly identifies the type, location, and width of the water systems. Additionally, the AlexNet transfer learning method is applied to locate the river skeleton within the extracted water system area. Morphological methods are then used to extract river morphology and calculate its length and width. Finally, a developed algorithm for water system information extraction detects the presence of water system coverage areas in Chongli District. Through experiments, a total of 2792 mountain peak data points were successfully extracted. In terms of water system information extraction, the Faster R-CNN and CNN-based methods demonstrate high efficiency and low detection rates. This study provides information services and data support for the operation and maintenance of the Chongli Winter Olympics. Furthermore, it serves as a reference for future research conducted by scholars in the Chongli area, offering insights into various fields such as environmental changes, vegetation classification, and geomorphic features.

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. The study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Crowd abnormal behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd, and monitor the status of the crowd system in public places in real-time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the crowd abnormal behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by crowd abnormal behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework; Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed; Finally, the full text is summarized and the future development direction of crowd abnormal behavior recognition technology is prospected.

As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.

Because the integrity, availability, and confidentiality of digital assets are so important, cybersecurity is key to both the education sector and the critical infrastructure sector. This article investigates the state of cybersecurity in education, emphasizing strategies for securing the classroom against cyberattacks. We go over important subtopics including risk assessment, putting security measures in place, and incident response preparation. Furthermore, we examine how industry-academia partnerships might improve cybersecurity resilience. We also explore cybersecurity for critical infrastructure, stressing the need for risk assessment, security control implementation, and incident reaction and recovery planning. We introduce subtopics such as public-private partnerships in critical infrastructure protection and continuous monitoring and threat detection systems. By use of an extensive examination, this article seeks to provide perspectives on efficacious cybersecurity tactics for preserving education and vital infrastructure, guaranteeing the sustained steadiness and adaptability of the community against constantly changing cyber threats.

The multi-stage, multi-level assembly job shop scheduling problem (MsMlAJSP) is commonly encountered in the manufacturing of complex customized products. Ensuring production efficiency while effectively improving energy utilization is a key focus in the industry. For the energy-efficient MsMlAJSP (EEMsMlAJSP), an improved imperialist competitive algorithm based on Q-learning (IICA-QL) is proposed to minimize the maximum completion time and total energy consumption. In IICA-QL, a decoding strategy with energy-efficient triggers based on problem characteristics is designed to ensure solution quality while effectively enhancing search efficiency. Additionally, an assimilation operation with operator parameter self-adaptation based on Q-learning is devised to overcome the challenge of balancing exploration and exploitation with fixed parameters, thus the convergence and diversity of the algorithmic search is enhanced. Finally, the effectiveness of the energy-efficient strategy decoding trigger mechanism and the operator parameter self-adaptation operation based on Q-learning is demonstrated through experimental results, and the effectiveness of IICA-QL for solving EEMsMlAJSP is verified by comparing with other algorithms.

In an era where digital technologies are integral to daily life, developing higher-order thinking skills with appropriate information and communication technology (ICT) support is crucial. The purpose of this study was to examine synergies of systems thinking and technology-enhanced learning from the perspective of flow theory. For that purpose, we surveyed more than 65 pre-service preschool teachers engaged in a design, technology, and engineering (DTE) course at the University of Ljubljana. Mapping of systems thinking revealed that pre-service preschool teachers needed support with regard to feedback and understanding the interrelationship dimen-sion of systems thinking. Predictive and mediation analyses yielded some interesting results. Those who participated in the ICT-enhanced DTE course showed higher cognitive and social engage-ment, which is correlated with better systems thinking, while their aesthetic engagement was lower. Experiencing a flow state during ICT activities positively influenced systems thinking, par-ticularly in terms of clear goals and autotelic experiences. Flow theory seems to be a solid and ap-propriate framework to use for studying synergies in technology-enhanced systems thinking for sustainable education. These findings underscore the importance of integrating systems thinking into curricula to enhance learning outcomes and prepare students for future challenges toward the achievement of Sustainable Development Goals.

To achieve real-time identification of soybean seedlings and weeds in soybean seedling fields on agricultural intelligent terminal devices, this article constructs a real-time evolutionary identification system ESWIM driven by computational complexity under few-shot training. Firstly, based on the identification task, high-quality image samples are collected and annotated. By analyzing the terrain, the weather during the growth period, image acquisition methods, and the status of equipment fieldwork, we obtained a suitable enhancement algorithm. This algorithm not only effectively simulates the diversity of actual images, but also solves the problem of significant imbalance in the number of targets. Then, using the computational complexity driven approach and YOLOv5n as the foundation model, a soybean seedling and weed recognition model SWIM is constructed. The model's computational space and time complexity are reduced by 45.19% and 42.86%, respectively, while mAP (0.5) increased by 0.1%. Finally, by utilizing human-computer interaction to enable real-time and effective evolution of the dataset, an evolutionary strategy is designed to rapidly iterate and update model parameters, resulting in the development of the evolutionary system ESWIM. Laboratory experiments have shown that the ESWIM system can quickly adjust the recognition system during device operation to achieve target identification tasks in different environments.

Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnosis of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and final ground contacts (F1 score of 99% and 91%, respectively). It enables determination of temporal and spatial gait cycle characteristics (among others stride time and stride length) with good to excellent validity at a precision sufficient to monitor clinically relevant changes in walking speed, stride-to-stride variability, and side asymmetry. This study highlights the ear as a viable site for monitoring gait and proposes its potential integration with in-ear vital sign monitoring. Such integration offers a practical approach to comprehensive health monitoring and telemedical applications, by integrating multiple sensors in a single anatomical location.

Groundnut is an oil seed cash crop, that can be consumed directly by humans and animals. It is also used as an intelligence in the industrial production of butter and other products. However, Groundnut seeds can become infected with fungi, viruses, pests, physical damage, and high heat, which can negatively impact crop yield, quality, and economic value, and pose health hazards. To address these challenges, computer vision technology detects and classifies defects. This study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images. To enhance model performance, collected images are preprocessed using novel techniques tailored to different image types. Preprocessing techniques are chosen based on image-quality evaluation metrics. Watershed segmentation is applied followed by detecting the region of interest in the image using YOLOv3. The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. The ensemble model achieves an accuracy of 96.25% with a split ratio of 10% for testing, 10% for validation, and 80% for training set. This research benefits farmers looking to improve yield, consumers of groundnut products, and researchers studying seed defects. The work provides valid insights for future researchers regarding dataset creation, augmentation, methods, and feature selection for modeling. However, the model experienced misclassifications due to the similar appearance of sample images in different classes. Future researchers should address these challenges and consider factors such as oil content and defect grading.

Every country has a different philosophy of education, which in contemporary China manifests itself in ideological guidance at all levels and in all types of schools. China's education has entered a new era, but ideological and political education is still needed in schools and colleges, and its core interest is to cultivate newcomers of the times who can take on the important task of national rejuvenation; in other words, the education sector has to ensure the sustainability of the cultivation of talents as well as the sustainability of socialist development. In this context, ideological and political education has initiated a triple spatial shift, i.e., from classroom space to cyberspace, from physical space to virtual space, and from campus space to social space. The triple spatial transfer has a profound historical logic, which is the inevitable process of the continuous development of ideological and political education itself, and the historical convergence of the Party's proposition, people's expectation and social development in this era. The requirements of the external situation, the needs of internal development, and the needs of the political task constitute the direct reality of the triple spatial transformation. The future direction of the spatial transformation of ideological and political education will be reflected in the intersection of subject and space, the collision of individuality and commonality, and the fusion of unity and diversity.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

Neural network regularized learning has garnered significant attention in recent years. We give a systematic investigation on the performance of regularized regression associated zonal translation networks. We propose the concept of Marcinkiewicz-Zygmund inequality Setting (MZIS) for the scattered nodes collected from the unit sphere. We show that, under the MZIS, the corresponding convolutional zonal translation network has reproducing property. Based on these facts,we propose a kind of kernel regularized regression learning framework and provide upper bound estimate for the learning rate with the kernel approach. We also give proof for the density of the zonal translation network with spherical Fourier analysis.We provide the approximation error with a K-functional.

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. This study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. The system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

Early disease detection is crucial for maximizingcrop yields and minimizing financial losses in agriculture. Inorder to categorise leaf diseases in pepper, tomato, and potato plants, this study suggests a revolutionary CNN design. The algorithm collects disease-specific features from input photos by using data augmentation techniques to artificially enlarge the training dataset. ReLU-activated convolutional layers gradually capture features at different levels, from low to high, while max pooling layers minimise spatial dimensionality. After thecharacteristics are extracted, a fully-connected layers with a softmax activation function classify them into illness groups. A well chosen dataset containing labelled healthy and sick leaves is used to train and validate the model. Generalizability to untesteddata is ensured via performance evaluation on an independent testing set. The field of deep learning for agricultural applications benefits from this research. This strategy has the ability to rev- olutionise agricultural practices by enabling automated disease identification. This might result in early interventions, better crop health, and eventually sustainable agricultural production.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN’s use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyper-parameters—learning rate and batch size of the online learning phase—highlights their roles in optimizing the learning process and enhancing the framework’s overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Deep learning has increasingly been employed to identify minerals. However, deep learning can only be used to identify minerals in the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, the study introduces the approach that amalgamates One-Class Support Vector Machines (OCSVM) with the ResNet architecture for the out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the earlier layers of the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set's distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event OCSVM identifies the mineral out of the training set's distribution, it is unequivocally categorized as an unclassified or 'unknown' mineral. Empirical results substantiate the method's capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

The transportation industry is rapidly transitioning from Internal Combustion Engine (ICE) based vehicles to Electric Vehicles (EVs) to promote clean energy. However, large-scale adoption of EVs can compromise the reliability of the power grids by introducing large uncertainty in the demand. Demand response with a controlled charge scheduling strategy for EVs can mitigate such issues. In this paper, a deep reinforcement learning- based charge scheduling strategy is developed for individual EVs by considering user’s dynamic driving behavior and charging preferences. The temporal dynamics of user’s anxiety about charging the EV battery is rigorously addressed. A dynamic weight allocation technique is applied to continuously tune user’s priority for charging and cost-saving with respect to charging duration. The sequential charging control problem is formulated as a Markov decision process, and an episodic approach to the deep deterministic policy gradient (DDPG) algorithm with target policy smoothing and delayed policy update techniques is applied to develop the optimal charge scheduling strategy. A real-world dataset that captures user’s driving behavior, such as arrival time, departure time, and charging duration, is utilized in this study. The extensive simulation results reveal the effectiveness of the proposed algorithm in minimizing energy cost while satisfying user’s charging requirements.

Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment. Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage and performance optimised deep learning approach to predict the capacity based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a 1-dimensional convolutional and long-short term memory neural network and processes time, voltage and incremental capacity of partial charging curves as time series. The model is cross-validated on different aging scenarios reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real world application.

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants, the pollution reduction and carbon emissions reduction of coal-fired power plants will be a key task in the future. This article proposes an optimization operation of electrostatic precipitators(ESP), based on the working mechanism and historical data of ESP, voltage-current characteristic model and outlet dust concentration prediction model are constructed by deep learning algorithm, Particle Swarm Optimization(PSO) is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate prediction of secondary current and outlet dust concentration of ESP has been achieved, the Mean Absolute Percentage Error(MAPE) of secondary current is less than 1.5%, and the MAPE of outlet dust concentration of ESP on the test set is less than 5.2%. Finally, the optimization experiment is carried out in a 330MW coal-fired power plants, the results showed that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization, according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely.

The integration of Artificial Intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in the diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish a digital representation of a patient’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-Rays into five distinct categories of lung diseases, including "Normal," "Covid," "Lung Opacity," "Pneumonia," and "Tuberculosis". The system’s performance was evaluated on a chest X-Ray dataset, demonstrating an impressive average accuracy of 96.6% across all classes. Further tests (prediction) were conducted on the trained network using a third dataset available in the literature and completely unknown to the network, yielding an average accuracy of 98% across three classes. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-Rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient’s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights for personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.

There is a great need to develop research-based models for supporting collaboration between educational institutions. Collaboration models are needed, for example, to assist the transition from lower educational level to higher education or from higher education to work life. In this research, collaboration was conducted through a joint upper-secondary school and university course that addressed global challenges by using a project-based learning approach. There is a shortage of research exploring students’ perceptions of collaboration. Therefore, the main purpose of this qualitative case study is to explore the relevance of the joint course for students and university students. The research was guided by the following research question: What kind of relevance did the students experience in the joint project-based learning course? The data was gathered using questionnaires, interviews, and reflective study reports. The data was analyzed via theory-based content analysis where research-oriented relevance model was used as the analysis framework. The analysis framework enabled categorizing experienced relevance on individual, vocational and societal dimensions. Altogether, the analysis produced 7 relevance categories for upper-secondary students and 9 for university students. According to the analysis, upper-secondary school students felt that the course offered the most on an individual relevance dimension whereas university students experienced the vocational relevance dimension most diverse. As the main conclusion, this research produced new insights on the experienced relevance of upper-secondary level and higher education cooperation from the students’ point of view. The acquired knowledge can be useful for everyone developing new cross-institutional cooperation models.

Building deep learning models proposed by third parties can become a simple task when specialized libraries are used. However, a great deal of mystery still surrounds the design of new models or the modification of existing ones. These tasks require in-depth knowledge of the different components or building blocks as well as their dimensions. This information is limited and broken up in different literature. In this article, we collect and explain in-depth the building blocks to design deep learning models, starting from the artificial neuron to the concepts involved in building deep neural networks. Furthermore, the implementation of each building block is exemplified using the Keras library.

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Auto-mated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96, on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

Proxy re-encryption (PRE) is a momentous and widely used cryptographic technique. It enables a proxy to forward ciphertext without the need of decryption. PRE has received significant interest in applications like cloud computing, blockchain, and the Internet of Things. Despite its wide range of uses, PRE has also been subject to new security and privacy regulations. In PKC’19, Cohen $et~al.$~first drew attention to the weakness in PRE's security against chosen-plaintext attacks (CPA) and put up a more stringent security concept known as security against honest re-encryption attacks (HRA). Notably, Cohen provides a beneficial conclusion as well. It is proveded that PRE schemes with re-encryption simulatability property can be elevated from CPA to HRA security. It is also proved that CPA-secure PRE schemes with re-encryption simulatability property can be directly elevated to those satisfying HRA security. However, those PRE schemes with re-encryption simulatability are almost always based on pairings. In this study, to the best of our knowledge, we directly construct HRA-secure PRE with re-encryption simulatability for the first time based on the learning with errors (LWE) assumption, which was widely believed to be quantum-resistant. Based on the re-encryption key generation algorithm and the re-encryption algorithm construction method of the above scheme, we can modify attribute-based conditional proxy re-encryption (AB-CPRE) as well as the corresponding attribute-based proxy re-encryption (AB-PRE) algorithm to make them have re-encryption simulatability properties. Finally, by using this property, We boost the security of AB-CPRE scheme of ESORICS'21 from CPA to HRA and simplify the HRA-security proof for the AB-PRE of ESORICS'21.

Background: Coronavirus disease (COVID-19) has caused one of the worst global pandemics in recent decades. It has disrupted education systems worldwide, leading to a forced shift from traditional face-to-face to blended or fully distanced learning, requiring a higher level of student readiness for self-directed learning (SDL) and a more internal locus of control (LOC). Objective: This study explored the relationship between locus of control and level of readiness for SDL among Saudi nursing students and whether the COVID-19 pandemic has impacted this relation-ship. Methods: a cross-sectional correlational descriptive study was conducted to survey 277 Saudi nursing students enrolled in the bachelor program at one of the reputable universities in Saudi Arabia. An E-questionnaire containing two scales, the Self-Directed Learning Readiness Scale for Nursing Education, and the Locus of Control Scale, was used to collect data in addition to the selected participants’ characteristics. Results: Nursing students had a moderate-to-low level of readiness for SDL (mean = 144.0), and the majority had an external LOC. There was a significant association between the locus of control and level of readiness for self-directed learning (r = 0.19*, p = 0.001), and the internal locus of control was more significantly associated with self-directed learning (r = 0.22*, p = 0.0001) than with external locus of control. Conclusion: The study findings indicate a propensity of respondents towards an external locus of control, whereas most of the respondents’ reported levels of readiness ranged between low and moderate across all dimensions of self-directed learning. This study was not registered.

Drowning remains a prominent global pediatric health concern, necessitating preventive measures such as educational initiatives for children and caregivers. This study aimed to assess the feasibility and educational effectiveness of an interactive puppet show centered on water safety for children and parents. A 30-minute original theater performance, featuring two actors and three puppets (a girl, a crab, and a lifeguard), was conducted. Subsequently, 185 children (aged 4 to 8) and their 160 parents (134 mothers and 26 fathers) participated in this quasi-experimental study. Pre- and post-show tests were administered to evaluate knowledge and behaviors in aquatic environments. Prior to the puppet show, 78% of children exhibited basic aquatic competency. Only 33% considered swimming alone risky. Following the intervention, 81.6% of children changed their perception of the risks of solo beach activities, showing improved knowledge of emergency number activation (from 63.2% to 98.9%, p&lt;0.001). The intervention increased parents' intention to visit lifeguard-patrolled beaches and improved their CPR knowledge for drowning victims by 58.8%. In conclusion, a drowning prevention puppet show positively impacts children and parents, potentially enhancing safety behaviors during water-related leisure activities, warranting consideration as part of comprehensive drowning prevention strategies.

Purpose: This study builds upon an established effective training method to investigate the advantages of high variability phonetic identification training for enhancing lexical tone perception and production in Mandarin-speaking pediatric cochlear implant (CI) recipients, who typically face ongoing challenges in these areas. Method: Thirty two Mandarin-speaking children with CIs were quasi-randomly assigned into the training group (TG) and the control group (CG). The sixteen TG participants received five sessions of high variability phonetic training (HVPT) within a period of three weeks. The CG participants did not receive the training. Perception and production of Mandarin tones were administered before (pretest) and immediately after (posttest) the completion of HVPT via lexical tone recognition task and picture naming task. Both groups participated in the identical pretest and posttest with the same time frame between the two test sessions. Results: TG showed significant improvement from pretest to posttest in identifying Mandarin tones for both trained and untrained speech stimuli. Moreover, perceptual learning of HVPT facilitated trainees’ lexical tone production as rated by a cohort of 10 Mandarin-speaking adults with normal hearing. In contrast, CG did not exhibit significant changes in either perception or production. Conclusion: The results represent initial evidence of HVPT-induced transfer of perceptual learning to lexical tone production in CI users, which supports the application of this speech training protocol to aural rehabilitation practice.

The E-learning system has achieved great development after the pandemic. In this work, we proposed three artificial intelligence-based enhancements to our linguistic interactive E-learning system from different aspects. Compared with the original phonetic transcription exam system, our enhancements include an MFCC+CNN-based disordered speech classification module, a Transformer-based Grapheme-to-Phoneme converter, and a Tacotron2-based IPA-to-Speech speech synthesis system. This work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512x512 pixels were extracted using translation lesion boxes that encompass lesions in different positions and capture different contexts of perilesional skin. In total, 16488 translation augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.

It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.

Generalized Bayes is a Bayesian approach based on the learning rate parameter η. In this study, we examine the effect of parameter η on the estimation results considering joint type-I and type-II hybrid censored samples from k exponential populations. In addition to the learning rate parameter, we consider two loss functions, the Linex and general entropy loss functions in the Bayesian approach. Monte Carlo simulations are performed to compare the performances of the estimation results under losses and different values of η. An illustrative example is performed to study the effect of the learning rate parameter and the different losses with different parameters.

Data-driven models (DDMs) are extensively used in environmental modeling but face challenges due to limited training data and potential results not adhering to physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL included an energy constraint term from W2's thermal energy equilibrium into the cost function of the LSTM, besides the mean square error term. In PGDL, parameters were optimized by penalizing deviations from the energy law, ensuring adherence to physical constraints. Compared to LSTM, PGDL demonstrated enhanced satisfaction with the energy balance and superior performance in water temperature prediction. Even with less field data for training, PGDL outperformed both LSTM and calibrated W2 after pre-training with data generated using the uncalibrated W2. Therefore, integration of DDM with a PBM ensured physical consistency in water temperature prediction for complex stratified reservoirs with limited data. Moreover, pre-training the PGDL with PBM proved highly effective in mitigating bias and variance due to insufficient field measurement data.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from visual signals. Until today, one of the biggest challenges faced by intelligent robots or machines is the matching in stereovision. In this paper, we present the details of a new principle toward achieving a robust matching solution which leverages on the use and integration of top-down image sampling strategy, hybrid feature extraction, and RCE neural network for incremental learning (i.e., cognition) as well as robust match-maker (i.e., recognition). A preliminary version of the proposed solution has been implemented and tested with data from Maritime RobotX Challenge (www.robotx.org). The contribution of this paper is to attract more research interest and effort toward this new direction which may eventually lead to the development of robust solutions expected by future stereovision systems in intelligent robots, vehicles and machines.

With metal oxide semiconductors being a promising candidate for accurate indoor air quality assessments, multiple drawbacks of the gas sensors prevent their widespread use. Examples include poor selectivity, instability over time, and sensor poisoning. Complex calibration methods and advanced operation modes can solve some of those drawbacks. However, this leads to long calibration times, which are unsuitable for mass production. In recent years, multiple attempts to solve calibration transfer have been made with the help of direct standardization, orthogonal signal correction, and many more methods. Besides those, a new promising approach is transfer learning from deep learning. This article will compare different calibration transfer methods, including direct standardization, piecewise direct standardization, transfer learning for deep learning models, and global model building. The machine learning methods to calibrate the initial models for calibration transfer are feature extraction, selection, and regression (established methods) and a custom convolutional neural network TCOCNN. It is shown that transfer learning can outperform the other calibration transfer methods regarding the root mean squared error, especially if the initial model is built with multiple sensors. It was possible to reduce the number of calibration samples by up to 99.3 % (from 10 days to approximately 2 hours) and still achieve an RMSE for acetone of around 18 ppb (15 ppb with extended individual calibration) if six different sensors were used for building the initial model. Furthermore, it was shown that the other calibration transfer methods (direct standardization and piecewise direct standardization) also work reasonably well for both machine learning approaches, primarily when multiple sensors are used for the initial model.

AVs suffer reduced maneuverability and performance due to the degradation in sensor performances in fog. Such degradation causes significant object detection errors essential for AVs' safety-critical conditions. For instance, YOLOv5 performs significantly well under favorable weather but suffers miss detections and false positives due to atmospheric scattering caused by fog particles. Existing deep object detection techniques often exhibit a high degree of accuracy. The drawback is being sluggish at object detection in fog. Object detection methods with fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persist. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar's object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image on the camera image. Using the attention mechanism, we emphasized and improved important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our result shows that the proposed method significantly enhances the detection of distant and small objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed with an accuracy of 0.849 at 69 fps.

Forest education is pivotal for achieving the Sustainable Development Goals and promoting sustainable forest management in the face of global challenges. However, existing programs struggle to keep up with rapidly changing crises and uncertainties that contribute to deforestation and forest degradation. To address these challenges, integrating innovative approaches into forest education is crucial. This paper demonstrates the transformative use of a role-playing game as an innovative teaching method for integrated forest management. The game provides students with practical experience and a comprehensive understanding of landscape approaches, using the Congo Basin as a case study. Our research focuses on sustainable forest governance, stakeholder roles, and valuing natural assets and ecosystem services, supporting the transformation of economic, political, and social/cultural relationships at various scales. We highlight the potential of innovative forest education to foster sustainability, trigger critical thinking, resolve conflicts, and prevent costly forest losses.

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed by attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.

Nowadays, the European Union and the governments of the different countries have focused on the development of the Sustainable Development Goals (SDG) and the 2030 agenda, something that has been translated into education itself. Video Games, Gamification, and Game Based Learning have become different strategies and tools to enhance the learning process and some of the growing approaches used by teachers to develop sustainable education in the classrooms. This research aims to analyze the characteristics to promote sustainability in education using games and technology, specifically its learning benefits for Higher Education. A systematic review of the literature was conducted following the PRISMA methodology. At first, 2025 documents were found which, after the filtering phases, the number of articles has been reduced to nine, which subsequently were analyzed in depth. The results indicated that among the benefits of the use of games mediated by technologies are the following: it favors education for sustainability and it promotes the educational inclusion and the work of various social skills such as collaborative and cooperative work. Also, showed an increase of the number of publications between 2019 and 2023, reflecting the growing interest in the topic. However, there are some research gap in this field.

World Health Organization (WHO) has announced the monkeypox (MPX) epidemic a global public health emergency due to its re-emergence, remarkable increase in the number of MPX cases worldwide, and its potential spread. This paper introduces the symptoms, complications, and features of MPX; its transmission, diagnosis and testing, vaccines and treatment; MPX and sexually transmitted diseases, especially the human immunodeficiency virus (HIV); possible natural hosts or reservoirs of the monkeypox virus (MPXV). A useful tool for MPX and surgical safety recommendations are presented. The challenges in fighting the MPX epidemic, One Health strategy, and future research are discussed.

Automated lip reading is a research problem that has developed considerably in recent years. Lip reading is evaluated both visually and audibly in some cases. The lip reading model is a field of use for detecting specific words using images from security cameras, but it is not possible to use audio-visual databases in this situation. It is not possible to obtain the sound input of the pronounced word in all cases. We collected a new Turkish dataset with only the image in this study. The new dataset is produced using Youtube videos, which is an uncontrolled environment. For this reason, images have difficult parameters in terms of environmental factors such as light, angle, color, and personal characteristics of the face. Despite the different features on the human face such as mustache, beard, and make-up, the visual speech recognition problem was developed on 10 classes including single words and two-word phrases using Convolutional Neural Networks (CNN) without any intervention on the data. The proposed study using only-visual data obtained a model which is automated visual speech recognition with a deep learning approach. In addition, since this study uses only-visual data, the computational cost and resource usage is less than in multi-modal studies. It is also the first known study to address the lip reading problem with a deep learning algorithm using a new dataset belonging to the Ural-Altaic languages.

In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human-computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey provides an in-depth exploration of the vision-based human action recognition field, comprehensively offering the available techniques and their evolution, highlighting the cutting-edge ideas driving its development. We also analyze the most used keywords in research papers over the past years to identify trends and predict possible future directions. Hence, this concise survey helps researchers understand the breadth of existing approaches, evaluate current research trends, and stay up-to-date on potential developments.

The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions.

Reading proficiency is requisite in our read-to-learn educational system, yet two-thirds of American students are not proficient readers. Assuring educational equity means supporting all learners with multiple component reading interventions that individually scaffold students while remediating weak literacy skills and providing intensive and sustainable intervention early. This study (N = 855) measured the efficacy of two different multiple component reading programs for students in grades three, four, and five. Grade levels of students were assigned to either the treatment intervention or the typical practice condition; and all students were pre-and post-tested using EasyCBM Reading Benchmarks. Students scoring at/below the 30^th percentile on either benchmark were also assessed with the WRMT-3 Passage Reading Comprehension and Oral Reading Fluency measures. Students in the treatment condition received Readable English and students in typical practice condition continued to receive Amplify CKLA during their regular ELA times for 45—60 hours. Students receiving Readable English significantly outperformed students in the typical practice condition on measures of oral reading fluency, reading rate, accuracy, and passage comprehension. Raw scores, growth scale values, and grade equivalents are reported, and implications for practice are discussed. In a school year fraught with pandemic instructional interruptions and learning loss, elementary students in the intervention condition averaged a year’s worth of growth in reading fluency and nine months of growth in reading comprehension compared to three- and five-months fluency and comprehension growth in the typical practice condition. Students in the Readable English condition experienced meaningful gains in reading rate and accuracy that will give exponential word reading volume dividends to students able to read text faster and more accurately going forward. This study adds to accumulating evidence that multiple component reading programs designed to reinforce fluency skills also support reading comprehension gains for all students.

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identify potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.

The Coronavirus (COVID-19) pandemic has created big challenges and opportunities in Higher Education (HE). In this situation, several universities worldwide have responded with digital methods and hybrid classes in a short period of time. The aim of this paper is to show how the universities have adapted teaching methods to digital platforms in the academic year 2020–21. This case study is based on the experience of 37 postgraduate communication students in the course Business and Communication from a Communication Programme. The objective of this course was to promote the 17 sustainable development goals (SDGs) in business and following the Principles for Responsible Management Education (PRME) of the United Nations. To do this, the students provided different solutions related to the SDGs and developed a communication strategy to inform and engage the stakeholders in the companies analyzed in a hybrid class. The results show that using this methodology and working in digital platforms, students have learned the importance of SDGs through implementing specific solutions linked to the corporate social responsibility (CSR) strategy. Based on this analysis, they also developed a communication strategy showing how companies can improve society with specific actions through the lens of the SDG perspective.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

During the last years, epigenetic processes have emerged as important factors for many neurodegenerative diseases, such as Alzheimer’s diseases (AD). These complex diseases seem to have a heritable component; however, genome-wide association studies failed to identify the genetic loci involved in the eatiology. So, how can these changes be transmitted from one gen-eration to the next? Answering this question would allow us to understand how the environ-ment can affect human populations for multiple generations and explain the high prevalence of neurodegenerative diseases, such as AD. This review pays particular attention to the relationship among epigenetics, cognition, and neurodegeneration across generations, deepening the under-standing of the relevance of heritability in neurodegenerative diseases. In fact, we highlight some recent examples of EI induced by experiences, focusing on their contribution of processes in learning and memory, to point out new targets for therapeutic interventions. Here, we first describe the prominent role of epigenetic factors in memory processing. Then, we briefly discuss aspects of EI. And ends, we summarize evidence of how epigenetic marks inherited by experi-ence and/or environmental stimuli contribute to cognitive status offspring, since better knowledge of EI can provide clues in the appearance and development of age-related cognitive decline and AD.

Deep learning (DL), which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN or ConvNet), Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM), Self-organizing Map (SOM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), Generative Adversarial Network (GAN), Deep Transfer Learning (DTL or Deep TL), Deep Reinforcement Learning (DRL or Deep RL), or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyber-attacks, e.g. denial of service (DoS), fraud detection or cyber-anomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

Estimating the remaining useful life (RUL) of components is a crucial task to enhance the reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process, to estimate the future behavior. Although many signal processing and data-driven based methods were proposed to construct the , most of the existing methods are based on manual feature extraction techniques, and need the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique is used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model is trained by the healthy operation dataset. Finally, the Mahalanobis Distance (MD) between the healthy and failure stages is measured as the . The proposed method is tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibits a monotonically increasing degradation trend and has a good performance to detect incipient faults.

The paper presents the most comprehensive and large-scale study to date on how students perceive the impacts of the COVID-19 crisis on various aspects of their lives on a global level. With a sample of 30,383 students from 62 countries, the study reveals that amid the worldwide lockdown and transition to online learning students were most satisfied with the support provided by teaching staff and their universities’ public relations. Still, deficient computer skills and the perception of a higher workload prevented them from perceiving their own improved performance in the new teaching environment. Students were mainly concerned about issues to do with their future professional career and studies, and experienced boredom, anxiety and frustration. The pandemic has led to the adoption of particular hygienic behaviours (e.g. wearing masks, washing hands) and discouraged certain daily practices (e.g. leaving home, shaking hands). Students were also more satisfied with the role played by hospitals and universities during the epidemic compared to the government and banks. The findings also show that students with selected socio-demographic characteristics (male, part-time, first level, applied sciences, lower living standard, from Africa or Asia) were generally more strongly affected by the pandemic since they were significantly less satisfied with their academic work/life. Key factors influencing students' satisfaction with the role of their university are also identified. Policymakers and higher education institutions around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

Background: Ongoing molecular profiling studies enabled by advances in biomedical technologies are producing vast amounts of ‘omic’ data for early detection, monitoring, and prognosis of diverse diseases. A major common limitation is the scarcity of biological samples, necessitating integrative modeling frameworks that can make optimal use of available data for disease classification tasks. Related data sets are often available from different studies, but may have been generated using different technology platforms. Thus, there is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate the discovery of robust biomarkers that underlie disease regulatory processes. Results: In this paper, we introduce a novel framework called Knowledge Augmented Rule Learning (KARL), which incorporates two sources of knowledge, domain, and data, for pattern discovery from small and high-dimensional datasets, such as transcriptomic data. We propose KARL as a transfer rule learning framework in which knowledge of the domain is transferred to the learning process on data in order to 1) improve the reliability of the discovered patterns, and 2) study the knowledge of the domain when used along with data for modeling. In this work, we generated KARL models on gene expression datasets for five types of cancer, including brain, breast, colon, lung, and prostate. As our knowledge of the domain, we used the Ingenuity Knowledge Base (IKB) to extract genes related to hallmarks of cancer and annotated these prior relationships before learning classifiers from these datasets. Conclusions: Our results show that KARL produces, on average, rule models that are more robust classifiers than the baseline without such background knowledge, for our tasks of cancer prediction using 25 publicly available gene expression datasets. Moreover, KARL helped us learn insights about previously known relationships in these gene expression datasets, along with new relationships not input as known, to enable informed biomarker discovery for cancer prediction tasks. KARL can be applied to modeling similar data from any other domain and classification task. Future work would involve extensions to KARL to handle hierarchical knowledge to derive more general hypotheses to drive biomedicine.

Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g. hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause for the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: 1) a long short-term memory (LSTM); 2) a proposed spectrogram-based convolutional neural network model (pCNN); and 3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (manual) feature engineering. Results were evaluated on our own, publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from "BCI Competition IV". Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for increasing our understanding of brain architecture and function, language, memory and semantics, and the nature of consciousness. In this work, we propose a novel application deep learning to model perception in grapheme-color synesthesia. Achromatic letter images, taken from database of handwritten characters, are used to induce synesthesia. Results show the model learns to accurately create a colored version of the inducing stimulus, according to a statistical distribution from experiments on a sample population of grapheme-color synesthetes. The spontaneous, creative mental imagery characteristic of the synesthetic perceptual experience is reproduced by the model. A model of synesthesia that generates testable predictions on brain activity and behavior is needed to complement large scale data collection efforts in neuroscience, especially when articulating simple descriptions of cause (stimulus) and effect (behavior). The research and modeling approach reported here begins to address this need.

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

Parallel-serial (hybrid) manipulators represent robotic systems composed of kinematic chains with parallel and serial structures. These manipulators combine the benefits of both parallel and serial mechanisms, such as increased stiffness, high positioning accuracy, and a large workspace. This study discusses the existing architectures and applications of parallel-serial robots and the methods of their design and analysis. The paper reviews around 500 articles and presents over 150 architectures of manipulators used in machining, medicine, and pick-and-place tasks, humanoids and legged systems, haptic devices, simulators, and other applications, covering both lower mobility and kinematically redundant robots. After that, the paper considers how researchers have developed and analyzed these manipulators. In particular, it examines methods of type synthesis, mobility, kinematic, and dynamic analysis, workspace and singularity determination, performance evaluation, optimal design, control, and calibration. The review concludes with a discussion of current trends in the field of parallel-serial manipulators and potential directions for future studies.

The conventional method used for calculating pavement condition index (PCI) has two major drawbacks: safety problems during pavement inspection, and human error. This paper proposes a method for removing these problems. The proposed method uses surface deflection data in falling weight Deflectometer test to estimate PCI. The data used in this study were derived from 236 pavement segments taken from Tehran-Qom freeway in Iran. The data set was analyzed using multi layers perceptron (MLP) and radial basis function (RBF) neural networks. These neural networks were optimized by levenberg-marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic (RBF-GA) algorithms. After initial modeling with four neural networks mentioned, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of analysis have been verified by the four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SD). The best reported results belonged to CMIS, including APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

Computer Vision using Deep Learning algorithms has served numerous human activity identification applications, particularly those linked to safety and security. However, despite the fact that autistic children are frequently exposed to danger as a result of their activities, many Computer Vision experts have shown little interest in their safety. High-grade autistic children frequently experience the Meltdown Crisis condition, characterized by hostile behaviors and loss of control. This study aims to introduce a monitoring system capable of predicting the Meltdown Crisis condition early and alerting the children’s parents or caregivers before entering more difficult settings. For this endeavor, the suggested system was constructed using a combination of a pre-trained Vision Transformer (ViT) model (Swin-3D-b) and a Residual Network (ResNet) architecture to extract robust features from video sequences in order to extract and learn the spatial and temporal features of the Stereotyped Motor Movements made by autistic children at the beginning of the Meltdown Crisis state, which is referred to as the Pre-Meltdown Crisis state. In order to attain a 92% recall and F1 Score, the final decisions made for data preparation, model construction, and training parameters were tweaked and established experimentally. The best loss value obtained was 0.08. The MeltdownCrisis dataset, which includes realistic scenarios of autistic children’s behaviors in the Pre-Meltdown Crisis state and Normal state the Normal state data being used as a negative class was utilized for evaluation.

ChatGPT, developed by OpenAI, is an advanced large language model (LLM) that leverages artificial intelligence (AI) and deep learning to generate human-like responses. This paper explores its applications in digital health and healthcare, focusing on enhancing patient engagement through medical history collection, symptom assessment, and decision support to improve diagnostic accuracy. We also examine its role in promoting health literacy and clinical decision-making across various organ systems. Despite its advantages, the integration of ChatGPT in healthcare poses limitations and risks, including the potential for inaccurate information, overreliance by users, and ethical concerns related to informed consent. We discuss the challenges of keeping pace with rapidly evolving medical knowledge and highlight the need for continuous updates. Additionally, the paper addresses intellectual property and regulatory issues associated with AI deployment in medicine. Through this analysis, we provide insights into the benefits and potential risks of employing ChatGPT in healthcare.

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional Intrusion Detection Systems (IDSs) have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns, as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks that addresses the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90\% accuracy across various metrics, including precision, recall, and f1-score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyber attacks, including DoS, DDoS, Data Injection, Ransomware, and several others showcasing its robustness. This research makes a great advancement to the intrusion detection systems by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models.

Background: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n=5844) was used to infer a convolutional neural network for wheal segmentation (ML model). Three methods for inferring wheal dimensions were evaluated: the ML model; the standard protocol (MA1); and approximation of the area as an ellipse using diameters measured by an allergist (MA2). The results were compared with assisted image segmentation (AIS), the most accurate method. Bland-Altman, distribution analyses, and correlation tests were applied to compare the methods. The study also compared the percentage deviation among these methods in determining the area of wheals with regular geometric shapes (n = 150) and with irregular shapes (n=150). Results: The Bland-Altman analysis showed that the difference between methods was not correlated with the absolute area. The ML model achieved a segmentation accuracy of 85.88% and a strong correlation with the AIS method (ρ=0.88), outperforming all other methods. Additionally, MA1 showed significant error (13.44±13.95%) for pseudopods. Conclusions: The ML protocol can potentially automate the reading of SPT, offering greater accuracy than the standard protocol.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could suggest patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but to be challenged in real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning based PAFNet model that integrates a sliding window technique on raw R-R interval of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onsets. This integration enable the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, specifically its effectiveness in making a new prediction with each new heartbeat. Catering to potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments was collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with a 100 R-R intervals was achieved in terms of the sensitivity of 97.12%, the specificity of 97.77%, and the accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding windows step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 minutes in advance. The present results suggest that the sliding window technique on raw R-R interval sequences along by deep learning based algorithms may offers the possibility of providing an accurate, real-time, and end-to-end clinical tool for mass monitoring of PAF.

Cold-water coral (CWC) reefs represent vulnerable marine ecosystems (VMEs) of critical ecological importance. This study presents a novel approach for the automated detection and segmentation of key CWC species, Desmophyllum pertusum and Madrepora oculata, in underwater imagery using the YOLOv8l-seg deep learning model. The model was applied to images collected at two Natura 2000 sites in the Cantabrian Sea: the Avilés Canyon System (ACS) and El Cachucho Seamount (CSM). Results demonstrate the model's high accuracy in identifying and delineating individual coral colonies. Significant variability in coral cover was observed between and within the study areas, highlighting the patchy nature of CWC habitats. Three distinct coral community groups were identified based on percentage coverage composition and abundance, with the highest coral cover group being located exclusively in the La Gaviera canyon head within the ACS. This research demonstrated the potential of deep learning models for efficient and accurate monitoring of VMEs, facilitating the acquisition of high-resolution data essential for understanding CWC distribution, abundance, and community structure, and contributing to the development of effective conservation strategies.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms—deep neural network (DNN), support vector machine (SVM), and random forest (RF)—were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R² = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.

Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.