The aim of this study is (a) to develop, test, and employ a combined method of unsupervised machine learning to objectively assess the condition of sports facility in primary schools (PSSFC) and (b) examine the examine the geographical and typological association with PSSFC. Based on the Sixth National Sports Facility Census (NSFC), six PSSFC indicators (indoor and outdoor facility included) were selected as the measurements and decomposed by using the t-stochastic neighbor embedding (t-SNE). Thereafter, the Fuzzy C-mean (FCM) algorithm was used to cluster the same type of PSSFC with selecting the optimum numbers of evaluation level. Overall 845 primary schools in Shanghai, China were recruited and tested by this combined approach of unsupervised machine learning. In addition, the two-way analysis of covariance was used to examine the location and types of school associated with PSSFC variables in each level. The combined method was found to have acceptable reliability and good interpretability, differentiating PSSFC into five gradient levels. The characteristics of PSSFC differ by the location and school type of individual school. Our findings are conducive to the regionalized and personalized intervention and promotion on the children’s physical activity (PA) upon the practical situation of particular schools.

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

Smartphone devices, particularly android devices used by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attackers that is a network of interconnected nodes operated by command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks carried Distributed denial of services (DDoS) that causes to steal sensitive data, remote access, spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet using static or dynamic analysis. In this paper, we have proposed a novel hybrid model, which is a combination of static and dynamic method that relies on machine learning to identify android botnet applications having C&C capability. The results evaluated through machine learning classifiers in which Random forest classifier outperform other ML techniques, i.e. Naïve Bayes, Support Vector Machine, and Simple logistics. Our proposed framework can achieve 97.48% accuracy in detecting such harmful applications. Furthermore, we highlight some research directions and possible solutions regarding botnet attacks for the entire community.

As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested based approaches in terms of energy consumption, network control overhead, and packet loss rate.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our compute kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager-worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a 10-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198s using 128 nodes of Piz Daint. This translates to an overall performance of 352TFLOPS. On the node-level, we provide results for two GPUs, Nvidia's Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all compute kernels and devices, demonstrating the performance portability of our approach.

The impact of instructional guidance on learning outcomes in higher biomedical education is subject of intense debate. There is the teacher-centered or traditional way of teaching (TT) and, on the other side, the notion that students learn best under minimal guidance (problem-based learning, PBL). Although the benefits of PBL are well-known, there are aspects susceptible to improvement. Hence, a format merging TT and PBL (hybrid-PBL, h-PBL) may advance education in biomedical sciences. Here, we systematically reviewed studies that employed h-PBL in higher biomedical education compared to TT and/or pure PBL. We found that h-PBL resulted in better overall students’ performance and perception than TT or pure PBL. These findings encourage more research on investigating the pedagogical benefits of h-PBL and posit an eclectic system in which the pedagogical tools from TT and PBL are used cooperatively in the best interest of the education and satisfaction of the students.

Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and healthcare practitioners. Skin lesion restoration is an essential preprocessing step for lesion enhancements for accurate automated analysis and diagnosis. Digital hair removal is a non-invasive method for image enhancement by solving the hair-occlusion artefact in previously captured images. Several hair removal methods were proposed for skin delineation and removal. However, manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or using benchmarking datasets for comparison purposes. In the presented work, we propose a realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with the ground-truth mask for hair location. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximise the synthesis quality. For quantitatively evaluate the realism of image synthesis, the t-SNE feature mapping and Bland-Altman test are employed as objective metrics. Experimental results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture.

Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space consumed and accuracy. The major enhancement of the FPBST includes converting the resultant BST to a decision tree, in order to remove the need for the slow K-nearest neighbors (KNN), and to obtain a smaller tree, which is useful for memory usage, speeding both training and testing phases and increasing the classification accuracy. The proposed decision trees are based on calculating the probabilities of each class at each node using various methods; these probabilities are then used by the testing phase to classify an unseen example. The experimental results on some (small, intermediate and big) machine learning datasets show the efficiency of the proposed methods, in terms of space, speed and accuracy compared to the FPBST, which shows great potential for further enhancements of the proposed methods to be used in practice.

Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still signicant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certied and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certication is costly and time consuming. The study specically analyses Selective Laser Melting (SLM) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as `faulty' or `acceptable'. A fully supervised approach identied faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certication data. The results show that semi-supervised learning is a promising approach for the automatic certication of AM builds that can be implemented at a fraction of the cost currently required.

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called Sparsity based Locality-Sensitive Discriminative Dictionary Learning(SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

The betel nut chewing habit of taxi drivers has shown that can promote companionship, relaxation, and emotional stability at work. 19.3% of betel nut behaviors are instigated by an invitation among drivers to engage in the habit. The objective of this study was to explore the impact of social learning, subjective norm, and self-efficacy on betel nut chewing in taxi drivers in Taiwan. We conducted a cross-sectional study on taxis’ driver of betel nut behaviour and measured their evaluations of its consequences using a self-report questionnaire. Other variables include socio-demographic characteristics and chewing-related experience. The overall variance is 44.5% in socio-demographic variable, social learning, self-efficacy, and subjective norm. Results show that the variables of affected betel nut chewing behavior, driving years has the strongest influence, followed by learning about betel nut from the media, relatives, and friends, and education level, which positively affect betel nut chewing behavior. Findings indicated that education related to quitting betel nut chewing, significant others, and rules of the taxi fleet are adopted to design a plan for quitting betel chewing to reduce the positive expectations of taxi drivers for betel nut chewing and decrease their betel nut chewing behavior.

Turnovers in the National Football League (NFL) occur whenever a team loses possession of the ball due to a fumble, or an interception. Turnovers disrupt momentum of the offensive team, and represent lost opportunities to advance downfield and score. Teams with a positive differential turnover margin in a given game win $70\%$ of the time. Turnovers are statistically rare events, occurring apparently randomly. These characteristics make them difficult to predict. This investigation advances the hypothesis that turnovers are predictable in NFL football. Machine learning models are developed to learn the concept: At any point within a football game, what is the likelihood that a turnover will be observed on the next play from scrimmage? Results presented suggest evidence to support the working hypothesis. Under certain conditions, both fumbles and interceptions can be anticipated at low false discovery rates (less than $15\%$). This approach may be useful to inform in-game strategies to mitigate the negative consequences of turnovers by an offensive team, or to maximize their probability by a defensive squad.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Based on the detailed exploration of AI techniques for the early identification of learning difficulties in education, this study underscores the pivotal role of educational data analysis in enhancing student outcomes. Educators can proactively identify and address learning challenges through systematic data collection and analysis, thereby minimizing long-term academic obstacles and emotional stress. The integration of AI, particularly through natural language processing and computer vision technologies, facilitates personalized learning paths and real-time interventions, offering tailored support that maximizes each student's learning potential. These advancements optimize educational resource allocation and foster a more inclusive and effective learning environment, paving the way for future advancements in personalized education and intelligent teaching practices.

Keywords: Artificial Intelligence in Implantology; AI in Dental Implantology; Machine Learning in Dental Implantology; Deep Learning in Implant Dentistry; Robotics in Dental Implantology; AI-Based Implant Planning.

The rapid advancements and the merging of hybrid quantum-classical computing, artificial intelligence (AI), machine learning (ML), and deep learning (DL) pose a potentially unseen and significant threat to encryption that may impact Post-Quantum Cryptography (PQC) transition timelines. There is a direct hybrid quantum-classical computing threat on cryptography, and there is also a direct threat AI/ML on cryptography. However, the synergistic combination of these technologies presents known and unknown threats that need attention, focus action, and research. This paper reviews Grover's Adaptive Search (GAS), which combines Grover's Algorithm with adaptive techniques to optimize search further, potentially making it even more efficient for attacking encryption. This work also examines the quantum-accelerated Harrow-Hassidim-Lloyd (HHL) Algorithm, designed to solve systems of linear equations exponentially faster than classical algorithms in certain conditions. The HHL algorithm can solve some lattice-based problems, which have implications for lattice-based encryption. This technological confluence and its potential impact on cryptography and encryption necessitate a proactive and coordinated approach to developing and implementing quantum-resistant AI/ML cryptographic solutions. This paper reviews the technological confluence and its potential implications for classical cryptography and PQC transition timelines and calls for further research.

The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust.

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil - water systems is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on two-phase oil - water relative permeability is still a major challenge for precise studying and evaluation of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable, accurate, simple and quick to use paradigms to predict the temperature dependency of relative permeability in oil - water systems (K_rw and K_ro). To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, including oil and water viscosities, absolute permeability and water saturation, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP based correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K_rw and K_ro, respectively. Lastly, the comparison of the performances of our correlations against those of the preexisting ones indicated the large superiority of the introduced correlations compared to previously published methods. The findings of this study can help for better understanding and studying the temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes.

Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes Stress and Strain of the alternatives that are designed to be employed in the new quantitative ROAM approach.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems in which systematic architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope up with current Information and Communication Technologies (ICT). The distributed generation in smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review on architecture for smart grids. An in depth analysis of layered and agent based architectures is presented and compared under various domains.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

As education continues to evolve in response to technological advancements, the concept of Education 5.0 has gained prominence. However, it is crucial to critically analyze the limitations and challenges of this model. This article presents a comprehensive examination of Education 5.0, identifies its drawbacks, and proposes a vision for Education 6.0. Through insightful case studies and discussions, this article aims to provide a nuanced perspective on the future of education.

Accurate prediction of investment returns is crucial for financial decision-making. Traditional models often struggle with the complexity and variability inherent in financial data. This article presents an advanced predictive model integrating three deep neural networks, pretraining, and ensemble learning to enhance prediction accuracy. By leveraging a combination of DNNs for handling different complexities of data, and a CNN for efficient feature extraction, our approach significantly outperforms traditional methods. Our model incorporates sophisticated data handling techniques, including extensive preprocessing and data augmentation, and benefits from pretraining and ensemble methods to improve robustness and performance. This comprehensive framework offers a robust solution for investment return prediction, addressing the shortcomings of existing models and providing enhanced accuracy and reliability.

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding-decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by UAVs. The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) was obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers)proposed in this paper outperforms other methods. The main evaluation indicator mIoU of HHSN-25 is higher than other methods, reaching 87.00%, and Unet is 85.50%, Unet++is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Within education, we make assumptions the correct manner of teaching a student, and with that, we assume further that regardless of the intellectual content that a student should engage with, the acculturation for the customary (and always rather traditional) means of religious apprehension for the purpose of an assumedly universal and beneficial role. This article instead promotes the view that such determinations are (1) unsuitable for modern society and (2) causal to a disjunction with a truly proper manner of education in the form of the matheticism – a term rather unfortunately applied in educational theory.

The use of digital twin and shadow concepts for industrial material processes have promoted new notions to bridge the gap between physical and cyber manufacturing processes. Hence, many multi-disciplinary areas like advanced sensor technologies, material science, data analytics, and machine learning algorithms are employed to create such hybrid systems. On the other hand, new additive manufacturing (AM) processes for metals and polymers based on novel emerging technologies have already shown a promising horizon for manufacturing of sophisticated parts with complex geometries. These processes are going through a major transformation with the emergence of digital technology, hybrid physical-data driven modelling and fast reduced models. This paper presents a fresh look at the hybrid physical-data driven and reduced order modelling (ROM) techniques for digitalization of AM processes within a digital-twin concept. The main contribution is to show the benefits of ROM and machine learning (ML) technology for process data handling, optimization\controlling and their integration into the real-time assessment of AM processes. Hence, a novel combination of efficient data solver technology and architecturally designed neural network (NN) module has been developed for the transient manufacturing processes with high heating\cooling rates. Furthermore, a real-world case study has been presented where the combination of hybrid modelling along with ROM and ML schemes are used for an industrial wire arc AM (WAAM) process.

This study investigated the effectiveness of virtual reality (VR) technology in table tennis education compared to traditional training methods. A 12-week randomized controlled trial was conducted with 120 participants divided equally between VR and traditional training groups. Performance metrics, learning motivation, and satisfaction were assessed at regular intervals. Results demonstrated significant advantages of VR training, with the VR group showing superior improvements in serve accuracy (23.5% vs. 15.8%, p < 0.001), rally endurance (increase of 8.2 vs. 5.7 shots, p < 0.01), and overall skill scores (18.7 vs. 13.2 points improvement, p < 0.001). The VR group also exhibited higher increases in learning motivation (23.5% vs. 12.8%, p < 0.001) and satisfaction (31.5% vs. 18.7%, p < 0.001). Subgroup analysis revealed particular benefits for novice players and younger participants. These findings suggest that VR technology offers a promising approach to enhance table tennis education, potentially revolutionizing sports training methodologies. Future research should focus on long-term skill retention and the optimization of VR training protocols.

Attention mechanisms have become a fundamental component of deep learning, including the field of computer vision. The key idea behind attention in computer vision is to help the model focus on the relevant spatial regions of the input image, rather than treating all regions equally. The traditional approaches to attention mechanisms in computer vision often suffer from distribution inconsistencies in the attention maps, resulting in sharp transitions that negatively affect model’s focus and lead to poor generalization on complex shapes. The problem of spatial incoherence is particularly pronounced in the task of semantic segmentation, where accurate pixel-level predictions require a detailed understanding of the spatial relationships within the image. In this paper, we propose an attention mechanism called Smooth Attention designed for convolutional neural networks to address the problem of spatial inconsistency in attention maps through multidimensional spatial smoothing. We conduct a series of experiments to evaluate the effectiveness of the proposed mechanism and demonstrate its superior performance compared to traditional methods.

This study aimed to identify whether the combined use of functional electrical stimulation (FES) reduces the motor torque of a gait exercise rehabilitation robot in spinal cord injury (SCI) and to verify the effectiveness of the developed automatic assist level adjustment in people with paraplegia. Acute and chronic SCI patients (1 case each) performed 10 min of gait exercises with and without FES using a rehabilitation robot. Reinforcement learning was used to adjust the assist level automatically. The maximum torque values and assist levels for each of the ten walking cycles when walking became steady were averaged and compared with and without FES. The motor's output torque and the assist level were measured as outcomes. The assist level adjustment allowed both the motor torque and assist level to decrease gradually to a steady state. The motor torque and the assist levels were significantly lower with the FES than without the FES under steady conditions in both cases. No adverse events were reported. The combined use of FES attenuated the motor torque of a gait exercise rehabilitation robot for SCI. Automatic assistive level adjustment is also useful for spinal cord injuries.

The fundamental period is one of the most important parameters for the design of new structures , as well as the estimation of the capacity of existing ones. However, its calculation requires the solution of an eigenvalue problem. Especially for new buildings, this can involve material and geometric parameters that are not known beforehand. Thus, to estimate it, various design codes and researchers have adopted a several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for the derivation of analytical equations for the fundamental period of Reinforced Concrete (RC) structures. The methodology is based on Machine Learning (ML) explainability techniques and specifically the so-called SHapley Additive exPlanations (SHAP) values. The novel methodology allows these equations to be constructed sequentially and in an informed manner, controlling the balance between accuracy and complexity. An extended dataset comprised of 4026 data points is employed. The results showed excellent accuracy, with a coefficient of determination R2≈0.95. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.

Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, are limited. Objectives: The aim of this work is to develop a binary outcome mixed effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aims to assess its performance and compare it to fixed effects Xgboost and traditional logistic regression models. Methods: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 Jan 2012 and 31 Mar 2019 were included. The dataset was split into two cohorts: Training/Validation (n = 157196; 2012-2016) and Holdout (n = 69891; 2017-2019). The outcome variable was 30 days mortality with hospitals considered as clustering variable. The logistic regression, mixed effects logistic regression, Xgboost and binary outcome mixed effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach. Results: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of mixed effects models. Unstandardized Xgboost BME demonstrated marked improvements in predictor power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glm) and generalized linear mixed-effects models (glmer) models followed similar results, with Xgboost models excelling also at greater sample sizes. Conclusions: These findings suggest that integrating mixed effects into machine learning models can enhance their applicability in clinical settings where sample size is small such as rare conditions.

Predictive maintenance (PdM) represents a transformative approach to ensuring the security and reliability of critical infrastructure systems. This proactive maintenance strategy leverages advanced data analytics, machine learning, and sensor technology to anticipate and address potential failures before they occur. By continuously monitoring the health of infrastructure components and analyzing performance data, predictive maintenance enables timely interventions, minimizes unplanned downtimes, and enhances overall system resilience. This paper explores the application of predictive maintenance in safeguarding critical infrastructure, focusing on its impact on security, operational efficiency, and cost-effectiveness. Case studies demonstrate how PdM has been successfully implemented across various sectors, including transportation, energy, and utilities, highlighting its role in mitigating risks and extending the lifespan of essential assets. The findings underscore the importance of integrating predictive maintenance into security strategies to protect vital infrastructure from emerging threats and ensure uninterrupted service delivery.

Recent advancements in Artificial Intelligence (AI) and generative models have significantly accelerated the evolution of internet services. In data centers, where data is often distributed across multiple machines, leveraging these machines to expedite the training of machine learning applications is common. However, this approach incurs substantial communication overhead. To mitigate this, data compression techniques such as quantization, sparsification, and dimension reduction are gaining attention, especially with iterative training algorithms that frequently exchange gradients. With the advancement of FPGA technology, FPGA-based bump-in-the-wire network interface controllers (smartNICs) have demonstrated notable benefits for data centers, matching ASIC switches in port-to-port latency and capacity. However, FPGA-based switches often have spare hardware resources beyond their switching functions, which are typically wasted. we propose a novel architecture that integrates FPGA-based switches with ensemble learning methods, utilizing these spare resources on FPGA-based network switches. This architecture employs random projection as a compression method to accelerate machine learning tasks while maintaining high accuracy. Our system accelerates the training process for Multilayer Perceptron (MLP) models with a speedup of 2.1-6.7 times across four high-dimensional datasets. In addition, it leverages the Hamming distance metric for k-Nearest Neighbors (kNN) classification, combining random projection with Hamming encoding, to offload large-scale classification tasks from downstream servers. This achieves a worst-case speedup of 2.3-8.7 times and a memory reduction of 3.9-27.7 times compared to CPU-based kNN classifiers using cosine distance. Our architecture addresses the challenges of high-dimensional data processing and exemplifies the potential of AI-driven solutions in accelerating and optimizing internet services. Moreover, the programmability of FPGAs allows the system to adopt various compression methods, extending its applicability. Offloading tasks to FPGAs can significantly reduce query response times, showcasing a crucial step towards achieving greater levels of autonomic computing and the rapid evolution of internet systems.

Fire detection is crucial for ensuring human safety and minimizing property damage. By utilizing advanced technologies, we can identify fires early, before they escalate. Autonomous fire detection systems are particularly vital in high-risk areas with minimal human presence. To address this need, we propose an automated fire-detection model based on YOLOv8. We modified the architecture of YOLOv8 and achieved impressive performances. Through comprehensive analysis of different YOLO models and their predecessors, we identified and addressed substantial gaps in this field. Also, by augmenting the framework with additional capabilities, we surpassed previous models, enabling real-time fire detection. Our proposed model achieves impressive performances, with 98% accuracy in fire detection and 97.8% in smoke detection. Moreover, recall rates for fire and smoke are 97.1% and 97.4%, respectively, with a mean average precision (mAP) accuracy of 99.1% for both cases. Finally, we applied an explainable artificial intelligence technique to interpret our proposed model and its results. This study lays the foundation for future research aimed at enhancing fire detection efficiency. The reliability of our modified model presents promising opportunities for future advancements, ensuring increased effectiveness and precision in efficient fire detection.

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3- Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning (ML) metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

The Greater Amanzule Peatland (GAP) in Ghana represents an important ecosystem facing dynamic land cover changes as a result of both natural and anthropogenic factors. This study integrates multispectral and radar remote sensing data from Landsat-7 and -8, L-band SAR, and SRTM to conduct a machine learning-based analysis of these changes from 2010 to 2020 and to predict future scenarios up to 2040. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6,570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5,965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, these do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the GAP and offer critical insights for the development of targeted conservation strategies. Additionally, the methodological framework presented provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands, applicable to other regions facing similar environmental challenges.

Building small populations mortality tables has great practical importance in actuarial applications. In recent years, several works in literature explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations: many models dealing with this problem usually use a two-population approach, modelling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small population mortality tables, a general structure for a two-step two-populations model is proposed, its main element of novelty residing in a machine-learning based approach to mortality spread estimation. In order to obtain this, Contrast Trees and related Estimation Contrast Boosting techniques have been applied. A quite general machine learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model.

This study introduces an advanced method for climate downscaling using a Super-Resolution Generative Adversarial Network (SRGAN), referred to as Elevation-integrated TEmperature and Precipitation SRGAN downscaling (E-TEPS), to enhance the spatial resolution of climate data, specifically 2m-temperature above the surface and total precipitation, from the high-resolution Euro-Mediterranean Center on Climate Change (CMCC) dataset over Italy. Traditional Numerical weather prediction (NWP) as well as global climate models (GCMs) generally lack the resolution required for local applications, and this research addresses that gap by refining GCM outputs to provide detailed local climate insights. E-TEPS incorporates elevation maps as auxiliary inputs, significantly improving the accuracy of downscaled temperature and precipitation data. Utilizing Google Cloud's computational resources, the model processes large climate datasets and runs complex training operations efficiently. The system is exceptionally fast, delivering all results in under 10 seconds. The model demonstrates superior performance over two interpolation methods (bicubic and linear), used as traditional downscaling methods, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), higher Pearson Correlation for temperature, and higher agreement values for the precipitation. Visual comparisons confirm that E-TEPS generated high-resolution outputs exhibit fewer artifacts and better detail preservation, particularly in regions with complex terrain and variable climatic conditions. These findings underscore the potential of our model in delivering precise, high-resolution climate predictions, thereby enhancing the performance of early-warning systems and supporting more effective climate-related decision-making. This paper is the first part of a two-part series; the second part will demonstrate the integration of this downscaling method with the FourCastNet global forecasting model to enhance climate predictions at a regional level, particularly focusing on high-resolution forecasts for Italy.

Our study is part of the ongoing discussion concerning influencer marketing as there seems to have been a shift and social media marketing is bigger than ever. Social media outlets are excellent vehicles for fostering relationships with consumers who can be susceptible to influencer advertising. Therefore, companies now pay for posts on users’ profiles to generate popularity for products. How this approach affects consumers’ buying behavior is an interesting research question. We reference Herbert Simon’s economics of attention as an appropriate theoretical construct for describing the reality of the internet. Based on unique survey data from 513 Polish respondents, the study applies econometric methods and supervised and unsupervised machine learning techniques to identify the primary consumer groups with different characteristics of engagement and sensitivity to influencer activities, revealing the possible drivers of purchase intent. The findings provide valuable insights into consumers’ informational preferences, demonstrating how social media influencers may attract attention and drive sales.

Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a &gt;50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

The next generation network requires not only ultra high data rate, global coverage and connectivity, but also reducing network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For a RIS-aided multiple-input and single-output (MISO) system, in this paper, to achieve the channel state information (CSI), we propose a principle component analysis (PCA) based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL) based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results verify our proposed channel estimation scheme based on PCA and beam tracking scheme based on DNN for the semi-active RIS-aided MISO systems can obtain approximate performances with much lower computational complexity.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), often resulting in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models, analyze the key geometric information in the G-code generated during CNC machining, and uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bezier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. Taking the optimization of a gear model’s process file as an example, the effectiveness of the proposed method is verified. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths, reduce machining defects, and has substantial application value.

The popularity of handheld devices for point-of-care ultrasound (POCUS) has increased in recent years due to their portability and cost-effectiveness. However, POCUS has the drawback of lower imaging quality compared to conventional ultrasound, because of hardware limitations. Improving the quality of POCUS through post-image processing would therefore be beneficial, with deep learning approaches showing promise in this regard. This review investigates the state-of-the-art progress of image enhancement using deep learning suitable for POCUS applications. A systematic search was conducted from January 2024 to February 2024 on PubMed and Scopus. From the 457 articles that were found, the full text was retrieved for 69 articles. From this selection, 15 articles were identified addressing multiple quality enhancement aspects. A disparity in the baseline performance of the low-quality input images was seen across these studies, ranging between 8.65–29.24 dB for the Peak Signal-to-Noise Ratio (PSNR) and 0.03-0.71 for the Structural Similarity Index Measure (SSIM). In six studies, where both PSNR and the SSIM metrics were reported for the baseline and the generated images a mean difference of 6.60 (SD ± 2.99) and 0.28 (SD ± 0.15) was observed for the PSNR and SSIM, respectively. The reported performances demonstrate the potential of deep-learning-based image enhancement for POCUS. However, variability in the extent of performance gain across datasets and articles was notable, and the heterogeneity across articles makes quantifying the exact improvements challenging.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep learning techniques, particularly advanced neural network architectures, in predicting photometric metallicity from time-series data. Moreover, our deep learning models demonstrated notable predictive performance over experimentation and analysis. Through GRU-based models we achieved a low mean absolute error of 0.056 and a high $R^2$ regression performance of 0.9401 measured by cross-validation, showcasing the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.

Previous literature has linked working for pay while attending university with negative academic and health outcomes, yet working students are often resilient when experiencing such adverse circumstances. This makes it crucial to explore potential psychological mechanisms which transform adverse experiences into sources of motivation and persistence for working students. We explore one mechanism – social identification – and its different foci – identifying as a student, employee, working student, or as a student of one’s discipline of study – as potential predictors of important academic (academic self-efficacy, approaches to learning, and academic achievement) and social (status in society) outcomes in three cross-sectional studies. In Study 1, part-time working hours (but not identification processes) were associated with academic self-efficacy. In Study 2, discipline identification and part-time working hours were associated with using deep approaches to learning. In Study 3, student identification was associated with increased status in society. Overall, discipline identification may be solely linked to academic outcomes, yet student identification should be explored further as a potential enhancer of social and graduate outcomes. We discuss additional mechanisms which can help to transform working students’ experiences through their social identities and suggest boundary conditions which can affect the link between these identities and important outcomes.

The development of Artificial Intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are deeply changing both from a theoretical and a practical perspective, even in the field of ship propulsion systems. Namely, conventional preventive maintenance schedules audits and checks at fixed times by using statistics on component failures, but it can be improved by a predictive maintenance based on the real component health status, which is inspected by proper sensors. In this way, time and costs are saved. More in details, data-driven models, through Machine Learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements coming from the asset, hence for a diagnosis based on the difference between expectations and observations. Then, a ML-based fault detection starts with the choice of the ML algorithm. This process is often not the consequence of an in-depth deep analysis of the different algorithms available in the literature. For that reason, the authors propose a simple procedure to support a first implementation stage of the so-called Condition Based Maintenance (CBM): a quick benchmarking of the most suitable ML algorithms useful for fault detection. The algorithms are compared by taking into account not only the algorithm output error, shown by the Mean Squared Error between predictions and data, but also the response time, which is a crucial variable for maintenance purpose.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Considering that emotional events diversify as one ages, this study formulates the Salient Episodic Memory Aggregation Hypothesis, which claims that aging induces homogenization of episodic memory and consequently increases the salience of emotional episodes. To test this hypothesis, the study focused on sadness episodic memory and conducted a web-based survey and a combined analysis consisting of natural language processing, machine learning, and network graph analysis. A sample of 2,954 adults aged between their 20s and over 70s participated in this study. They were asked to describe a sad event they had experienced and assess the psychological properties of the recalled episodes. After isolating and lemmatizing episodic words using natural language processing, vocabulary frequency was counted for each generation, and network graph analysis based on machine learning was performed to compare the structures of sadness episodes between generations. As a result, there were mainly two types of episodic words for which frequencies either declined or increased with age. The network structures of episodic words for the older generations possessed higher proximity (shortest path length) to higher concentrations of episodic words. These findings support the current hypothesis, indicating that the nature of sadness episodes differ between generations, and that older people have more aggregated episodic memory structures.

Land cover mapping, essential for understanding global land use patterns, relies on satellite imagery for monitoring changes, assessing ecosystem health, and supporting conservation ef- forts. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, and a lack of comparative studies for optimal deep learning model selection. Additionally, a scarcity of aerial datasets specifically tailored for agricultural areas exists. This study addresses these gaps by presenting a method for semantic segmentations of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the "Land Cover Aerial Imagery" (LICAID) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conduct a comparative study, employing cutting-edge deep learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately delineating land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpret-ability is essential for advancing the field of remote sensing and supporting sustainable environmental stewardship worldwide.

The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors, LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the Earth with the atmosphere. These interactions yield a flux of secondary particles that can be grouped into three distinct basic constituents: the electromagnetic, muonic, and hadronic components. In this work we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. After implemented OPTICS, a density-based clustering algorithm, to identify patterns in data collected by a single WCD, we have further refined our approach by implementing a method that categorizes and differentiates particle groups using advanced unsupervised machine learning techniques. Our analysis demonstrates that applying our enhanced methodology can accurately identify the originating particle with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of implementing machine-learning-based models throughout LAGO distributed detection network and other astroparticles observatories for semi-automated, onboard and real-time data analysis. When a particle enters a Water Cherenkov Detector (WCD), it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, this data is insufficient to effectively distinguish between the contributions of different secondary particles. This research extends our previous investigations, wherein we implemented OPTICS, a density-based clustering algorithm, to identify patterns in data collected by a single WCD. We have further refined our approach by implementing a method that categorizes and differentiates particle groups using advanced unsupervised machine learning techniques. This methodology facilitates the differentiation among particle types and utilizes the detector’s nuanced response to each particle type, thus pinpointing the principal contributors within each group. In this work, we utilize OPTICS to analyze simulated data, further validating the proposed method. We created a simulated dataset by combining the outputs of the ARTI and MEIGA frameworks. This dataset simulates the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 meters above sea level. To achieve this, real-time magnetospheric and local atmospheric conditions for February and March of 2012 were analyzed, and the resultant atmospheric secondary-particle flux was integrated into a specific MEIGA application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The output from MEIGA was modified for effective integration into our machine learning pipeline. Our analysis demonstrates that applying our enhanced methodology can accurately identify the originating particle with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of implementing machine-learning-based models throughout the LAGO distributed detection network for semi-automated, onboard and real-time data analysis. Our analysis demonstrates that OPTICS can accurately identify the originating particle with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of implementing machine-learning-based models throughout the LAGO distributed detection network for semi-automated, onboard data analysis.

: Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems, including zero-shot in-context learning scenarios. This study explores the ability of MLLMs in visually solving the Traveling Salesman Problem (TSP) and Multiple Traveling Salesman Problem (mTSP) using images that portray point distributions on a two-dimensional plane. We introduce a novel approach employing multiple specialized agents within the MLLM framework, each dedicated to optimizing solutions for these combinatorial challenges. Our experimental investigation includes rigorous evaluations across zero-shot settings and introduces innovative multi-agent zero-shot in-context scenarios. The re-sults demonstrated that both multi-agent models—Multi-Agent 1, which includes the Initializer, Critic, and Scorer agents, and Multi-Agent 2, which comprises only the Initializer and Critic agents—significantly improved solution quality for TSP and mTSP problems. Multi-Agent 1 ex-celled in environments requiring detailed route refinement and evaluation, providing a robust framework for sophisticated optimizations. In contrast, Multi-Agent 2, focusing on iterative re-finements by the Initializer and Critic, proved effective for rapid decision-making scenarios. These experiments yield promising outcomes, showcasing the robust visual reasoning capabilities of MLLMs in addressing diverse combinatorial problems. The findings underscore the potential of MLLMs as powerful tools in computational optimization, offering insights that could inspire further advancements in this promising field.

The higher education sector in Africa has experienced remarkable growth over the past decades, enrolling and graduating millions of students annually. Despite this progress, high unemployment rates among graduates and pervasive underdevelopment have raised questions about the relevance of higher education in Africa. Critics have intensely scrutinized the quality of curricula, highlighting a significant mismatch between the skills offered and the demands of the job market. This disconnect is often due to curricula that are unresponsive to both job requirements and students' needs. Enhanced pedagogical approaches are essential to address this issue. Community engagement and service learning (CE&SL) offer promising solutions by bridging the gap between theoretical knowledge and practical application. Recognizing the potential benefits, higher education institutions across the continent are gradually integrating CE and SL into their teaching and research frameworks. However, the adoption of these approaches has been slow. This paper explores the opportunities and challenges hindering the effective and widespread implementation of CE and SL in African higher education institutions and proposes practical strategies to overcome these obstacles. By embracing these innovative approaches, African universities can better prepare graduates for the dynamic job market and contribute more effectively to the continent's development.

In this work, we introduce the first pipeline that combines a refraction-aware Structure-from-Motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach the real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements on accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at 2 locations in the southern Mediterranean Sea, with varying seafloor types which demonstrate the benefits of refraction correction for the deep learning framework. We open-source our refraction-aware SfM, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.

Every year, the Montreal police are confronted with countless crimes committed by criminals. These crimes affect the quality of life of city residents and impose a socio-economic burden on the city. In this study, we conduct a comparative analysis based on several machine learning algorithms to develop a model to predict the crime category in Montreal. The performance of algorithms such as eXtreme Gradient Boosting (XGBoost), Decision Trees (DT) and Random Forest (RF) were analyzed. The performance analysis takes into account the performance metrics such as precision, accuracy, recall and F1-score. This analysis was based on crime data in Montreal from 2015 to 2023. This data is characterized by a strong imbalance between crime categories. To address the data imbalance problem, a data balancing approach based on the SMOTE-ENN algorithm was adopted. In the exploratory data analysis phase, temporal trends by crime category were highlighted. The results of the analysis showed that the XGBoost algorithm outperformed the other two. Specifically, the XGBoost algorithm achieves an accuracy of 92%, while DT and RF achieve an accuracy of 86% and 84%, respectively. As a result, XGBoost was deployed via a web application using the Flask and Swagger UI Python frameworks. This study provides the Montreal police with an effective tool to better utilize their resources in fighting crime. In addition, policymakers in the city of Montreal can use this tool to identify high-risk areas and give them more attention.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analysis based on artificial neural networks (ANN) were performed for robot manipulator systems used in the textile industry and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analysis of a 6 degree of freedom (DOF) industrial denim robot manipulator were carried out via 4 different learning algorithms of Delta-Bar-Delta (DBD), Online Back Propagation (OBP), Quick Back Propagation (QBP) and Random Back Propagation (RBP) for proposed neural network predictor. From the results obtained, it was observed that QBP based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modelling of trajectory control. In addition, 3-5-6 type ANN structure was also improved and its Root Mean Square Error (RMSE) and statistical R2 performances were compared to that of 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Predominantly, conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, whose potential has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at comfort temperature showed high motivation to perform the behaviors of Preening (P), Foraging (F), Lying down (Ld), Eating (E), and Walking (W) whose sequences &lt;{Ld,P}&gt;; &lt;{Ld,F}&gt;; &lt;{P,F,P}&gt;; &lt;{Ld,P,F}&gt; and &lt;{E,W,F}&gt; were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

The use of didactic and interactive materials is essential in generating interest among students and teachers in utilizing mathematical models and digital technologies for the educational process. These materials create a dynamic and engaging learning environment that promotes a deeper understanding of complex concepts and fosters a passion for learning. Additionally, integrating interactive materials into pilot courses can assist teachers in better assessing student learning and adjusting the teaching strategies accordingly and the teachers can receive valuable insights into students' strengths and weaknesses, allowing them to provide targeted support and intervention when needed. In this context, the purpose of this paper is to introduce an approach for solving mathematics problems embedded in technical scenarios within the defense and security fields with the aid of digital technology. In this way, students can visualize abstract concepts, experiment with different scenarios, and receive instant feedback on their understanding. This paper helps to reinforce key concepts and enhance problem-solving skills, sparking curiosity, creativity, and encouraging active participation and collaboration. Throughout the development of this proposal, good practices are adopted, and advice for improvement is collected while having a wide range of users, giving feedback, discussions, and testing (pilot) short-term learning/teaching/training activities.

Securing the structural safety of blades has become crucial, owing to the increasing size and weight of blades resulting from the recent development of large wind turbines. Composites are primarily used for blade manufacturing because of their high specific strength and specific stiff-ness. However, in composite blades, joints may experience fractures from the loads generated during wind turbine operation, leading to deformation caused by changes in the structural stiff-ness. In this study, 7,132 debonding damage data, classified by damage type, position, and size, are selected to predict debonding damage based on natural frequency. The change in the natural frequency caused by debonding damage is acquired through finite element (FE) modeling and modal analysis. Synchronization between the FE analysis model and manufactured blades is achieved through modal testing and data analysis. Finally, the relationship between the debond-ing damage and the change in natural frequency is examined using artificial neural network techniques

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Methodologies for future-oriented research are mutually beneficial to highlight different methodological perspectives and proposals for extending higher education didactics towards sustainability. This study explores how different augmented reality applications can enable new ways of teaching and learning. It systematically investigates how student teachers (n=18) in higher education experienced ongoing realities while designing learning activities for a hybrid conference and interconnecting sustainability knowings via didactic modelling and design thinking. This qualitative study aims to develop a conceptual hybrid framework concerning the implications of student teachers incorporating the design thinking and inner transition into their professional work with future-oriented methodologies on didactic modelling for sustainability commitment. With a qualitative approach data was collected during and after a hackathon-like workshop through student teachers’ reflections, post-workshop surveys, and observation field notes. The thematic analysis shed light on transgressive learning and a transition in sustainability mindset through activation of inner dimensions. Findings reinforcing sustainability commitment evolved around the following categories: being authentic (intra-personal competence), collaborating co-creatively (inter-personal competence), thinking long-term-oriented (futures-thinking competence on implementing didactics understanding), relating with creative confidence (values-thinking competence as embodied engagement), and acting based on perseverant professional knowledge driven change (bridging didactics) by connecting theory-loaded empiricism and empirically loaded theory. The results highlight some of the key features of future-oriented methodologies – and approaches to future-oriented methodologies feature collaboration, boundary crossing and exploration – and show the conditions that can support or hinder methodological development and innovation.

Accurate segmentation of the Left Ventricle (LV) in Echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing the YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed-accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction, while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO based model on a well-annotated Echo image dataset, we can leverage the model's strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31%and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care.

Turbidity is an important indicator of water quality in hydrology. More traditional ways to monitor turbidity can provide reliable results. However, they are prone to human error, have elevated costs, and lack real-time monitoring capacity. Addressing these hindrances, in this work we combine spectral bands and indices from Sentinel-2 with several machine learning paradigms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations encompassing the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML paradigms for turbidity modeling were satisfactory at locations with a larger range and standard deviation values, achieving a mean R2 value of 59.5%. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. When all the samples from the monitoring stations were used, the XGBoost provided superior output for turbidity modeling, reaching an R2 equal to 75.7%. A comprehensive comparison with the literature found values showed that the models implemented using this study’s methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.

Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulation, and between red or green light stimulation in awake volunteers with closed eyelids. This new method of EEG analysis is visionary in the understanding of visual signal processing and will facilitate anaesthetic depth research. Methods: X-gradient boosting models were used to classify the cortical response to VEP stimulation (no light vs. any light stimulation; red vs. green light stimulation). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), training across all but one participant with prediction in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulation (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96±0.05, accuracyindividual: 0.94±0.05; AUCROConeout: 0.98±0.04; accuracyo-neout:0.96±0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between green and red colour stimulation (AUCROCall: 0.97; accuracyall: 0.91; AUCROCin-dividual: 0.98±0.04, accuracyindividual: 0.96±0.04; AUCROConeout: 0.96±0.05; accuracyoneout: 0.93±0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or any light stimulation and between green or red colour stimulation using VEPs in awake female and male volunteers with eyes closed. Our findings may open new possibilities for using VEPs in the intraoperative setting.

Facial Emotion Recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This paper investigates the impact of histogram equalization, data augmentation and model optimization strategies on three well-known FER datasets using pre-trained VGG models. Additionally, this paper showcases the effectiveness of different regularization techniques, callbacks, and learning schedulers in enhancing model performance by conducting extensive experiments. The model evaluation is discussed in terms of the following metrics: Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG models exhibit state-of-the-art performance compared to more complex conventional transfer learning models resulting in accuracies of 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.

The application of space robotic manipulators and heightened autonomy for ios represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on drl to solve for the space manipulator path-planning during the motion synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-dof robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The ppo drl algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joints rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an ios mission.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

Juvenile Polyposis Syndrome (JPS) is a rare autosomal dominant disorder characterized by multiple juvenile polyps in the gastrointestinal tract, often associated with mutations in genes such as Smad4 and BMPR1A. This study explores the impact of Smad4 knockout on the development of intestinal polyps using Collaborative Cross (CC) mice, a genetically diverse model. Our results reveal a significant increase in intestinal polyps in Smad4 knockout mice across the entire population, emphasizing the broad influence of Smad4 on polyposis. Sex-specific analyses demonstrate higher polyp counts in knockout males and females compared to their WT counterparts, with distinct correlation patterns. Line-specific effects highlight the nuanced response to Smad4 knockout, underscoring the importance of genetic variability. Multimorbidity heat maps offer insights into complex relationships between polyp counts, locations, and sizes. Heritability analysis reveals a significant genetic basis for polyp counts and sizes, while machine learning models, including k-Nearest Neighbours and Linear Regression, identify key predictors, enhancing our understanding of juvenile polyposis genetics. Overall, this study provides a comprehensive understanding of the intricate genetic interplay in the context of Smad4 knockout, offering valuable insights into potential therapeutic targets for juvenile polyposis and related diseases.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in terms of predictive ability. The dataset was obtained from an observational case-control study in Northern Rwanda. Various statistical models and machine learning techniques were evaluated, including logistic regression, logistic re-gression with elastic-net regularisation, Random Forest, Extremely Randomized Trees, and gradient boosted trees with XGBoost. Random Forest emerged as the best predictive model for PPH across three different train-test data partitions. The important predictors identified in the study were haemoglobin level during labour and maternal age. However, there were differences in PPH risk factor importance in different data partitions, highlighting the need for further investigation. These findings contribute to understanding PPH risk factors, highlight the importance of considering different data partitions and implementing cross-validation in predictive modelling, and emphasise the value of identifying the appropriate prediction model for the application. Effective PPH pre-diction models are essential for improving maternal health outcomes on a global scale. This study provides valuable insights for healthcare providers to develop predictive models for PPH to identify high-risk women and implement targeted interventions

Cardiovascular diseases (CVDs) remain the leading cause of mortality. Traditional approaches to diagnosing and treating CVDs often involve interpretation, which can lead to errors. The emergence of Artificial Intelligence (AI) and Machine Learning aids the management of CVDs. AI examines amounts of data and identifies patterns offering solutions for disease detection, risk prediction, and patient care. The American Heart Association's 2023 Scientific Sessions (AHA23) showcased studies that highlight AIs potential to revolutionize cardiac healthcare. This article presents a review of these cutting-edge studies exploring how AI is applied in health, the outcomes of these applications, and their implications for clinical practice. The insights gained from this review will contribute to the discussion on AI's role in healthcare and its capacity to enhance outcomes.

When wildlife and motor vehicles collide, the result for the animals is often death (roadkill). A commercial roadkill Virtual Fence (VF) mitigation device (iPTE Traffic Solutions) was used in a field trial to test its effectiveness along a 4.9-km segment of road on Bruny Island, Tasmania. A total of 585 days of monitoring of roadkill by species was conducted, with six sections alternatively switched on or off, according to a variation of a Crossover and Multiple Before-After-Control-Impact (MBACI) experimental designs that divided monitoring into “off-on” then “on-off” blocks of periods with each “on” period followed by a 16 d “wash-out” period. For the six sections over the four periods that exclude wash-outs, the 24 aggregated values of daily counts of roadkill of Tasmanian pademelons (Thylogale billardierii), that gave a total count of 222, were modelled. Other species were killed in insufficient numbers for analysis to be informative. The statistical analysis exploited the MBACI design to estimate the VF effect using a log-odds ratio parameter (LORP) while accounting for local spatio-temporal effects. Both versions of the analysis, either averaging over the three spatial replicates (paired sections) or two temporal replicates (blocks) gave no statistically significant effect of the VF judged as a LORP estimate not sufficiently below zero. Corresponding percentage reduction estimates of 9% and 16% were derived from the LORP estimates and given parameter uncertainty estimates the power to detect as significant a LORP corresponding to a 50% reduction in rate were 0.5 and 0.6, respectively. This study confirms results from a similar field trial carried out in 2018 also in southern Tasmania and adds to the evidence that this VF is likely to give if anything only a minor reduction in roadkill for a commonly killed species for which researchers have the best chance of detecting any practically useful reduction.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

Cancer diagnosis stands at the cusp of a profound transformation, driven by the burgeoning capabilities of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review illuminates the extraordinary impact of AI and ML in the field, unraveling their multifaceted roles in the realm of oncology. In the arena of cancer diagnosis, AI and ML serve as invaluable allies, empowering healthcare professionals with unparalleled tools for precision and efficiency. Notably, AI's prowess in analyzing medical images, including radiological scans and pathology slides, elevates the early detection of malignancies to new heights. Coupled with its ability to dissect genomic data, AI tailors therapeutic strategies to the individual, promising optimized treatment outcomes. However, the incorporation of AI and ML into clinical practice necessitates a careful navigation of ethical considerations, data privacy, and regulatory landscapes. Safeguarding patient data, ensuring transparency, and addressing algorithmic biases emerge as pivotal challenges that require vigilant attention. Yet, the future of AI and ML in cancer diagnosis is brimming with promise. The integration of multi-modal data, real-time monitoring, and Explainable AI (XAI) methods holds the potential to enrich diagnostic capabilities and engender patient trust. Global collaboration and data sharing initiatives are fostering the development of robust AI models. Furthermore, AI's role in cancer prevention, identifying high-risk individuals and enabling targeted preventive strategies, is poised to revolutionize healthcare. AI and ML are forging a path toward an era of unparalleled accuracy, efficiency, and personalization in cancer diagnosis. Challenges notwithstanding, these technologies bear the promise of fundamentally reshaping patient care, elevating outcomes, and advancing the fight against cancer. The ongoing exploration and responsible implementation of AI and ML in oncology will be pivotal in harnessing their full potential and paving the way for a brighter future for cancer patients.

The rapid evolution of technology has given rise to a connected world, where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While IoT offers incredible convenience and efficiency, it presents a significant challenge in cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques are employed within Intrusion Detection Systems (IDS) to enhance their capabilities in identifying and responding to security threats. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. Experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 seconds.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the Energy transition. This study focused on exploring the feasibility of identifying and quantifying Sb mineralizations through the spectral signature of soils using reflectance spectroscopy, a non-invasive remote sensing technique, and by employing deep learning algorithms such as Convolutional Neural Networks (CNNs). Common signal preprocessing techniques were applied to the spectral data, and the soils were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite achieving high R-squared values, the study faces a significant challenge of generalization of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.

Despite widespread vaccination, early treatments, and improved understanding of the disease, the effects of SARS-CoV-2 infection remain significant worldwide. Many patients still suffer from severe COVID-19, necessitating admission to intensive care units. Remdesivir is a primary treatment option among viral RNA polymerase inhibitors for hospitalized SARS-CoV-2 patients. However, there is a lack of studies examining factors influencing its effectiveness in this context. We conducted a retrospective study throughout 2022, analyzing clinical, laboratory, and sociodemographic data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared and validated to predict factors associated with the loss of clinical benefit from remdesivir in terms of mortality and hospital stay. Data were extracted from electronic health records. The eXtreme Gradient Boost (XGB) method achieved the highest balanced accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes of remdesivir use in terms of mortality included limitation of life support treatment, need for ventilatory support (especially invasive mechanical ventilation) on day 14 after the first dose of remdesivir, lymphopenia, low levels of albumin and hemoglobin, presence of flu and/or coinfection, and cough. Factors associated with worse outcomes of remdesivir use in terms of hospital stay included the number of doses of the COVID-19 vaccine, patchy lung density, bilateral pulmonary radiological status, number of comorbidities, oxygen therapy, troponin and lactate dehydrogenase levels, and asthenia. These findings highlight the effectiveness of XGB as a strong candidate for accurately categorizing COVID-19 patients undergoing remdesivir treatment.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

Object detection is complex and involved diverse requirements for different applications. In critical application such as visual impaired navigation guide and real-time surveillance for instance, identifying a particular object of instance is required and this involved a complex approach for realization. Literatures were reviewed, starting with the application of deep learning techniques for object detection; their gaps were identified and addressed using real-time object detection models literature review. From the review gaps were also detected and addressed using literature review on occlusion detection techniques. Object of instance detection in clustered scene with similar object of interest was identified as an open gap not addressed in the existing literature; even through it is vital for many applications. This research recommended an Occlusion Based Object of Instance Detection (OBOID) technique which used the spatial information to identify instant object of interest in clustered scene with many similarly object of interest. Limitation of the recommended OBOID is that it requires only system where position and distance of object is necessary to inform other decision.