Intestinal parasitic infections pose a significant public health challenge in Northern Nigeria, with prevalence rates ranging from 20% to 70%. Traditional diagnostic methods, primarily microscopic examination of stool samples, face limitations such as low sensitivity and high costs. This research addresses these challenges by proposing an Artificial Intelligence (AI)-based platform for the identification and counting of intestinal parasites. A total of 651 samples were collected from the 7 Northern State of Nigeria along with questionnaire collecting data on demographic, socio-economic, hygiene habits and environmental factors. Leveraging the You Only Look Once (YOLO) V8 model, trained on a dataset of 360 pre-processed and 467 annotated images, the AI model demonstrated promising performance metrics. Information-Theory-Based approaches including (CIFE, CMIM, DISR and ICAP) were used on various machine learning models (Naïve Bayes, Random Forest, Support Vector Machine and Decision Tree) to analyse the risk factors associated with the parasitic disease. The precision-recall curve, average precision, mean average precision, and F1 score indicated reliable detection and classification across various parasite classes. The accuracy (97%) and AUC (99%) scores shows that CIFE with Random Forest has the best performance indicating the significant risk factors associated with the parasitic diseases. The model exhibited a well-balanced trade-off between precision and recall, showcasing its potential as a cost-effective and accessible tool for improving the diagnosis and treatment of intestinal parasitic infections in resource-limited settings.

Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT. Although deep neural models produce state-of-the-art results in many translation tasks, they are found to under-perform on resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and NMT on two rarely-tested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration. This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation and rankings of our MT systems via a social media platform-based human evaluation scheme.

Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma and 0.98 for gonadotroph adenoma. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

This study aims to investigate the effectiveness of sustainable urban regeneration projects in mitigating Urban Heat Island (UHI). A place-based approach utilizing Geographic Information Systems (GIS) integrates spatial data and satellite imagery on the urban environment, human ac-tivity, demographics, and climate conditions, facilitating a deeper understanding of UHI dy-namics. Data-driven modelling with Machine Learning algorithms were developed to identify key variables influencing UHI effects, such as building density, greening extent, and surfaces’ albedo. A comparative pre- and post-intervention analysis in Turin (Italy), focusing on the Teksid ex-industrial area, was investigated. This analysis reveals a reduction in surface UHI intensity of -0.94 in summer and -0.54 in winter, highlighting the positive impact of mitigation strategies employed in the regeneration plan. The implications for urban and territorial policies were in-vestigated, focusing on urban planning tools for UHI mitigation. Specifically, the Local Climate Zones method was examined and compared with actual urban morphologies to find more effective and tailored solutions for policy makers and urban planners. The combined approach of geo-graphical and satellite information, Machine Learning models and Local Climate Zones joins quantitative and qualitative data, allowing to find more tailored, effective and alternative solu-tions in critical contexts such as the urban ones.

N-shot learning has emerge in recent year as potential learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample, there remains a big challenge on an actual out-of-distribution zero-day without any similarity to previously known malware family or new variant of an existing malware family. This ultimately questions the effectiveness of current state-of-the-art few-shot learning approach. In this research, we did an in-depth investigation into the performance of state-of-the-art Zero-shot, Single-shot, and few-shot learning approaches on zero-day out-of-distribution malware attack detection based on their static properties using Malimg and Malevis malware dataset. We ensure our model was aware of an out-of-distribution class during training while varying the number of samples in the out-of-distribution class accordingly zero-shot(no sample), single-shot (1 sample), few-shot(5 samples) while using confusion matrix to get the actual number of correct prediction on out-of-distribution malware validation samples. we assert that the model should be smart enough to detect and classify previously unseen data into an empty family as an out-of-distribution considering that the model was made to be aware of the existence of such distribution during training. Result shows 0, 0, and 3 correct out-of-distribution predictions on Zero-shot, single-shot, and few-shot experiments respectively, thereby showing limitation of the current state-of-the-art N-shot approaches on out-of-distribution attack.

The emergence of few-shot learning as a potential approach to address the problem of data scarcity by learning underlying pattern from a few training sample had so far given a mix-result especially on the suitability of model-agnostic meta learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample. In this research, we did an in-depth evaluation of meta- learning to determine their plausibility and suitability for previously unknown cyberattack detection by first retrieving the original research artifacts of current state of the art meta learning to repeat the experiment with original dataset before replicating the experiment with two different malware dataset which had not been previously done with meta-transfer learning. On each of the experiments, meta-transfer learning gave good results on digital character recognition dataset but abysmal result on Malimg and Malevis malware images datasets thereby indicating its unreliability for detecting cyberattacks and the need for an improvement to the state-of-the-art meta transfer learning towards a better attack detection. Transfer learning performance is independent on imbalance and hence does not influence its performance since both malware dataset used for this experiment result in high validation loss and balancing the dataset doesn't result in reduced validation loss,the successful learning transfer seen on digital character recognition dataset is not unconnected to the fact that several languages have similar characters and digits thereby enhancing the successful learning transfer unlike malware datasets, and more importantly the finding that current meta-learning transfer approach doesn't generalize well on malware dataset and hence not suitable for detecting previously unseen out-of-distribution attack.

Antisense oligonucleotide (ASO)-mediated exon skipping has emerged as a powerful tool for examining the function of genes and exons in basic research, as well as gene therapy. Computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping using machine learning. However, these methods can be computationally demanding and the prediction accuracy of the tool is not yet optimal. In this study, we propose an approach to reduce computational burden and improve prediction performance by utilizing feature selection within machine learning algorithms and employing ensemble learning techniques. The method was evaluated using a dataset of genes with experimentally validated exon skipping events. The dataset was divided into training and testing sets to assess the accuracy of the algorithm. Our results demonstrate that using a 3-way voting approach with random forest, gradient boosting, and XGBoost can significantly reduce computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2’-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. These findings suggest that this approach has the potential to enhance the efficiency and accuracy of predicting ASO efficacy via exon skipping, facilitating the development of novel therapeutic strategies.

This paper presents MADS (Multi-Agents for Data Science), an innovative and simple open-source multi-agent framework designed for systemic pipeline execution in applied supervised machine learning. By leveraging the capabilities of multi-agent systems (MAS), we introduce a universal approach to optimize and streamline machine learning pipelines. Our framework highlights the differences between various types of agents, such as reinforcement learning (RL) agents and large language model (LLM) agents, and their distinct contributions to the process. While we currently employ LLM agents to automate and enhance machine learning tasks, we acknowledge the potential of incorporating RL agents in future iterations to further improve performance and adaptability. The primary objective is to enhance the efficiency, scalability, and adaptability of supervised learning applications across various domains. This integration addresses the complexity and manual effort typically associated with machine learning workflows, paving the way for more automated, robust, and scalable solutions. Our approach demonstrates significant improvements in task automation, reduced human intervention, and enhanced model performance. The MADS framework, which will soon be available as an open-source implementation, represents a pivotal contribution to the field of machine learning, promising to facilitate broader adoption and collaborative advancement.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

In this paper we describe and exploit a geometric framework for Gibbs probability densities and the associated concepts in statistical mechanics, which unifies several earlier works on the subject, including Souriau's symplectic model of statistical mechanics, its polysymplectic extension, Koszul model, and approaches developed in quantum information geometry. We emphasize the role of equivariance with respect to Lie group actions and the role of several concepts from geometric mechanics, such as momentum maps, Casimir functions, coadjoint orbits, and Lie-Poisson brackets with cocycles, as unifying structures appearing in various applications of this framework to information geometry and machine learning. For instance, we discuss the expression of the Fisher metric in presence of equivariance and we exploit the property of the entropy of the Souriau model as a Casimir function to apply a geometric model for energy preserving entropy production. We illustrate this framework with several examples including multivariate Gaussian probability densities, and the Bogoliubov-Kubo-Mori metric as a quantum version of the Fisher metric for quantum information on coadjoint orbits. We exploit this geometric setting and Lie group equivariance to present symplectic and multisymplectic variational Lie group integration schemes for some of the equations associated to Souriau symplectic and polysymplectic models, such as the Lie-Poisson equation with cocycle.

Fine-needle aspiration cytology (FNAC) is a pivotal diagnostic tool in oncology, utilized for evaluating suspicious lesions and stratifying tumors. The incorporation of machine learning (ML) into FNAC has revolutionized accuracy, efficiency, and diagnostic precision. This comprehensive review explores recent advances in FNAC, emphasizing the transformative role of ML in image-guided sample collection and tumor stratification. Leveraging deep learning and other ML algorithms, researchers have improved diagnostic accuracy, minimized unnecessary biopsies, and optimized treatment selection. This article highlights the transformative applications of machine learning in FNAC while addressing its current limitations and future potential.

The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach ("VarioCNN"). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image data sets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.

Suicide dynamics form a complex system deeply influenced by interrelated factors, necessitating advanced methodologies for comprehensive analysis. This study pioneers the application of Particle Markov Chain Monte Carlo (PMCMC) methods in suicide research, recognized for their enhanced sampling efficiency over traditional techniques in complex, high-dimensional models. PMCMC is particularly adept at handling non-Gaussian distributions and navigating parameter spaces efficiently, preventing the entrapment in local optima. Utilizing PMCMC, our research simulated a broad spectrum of potential outcomes, clarifying the probable scenarios and their likelihoods, thus enriching the predictive accuracy and understanding of suicide dynamics. These methods facilitated the exploration of dynamic interactions among key risk factors such as mental health issues, trauma, substance use, social isolation, and access to harmful means, whose complex interplays challenge predictive modeling. The study extends previous applications of PMCMC in complex systems like H1N1, opioid crises, and COVID-19 to the domain of suicide, suggesting its potential in enhancing decision-making and intervention strategies. However, limitations due to the model's simplified assumptions and the specificity of the data to certain populations underscore the necessity for broader application to validate findings across varied demographics. In summary, while PMCMC offers robust capabilities for the dynamic modeling of suicide, it requires careful parameter selection and consideration of computational demands. Future research should continue to leverage this approach in more complex settings, enhancing our ability to predict and mitigate suicide risks effectively.

The use of biomass as a source of energy has been identified to be energy intensive which involves high cost of handling. However, pelletization reduces the bulk density of biomass, thereby reducing the cost of handling and enhancing ease of use. This study modified and evaluated an existing hand operated fish feed pelleting machine. The parts of the machine that were redesigned are hopper and power transmission unit. Corncob was used to evaluate the modified machine using die hole diameter (5,6 and 7 mm) and binder quantity (0, 2.5 and 5 wt%) as factors. The average results obtained for machine efficiency, throughput, pellet length and bulk density were 58.83%, 4.24 kg/h, 15.51 mm and 0.160 g/cm3. Die hole diameter had significant effect on pellet length only. Binder quantity had significant effect on machine efficiency, throughput and pellet length. Machine efficiency and throughput reduces as the quantity of binder increases and pellet length increases with increasing quantity of binder.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Keywords: Electrical power systems, Support vector machines, Random Forest, Machine learning, Wavelet transform, Transmission lines fault, Electrical power quality, Short circuit, Classification of faults, Localization of faults, Decision trees, Ensemble learning, K-nearest neighbors.

Global rapidly evolving events, e.g., COVID-19, are usually followed by countermeasures and policies. As a reaction, the public tends to express their emotions on social media platforms. Therefore, predicting emotional responses to events is critical to put a plan to avoid risky behaviors. This paper proposes a machine learning-based framework to detect public emotions based on social media posts in response to specific events. It presents a precise measurement of population-level emotions which can aid governance in monitoring public response and guide it to put in place strategies such as targeted monitoring of mental health, to react to a rise in negative emotions in response to lockdowns, or information campaigns, for instance in response to elevated rates of fear in response to vaccination programs. We evaluate our framework by extracting 15,455 tweets. We annotate and categorize the emotions into 11 categories based on Plutchik’s study of emotion and extract the features using a combination of Bag of Words and Term Frequency-Inverse Document Frequency. We filter 813 COVID-19 vaccine-related tweets and use them to demonstrate our framework’s effectiveness. Numerical evaluation of emotions prediction using Random Forest and Logistic Regression shows that our framework predicts emotions with an accuracy up to 95%.

Generative large language models (LLMs), e.g., ChatGPT, have demonstrated remarkable proficiency across several NLP tasks such as machine translation, question answering, text summarization, and natural language understanding. Recent research has shown that utilizing ChatGPT for assessing the quality of machine translation (MT) achieves state-of-the-art performance at the system level but performs poorly at the segment level. To further improve the performance of LLMs on MT quality assessment, we conducted an investigation into several prompting methods. Our results indicate that by combining Chain-of-Thoughts and Error Analysis, a new prompting method called Error Analysis Prompting, LLMs like ChatGPT can \textit{generate human-like MT evaluations at both the system and segment level}. Additionally, we discovered some limitations of ChatGPT as an MT evaluator, such as unstable scoring and biases when provided with multiple translations in a single query. Our findings aim to provide a preliminary experience for appropriately evaluating translation quality on ChatGPT while offering a variety of tricks in designing prompts for in-context learning. We anticipate that this report will shed new light on advancing the field of translation evaluation with LLMs by enhancing both the accuracy and reliability of metrics. The project can be found at https://github.com/Coldmist-Lu/ErrorAnalysis_Prompt.

Traffic Inspection (TI) work in this article is positioned as a specific module of road traffic with its primary function oriented towards monitoring and controlling safe traffic and the execution of significant events within a particular geographic area. Exploratory research on the significance of event execution in simple, complicated and complex traffic flow and process situations is related to the activities of monitoring and controlling functional states and performance of categorical variables, which include objects and locations of road infrastructure, communication infrastructure and networks of traffic inspection resources. It is emphasized that the words “work” and “traffic” have the semantic status as synonyms (in one world language), which is explained in the design of the Agent-based model of the complexity of content and contextual structure of TI work at the singular and plural level with 12 points of interest (POI) in the thematic research. An Event Execution Log (EEL) was created for on-site data collection with eight variables, seven of which are independent (event type, activities, objects, locations, host, duration-period and periodicity of the event) and one dependent (significance of the event) variable. The structured data set includes 10,994 input-output vectors in 970 categories collected in the EEL created by 32 human agents (traffic inspectors) over a 30-day period. An algorithmic presentation of the methodological research procedure for preprocessing and final data processing in the ensemble of machine learning models for classification and selection of TI tasks is given. Data cleaning was performed on the available dataset to increase data consistency for further processing. Vector elimination was carried out based on the Location variable, such that the total number of vectors equals the number of unique categories of this variable, which is 636. The main result of this research is the Classification modeling of the significance of events in TI work based on machine learning techniques and the Stacking ensemble. The created machine learning models for event significance classification modeling have high accuracy values. To evaluate the performance metrics of the Stacking ensemble of models, the confusion matrix, Precision, Recall, ND F1-score are used.

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a Support Vector Machine (SVM) for classification. Results showed that this approach achieved an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates the mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

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.

Quorum Sensing (QS) and Machine Learning (ML) hybrid systems represent a groundbreaking innovation in synthetic biology, offering unprecedented control and adaptability in microbial gene regulation and metabolic processes. QS, a microbial communication mechanism, is crucial for coordinating gene expression in response to population density, impacting behaviors such as biofilm formation, virulence, and resource optimization. However, traditional QS systems are constrained by their reliance on static, pre-programmed feedback loops, limiting their flexibility in dynamic, complex environments. This review highlights how integrating advanced ML algorithms—such as reinforcement learning and deep learning—into QS systems can overcome these limitations by enabling real-time data processing, predictive modeling, and dynamic feedback control. Through these innovations, QS-ML systems can autonomously adjust gene expression and metabolic outputs, making them more efficient and scalable in applications ranging from pathogen control to precision medicine and industrial biomanufacturing. Key case studies illustrate the successful deployment of QS-ML systems to combat antimicrobial resistance, optimize bio-production, and enhance therapeutic precision in cancer and immune modulation. Despite the clear advantages, challenges remain in data integration, system robustness, and regulatory oversight. Addressing these hurdles through interdisciplinary collaboration and developing scalable, multi-omics data platforms will be critical for advancing QS-ML systems from experimental settings to real-world applications. This review underscores the transformative potential of QS-ML systems in revolutionizing synthetic biology, with profound implications for personalized medicine, sustainable biomanufacturing, and environmental health.Quorum Sensing (QS) and Machine Learning (ML) hybrid systems represent a groundbreaking innovation in synthetic biology, offering unprecedented control and adaptability in microbial gene regulation and metabolic processes. QS, a microbial communication mechanism, is crucial for coordinating gene expression in response to population density, impacting behaviors such as biofilm formation, virulence, and resource optimization. However, traditional QS systems are constrained by their reliance on static, pre-programmed feedback loops, limiting their flexibility in dynamic, complex environments. This review highlights how integrating advanced ML algorithms—such as reinforcement learning and deep learning—into QS systems can overcome these limitations by enabling real-time data processing, predictive modeling, and dynamic feedback control. Through these innovations, QS-ML systems can autonomously adjust gene expression and metabolic outputs, making them more efficient and scalable in applications ranging from pathogen control to precision medicine and industrial biomanufacturing. Key case studies illustrate the successful deployment of QS-ML systems to combat antimicrobial resistance, optimize bio-production, and enhance therapeutic precision in cancer and immune modulation. Despite the clear advantages, challenges remain in data integration, system robustness, and regulatory oversight. Addressing these hurdles through interdisciplinary collaboration and developing scalable, multi-omics data platforms will be critical for advancing QS-ML systems from experimental settings to real-world applications. This review underscores the transformative potential of QS-ML systems in revolutionizing synthetic biology, with profound implications for personalized medicine, sustainable biomanufacturing, and environmental health.

This paper presents a new means to control the processes, which involve the electric energy conversion. Electric machines fed by electronic converters provide the useful power defined by the inner product of two generalized energetic variables: the effort and the speed. The novelty of the paper idea is to control the desired energetics variables by a direct Data Driven Control (DDC) law in terms of the pair effort, speed and corresponding voltage controls of the electronic converter. It must be mentioned that the same desired useful power might be obtained with different controls at different efficiencies. The regularization problem was solved by selecting from a knowledge database, the maximum efficiency operation points. Knowing a reasonable number of optimal efficiency operation points, an interpolative Radial Base Function (RBF) control was built up. The RBF algorithm can find by interpolation the optimal controls for any admissible operation points of the process. The control scheme developed for this purpose has an inner DDC loop, that perform the converter control based on measured speed and demanded torque by the outer loop which handles the speed. A comparison of the DDC with the Model Predictive Control (MPC) schemas highlights the advantages of the new proposed control.

Cloud computing transformed IT organizations with its on-demand model, notably Infrastructure as a Service (IaaS). Cloud providers manage extensive physical devices, consuming substantial energy and bandwidth for data traffic during virtual machine deployment. Balancing energy and bandwidth are critical. This thesis presents BOHGOA integrated with ACO to optimize virtual machine placement in clouds, simultaneously considering bandwidth and energy. It yields Pareto-optimal solutions for this trade-off. Using CloudSim, BOHGOA outperformed GA, ACO, and FFD algorithms, reducing bandwidth consumption by 54.14%, 32.11%, 57.47% (240 VMs) and 38.12%, 22.76%, 47.05% (500 VMs) respectively. Additionally, it decreased physical machine energy usage by 37.70%, 34.01%, 40.14% (240 VMs) and 27.50%, 22.28%, 30.52% (500 VMs) respectively. These results underscore BOHGOA's effectiveness in optimizing cloud VM placement.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as PFAS, which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a Hybrid Neural Network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures and enables the creation of classification models for binary and multiclass classification and regression. Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including Random Forest, Bootstrap Aggregating, Adaptive Boosting, Support Vector Regressor, Gradient Boosting, Kernel Ridge, Decision Tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and non-carcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieve average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multi-mixtures.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses some info-geometric applications to Machine Learning are highlighted. Closing remarks with some challenging open problems combined with the next phase of research are given.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

In order to conduct an accurate classification of the heterogeneous landscape in Jiului Valley, Romania mining basing, four machine learning algorithms (SVMs) and two common algorithms (MLC and MD) have been compared, using a temporal series of Landsat satellite images from the period 1988-2017. By using independent validation, an accuracy assessment was established together with the analysis of the differences between the classification algorithms used. Although all six algorithms used have shown a high overall accuracy (ranging from 80.29% to 93.14%) and Kappa values (from 0.77 to 0.92), SVM-RBF appears to have a higher overall applicability in describing the spatial distribution and the cover density of each land cover category. Results have indicated a large difference in classification accuracy between the SVM-RBF algorithm and commonly used algorithms, the SVM-RBF algorithms have slightly outperformed the MLC with an overall accuracy of 7.14–8.86% and by 0.0833–0.1033 kappa coefficient. On the other hand, the same algorithm have outperformed the MD by and overall accuracy of 9.71–10.86% and by 0.1133–0.1267 kappa coefficient. By using SVM-RBF, certain classified maps have been developed and used for assessing changes by post classification comparison. The results have shown an average growth of 6.5% in mined areas over the studied period.

This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence" and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which 59 were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. It was found that various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, as well as various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

In 1969, Jean-Marie Souriau introduced a “Lie Groups Thermodynamics” in Statistical Mechanics in the framework of Geometric Mechanics. This Souriau’s model considers the statistical mechanics of dynamic systems in their "space of evolution" associated to a homogeneous symplectic manifold by a Lagrange 2-form, and defines in case of non null cohomology (non equivariance of the coadjoint action on the moment map with appearance of an additional cocyle) a Gibbs density (of maximum entropy) that is covariant under the action of dynamic groups of physics (eg, Galileo's group in classical physics). Souriau Lie Group Thermodynamics was also addressed 30 years after Souriau by R. F. Streater in the framework of Quantum Physics by Information Geometry for some Lie algebras, but only in the case of null cohomology. Souriau method could then be applied on Lie Groups to define a covariant maximum entropy density by Kirillov representation theory. We will illustrate this method for homogeneous Siegel domains and more especially for Poincaré unit disk by considering SU(1,1) group coadjoint orbit and by using its Souriau’s moment map. For this case, the coadjoint action on moment map is equivariant. For non-null cohomology, we give the case of Lie group SE(2). Finally, we will propose a new geometric definition of Entropy that could be built as a generalized Casimir invariant function in coadjoint representation, and Massieu characteristic function, dual of Entropy by Legendre transform, as a generalized Casimir invariant function in adjoint representation, where Souriau cocycle is a measure of the lack of equivariance of the moment mapping.

This paper aims to explore the potential application of Extreme Learning Machines (ELM) in stock price prediction and optimise the ELM model by combining the Crown Porcupine Optimisation Algorithm and the Adaptive Bandwidth Kernel Function Density Estimation Algorithm. Specifically, the model's root mean square error (RMSE) on the test set is 0.018, which shows its good prediction accuracy. Meanwhile, the correlation coefficient R reaches 0.968, proving the model's effectiveness in predicting stock prices. In addition, the coefficient of determination R² of the training set and test set are 0.987 and 0.954, respectively, which are not much different, indicating that the model has good generalisation ability and is not prone to overfitting. This is crucial for practical application, as it implies that the model fits the existing data well and makes accurate predictions for future data. In terms of other evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the performance is excellent in both the training and test sets. Together, these results indicate that after optimising the ELM by introducing the crown porcupine optimisation algorithm and the adaptive bandwidth kernel function density estimation algorithm, the model can effectively capture the changing stock price pattern, thus achieving efficient and accurate stock price prediction. In summary, this study not only demonstrates the great potential of ELM in stock price prediction but also highlights the importance of improving the model performance through advanced algorithm optimisation, which provides a new way of thinking and methodology for financial market analysis. This strongly supports investors' decision-making and lays the foundation for related research.

Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML with the power of quantum computing holds great potential for data-driven decision-making, as well as the development of more powerful models capable of handling more complex datasets with faster processing time. This area offers potential for improving the accuracy of real-time forecasting renewable energy production. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to design, implement, and evaluate the performance of a quantum neural network forecast model for solar irradiance up to 3-hours ahead. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. Using the best quantum neural network configuration found, the proposed framework could provide competitive results when compared to its competitors, considering forecasting intervals of 5- to 120-minutes ahead, where it was the fourth best-performing paradigm. For 3-hours ahead predictions, the QNN was able to overcome its clas-sical counterparts, but the XGBoost. This fact can be an indication that the quantum model may identify and retrieve relevant spatiotemporal information from the input dataset in such a manner not attainable by the current classical approaches.

The solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the quantity of solar panels and the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to forecast the solar irradiance of typical irrigation areas. The impact of the meteorological parameters on the solar irradiance is studied, and four different parameter combinations are formed and considered as input to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict the solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as the input parameters. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model, with predicted values falling within acceptable ranges. The model developed using mixed data of multiple stations can be applied for forecasting solar irradiance in different regions. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

Humans’ performance varies due to the mental resources that are available to successfully pursue a task. To monitor users’ current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded, respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

Over the last decade, the prevalence of health issues has increased by approximately 29.1%, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly, however many present approaches are unsuitable for real-world implementation due to high memory footprints and lack of interpretability. We introduce DeClEx, a pipeline designed to address these issues. DeClEx ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state of the art pre-trained models while achieving threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

The Robotics Vision Lab of Northwest Nazarene University has developed Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 seconds. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted with the lowest noise in the images. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94%. compared to 88% during the daytime operations.

Query reformulation is a fundamental task in information retrieval systems aimed at improving the accuracy and relevance of search results. This abstract presents a query reformulation approach that utilizes a domain-specific ontology to enhance the effectiveness of query reformulation. The use of ontology enables a more precise understanding of the domain and its associated concepts, leading to more accurate query interpretation and reformulation. The proposed approach consists of two main steps: ontology-based query interpretation and ontology-guided query expansion. The proposed approach use Power thesaurus tool that perform sentiment analysis for suggestion of the synonyms of the query words to improve the accuracy of the retrieved results. The proposed methodology gives high accuracy as compared to the previous approaches that used Wordnet for the suggestions of synonyms. The proposed approach demonstrates the potential of utilizing ontologies in query reformulation, offering a more accurate and context-aware search experience in domain-specific information retrieval systems.

This paper provides a proof of concept for a linear electric generator that can be used to harvest energy from various sources of linear motion, such as vibrations, free-piston engines and wave energy. The generator can be used to power small electronic devices, such as sensors, or charge household batteries. Literature was reviewed to develop an understanding about the applications, control methods, excitation methods and mechanics of rotating and linear electric machines. A bidirectional, two-sided linear machine was designed with two stator cores and a single mover core. The stator windings and mover winding can be independently excited, allowing for three modes: no mover excitation, DC excited mover, and AC excited mover. Simulations results showed that the magnetic flux generated by DC excited stator cores were concentrated in the centre of the mover core. The use of two stator cores eliminates lateral flux in the mover core when it is not excited, minimising attraction and repulsion forces. Parametric analysis showed that flux cutting occurred in all operation modes, verifying that the generator will produce power when operating. Hardware tests produced an output current when the machine is electrically and mechanically excited, verifying the proposed design.

The objective of the study was to predict the risk of exposure to ammonia concentration in the production of broiler breeds of slow and fast-growing with low and high production density and correlate with the incidence of health injuries in broiler chickens using a learning machine. Two commercial lines of broiler chicken were used, one with fast-growing (Ross®, slaughter age 42 days) and another with slow-growing (Hubbard®, slaughter age 63 days). All slow-growing birds were housed at a density of 32 kg/m2. Fast-growing birds were housed in two different housing densities: low housing density with a final housing density of 16 kg/m2 and high density with a final housing density of 32 kg/m2. A total of 1250 birds were used in this experiment, 450 of which were fast-growing birds and 800 were slow-growing birds. In each room, 306 birds were randomly distributed in 18 boxes (6 boxes for each treatment and 17 birds per box), each with 3 nipple drinkers and a manual feeder. The dimensions of the high-density boxes were 1 x 1.3 m2 , while the low-density boxes had the same number of animals housed in a larger area, with dimensions of 2 x 1.3 m2 . The remaining 319 birds were housed randomly throughout the room and outside the pens to simulate a commercial production system condition (stock density). All birds were fed with an initial commercial feed from day 1 to day 18 of the experiment, and from day 18 until the end, each breed was fed with different feeds according to their nutritional demands. The following data analysis steps were performed: data selection, pre-processing, transformation, mining, analysis, and, interpretation of results. The classification algorithms, decision tree (J48), SMO (Sequential Minimal Optimization), Naive Bayes, and Multilayer Perceptron were applied to the training and test data sets to build a rule model for predicting ammonia risk levels in broiler chickens. The cross-validation technique was used to parameterize the analysis in all models. From the database of the first phase of analysis, with the classifier to predict the risk condition of ammonia concentration, the Spearman correlation coefficient (ρ, rho), considering the presence of pododermatitis, vision/affected and mucosal injury, which includes assessments of the trachea, bronchi, lungs, eyes, paw injuries, and other injuries. A non-parametric correlation measure was applied to injury incidence data as a function of ammonia risk level (1 and 10 ppm) to correlate injury incidence and ammonia level in the conditions studied. The best predictive model capable of evaluating and obtaining better performance was the Multilayer Perceptron when we considered greater accuracy by the level of risk of exposure to ammonia in the broiler chicken production process, including fast and slow-growing. Birds exposed to higher levels of ammonia concentration have a higher correlation coefficient when the relationship between the variables is strong. The Spearman correlation coefficient shows a stronger association between increased risks of ammonia exposure and the incidence of chicken injuries.

Drone navigation is critical, particularly during the initial and final phases, such as the initial ascension, where pilots may fail due to strong external disturbances that could lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters with external disturbances simulating wind pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

The incidence of liver diseases is on the leading rise worldwide, but there is a lack of exact research models mimic liver diseases. Using the normothermic machine perfusion (NMP) platform to study diseased livers recovered from the liver transplantation (LT) recipients seems promising. Therefore, the aim of this study was to establish the first machine perfusion system called Life-XDL to cultivate diseased liver. Four diseased livers recovered from LT recipients within different diagnoses were collected. The diseased livers were connected to the Life-XDL machine perfusion system that circulated Life-XDL perfusate providing oxygen and nutrients. The pressure and flow of the device was recorded and blood gas analysis and laboratory test of perfusate and bile was examined to analyze the function of the diseased livers. Liver tissues after perfusion were collected for histological analysis. Experiments showed that Life-XDL kept the diseased livers at the physiological state with providing stable hemodynamics and suitable partial pressure of oxygen and carbon dioxide. The results of blood gas analysis and laboratory test results demonstrated restoration and maintenance of metabolism and less damage. Moreover, the bile production of case 1, 3 and 4 diseased livers represented its vivid functionality. The pathological characteristics were kept stable before and after NMP. Overall, we successfully establish the Life-XDL machine perfusion system which specially for diseased human whole liver and develop a new diseased human whole organ model in vitro through normothermic machine perfusion that recapitulate the main features of the human liver disease, which showing great promise as disease modeling platforms.

This paper presents a complete derivation and design of a physics-informed neural network (PINN) applicable to solve initial- and boundary value problems described by linear ordinary differential equations. The objective not to develop a numerical solution procedure which is more accurate and efficient than standard finite element or finite difference based methods, but to give a fully explicit mathematical description of a PINN and to present an application example in the context of hydrodynamic lubrication. It is, however, worth noticing that the PINN developed herein, contrary to FEM and FDM, is a meshless method and that training does not require big data which is typical in machine learning.

The vibrations of a Cartesian cutting machine caused by the pneumatic tool are studied with a sub-system approach. The cutting head is modeled as an equivalent robot arm which is able to mimic the measured resonances, The Cartesian structure is modeled according to the mode superposition approach. The full model is able to predict the variations in the response of the machine to tool excitation that are caused by the motion of the head along the rails of the Cartesian structure. Comparisons with experimental results are made. Finally, the mathematical model is used for assessing the effectiveness of a vibration absorber tuned to main resonance frequency of the cutting head.

Classification algorithms are very widely used algorithms for the study of various categories of data located in multiple databases that have real-world implementations. The main purpose of this research work is to identify the efficiency of classification algorithms in the study of breast cancer analysis. Mortality rate of women increases due to frequent cases of breast cancer. The conventional method of diagnosing breast cancer is time consuming and hence research works are being carried out in multiple dimensions to address this issue. In this research work, Google colab, an excellent environment for Python coders, is used as a tool to implement machine learning algorithms for predicting the type of cancer. The performance of machine learning algorithms is analyzed based on the accuracy obtained from various classification models such as logistic regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree and Random forest. Experiments show that these classifiers work well for the classification of breast cancers with accuracy>90% and the logistic regression stood top with an accuracy of 98.5%. Also implementation using Google colab made the task very easier without spending hours of installation of environment and supporting libraries which we used to do earlier.

Constitutive models for plastic deformation of metals are typically based on flow rules determining the transition from elastic to plastic response of a material as function of the applied mechanical load. These flow rules are commonly formulated as a yield function, based on the equivalent stress and the yield strength of the material, and its derivatives. In this work, a novel mathematical formulation is developed that allows the efficient use of machine learning algorithms describing the elastic-plastic deformation of a solid under arbitrary mechanical loads and that can replace the standard yield functions with more flexible algorithms. By exploiting basic physical principles of elastic-plastic deformation, the dimensionality of the problem is reduced without loss of generality. The data-oriented approach inherently offers a great flexibility to handle different kinds of material anisotropy without the need for explicitly calculating a large number of model parameters. The applicability of this formulation in finite element analysis is demonstrated, and the results are compared to formulations based on Hill-like anisotropic plasticity as reference model. In future applications, the machine learning algorithm can be trained by hybrid experimental and numerical data, as for example obtained from fundamental micromechanical simulations based on crystal plasticity models. In this way, data-oriented constitutive modeling will also provide a new way to homogenize numerical results in a scale-bridging approach.

Machines for direct digital inkjet printing on cylindrical containers are a new technology out on the market. Their commercialization in the industrial sector has been affected by their high precision. This leads to the use of mechanisms with narrow manufacturing tolerances and to the search for topologies that have the least accumulated error without affecting quality. Machines with topologies to work on flat substrates have printing and productivity problems working on cylindrical substrates. This research paper presents the qualitative design of a direct digital inkjet printer working over cylindrical substrates comparing five mechanical topologies; three topologies with radial distribution and two topologies with parallel distribution. The aim of these topologies is to find the precision, quality and efficiency of the printer taking into account the restrictions present in its construction. Each topology has separate constitutive mechanisms, it is analyzed the tolerance ranges between the print head and the substrate whose cumulative error maximizes the inkjet print resolution to determine precision. From five Topologies, number 1, 2 and 5 meet the requirements. the topology 2 meets the requirements but it is not able to be developed due to current technological limitations.

This paper introduces an innovative theoretical framework for quantum-inspired data embeddings, grounded in foundational concepts of quantum mechanics such as superposition and entanglement. This approach aims to advance semi-supervised learning in contexts characterized by limited labeled data by enabling more intricate and expressive embeddings that capture the underlying structure of the data effectively. Grounded in foundational quantum mechanics concepts such as superposition and entanglement, this approach redefines data representation by enabling more intricate and expressive embeddings. Emulating quantum superposition encodes each data point as a probabilistic amalgamation of multiple feature states, facilitating a richer, multidimensional representation of underlying structures and patterns. Additionally, quantum-inspired entanglement mechanisms are harnessed to model intricate dependencies between labeled and unlabeled data, promoting enhanced knowledge transfer and structural inference within the learning paradigm. In contrast to conventional quantum machine learning methodologies that often rely on quantum hardware, this framework is fully realizable within classical computational architectures, thus bypassing the practical limitations of quantum hardware. The versatility of this model is illustrated through its application to critical domains such as medical diagnosis, resource-constrained natural language processing, and financial forecasting—areas where data scarcity impedes the efficacy of traditional models. Experimental evaluations reveal that quantum-inspired embeddings substantially outperform standard approaches, enhancing model resilience and generalization in high-dimensional, low-sample scenarios. This research marks a significant stride in integrating quantum theoretical principles with classical machine learning, broadening the scope of data representation and semi-supervised learning while circumventing the technological barriers of quantum computing infrastructure.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

This paper proposes an analytical model of the double-stator spoke-type permanent-magnet vernier machine (DSSTVM) using the subdomain model (SDM), which can be used to calculate the mag-netic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained correspondingly. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.

Building Information Modelling (BIM) has gained significant relevance in civil engineering, particularly in the infrastructure sector. It offers the potential to increase efficiencies, optimize processes, and enhance sustainability throughout infrastructure projects’ life cycles. BIM consolidates information about asset components into a single model, enabling real-time updates, cost reduction, and improved consistency. Digital twins serve as valuable extensions of BIM in infrastructure, playing a crucial role during operation. By capturing real-time data from sensors and IoT devices, digital twins enabls continuous monitoring, proactive maintenance, efficient energy management, and informed decision-making for optimized operational efficiency. Advancements in 3D point cloud technologies, such as 3D laser scanning, have expanded BIM’s application in operation and maintenance. These technologies rapidly capture accurate and detailed three-dimensional information, prompting the exploration of automated alternatives to manual point cloud modeling. This paper demonstrates the application of supervised machine learning, specifically support vector machines, for analyzing and segmenting 3D point clouds, a crucial step in 3D modeling. Various approaches for semantic segmentation are introduced, investigated, and evaluated using diverse data sets. The results highlight the effectiveness of supervised machine learning techniques in achieving accurate segmentation of 3D point clouds.

This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the study’s discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithm’s ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.

Non-neural Machine Learning (ML) and Deep Learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM and Transformers) are compared using multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure.

Nollywood, based on the idea of Bollywood from India, is a series of outstanding movies that originate from Nigeria. Unfortunately, while the movies are in English, they are hard to understand for many native speakers due to the dialect of English that is spoken. In this article, we accomplish two goals: (1) create a phonetic sub-title model that is able to translate Nigerian English speech to American English and (2) use the most advanced toxicity detectors to discover how toxic the speech is. Our aim is to highlight the text in these videos which is often times ignored for lack of dialectal understanding due the fact that many people in Nigeria speak a native language like Hausa at home.

We consider a coded compressed sensing protocol for unsourced massive random access (URA) that concatenates a shared Patterned Reed-Muller (PRM) inner codebook to an outer error-correction code. In this paper, an iterative list PRM projection algorithm is proposed to supplant the signal detector associated with the inner PRM sequences. In particular, we first propose an enhanced paths-saving algorithm called list PRM projection detection for the single-user scenario that keeps multiple candidates for the first few layers to remedy the risk of spreading errors. On this basis, we further propose an iterative list PRM projection algorithm for the multi-user scenario. The vectors for PRM codes and channel coefficients are jointly detected in an iterative manner, which offers significant improvements regarding the convergence rate for signal recovery. Furthermore, the performances of the proposed algorithms are analyzed mathematically, and we verified that the theoretical simulations are consistent with the numerical simulations. Finally, we concatenate the inner PRM codes that employ the iterative list detection to two practical error-correction outer codes. According to the simulation results, we conclude that the packetized URA with the proposed iterative list projection detection works better than benchmarks in terms of the number of active users it can support in each slot and the amount of energy needed per bit to meet an expected error probability.

The General Theory of Information (GTI) tells us that information is represented, processed and communicated using physical structures. The physical universe is made up of structures combining matter and energy. According to GTI, “Information is related to knowledge as energy is related to matter.” GTI also provides tools to deal with transformation of information and knowledge. We present here, the application of these tools for the design of digital autopoietic machines with higher efficiency, resiliency and scalability than the information processing systems based on the Turing machines. We discuss the utilization of these machines for building autopoietic and cognitive applications in a multi-cloud infrastructure.

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

Exploiting the linguistic knowledge of the source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language pairs. However, since the Turkish→English machine translation task is low-resource and the source-side Turkish is morphologically-rich, there are limited resources of bilingual corpora and linguistic information available to further improve the NMT performance. Focusing on the above issues, we propose a multi-source NMT approach that models the word feature in parallel to external linguistic features by using two separate encoders to explicitly incorporate linguistic knowledge into the NMT model. We extend the word embedding layer of the knowledge-based encoder to accommodate for each word’s linguistic annotations in the context. Moreover, we share all parameters across encoders to enhance the representation ability of the NMT model on the source language. Experimental results show that our proposed approach achieves substantial improvements of up to 2.4 and 1.1 BLEU scores in Turkish→English and English→Turkish machine translation tasks, respectively, which points to a promising way to utilize the source-side linguistic knowledge for the low-resource NMT.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

Poverty elimination is an essential and unavoidable step in human development. Predicting poverty is the first crucial step in addressing this issue, especially in developing countries where it remains a significant concern. The main objectives of this paper are: (i) to explore and analyze the multidimensional poverty data in Pattani province, and (ii) to apply feature selection techniques (Chi-Square, Mutual Information, and Gini Index) to enhance the prediction process. These techniques help in reducing irrelevant and redundant features, leading to more efficient models and better insights. This paper presents the development of a predictive model aimed at classifying poverty and providing actionable recommendations for policymakers. Machine learning models, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to assess the impact of feature selection methods on model performance. The effectiveness of each model was evaluated through various metrics, including accuracy. The experimental results show that the success of these models in predicting poverty, with DT, RF, and SVM obtaining 93%, 95%, 94% of accuracy, respectively. The findings underline the importance of feature selection in improving the performance of machine learning models.

Recommender systems in education are becoming more widespread, typically focusing on recommending courses or study materials. This study proposes a machine learning approach to recommend a university degree based on high school and university standardised test results, incorporating students' socioeconomic information as input variables. The objective is to develop a tool for students’ decision-making, supporting the sustainable development goal of Quality Education by providing a data schema to maximise the likelihood of a successful match between the student's profile and the academic program. With its focus on equity in education, this study provides a data-driven approach to assist students in selecting suitable university degrees, aiming to improve educational outcomes and inspire a more equitable education system.

We study how abstract representations emerge in a Deep Belief Network (DBN) trained on benchmark datasets. Our analysis targets the principles of learning in the early stages of information processing, starting from the “primordial soup" of the under-sampling regime. As the data is processed by deeper and deeper layers, features are detected and removed, transferring more and more “context-invariant” information to deeper layers. We show that the representation approaches an universal model – the Hierarchical Feature Model (HFM) – determined by the principle of maximal relevance. Relevance quantifies the uncertainty on the model of the data, thus suggesting that “meaning" – i.e. syntactic information – is that part of the data which is not yet captured by a model. Our analysis shows that shallow layers are well described by pairwise Ising models, which provide a representation of the data in terms of generic, low order features. We also show that plasticity increases with depth, in a similar way as it does in the brain. These findings suggest that DBNs are capable of extracting a hierarchy of features from the data which is consistent with the principle of maximal relevance.

Abstract — Game development using technology was started in 1950 around the first video game created Tennis for Two by Higginbotham as a fun diversion, to show the power of technology. The advanced development of Artificial intelligence (AI), has turned out to be the heart of game development technology and AI has revolutionized gaming and brought so many features to life.in reality, it isn't easy to give a definition and meaning to it even though games are becoming a part of human survival. It is difficult to define the real meaning of a game but it is part of human survival as it usually refers to the cleverness of the way the human being involvement in the characters of the game. As a part of game development, many technologies were used to create a game for the players with creativity, complex design, variety of levels, and aspects for the player to use choices effectively and efficiently to interact with the character in the game and enhance the experience to the next levels. This paper initially gives the importance of game development and need for the technology usage to enhance the current state of games. Then analyzes the history and present state of Artificial intelligence in game development. Finally, it explains the highlights of potential changes in game development and the impact and influence of artificial intelligence and machine learning models in the future development of games.

An Oscillating Water Column device, specifically a Savonius turbine, underwent comprehensive testing within an air duct to evaluate its performance under varying rotational speeds, flow directions, and the presence of power augmenters positioned both in front of and behind the device. The experimental setup involved the utilization of load cells and pressure transducers, with their data utilized to calculate pressure differentials across the turbine and torque. Subsequently, a predictive model based on decision trees was developed using Machine Learning. This model was then used to analyze the influence of various features on predicting the pressure difference, considered as the output. The results of the validation (10-fold cross-validation) and test phases were thoroughly investigated. Moreover, obtaining a predictive model allows for the exploration of different scenarios without relying solely on physical experimentation, thereby broadening the scope for further testing.

This paper proposes an enhanced Quantum Key Distribution (QKD) protocol by incorporating a Three-Dimensional Angular Momentum Representation (3D AMR) to address vulnerabilities inherent in traditional two-dimensional polarization states-based systems. The 3D AMR method expands the state space, potentially increasing security against sophisticated quantum attacks. We present a theoretical and computational framework that transitions from the conventional two-dimensional polarization to a three-dimensional angular momentum space, which inherently increases the complexity and security of the quantum states used in QKD.

The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.

Auxins are chemical compounds of wide interest, mostly due to their role in plant metabolism and development. Since the latter half of the XX century, synthetic auxins have been used as herbicides, presenting notable advantages, such as low toxicity in humans. As of today, extensive descriptions of syntetic and natural auxins have been made; yet studies centered on the molecular recognition process of auxins are scarce. Herein, we present a comprehensive in silico characterization of auxin receptor TIR1, using structure based methods to aid de novo design of putative ligands. In addition, we conducted binding site characterization using cosolvent molecular dynamics and enhanced sampling simulations with reference ligands. We also present a database of putative auxins and decoy compounds which can aid further studies focusing on synthetic auxin design. To the best of our knowledge, this study is the first of its kind focusing on TIR1.

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

This work develops a new method for vector data augmentation. The proposed method applies the Principal Component Analysis (PCA) and determines the Eigenvectors of a set of training vectors for a Machine Learning (ML) method and uses them to generate the distilled vectors. The training and the PCA distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors the extended set trains a ML classifier to achieve a required accuracy. Hence, the novelty of this study is, the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: Neural Network, Logistic Regression, Support Vector Machine with linear and polynomial kernels. For the purpose of augmentation, we conduct several distillations including nested (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We train the classifiers with three sets of vectors: original vectors; original augmented with vectors distilled by PCA; original augmented with distilled and double distilled, by PCA, vectors. The experimental results are presented in the paper, and they confirm the advantage that the PCA distilled vectors increase the classification statistics of ML methods, if the distilled vectors augment the original training vectors.

Regular inspections during construction work verify that the work completed is consistent with the plans and specifications and ensure that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision-based (CV) techniques for the measurement of as-built elements, predominantly use 3D laser scanning or 3D Photogrammetry modelling to determine the geometrical properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates making the deployment of these techniques on dynamic construction project sites a challenging task. In this paper, we propose a pipeline for automating the measurement of as-built components using artificial intelligence (AI) and computer vision (CV) techniques. The pipeline requires a single image obtained with a stereo-camera system to measure the size of selected objects or as-built components. We demonstrate our approach by measuring the size of concrete walls and columns. The novelty of this work is attributed to the fully automated CV-based method for measuring any given element using a single image only. The proposed solution is suitable for use in measuring the sizes of as-built components of built assets. It has the potential to be further developed and integrated with BIM models for use on construction projects for progress monitoring.

The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes such as machine learning has the potential to improve efficiency, reduce costs, and improve patient outcomes. AI can assist with decision-making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. However, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology.

IKEA is a massive international conglomerate company, and the purpose of this exploratory qualitative research is to investigate how the burgeoning academic field known as "artificial intelligence" (AI) could assist IKEA in resolving some of the challenges that it is currently dealing with in relation to its efforts to source sustainable materials. According to the conclusions of this study, artificial intelligence has the potential to aid in the resolution of at least some of the issues associated with sustainable sourcing, at least to some degree. This was developed by mapping the issues, analyzing the potential for adopting artificial intelligence as a solution, and defining the components that are required for efficient adoption of the technology. A number of prerequisites need to be satisfied by businesses before artificial intelligence may be used in the area of sustainable sourcing. In addition, a number of artificial intelligence's constraints need to be addressed before the technology can be put into practice by the same businesses. In conclusion, the findings of this study offer some management implications and ideas for businesses that have the goal of sourcing in a way that is less harmful to the environment by making use of technological breakthroughs such as artificial intelligence. This can be accomplished through the utilization of innovative technologies.

Child sexual abuse inflicts lifelong devastating consequences for victims and is an ongoing social concern. In most countries, child sexual abuse material (CSAM) distribution is illegal. As a result, there are many research papers in the literature which proposed technologies to detect and investigate CSAM. In this survey, a comprehensive search of the peer-reviewed journal and conference paper databases (including preprints) is conducted to identify high-quality literature. We use the PRISMA methodology to refine our search space to 2,761 papers published by Springer, Elsevier, IEEE and ACM. After iterative reviews of title, abstract and full text for relevance to our topics, 43 papers are included for full review. Our paper provides a comprehensive synthesis of the tasks of the current research and how the papers use techniques and datasets to solve their tasks and evaluate their models. To the best of our knowledge, we are the first to focus exclusively on online CSAM detection and prevention with no geographic boundaries, and the first survey to review papers published after 2018. It can be used by researchers to identify gaps in knowledge and relevant publicly available datasets that may be useful for their research.

Optimizing intensive care resources using predicting modeling is paramount for fighting the COVID-19 pandemic. In this paper, we model the admission of COVID-19 patients in intensive care units (ICU) in Colombia using openly available data gathered from 18 March 2020 to 14 October 2020. After an intensive preprocessing of the data, we trained four different machine learning models using four different strategies for handling the imbalanced features. Our findings show that our best model (XGBoost) effectively predicts an Area Under the Curve (AUC-ROC) of 0.94, in line with the state-of-the-art results obtained in other predictive models obtained with medical data.

Over the past decade, research in the field of Deep Learning has brought about novel improvements in image generation and feature learning; one such example being a Generative Adversarial Network. However, these improvements have been coupled with an increasing demand on mathematical literacy and previous knowledge in the field. Therefore, in this literature review, I seek to introduce Generative Adversarial Networks (GANs) to a broader audience by explaining their background and intuition at a more foundational level. I begin by discussing the mathematical background of this architecture, specifically topics in linear algebra and probability theory. I then proceed to introduce GANs in a more theoretical framework, along with some of the literature on GANs, including their architectural improvements and image-generation capabilities. Finally, I cover state-of-the-art image generation through style-based methods, as well as their implications on society.

Real estate has the dual characteristics of consumption and investment. Under the inherent concept of "where there is land, there is wealth", real estate has become the investment target of the main asset. In addition to the supply and demand of the market will affect the price of real estate, many general economic factors will also have a certain impact on the price of real estate. This paper pays a special focus on the effects of MRT on housing prices, finding the closer a house is to an MRT station, the higher its price. In addition, convenience stores are very popularized, and this research report is also interested in whether the number of convenience stores within walking distance will affect the house price. In a word, these two factors do affect housing prices significantly.

This study evaluated the using of machine vision in combination with deep learning to identify weeds in real-time for wheat production system. PMAS-Arid Agriculture university research farm were selected for collection of images (6000 total images) of weeds and wheat crops under different weather condition. During growing season, the database was constructed to identify the weeds. For this study two framework were used TensorFlow and PyTorch under CNNs and Deep learning. Deep learning perfromed better with in PyTourch value as compared to another model in Tensorflow. comparing with other networks such as YOLOv4, we concluded that our network reaches a better result between speed and accuracy. Specifically, the maximum precision of weed and wheat plants were 0.89 and 0.91 respectively with 9.43 ms and 12.38 ms inference time per image (640 × 640) NVIDIA RTX2070 GPU.

Cyber-threat is now a global concern to all internet users, as the rate of crimes has tremendously changed from physical to virtual. Implementing awareness programs at various organization levels is required, Also there is a need to know the methodologies used in measuring cybersecurity awareness program effectiveness. The paper aims to review and explore the previous methodologies applied, target organizations, and application of Machine Learning techniques in the assessment of awareness programs. Systematic literature review techniques were used to conduct the search using pre-defined keywords and published papers from 2006 to 2019, the information was used in providing an answer to the research questions. The finding shows questionnaire was widely used as a method of evaluating cybersecurity awareness and also organizations implement more awareness programs, the gap found is the lack of using ML in assessing the effectiveness of design, and implemented cybersecurity awareness programs

According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, we present a new unsupervised machine learning blink detection strategy using existing eye-tracking technology. The method is compared to two existing techniques. All three algorithms make use of eye aspect ratio values for blink detection. Accurate and rapid blink detection complements existing eye-tracking research and may provide a new informative index of physical and mental status.

The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

Edible mushrooms have been recognized as highly nutritional food for a long time, due to their specific flavor, texture and also for therapeutic effects. This study proposes a new simple approach, based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushrooms species from Romanian spontaneous flora, namely Armillaria mellea, Boletus edulis and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) manage to 100% classify the three species and the cross validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for holdout set the percent raised at 94.74%. The lowered values obtained for the training set was due to one C. cibarius sample, two B. edulis and five A. mellea, which were placed to other species. Anyway, for holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified investigated mushroom samples according to their species, meaning that in every partition the predominant specie had the biggest DOMs, while samples belonging to other specie had lower DOMs.

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

With the evolution of technology over the years, it has become possible to develop intelligent environments based on the concept of the Internet of Things, distributed systems, and machine learning. Such environments are incorporated with various solutions to solve user demands from services. One of these solutions is UBIPRI middleware, whose central concept is to maintain privacy in smart environments and to receive notifications as one of its services. However, this service is freely performed, disregarding the privacy that the environment employs. Another consideration is that based on the researched related works, it was possible to identify that the authors do not use statistical hypothesis tests in their solutions developed in the presented context. This work proposes an architecture for notification management in smart environments, composed by a notification manager named PRIPRO to assign it to UBIPRI and to aim to perform tests and comparisons between classification algorithms to delimit which one is most feasible to implement in the PRINM decision-making mechanism. The experiments showed that the J48 algorithm obtained the best results compared to the other algorithms tested and compared.

The multidimensionality of chronic pain forces us to look beyond isolated pain assessment such as pain intensity, which does not consider multiple key parameters, particularly in patients suffering from post-operative Persistent Spinal Pain Syndrome (PSPS-T2). Our ambition was to provide a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and objective quantitative pain mapping assessments, the objective being to capture patient condition instantaneously, using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in a real-life observational prospective PREDIBACK study with 12-month follow-up and received various treatments. From a multitude of questionnaires/scores, specific items were combined using exploratory factor analyses to create an optimally accurate MCRI; as a single composite index, using pairwise correlations between measurements, it appeared to better represent all pain dimensions than any other classical score. It appeared to be the best compromise among all existing indexes, showing the highest sensitivity/specificity related to Patient Global Impression of Change (PGIC). Novel composite indexes could help to refine pain assessment by changing the physician’s perception of patient condition on the basis of objective and holistic metrics, and by providing new insights to therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.

Machine-learning methods used for classification are challenged by the class imbalance problem, where a certain class is underrepresented. Over/under-sampling the majority/minority class observations or model selection for ensemble methods alone might not be effective if the class imbalance ratio is very high. To address this concern, a novel method is presented for generating synthetic data for minority class observations in conjunction with an optimal tree ensemble classifier (OTEC). This novel synthetic method first generates minority class instances to balance the training data. Then it applies OTEC, where models are selected based on their performance using out-of-bag observations and training subsamples. A total of 20 benchmark problems on binary classification with moderate to extreme class imbalance are used to assess the efficacy of the proposed method against other well-known methods, including optimal tree ensemble, smote random forest, under-sampling random forest, oversampling random forest, k-nearest-neighbor, support vector machine, tree, and artificial neural network, using classification error rate, sensitivity, specificity, precision, recall and F1-score as performance metrics to assess the efficacy of the proposed method. The analyses presented in this study revealed that the proposed method based on data balancing and model selection yielded better results than other methods.

During the development of new medicines mutually exclusive specificity appears to be required but hitherto is most often included at the late clinical trial stage. Here, I describe the use of Hedgehog signalling in Drosophila as a model to represent a mutually exclusive relationship within the context and report the identification of CG43658, a putative Rho guanine nucleotide exchange factor and orthologue of the Charcot-Marie-Tooth disease associated gene ARHGEF10. Furthermore, a Computer that is able to consider the whole and describe the sum of everything in it absolutely in unambiguous terms is a useful tool. Here I describe such a computer with a suggestion for an application to examine any component in the context where a medicine is sought. Taken, together I would like to propose this method as a fundamental necessity and improvement upon current existing methods related not only to Hedgehog signalling but plausibly to life science research and the search for medicines in general.

Abstract：Ballast pick-up velocity is a crucial parameter in the design of a slurry system in excavating devices, it influences transportation stability, safety, and economy of the system. In this study, a vertical pipe hydraulic experiment with different ballast sizes under water condition was conducted to investigate the ballast pick-up velocity in the shield slurry system. Thereafter, the coupling method of computational fluid dynamics and the discrete element method (CFD-DEM) was employed to establish a numerical model of the ballast pick-up velocity in the shield slurry system, and its applicability was verified by comparing the experiment results. Additionally, the ballast pick-up process under actual working conditions was studied, and the influence of ballast size and concentration on pick-up velocity was investigated. Overall, the primary findings of the study are as follows: 1) The numerical method can be applied to predict both the single ballast and ballast group pick-up velocity. 2) The pick-up process of the ballast group can be divided into four stages. 3) The conveying velocity of the ballast should be higher than the pick-up velocity of the single ballast to facilitate smooth discharge. 4) For a certain increase in ballast concentration, the pick-up velocity tends to be stable.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.

In this paper, we explore the theoretical foundation of Graph Neural Network (GNN) model and apply it to various traditional supply chain logistics problem. In particular, we study the Route Optimization Problem, Demand Forecasting Problem, Risk Assessment and Anomaly Detection, Supplier Section and Procurement Optimization, Inventory Optimization Problem, and Green Supply Chain Problem. The study helps us the expand the use the GNN and gives daily supply chain management a boost.