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Machine Learning (ML) has emerged as a pivotal force in enhancing Proton Exchange Membrane Water Electrolyzer (PEMWE) devices. These devices are critical for transforming renewable electricity into hydrogen, a key clean energy vector. Despite their prospects, the broader implementation of PEMWE is hindered by cost and efficiency barriers. PEMWEs are inherently complex, involving multi-scale processes such as electrochemical reactions, reactant transportation, and thermo-electrical interactions. This complexity has previously limited optimizations to isolated components like electrocatalysts, membrane electrode assemblies (MEAs), Bipolar plates (BPs), and Gas Diffusion Electrodes (GDEs). ML presents a revolutionary pathway to address these obstacles by enabling system-wide optimization. In this paper, we offer an in-depth review of cutting-edge ML applications for improving PEMWE performance and efficiency. ML's ability to process large datasets and identify intricate patterns accelerates the research and development of PEMWEs, thereby reducing costs and boosting efficiency. We describe a variety of algorithms, such as Artificial Neural Networks (ANN), Deep Learning (DL), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM), commonly used in PEMWE applications, highlighting their significance in enhancing PEMWE systems. Additionally, we explore hybrid methods that combine various ML techniques to further improve PEMWE performance and efficiency. The review provides a concise overview and forward-looking perspective on the role of ML in advancing PEMWE technology, marking a significant step towards their cost-effective and scalable deployment. • A thorough evaluation of PEMWE components and their specific functions is presented. • A wide range of ML algorithms for optimizing PEMWE are summarized. • Recent advancements in ML models for the design and optimization of PEMWE are reviewed. • The main challenges and future research opportunities in ML-driven PEMWE are discussed. [ABSTRACT FROM AUTHOR]

This work surveyed the vibration behavior and stability analysis of an underwater moving flexible nanosize beam, implementing numerical and analytical methods as well as tree-based machine learning (ML) algorithms. Dynamic modeling is performed based on the nonlocal stress-strain gradient theory (NSSGT), incorporating variable environmental conditions, surface energy, and rotational inertia effects. The natural frequencies and instability threshold are computed numerically. The exact closed-form mathematical expression for the critical towing velocity of the nanosize beam is determined analytically. Also, decision tree regression and least-squares boosting tree (LSBT) regression algorithms are exploited to predict stability boundaries and the fundamental vibration frequency, and their efficiency is surveyed accordingly. Comparative studies and parametric investigations are conducted. The impressions of geometry, added mass coefficient, scale ratio parameter, surface layer characteristics, fluid mass ratio, humidity, temperature rise, and external magnetic field intensity on the nanosystem dynamics are examined and illuminated. It is detected that the results of the analytical method and ML-based approaches are consistent with those reported by the numerical technique and literature. The results asserted that the proposed ML algorithms have acceptable performance and excellent effectiveness in computational time and cost. It is comprehended that the dynamic features of submerged traveling nanobeams drastically depend on the rotational inertia effects and thickness-dependent scale effects. The stability of the immersed movable nanoscale beams is perceived to improve by increasing the scale ratio parameter and the added mass coefficient. From the perspective of the optimal design of engineering instrumentation tools, the outcomes of the current article can be applied as an inclusive benchmark. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) are high‐energy consumers and major Greenhouse Gas (GHG) emitters. This review offers a comprehensive global overview of the current utilization of machine learning (ML) to optimize energy usage and reduce emissions in WWTPs. It compiles and analyses findings from over a hundred studies primarily conducted within the last decade. These studies are organized into five primary areas: energy consumption (EC), aeration energy (AE), pumping energy (PE), sludge treatment energy (STE) and greenhouse gas (GHG). Additionally, they are further categorized based on learning type, the scale of application, geographic location, year, performance metrics, software, etc. ANNs emerged as the most prevalent, closely trailed by FL and RF. While GA and PSO are the predominant metaheuristic approaches. Despite increasing complexity, researchers are inclined towards employing hybrid models to enhance performance. Reported reductions in energy consumption or GHG emissions spanned various ranges, falling within the 0–10%, 10–20% and >20% brackets. Highlights: Investigating ML applications for energy optimization and GHG reduction in WWTPs.Over a hundred technical publications were reviewed mainly from 2013 to 2023.ML algorithms were classified and evaluated based on framework and performance.Comparative analyses were conducted on key features, pros and cons of ML algorithms. [ABSTRACT FROM AUTHOR]

One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions. [ABSTRACT FROM AUTHOR]

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices. [ABSTRACT FROM AUTHOR]

Abstract One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions.

This paper intends to provide the reader with an overview of the main processes that are introducing artificial intelligence (AI) into healthcare services. The first part is organized according to an evolutionary perspective. We first describe the role that digital technologies have had in shaping the current healthcare methodologies and the relevant foundations for new evolutionary scenarios. Subsequently, the various evolutionary paths are illustrated with reference to AI techniques and their research activities, specifying their degree of readiness for actual clinical use. The organization of this paper is based on the interplay three pillars, namely, algorithms, enabling technologies and regulations, and healthcare methodologies. Through this organization we introduce the reader to the main evolutionary aspects of the healthcare ecosystem, to associate clinical needs with appropriate methodologies. We also explore the different aspects related to the Internet of the future that are not typically presented in papers that focus on AI, but that are equally crucial to determine the success of current research and development activities in healthcare. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks pose a significant threat to online services by flooding targets with unusually high volumes of traffic or data, disrupting services and causing financial and reputational damage. This paper explores the workings and impact of DDoS attacks, with a variety of methods used by attackers to exploit vulnerabilities in the target infrastructure. To address these risks, this paper advocates the application of Machine Learning (ML) techniques. ML allows computers to learn patterns from data and make decisions autonomously and offers a proactive defense against DDoS attacks. Using UNSW-NB15 Dataset, this study evaluates the performance of eight ML algorithms Logistic Regression, KNN, SVM, Random Forest, Decision Tree, LSTM, MLP, and GRU, the evaluation of this study is based on Accuracy, Precision, Recall, and F1 Score metrics. The results show that Random Forest is the most effective algorithm, showing superior performance across all metrics with 97.68% accuracy. This explains the potential of Machine Learning, specifically the Random Forest algorithm. can be considered to improve security and reduce the risk posed by DDoS attacks. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.

Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization.

Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization. [ABSTRACT FROM AUTHOR]

The so-called black swans, COVID-19 and the invasion in Ukraine, have led to an unprecedented increase in electricity prices. Since 2021, after lockdowns, the electricity price has started to increase due to economic recovery, rising prices of the tCO2 and other primary sources that become unavailable or at much higher prices. In this context, we noticed that the variation of electricity prices in one country can be explained by the price fluctuations of the previous day in the neighboring countries. For instance, the prices for the current day (d) in the Romanian Day-Ahead Market is strongly correlated with the prices of the previous day (d−1) on DAMs of its neighboring countries. It is worth mentioning that the target can be switched by the rest of the variables. Not only the price in Romania can be estimated using the proposed Electricity Price Forecast (EPF) method, but also the prices in other neighboring countries can be a target for prediction because the regional prices on similar markets contain most of society's distress. Another interesting aspect is that the proposed forecasting methodology is robust, as proved by testing it on a varied and longer time interval (from January 2019 to August 2022). Furthermore, the proposed price forecasting methodology includes the adjustment of training interval according to the price standard deviation and weighing the results of the five individual Machine Learning (ML) algorithms to further improve the prediction performance. The set consists of data collected between 1st of January 2019—one year before COVID-19 pandemic outburst and 31st of August—several months after the war has started in the Black Sea region. [ABSTRACT FROM AUTHOR]

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

Original and pure seeds are the most important factors for sustainable agricultural production, development, and food security. Conventionally, seed protection and certification programs are carried out on several classes from breeders to certify seed based on a physical, biochemical, and genetic evaluation to approve seed as a cultivar. In seed industries, quality assurance programs depend on different methods for certifying seed quality characteristics such as seed viability and varietal purity. Those methods are mostly conducted in a less cost-effective and timely manner. Combining machine learning (ML) algorithms and optical sensors can provide reliable, accurate, non-destructive, and quick pipelines for seed quality assessments. ML employs various classifiers to authenticate and recognize varieties through K-means, Support Vector Machines (SVM), Discriminant Analysis (DA), Naive Bayes (NB), Random Forest (RF) and Artificial Neural Networks (ANNs). In recent years, progress in ANN algorithms as deep learning simplifies big data analytics procedures by categorizing learning and extracting distinct levels of multiplex data. Deep learning opened a new door for developing a smartphone as a fast and robust substitute for the online seed variety discrimination stage through developing a Convolutional neural networks (CNNs) model-based mobile app. This review presents machine learning and seed quality assessment areas to recognize and classify seeds through long-standing and novel ML algorithms.

Heart disease (HD), including heart attacks, is a primary cause of death across the world. In the area of medical data analysis, one of the most difficult problems to solve is determining the probability of a patient having heart disease. Death rates can be lowered by the early detection of heart diseases and the constant monitoring of patients by physicians. Unfortunately, heart disease cannot always be detected accurately, and a doctor cannot be in touch with a patient 24/7. Machine learning (ML) has the potential to aid in diagnostics by providing a more precise basis for prediction and making decisions using data given by healthcare sectors throughout the world. This study aims to employ several feature selection methods to develop an accurate ML technique for heart disease prediction in its earliest stages. The feature selection process was performed using three distinct methods, namely, chi-square, analysis of variance (ANOVA), and mutual information (MI). The three feature groups that were ultimately selected were referred to as SF-1, SF-2, and SF-3, respectively. Then, ten different ML classifiers were used to determine the best technique, and which feature subset was the greatest fit. These classifiers included Naive Bayes, support vector machine (SVM), voting, XGBoost, AdaBoost, bagging, decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), and they were denoted as (A1, A2, …, A10). The proposed approach for predicting heart diseases was evaluated using a private dataset, a publicly available dataset, and multiple cross-validation methods. To find the classifier that generates the best rate of accurate heart disease predictions, we applied the Synthetic Minority Oversampling Technique (SMOTE) to fix the issue of unbalanced data. The experimental findings demonstrated that the XGBoost classifier achieved the optimal performance using the combined datasets and SF-2 feature subset with the following rates: 97.57% for accuracy, 96.61% for sensitivity, 90.48% for specificity, 95.00% for precision, 92.68% for F1 score, and 98% for AUC. The development of an explainable artificial intelligence approach that makes use of SHAP methodologies is being done to get an understanding of how the system predicts its ultimate results. The proposed technique had great promise for the healthcare sector to predict early-stage heart disease with cheap cost and minimal time. Ultimately, the best ML method has been used to make a mobile app that lets users enter HD symptoms and quickly receive a heart disease prediction.

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security. [ABSTRACT FROM AUTHOR]

Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervisedmachine learning method to detect fake reviews on any website, among other things. Online reviews are classified using a semi-supervised approach (PU-learning) since there is a shortage of labeled data, and they are dynamic. Then, classification is performed using the machine learning techniques Support Vector Machine (SVM) and Nave Bayes. The performance of the suggested system has been compared with standard works, and experimental findings are assessed using several assessment metrics. [ABSTRACT FROM AUTHOR]

The global population's rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future. [ABSTRACT FROM AUTHOR]

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall’s correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, ‘Bayesian optimization with KNN’, was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.

This paper intends to provide the reader with an overview of the main processes that are introducing artificial intelligence (AI) into healthcare services. The first part is organized according to an evolutionary perspective. We first describe the role that digital technologies have had in shaping the current healthcare methodologies and the relevant foundations for new evolutionary scenarios. Subsequently, the various evolutionary paths are illustrated with reference to AI techniques and their research activities, specifying their degree of readiness for actual clinical use. The organization of this paper is based on the interplay three pillars, namely, algorithms, enabling technologies and regulations, and healthcare methodologies. Through this organization we introduce the reader to the main evolutionary aspects of the healthcare ecosystem, to associate clinical needs with appropriate methodologies. We also explore the different aspects related to the Internet of the future that are not typically presented in papers that focus on AI, but that are equally crucial to determine the success of current research and development activities in healthcare.

Online customer feedback is essential for promoting online buying. The e-commerce sector has experienced exponential growth since COVID-19. Now days the growth of any business in e-commerce industry is highly influenced by the online consumer reviews and a lot of research has been conducted by numerous researchers in this regard to determine the reviews helpfulness for experience and search based products. In the study, the author's aims to develop a convolutional neural network based binary classification model for assessing the usefulness of the products reviews through the analysis of consumer evaluations with reference to products and services in e-commerce. In this experiment, predicting helpfulness is considered as binary research problem in order to determine review helpfulness in association with the structural, emotional, linguistic, emotive, lexical, and voting feature sets. In this study, various machine learning algorithms viz., K-nearest neighbor (KNN), Linear regression (LR), Gaussian naive bays (GNB), linear discriminant analysis (LDA), support vector machine (SVM) and convolution neural networks (CNN) were used to build the classification models and their results were compared with each other and other existing state of art models. From the simulation results, it was observed that CNN outperformed over the above stated algorithms and other existing state-of-the-art classification models, achieving 99.72% and 99.97% accuracy for two different search and experience based datasets. Furthermore, the performance of these models were also evaluated in terms of precision, recall, and F1 score. The findings presented in this paper reveals the importance of machine learning models in selecting the quality products. [ABSTRACT FROM AUTHOR]

For the advancement of technologies in vehicular industry, an intelligent Software Defined Vehicular Ad-hoc Network (SDVANET) which decoupled its data and control plane providing a golden opportunity to the researchers and academicians working in the related field. The limitation associated with SDVANET lies in the fact that it has problem in transferring reliable multimedia information among different users. To address this limitation, researchers of the domain tried to provide more intelligence to SDVANET by deploying Machine Learning (ML) algorithms to this network. This deployment of ML algorithms provided a safe, reliable, secure, optimal methodology for transferring multimedia information among SDVANET networks. In this work, researchers have used two datasets: small dataset and big dataset according to number of iterations used for collection. Total six supervised ML algorithms: Random Forest (RF), Decision-Tree (DT), Logistic Regression (LR), Naive-Bayes (NB), Support Vector Machine (SVM), K-Nearest Neigbour (kNN) are chosen to participate in the training and testing process. For result analysis, two simulation parameters: Receiver operating characteristic-Area under the ROC curve (ROC-AUC) and Confusion matrix are utilized. Numerical Results: Classification Accuracy (CA) (88.3%), F1-Measure (89.3%), precision (94.3%), recall (93.3%), AUC (92.3%) indicate that ML algorithms used for small dataset (LR, SVM and NB), NB is outperforming and ones used for big dataset (DT, RF and kNN), DT gives best performance based on numerical results: CA (95.4%), F1-Measure (93.4%), precision (92.3%), recall (98.2%), AUC (94.9%). So, NB and DT are the best ML algorithms for transferring the secure multimedia information, avoiding accidents among SDVANET networks. [ABSTRACT FROM AUTHOR]

Hospitals are facing difficulties in predicting, evaluating, and managing cost-affecting parameters in patient treatments. Inaccurate cost prediction leads to a deficit in operational revenue. This study aims to determine the ability of Machine Learning (ML) algorithms to predict the cost of care in bariatric and metabolic surgery and develop a predictive tool for improved cost analysis. 602 patients who underwent bariatric and metabolic surgery at Wetzikon hospital from 2013 to 2019 were included in the study. Multiple variables including patient factors, surgical factors, and post-operative complications were tested using a number of predictive modeling strategies. The study was registered under Req 2022–00659 and approved by an institutional review board. The cost was defined as the sum of all costs incurred during the hospital stay, expressed in CHF (Swiss Francs). The data was preprocessed and split into a training set (80%) and a test set (20%) to build and validate models. The final model was selected based on the mean absolute percentage error (MAPE). The Random Forest model was found to be the most accurate in predicting the overall cost of bariatric surgery with a mean absolute percentage error of 12.7. The study provides evidence that the Random Forest model could be used by hospitals to help with financial calculations and cost-efficient operation. However, further research is needed to improve its accuracy. This study serves as a proof of principle for an efficient ML-based prediction tool to be tested on multi-center data in future phases of the study. [ABSTRACT FROM AUTHOR]

The study presents a sophisticated hybrid machine learning methodology tailored for predicting energy loads in occupied buildings. Leveraging eight pivotal input features—building compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution—we elucidate the intricate relationships between building characteristics and their corresponding heating load (HL) and cooling load (CL). We meticulously analyze these features across 12 diverse structural forms, each emblematic of unique architectural designs and building materials. Using a dataset encompassing 768 buildings, we demonstrate the prowess of our proposed models. Among the algorithms we employed, the extreme gradient boosting algorithm stands out, registering impressive accuracy metrics (HL: RSQ = 0.9986, RMSE = 0.3797, MAE = 0.2467 and MAPE = 1.1812; CL: RSQ = 0.9938, RMSE = 0.7578, MAE = 0.4546 and MAPE = 1.6365). We further integrate SHAP analysis, revealing that relative compactness positively influences both HL and CL the most, closely followed by surface area and glazing area. By merging an explainable extreme gradient boosting algorithm with a Bayesian-based metaheuristic optimization technique, we ensure both high predictive accuracy and interpretability. This study holds profound implications for enhancing building energy efficiency, curbing waste, and championing the shift to sustainable energy sources, aligning seamlessly with SDG 7.

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use. [ABSTRACT FROM AUTHOR]

In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health "Dr. Milan Jovanovic Batut" in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has emerged as a powerful tool for prediction of systems behavior in many engineering disciplines. A few applications of ML techniques are available in geotechnical engineering and other fields of civil engineering. The existing review studies on the application of ML techniques in conventional geotechnical engineering and earthquake geotechnics are available in the broader areas but not specific to liquefaction phenomenon. Studies exist on the liquefaction potential of cohesionless soils using ML techniques with a varying degree of success. More studies are needed to formalize the use of ML techniques in the seismic liquefaction assessment. In this review, an attempt is made to critically review the existing literature on ML techniques as applied to the liquefaction analysis. The published literature from 1994 to 2021 has been collected, critically reviewed, and presented systematically in the form of easy to understand tables and figures. The tables are labeled based on the data requirement in ML techniques, conventional methods, and in-situ tests. A summary table highlights the relative importance of input variables in the dataset required for the liquefaction potential assessment. Limitations of conventional methods and existing ML models, the importance of a large database, relative comparison of the four developed ML models, and the requirement of probabilistic analysis are included. A gap in the existing literature is outlined and followed by a way forward for future research. It is concluded that there is a need to update the liquefaction database and modify existing ML algorithms so that they become computationally efficient and reliable for liquefaction potential assessment. [ABSTRACT FROM AUTHOR]

This study explores the critical realm of Diabetic Foot Ulcers (DFUs) and proposes an innovative approach for early detection using Internet of Things (IoT) and Machine Learning (ML). A chronic metabolic condition with elevated blood glucose levels is called diabetes mellitus. A foot ulcer is an open wound that is typically located beneath the feet. It can be shallow and less severe, occurring just below the skin's surface, or it can be deep and expose the bones, tendons, and joints. However, diabetes patients may be able to avoid complications from diabetic foot ulcers if early prophylaxis is practiced. One of the complications that this condition is frequently linked to is diabetic foot ulcers. Focusing on Diabetes Mellitus, the chronic metabolic condition leading to DFUs, the study introduces a wearable shoe prototype equipped with temperature and pressure sensors. This IoT-enabled device facilitates daily foot evaluation at home, allowing for timely identification of early symptoms and severity monitoring. By integrating ML algorithms, the real-time ulcer detection system aims to prevent complications, reduce amputations, and enhance proactive diabetic care.

Since the 1990s, remote sensing images have been used for land cover classification combined with Machine Learning algorithms. The satellites and airborne sensors pass over a specific point of land surface periodically, it is possible to assess the change in land cover over a long time. With the advent of Machine Learning (ML) methods, automated land cover classification has been at the center of research for the last few decades. From 2015 forward, a technical shift has been noticed with the emergence of several branches of Neural Networks (NN) and Deep Learning (DL). This paper examines current practices, problems, and trends in satellite image processing. This formal review focused on the summarization of major classification approaches from 1995. Two dominant research trends have been noticed in automated land cover classification, e.g., per pixel and subpixel analysis. Classical machine learning algorithms and deep learning methods are mainly used for per-pixel analysis, whereas fuzzy logic algorithms are used for sub-pixel analysis. The current article includes the research gap in automated land cover classification to provide comprehensive guidance for subsequent research direction. [ABSTRACT FROM AUTHOR]

Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi's orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is a widely used technique in the field of spectroscopy, enabling the determination of the chemical composition of a variety of samples using typically single laser pulses. This paper presents the successful integration of femtosecond LIBS using double pulses that combined with machine learning algorithms enhanced the discrimination between two animal tissue types (liver and muscle) through the detection of atomic and molecular emissions. The double-pulse configuration was optimized on liver tissue, and the results demonstrated that at 1100 ps pulse delay, the signal was the highest overall for all identified lines, with a fivefold increase compared to single-pulse configuration at comparable energies. By employing femtosecond double-pulse LIBS, it is possible to achieve enhanced signal quality with a better signal-to-noise ratio. Both algorithms used here (Artificial Neural Network and Random Forest) consequently demonstrate superior performance in tissue type prediction when double pulses are employed, as compared to single pulses. The combination of femtosecond double-pulse LIBS with machine learning algorithms has the potential to be an effective technique for thin biological samples (for example biopsy sections), with minimal ablation. [Display omitted] • Femtosecond double pulses LIBS was applied to enhance signal. • Double-pulse configuration was optimized on liver tissue. • Enhanced signal quality and better signal-to-noise ratio were achieved. • Machine learning algorithms were used to discriminate between tissue types. • Double pulses improved discrimination as compared to single pulses. [ABSTRACT FROM AUTHOR]

The global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future.

BackgroundMachine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery.MethodsThe study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process.ResultsA total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation.ConclusionsThis study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.