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Introduction Design Methods Results Conclusion Clinical Relevance The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI‐integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms.The scoping review followed the Preferred Reporting Items for Systematic Review and Meta‐Analysis Extension for Scoping Reviews (PRISMA‐ScR) guidelines.A structured search was performed across seven databases according to PRISMA‐ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI).Twenty‐six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate‐ to high‐quality studies. The most used ML algorithm was Random Forest. The four second‐most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s).The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains.The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches. • We evaluated process-based (PB) and machine-learning (ML) models for SOC prediction. • Random forest & support vector machine predicted SOC superior to PB models. • Reduced data availability and quality significantly weakened ML model performance. • ML-assisted PB model ensembles strongly improved model performance. [ABSTRACT FROM AUTHOR]

With the rapid development of wearable electronics, flexible pressure sensors have attracted wide attention in human-computer interaction and intelligent machines. However, it is a challenge to achieve a sensor with high sensitivity, wide measurement range, and wearing comfortability. Here, we develop an oriented electrospinning thermoplastic polyurethane/polyacrylonitrile (TPU/PAN) nanofibers (OETPN) based piezoresistive pressure sensor (PONPS) in which the active layer and the electrode are assembled perpendicularly. The interdigital electrode is fabricated by spraying silver nanowires (AgNWs) on the OETPN through a mask plate. The active layer is composed of OETPN coated with MXene, encapsulated on the electrode by polyurethane (PU) film. The porous structure of nanofibers membrane broadens the measurement range of the sensor. Employing oriented nanofibers as active layer can improve the sensitivity in low pressure, because oriented nanofibers without interweaving nanofibers are more compressible than disordered nanofibers. Electrode prepared using the spraying method creates nanoscale microstructure and increases sensitivity. The perpendicular assembly has greater response between the active layer and the electrode than the parallel assembly to improve the sensitivity. The sensor exhibits high sensitivity (6.71 kPa−1, 0.02–2 kPa) and wide measurement range (0.02–700 kPa). The sensor can detect weak signals such as radial artery. A pressure array constructed precisely represents the distribution of pressure. An intelligent throat is created by combining machine learning algorithms with the PONPS. It can detect and recognize subtle throat vibrations while speaking, achieving recognition accuracy up to 100% using support vector machine (SVM) for five words with different syllables. The fabricated sensor shows promising prospects in personal healthcare and intelligent robots. [ABSTRACT FROM AUTHOR]

This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems. [ABSTRACT FROM AUTHOR]

Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients' engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients' movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system. [ABSTRACT FROM AUTHOR]

Globally, slope failures cause severe disasters and substantial financial losses annually. Recent advancements in machine learning (ML) algorithms and dataset collection have created alternate solutions for complex slope stability problems. However, rock slope stability prediction remains a challenging problem due to factors such as inadequate data and insufficient generalization performance of rock slope prediction models. The black-box nature of AI models also causes further criticism of using such models to address issues such as slope stability. In this study, we proposed an artificial intelligence (AI) based technique for rock slope stability prediction based on evolutionary and ML algorithms. The proposed GA-XGBoost model uses XGBoost to model the relationship between the input and output parameters of rock slopes, while Genetic Algorithm (GA) optimizes the hyperparameters of XGBoost. A comprehensive rock slope database of 7525 slope cases is implemented in this study to develop and verify the model. The model attains an impressive performance score of R 2 = 0.9999 , M A E = 0.8006 , and R M S E = 1.8624 on the training dataset and R 2 = 0.9934 , M A E = 2.2793 , and R M S E = 11.1090 on the testing dataset. Furthermore, to assess the relative significance of the various influential slope parameters, the SHapley Additive exPlanations (SHAP) algorithm is implemented. This step enables the physical and quantitative interpretations of dependencies between the input and output variables. Generally, this relationship is hidden in traditional machine learning algorithms. [ABSTRACT FROM AUTHOR]

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization. [ABSTRACT FROM AUTHOR]

Predicting climatic conditions plays a very vital role in the different prominent industries including farming, transportation in affiliation with supply chain, construction, travel and tourism industries, etc. Capturing the changes in the weather is necessarily important and highly efficient for predicting the upcoming changes in the atmosphere. Hence the developed framework approach monitors the weather using the IOT technology along with an approach to forecast and predict it with efficient accuracy. The system is distinctly useful for smaller areas. Similarly, the system emphasizes weather prediction using different ML algorithms using Multiple Linear Regression, Random Forest Classifier, Decision Tree, and Gradient Boosting in a phased manner. Additionally, it searches for the best algorithm with the maximum prediction accuracy. The system compares the performances of the ML algorithms. The system works on the patchwork of IOT technology and ML algorithms to create a platform to view the weather conditions and predict the weather conditions. The combination of both technologies gives insights to users about weather conditions and predictions on the same platform. [ABSTRACT FROM AUTHOR]

The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimer 's disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimer's disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics. [ABSTRACT FROM AUTHOR]

Dementia, a neurodegenerative disorder, is more prominent among elderly people. This disease is one of the primary contributors amongst other diseases having a high social impact in continents of Europe and America. Treatment of the neurological disorders of dementia patients have become possible due to the Advances in medical diagnosis as in the use of Magnetic Resonance Imaging (MRI). Artificial Intelligence (AI) and Machine Learning (ML) techniques have provided solutions that enable fast, accurate and autonomous detection of diseases at their early stage. This in turn has improvised the entire health care system. This study proposes a diagnostic method, based on ML, for detecting dementia disease. The Open Access Series of Imaging Studies (OASIS) database and Alzheimer's dataset (4 class of images) have been used for testing and training of various ML models. This involves the classification of the dependent variable into demented and non-demented patient. ML models as in Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, k-nearest neighbor (KNN), Random Forest, Adaptive Boosting (ADA boost), Gradient Boosting, XG Boost, were trained and tested using OASIS dataset. Models were trained with 70% of data and tested on 30% of data. Hyper tuning of parameters of these models was also carried out to check for improvement in the results. Analysis showed that Naïve Bayes was the best amongst all giving 95% accuracy, 98% precision, 93% recall and 95% F1-score. [ABSTRACT FROM AUTHOR]

Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth–ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their classification virtually impossible to carry out manually. The lightning classification is important to distinguish the types of thunderstorms and to know their severity. Lightning type is also related to aerosol concentration and can reveal wildfires. Artificial Intelligence (AI) is a good approach to recognizing patterns and dealing with huge datasets. AI is the general denomination for different Machine Learning Algorithms (MLAs) including deep learning and others. The constant improvements in the AI field show us that most of the Lightning Location Systems (LLS) will soon incorporate those techniques to improve their performance in the lightning-type classification task. In this study, we assess the performance of different MLAs, including a SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), FCN (Fully Convolutional Network), and Residual Neural Network (ResNet) in the task of LEMP classification. We also address different aspects of the dataset that can interfere with the classification problem, including data balance, noise level, and LEMP recorded length. [ABSTRACT FROM AUTHOR]

This study presents a new model to predict the static formation temperature in multiple geothermal fields using multiple machine learning algorithms. Both artificial intelligence and non-artificial intelligent algorithms were used for this prediction. The model developed in this study predicts static formation temperature using several machine learning algorithms, including artificial neural networks, fuzzy logic, k-nearest neighbors, and random forest algorithms. Results are compared with the real temperatures obtained from two geothermal wells. The random forest, fuzzy logic, and k-nearest neighbors algorithms outperformed the artificial neural network, with the random forest algorithm achieving higher accuracy. The MAE of the random forest algorithm was 0.7% and the RMSE was 0.9%. The MAE values for the other machine learning algorithms ranging from 1.5 to 8.4%, and the RMSE values ranged from 2 to 20.8%. Artificial intelligence methods were found to be more accurate than non-artificial intelligence (mathematical) methods, with varying percentages. The results generated by the unique new model computation and the measured test data are substantially matched when compared to computed data and observed temperature data. The novelty of this newly developed artificial intelligence model is that it serves as a practical and inexpensive tool for static formation temperature determination in geothermal and petroleum wells. [ABSTRACT FROM AUTHOR]

Aim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the "black box" problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p ≤ 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients. [ABSTRACT FROM AUTHOR]

The intricate and multifaceted nature of diabetes disrupts the body's crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew's correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain. [ABSTRACT FROM AUTHOR]

Technology has led to advances that improve the daily lives of visually impaired people. B-Fit Glasses, smart glasses with sophisticated machine learning algorithms and sensors, cameras, microphones, and processing units orchestrated by a server, are the driving forces behind this technological evolution. The glasses automatically detect and alert users to surrounding obstacles, improving spatial awareness. Their skills go beyond this. Voice commands can transition to facial recognition mode to recognize and name familiar people or document recognition mode to read printed or handwritten information. Currency denomination detection and verbalization are other notable features. Onboard processors rapidly analyze and store data, ensuring real-time responsiveness and accuracy. The comprehensive, user-centric B-Fit Glasses give the visually impaired an enriched, autonomous, and safer method to interact with their surroundings, revolutionizing wearable assistive technology. [ABSTRACT FROM AUTHOR]

A data management system is crucial to businesses as it efficiently organizes, secures, and leverages data, enabling informed decision-making, streamlined operations, and improved competitiveness. Machine learning enhances data management by automating insights extraction, predictive analytics, and pattern recognition, optimizing data utilization and driving informed business strategies. DOCU SAFE is a web base secure data management system that uses machine learning and NLP to enable organizations to store, manage, and analyze their data securely. The system addresses the challenges associated with data uploading, moving, and processing, including size/storage/volume of data, inconsistency and variety of data, formatting of data, security of data, analyzing and mapping of data, and cost management issues. DOCU SAFE provides a scalable solution for data management, enabling organizations to handle large volumes of data efficiently. The system uses machine learning algorithms to ensure that data is consistent, accurate, and secure, providing organizations with insights that can be used to make informed decisions. The data we enter here can be directed to four categories of classification, hidden and highlight, encryption, and hygiene solutions according to our needs. They currently have many tools in this regard, but there are problems with them. In addition, there are separate tools for data classification, data hidden and highlight, data encryption, data hygiene solutions, and there is no possibility to do everything with one tool/system. Here, all the above issues are the provided by same tool. Overall, DOCU SAFE is an effective solution for organizations that handle sensitive data and need a secure and efficient data management system. [ABSTRACT FROM AUTHOR]

Background: Protein methylation, a post-translational modification, is crucial in regulating various cellular functions. Arginine methylation is required to understand crucial biochemical activities and biological functions, like gene regulation, signal transduction, etc. However, some experimental methods, including Chip–Chip, mass spectrometry, and methylation-specific antibodies, exist for the prediction of methylated proteins. These experimental methods are expensive and tedious. As a result, computational methods based on machine learning play an efficient role in predicting arginine methylation sites. Results: In this research, a novel method called PRMxAI has been proposed to predict arginine methylation sites. The proposed PRMxAI extract sequence-based features, such as dipeptide composition, physicochemical properties, amino acid composition, and information theory-based features (Arimoto, Havrda-Charvat, Renyi, and Shannon entropy), to represent the protein sequences into numerical format. Various machine learning algorithms are implemented to select the better classifier, such as Decision trees, Naive Bayes, Random Forest, Support vector machines, and K-nearest neighbors. The random forest algorithm is selected as the underlying classifier for the PRMxAI model. The performance of PRMxAI is evaluated by employing 10-fold cross-validation, and it yields 87.17% and 90.40% accuracy on mono-methylarginine and di-methylarginine data sets, respectively. This research also examines the impact of various features on both data sets using explainable artificial intelligence. Conclusions: The proposed PRMxAI shows the effectiveness of the features for predicting arginine methylation sites. Additionally, the SHapley Additive exPlanation method is used to interpret the predictive mechanism of the proposed model. The results indicate that the proposed PRMxAI model outperforms other state-of-the-art predictors. [ABSTRACT FROM AUTHOR]

HEZER, a ground-breaking analyzer for higher education that aims to improve the decision-making process for people starting their educational journeys. The developed system makes use of web scraping techniques and cutting-edge machine learning algorithms to deliver individualized recommendations, with an emphasis on solving issues like limited advice, financial limits, career possibilities, and limited options. Students are helped in choosing their ideal degree programs by insightful opinions from both newcomers and professionals in the sector that have been gathered through surveys and questionnaires. HEZER includes a full range of functionality, such as budget planning, financial estimation, career analysis, and loan payback predictions. It is powered by cutting-edge technologies like React.js, Python, and machine learning methods like Random Forest Regression. It allows students to arrange their finances effectively by allowing them to predict future course prices. Carefully crafted research components offer individualized recommendations, financial advice, and perceptive analysis of commercial potential connected to chosen projects. The system's accuracy and dependability have been confirmed through meticulous integration and testing. HEZER establishes a new benchmark for customized recommendation systems in higher education, providing students with crucial tools to negotiate the challenging environment and make well- informed decisions. choices, ultimately leading to successful and fulfilling careers. [ABSTRACT FROM AUTHOR]

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Email is a useful communication medium for better reach. There are two types of emails, those are ham or legitimate email and spam email. Spam is a kind of bulk or unsolicited email that contains an advertisement, phishing website link, malware, Trojan, etc. This research aims to classify spam emails using machine learning classifiers and evaluate the performance of classifiers. In the pre-processing step, the dataset has been analyzed in terms of attributes and instances. In the next step, thirteen machine learning classifiers are implemented for performing classification. Those classifiers are Adaptive Booster, Artificial Neural Network, Bootstrap Aggregating, Decision Table, Decision Tree, J48, K-Nearest Neighbor, Linear Regression, Logistic Regression, Naïve Bayes, Random Forest, Sequential Minimal Optimization and, Support Vector Machine. In terms of accuracy, the Random Forest classifier performs best and the performance of the Naïve Bayes classifier is substandard compared to the rest of the classifiers. Random Forest classifier had the accuracy of 99.91% and 99.93% for the Spam Corpus and Spambase datasets respectively. The naïve Bayes classifier had the accuracy of 87.63% and 79.53% for the Spam Corpus and Spambase datasets respectively. [ABSTRACT FROM AUTHOR]

The interest in artificial intelligence (AI) predictive models in the domains of petrophysics and well logs has been rapidly growing as it prevails as a powerful tool, given the relative data abundance. Formation resistivity prediction, despite its existing necessity, remains a challenge. The objectives of this study are to provide a framework of considerations and limitations of resistivity prediction and to introduce AI models to predict resistivity in horizontal low formation quality well. Logging while drilling data were obtained for the study from a 12″ section of a horizontal low formation quality well. Statistical analyses were carried out to identify and remove insignificant features. RHOB, DTC, DTS, NPHI, and GR logs were used as input to build and train the model. Data scaling and transformation techniques were applied to improve the model's accuracy and accelerate the rate of convergence. Four models were built and trained using artificial neural network (ANN), adaptive neuro-fuzzy inference system types 1 and 2 (ANFIS 1 & ANFIS 2), and Support vector machine. Cross plots, coefficient of determination (R2) and mean absolute percentage error (MAPE) were used to evaluate the effectiveness of the prediction. All of the four predictive models yielded comparable results, where R2 values ranged between 0.90 and 0.95 for the training data set, and 0.89, to 0.91 for testing dataset. ANN model had an inherent complexity with two hidden layers, 30 neurons each. The main applications of resistivity predicted values are to be used qualitatively for geo-steering applications and to estimate the saturation profile of logged intervals. For those two applications, the resistivity prediction accuracy is subject to the relative significance of the value magnitude. [ABSTRACT FROM AUTHOR]

Study into decision support applications in healthcare, such as those dealing to diagnosis, forecasting, treatment planning, and various aspects of patient care, among other things, have seen a significant uptick in interest over the past few years. This surge in interest has been accompanied by a substantial increase in funding. This expansion has taken place as a consequence of developments in artificial intelligence and machine learning, as well as an increase in the availability of computer resources, as well as an increase in the quantity of data that is readily available. Every day, examples of studies that are both very intriguing and have been peer-reviewed are published in journals, which demonstrates the promise of this topic. At the same time, however, there are certain expectations and assumptions regarding the development, validation, and acceptance of such techniques that are unrealistic and at times overly optimistic. These expectations and assumptions exist at the same time as the unrealistic and overly optimistic ones. There are in-depth lectures provided on the topics of machine learning and deep learning, both of which are often used for health recommender systems, as well as how they are applied in the field of health informatics. The purpose of this study is to investigate many approaches that have been used in the area of health informatics in the past. [ABSTRACT FROM AUTHOR]

Blood banks play a critical role in ensuring a steady supply of safe blood for medical procedures. However, donor recruitment and retention pose significant challenges to the sustainability of blood banks. This study proposes an AI-enabled decision-support system to optimize donor recruitment and retention strategies in blood banks. The system leverages machine learning algorithms to analyze historical donor data, demographic information, and external factors to predict donor behavior and identify potential strategies for improving recruitment and retention. By incorporating AI into decision-making processes, blood banks can make data-driven decisions, enhance the efficiency of donor management, and allocate resources effectively. This paper presents the methodology used to develop the AI-enabled system and discusses its potential benefits and implications for blood bank operations. Experimental results demonstrate the effectiveness of the system in identifying successful recruitment and retention strategies. Overall, the research offers valuable insights into the application of AI in blood bank management, ultimately leading to more sustainable and efficient donor recruitment and retention practices. [ABSTRACT FROM AUTHOR]

Background: Single nucleotide polymorphisms influence the effects of tacrolimus and cyclosporine in renal transplants. Aim: We set out to use machine learning algorithms (MLAs) to identify variables that predict the therapeutic effects and adverse events following tacrolimus and cyclosporine administration in renal transplant patients. Method: We sampled 120 adult renal transplant patients (on cyclosporine or tacrolimus). Generalized linear model (GLM), support vector machine (SVM), artificial neural network (ANN), Chi-square automatic interaction detection, classification and regression tree, and K-nearest neighbors were the chosen MLAs. The mean absolute error (MAE), relative mean square error (RMSE), and regression coefficient (β) with a 95% confidence interval (CI) were used as the model parameters. Results: For a stable dose of tacrolimus, the MAEs (RMSEs) of GLM, SVM, and ANN were 1.3 (1.5), 1.3 (1.8), and 1.7 (2.3) mg/day, respectively. GLM revealed that the POR*28 genotype and age significantly predicted the stable dose of tacrolimus as follows: POR*28 (β −1.8; 95% CI −3, −0.5; p = 0.006), and age (β −0.04; 95% CI −0.1, −0.006; p = 0.02). For a stable dose of cyclosporine, MAEs (RMSEs) of 93.2 (103.4), 79.1 (115.2), and 73.7 (91.7) mg/day were observed with GLM, SVM, and ANN, respectively. GLM revealed the following predictors of a stable dose of cyclosporine: CYP3A5*3 (β −80.8; 95% CI −130.3, −31.2; p = 0.001), and age (β −3.4; 95% CI −5.9, −0.9; p = 0.007). Conclusion: We observed that various MLAs could identify significant predictors that were useful to optimize tacrolimus and cyclosporine dosing regimens; yet, the findings must be externally validated. [ABSTRACT FROM AUTHOR]

The world's water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a brain condition that causes involuntary or uncontrolled movements, including tremors, rigidity, and problems with balance and coordination. People of various racial and cultural backgrounds are affected by Parkinson's disease. Early diagnosis of Parkinson's disease is essential to slow neurodegeneration, making the disease's prognosis even more important. This paper explores the prediction of Parkinson's disease utilizing various feature selection techniques and combinations of classifiers. Four distinct feature selection techniques: variance threshold, information gain, chi-square, and principal component analysis (PCA) are utilized in this research. We have adopted Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gaussian Naive Bayes, XGBoost, and AdaBoost classification techniques to predict Parkinson's disease. For the experimental evaluation, we have used the UCI machine learning Parkinson's speech recording signal dataset. The combination of PCA and KNN for correlation distance function provides 92.10% accuracy which is superior performance compared to other combinations of feature selection techniques and machine learning classifiers. In the future, if AI-based predictive models of Parkinson's disease can be developed, healthcare professionals will benefit from reducing neurodegeneration. [ABSTRACT FROM AUTHOR]

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets). [ABSTRACT FROM AUTHOR]

Climate plays a dominant role in influencing the process of evaporation and is projected to have adverse effects on water resources especially in the wake of a changing climate. In order to understand the impact of climate change on water resources, artificial intelligence models that possesses rapid decision-making ability, are used. This study was carried out to estimate evaporation in the Karaidemir Reservoir in Turkey with artificial neural networks (ANNs). The daily meteorological data covering the irrigation season were provided for a 30-year reference period and used to develop artificial neural network models. Predicted meteorological data based on climate change projections of HadGEM2-ES and MPI-ESM-MR under the Representative Concentration Pathway (RCP) 4.5 and 8.5 future emissions scenarios between 2000–2098 were utilized for future evaporation projections. The study also focuses on optimal crop patterns and water requirement planning in the future. ANNs model was run for each of the scenarios created based on ReliefF algorithm results using different testing-training-validation rates and learning algorithms of Bayesian Regularization (BR), Levenberg–Marquardt (L-M) and Scaled Conjugate Gradient (SCG). The performance of each alternative model was compared with coefficient of determination (R2) and mean square error (MSE) measures. The obtained results revealed that the ANNs model has high performance in estimation with a few input parameters, statistically. Projected surface water evaporation for the long term (2080–2098) showed an increase of 1.0 and 3.1% for the RCP4.5 scenarios of the MPI and HadGEM model, and a 14% decrease and 7.3% increase for the RCP8.5 scenarios, respectively. [ABSTRACT FROM AUTHOR]

Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project's success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects' risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets. [ABSTRACT FROM AUTHOR]

Road Traffic Accidents (RTA) cause human losses and irreparable physical and psychological damage to many of the victims. They also involve a very relevant economic dimension. It is urgent to improve the management of human and material resources for more effective prevention. This work makes an important contribution by presenting a methodology that allowed for achieving a predictive model for the occurrence of RTA on a road with a high RTA rate. The prediction is obtained for each road segment for a given time and day and combines results from statistical methods, spatial analysis, and artificial intelligence models. The performance of three Machine Learning (ML) models (Random Forest, C5.0 and Logistic Regression) is compared using different approaches for imbalanced data (random sampling, directional sampling, and Random Over-Sampling Examples (ROSE)) and using different segment lengths (500 m and 2000 m). This study used RTA data from 2016–2019 (training) and from May 2021–June 2022 (test). The most effective model was an ML logistic regression with the ROSE approach, using segments length 500 m (sensitivity = 87%, specificity = 60%, AUC = 0.82). The model was implemented in a digital application, and a Portuguese security force is already using it. [ABSTRACT FROM AUTHOR]

Background: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. Methods: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. Results: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849–0.856, calibration slopes 0.911–1.173, and Hosmer–Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. Conclusion: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine. Aim: We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD). Method: Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2 genotypes, were obtained for patients receiving warfarin for at least the previous six months. The C5.0 decision tree classification algorithm was used to predict poor ACS while classification and regression tree analysis (CART), in addition to the Chi-square automatic interaction detector (CHAID), was used to predict SWWD. The percentage of patients within 20% of the actual dose, root mean squared error (RMSE), and area under the receiver-operating characteristics curve (AUROC) were identified as performance indicators of the models. Results: In the C5.0 classification decision tree, the CYP4F2 genotype was the strongest predictor of ACS (AUROC = 0.53). In the CART analysis of SWWD, VKORC1 polymorphisms were the most significant predictor, followed by the CYP2C9 genotype (percentage of patients within 20% of the actual dose = 38.2%, RMSE = 13.6). For the CHAID algorithm, the percentage of patients within 20% of the actual dose was 49%, while the RMSE was found to be 13.4. Conclusion: Genetic and non-genetic predictive factors were identified by the MLAs for ACS and SWWD. Further, the need to externally validate the MLAs in a prospective study was highlighted. [ABSTRACT FROM AUTHOR]

In the last two decades there has been obtained tremendous improvements in the field of artificial intelligence (AI) especially in the sector of face/facial recognition (FR). Over the years, the world obtained remarkable progress in the technology that enhanced the face detection techniques use on common PCs and smartphones. Moreover, the steadily progress of programming languages, libraries, frameworks, and tools combined with the great passion of developers and researchers worldwide contribute substantially to open-source AI materials that produced machine learning (ML) algorithms available to any scholar with the will to build the software of tomorrow. The study aims to analyze the specialized literature starting from the first prototype delivered by Cambridge University until the most recent discoveries in FR. The purpose is to identify the most proficient algorithms, and the existing gap in the specialized literature. The research builds a FR application based on simplicity and efficiency of code that facilitates a person's face detection using a real time photo and validate the access by querying a given database. The paper brings contribution to the field throughout the literature review analysis as well as by the customized code in Phyton, using ML with Principal Component Analysis (PCA), AdaBoost and MySQL for a myriad of application's development in a variety of domains. [ABSTRACT FROM AUTHOR]

This research aims to increase people's awareness of fake news on online social networks and help them determine the reliability of the information they consume. It investigates methods for detecting fake news sources, authors, and subjects on online social networks. The project uses an open-source online dataset of fake and real news to determine the credibility of news. Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TFIDF feature extraction achieving the highest accuracy of 99.36%. Random Forest (RF) and Naive Bayes (NB) had accuracy scores of 98.25% and 94.74%, respectively. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100–8500 kW). The volume of Big Data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient ($R^{2}$) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA). [ABSTRACT FROM AUTHOR]

Amplifier design, both in traditional approaches and real-time circuit optimization, greatly benefits from fast and thorough extraction of information from measurement data. Using only a few performance samples at varying impedances, deep learning image completion techniques can be utilized to extrapolate an entire set of Smith chart load-pull contours. In addition to speeding nonlinear device characterizations, this extrapolation can be performed in an iterative fashion for use as a circuit optimization algorithm with a very low number of measurements. The techniques of this work have been tested in the measurement of a nonlinear, large-signal amplifier. The load impedance can be estimated with a typical error of < 0.1 linear units using as few as seven impedances and yields even better accuracy with larger sample sizes. [ABSTRACT FROM AUTHOR]

The high prevalence of Alzheimer-type dementia and the limitations of traditional neuropsychological tests motivate the introduction of new cognitive assessment methods. We discuss the validation of an all-digital, ecological and non-intrusive e-health application for the early detection of cognitive impairment, based on artificial intelligence for patient classification, and more specifically on machine learning algorithms. To evaluate the discrimination power of this application, a cross-sectional pilot study was carried out involving 30 subjects: 10 health control subjects (mean age: 75.62 years); 14 individuals with mild cognitive impairment (mean age: 81.24 years) and 6 early-stage Alzheimer's patients (mean age: 80.44 years). The study was carried out in two separate sessions in November 2021 and January 2022. All participants completed the study, and no concerns were raised about the acceptability of the test. Analysis including socio-demographics and game data supports the prediction of participants' cognitive status using machine learning algorithms. According to the performance metrics computed, best classification results are obtained a Multilayer Perceptron classifier, Support Vector Machines and Random Forest, respectively, with weighted recall values >= 0.9784 ± 0.0265 and F1-score = 0.9764 ± 0.0291. Furthermore, thanks to hyper-parameter optimization, false negative rates were dramatically reduced. Shapley's additive planning (SHAP) applied according to the eXplicable AI (XAI) method, made it possible to visually and quantitatively evaluate the importance of the different features in the final classification. This is a relevant step ahead towards the use of machine learning and gamification to early detect cognitive impairment. In addition, this tool was designed to support self-administration, which could be a relevant aspect in confinement situations with limited access to health professionals. However, further research is required to identify patterns that may help to predict or estimate future cognitive damage and normative data. [ABSTRACT FROM AUTHOR]

Machine prediction algorithms (e.g., binary classifiers) often are adopted on the basis of claimed performance using classic metrics such as precision and recall. However, classifier performance depends heavily upon the context (workflow) in which the classifier operates. Classic metrics do not reflect the realized performance of a predictor unless certain implicit assumptions are met, and these assumptions cannot be met in many common clinical scenarios. This often results in suboptimal implementations and in disappointment when expected outcomes are not achieved. One common failure mode for classic metrics arises when multiple predictions can be made for the same event, particularly when redundant true positive predictions produce little additional value. This describes many clinical alerting systems. We explain why classic metrics cannot correctly represent predictor performance in such contexts, and introduce an improved performance assessment technique using utility functions to score predictions based on their utility in a specific workflow context. The resulting utility metrics (u-metrics) explicitly account for the effects of temporal relationships and other sources of variability in prediction utility. Compared to traditional measures, u-metrics more accurately reflect the real-world costs and benefits of a predictor operating in a realized context. The improvement can be significant. We also describe a formal approach to snoozing, a mitigation strategy in which some predictions are suppressed to improve predictor performance by reducing false positives while retaining event capture. Snoozing is especially useful for predictors that generate interruptive alarms. U-metrics correctly measure and predict the performance benefits of snoozing, whereas traditional metrics do not. [ABSTRACT FROM AUTHOR]

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- $\alpha$ family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app , which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine. [ABSTRACT FROM AUTHOR]

In the 2015 Canadian case of Ewert v. Canada, risk assessment tools were put on trial in Canada’s Federal Court and eventually at the Supreme Court of Canada, their efficacy was challenged, and their reliability was upended ,. Risk assessment tools are used by the justice system to present a calculated prediction of an offender’s risk of future criminal behavior. These tools are viewed by the legal community as precursors to machine learning and technological advancements in artificial intelligence (AI) in the criminal justice system, using algorithmic and data-driven decision-making to provide courts with predictions regarding an offender’s risk if released. [ABSTRACT FROM AUTHOR]

Speech disorder is a significant problem for people affected with Parkinson's disease (PD) leading to a substantial disability to communicate with others. PD affects the voice, including changes in pitch, intensity, articulation, and syllable rate.We aimed to study the current status of artificial intelligence (AI) using machine learning algorithms (MLAs) in the assessment of speech abnormalities in PD along with the generation of intelligible synthetic speech for voice rehabilitation. We searched the literature for studies focusing on speech/voice disorder in PD and rehabilitation techniques till June 18, 2022. We searched PubMed and Engineering Village (Compendex and Inspec combined) databases. After careful screening of the title and evaluation of abstracts, we used select articles describing the use of AI or its various forms in the management of speech abnormalities in PD to synthesize this review. MLAs classify PD and non-PD patients with an accuracy of more than 90% using only voice features. Non-acoustic sensors can rehabilitate PD patient by converting dysarthric speech to highly intelligible speech using MLAs. MLAs can automatically assess several speech features and quantify the progression of speech abnormalities in PD. PD speech rehabilitation using MLAs may prove superior to other available therapies. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning use a wide variety of algorithms. More than 60 years of research have shown that intelligent behavior requires a variety of methods. We discuss the major classes of algorithms and their uses. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, "Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?" Design/methodology/approach: This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, "Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?" The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings: The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value: The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive. [ABSTRACT FROM AUTHOR]

Based on a country study related to money laundering and terrorist financing, the Financial Action Group downgraded Hungary's compliance with Recommendation R15 (use of new technologies). At the same time, between 2020 and 2021, the Magyar Nemzeti Bank imposed fines on several commercial banks operating in Hungary for shortcomings on complying with money laundering and terrorist financing regulations. As a gap-filling analysis, the study examines supervised (classification, regression), unsupervised (clustering, anomaly detection), and hybrid machine learning models and algorithms operating based on highly unbalanced dataset of anti-money laundering and terrorist financing prevention of banking risk management. The author emphasizes that there is no one ideal algorithm. The choice between machine learning algorithm is highly determined based on the underlying theoretical logic and additional comparative. Model building requires a hybrid perspective of the give business unit, IT and visionary management. [ABSTRACT FROM AUTHOR]

In today's healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN. [ABSTRACT FROM AUTHOR]

The end of Moore's Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the training remains confined to the cloud, imposing the transaction of high volumes of private data over a network and leading to unpredictable delays when ML applications attempt to adapt to adversarial environments. To fill this gap, we make the first attempt to evaluate the feasibility of ANN training in Arm Cortex-M MCUs. From the available optimization algorithms, stochastic gradient descent (SGD) has the best trade-off between accuracy, memory footprint, and latency. However, its original form and the variants available in the literature still do not fit the stringent requirements of Arm Cortex-M MCUs. We propose L-SGD, a lightweight implementation of SGD optimized for maximum speed and minimal memory footprint in this class of MCUs. We developed a floating-point version and another that operates over quantized weights. For a fully-connected ANN trained on the MNIST dataset, L-SGD (float-32) is 4.20× faster than the SGD while requiring only 2.80% of the memory with negligible accuracy loss. Results also show that quantized training is still unfeasible to train an ANN from the scratch but is a lightweight solution to perform minor model fixes and counteract the fairness problem in typical FL systems. [ABSTRACT FROM AUTHOR]

• DSPA and CNN are the best ML algorithms for sepsis prediction. • Imbalance-XGBoost outperforms other traditional ML algorithms. • DSPA can improve sepsis diagnosis and management. • Further research needed on poor-performing ML algorithms. With the integration of artificial intelligence and clinical medicine, machine learning (ML) algorithms have been applied to develop sepsis predictive models for sepsis management. The purpose is to systematically summarize existing evidence to determine the effectiveness of ML algorithms in sepsis. We conducted a systematic electronic search of databases including PubMed, Cochrane Library, Embase, and the Web of Science, and included all case-control and cohort studies using terms reflecting sepsis and ML up to September 2023. statistical software STATA was used for network meta -analysis, and QUADAS-2 tool was used to assess the certainty of evidence. The SUCRA results for sensitivity, specificity, and predictive accuracy of various models are as follows: DSPA (77.0 %) > Imbalance-XGBoost (72.9 %) > CNN + Bi-LSTM (69.7 %) > CNN (67.3 %) > LR (62.4 %) > Ensemble model (55.9 %) > RF (53.2 %) > ET (51.3 %) > XGBoost (49.1 %) > DNN (48.1 %) > MLP (47.5 %) > RBF (47.1 %) > KNN (45.8 %) > NB (33.3 %) > SVM (13.7 %) > Bi-LSTM (5.7 %); CNN (78.3 %) > CNN + Bi-LSTM (77.6 %) > DSPA (75.1 %) > ET (69 %) > Bi-LSTM (68.5 %) > MLP (51 %) > RBF (50.2 %) > KNN (47.3 %) > RF (47 %) > Ensemble Model (43.4 %) > XGBoost (38.1 %) > SVM (37.3 %) > NB (34.2 %) > DNN (31.1 %) > LR (30.4 %) > Imbalance-XGBoost (21.5 %); DSPA (85.9 %) > CNN + Bi-LSTM (82.6 %) > CNN (81.9 %) > Imbalance-XGBoost (76.8 %) > ET (67.8 %) > RF (51.1 %) > Ensemble model (47.7 %) > XGBoost (44.4 %) > LR (42.7 %) > MLP (38.1 %) > RBF (37.8 %) > KNN (37.3 %) > DNN(35.8 %) > Bi-LSTM(33.3 %) > NB(21.5 %) > SVM(15.3 %). DSPA and CNN may be the best ML algorithms for predicting sepsis. Imbalance-XGBoost algorithm outperformed other traditional ML algorithms in terms of sensitivity and predictive accuracy. This study has several implications for clinical practice and research, highlighting the potential benefits of using ML algorithms in sepsis management, particularly in improving sepsis detection and reducing mortality rates. Through our systematic review and network meta -analysis, we have provided a comprehensive and accurate assessment of the effectiveness of ML algorithms in sepsis prediction, emphasizing the need for further exploration and evaluation of these algorithms to advance sepsis management. [ABSTRACT FROM AUTHOR]

Algorithms are becoming ubiquitous. However, companies are increasingly alarmed about their algorithms causing major financial or reputational damage. A new industry is envisaged: auditing and assurance of algorithms with the remit to validate artificial intelligence, machine learning, and associated algorithms. [ABSTRACT FROM AUTHOR]