Search

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Total Reflection (ATR-FTIR) platform was applied to discriminate systemic diseases using saliva, however, the salivary diagnostic application in viral diseases is less explored. The study aimed to identify unique vibrational modes of salivary infrared profiles to detect CHIKV infection using chemometrics and artificial intelligence algorithms. Thus, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with CHIKV (20 µl, 1 X 10 5 PFU/ml, n = 6) or vehicle (20 µl, n = 7). Saliva and serum samples were collected on day 3 (due to the peak of viremia). CHIKV infection was confirmed by Real-time PCR in the serum of CHIKV-infected mice. The best pattern classification showed a sensitivity of 83%, specificity of 86%, and accuracy of 85% using support vector machine (SVM) algorithms. Our results suggest that the salivary ATR-FTIR platform can discriminate CHIKV infection with the potential to be applied as a non-invasive, sustainable, and cost-effective detection tool for this emerging disease., (© 2024. The Author(s).)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Large language models (LLMs) have gained prominence since the release of ChatGPT in late 2022., Objective: The aim of this study was to assess the accuracy of citations and references generated by ChatGPT (GPT-3.5) in two distinct academic domains: the natural sciences and humanities., Methods: Two researchers independently prompted ChatGPT to write an introduction section for a manuscript and include citations; they then evaluated the accuracy of the citations and Digital Object Identifiers (DOIs). Results were compared between the two disciplines., Results: Ten topics were included, including 5 in the natural sciences and 5 in the humanities. A total of 102 citations were generated, with 55 in the natural sciences and 47 in the humanities. Among these, 40 citations (72.7%) in the natural sciences and 36 citations (76.6%) in the humanities were confirmed to exist (P=.42). There were significant disparities found in DOI presence in the natural sciences (39/55, 70.9%) and the humanities (18/47, 38.3%), along with significant differences in accuracy between the two disciplines (18/55, 32.7% vs 4/47, 8.5%). DOI hallucination was more prevalent in the humanities (42/55, 89.4%). The Levenshtein distance was significantly higher in the humanities than in the natural sciences, reflecting the lower DOI accuracy., Conclusions: ChatGPT's performance in generating citations and references varies across disciplines. Differences in DOI standards and disciplinary nuances contribute to performance variations. Researchers should consider the strengths and limitations of artificial intelligence writing tools with respect to citation accuracy. The use of domain-specific models may enhance accuracy., (©Joseph Mugaanyi, Liuying Cai, Sumei Cheng, Caide Lu, Jing Huang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.04.2024.)

Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare., (Copyright © 2023 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)

Background: Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients., Objective: To devise an ML algorithm for early mortality prediction in PE patients by employing clinical and laboratory variables., Methods: This study utilized diverse oversampling techniques to improve the performance of various machine learning models including ANN, SVM, DT, RF, and AdaBoost for early mortality prediction. Appropriate oversampling methods were chosen for each model based on algorithm characteristics and dataset properties. Predictor variables included four lab tests, eight physiological time series indicators, and two general descriptors. Evaluation used metrics like accuracy, F1_score, precision, recall, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves, providing a comprehensive view of models' predictive abilities., Results: The findings indicated that the RF model with random oversampling exhibited superior performance among the five models assessed, achieving elevated accuracy and precision alongside high recall for predicting the death class. The oversampling approaches effectively equalized the sample distribution among the classes and enhanced the models' performance., Conclusions: The suggested ML technique can efficiently prognosticate mortality in patients afflicted with acute PE. The RF model with random oversampling can aid healthcare professionals in making well-informed decisions regarding the treatment of patients with acute PE. The study underscores the significance of oversampling methods in managing imbalanced data and emphasizes the potential of ML algorithms in refining early mortality prediction for PE patients., Competing Interests: Declaration of competing interest We, the authors of the paper entitled "Advancing Prognostic Precision in Pulmonary Embolism: A Clinical and Laboratory-Based Artificial Intelligence Approach for Enhanced Early Mortality Risk Stratification," hereby declare our interests in relation to the subject matter presented in the manuscript., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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).

Predictive models are widely used in clinical practice. Despite of the increasing number of published AI systems, recent systematic reviews have identified lack of statistical rigor in the development and validation of predictive models. This work reviewed the current literature for predictive performance measures and resampling methods. Furthermore, common pitfalls were discussed., (© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.)

Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns., Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker's immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields., Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration., Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.

Instead of only focusing on the targeted drug delivery system, researchers have a great interest in developing peptide-based therapies for the procurement of numerous class of diseases. The main idea behind this is to anchor the properties of the receptor to design peptide-based therapeutics. As these macromolecules have distinct physicochemical properties over small molecules, it becomes an obligatory field for the treatment of diseases. For this, various in silico models have been developed to speculate the proteins by virtue of the application of machine learning and artificial intelligence. By analysing the properties and structural alert of toxic proteins, researchers aim to dissert some of the mechanisms of protein toxicity from which therapeutic insights may be drawn. Numerous models already exist worldwide emphasizing themselves as leading paramount for toxicity prediction in protein macromolecules. Few of them comparatively compete with the other predictive protein toxicity models and convincingly give a high-performance result in terms of accuracy. But their foundation is quite ambiguous, and varying approaches are found at the level of toxicoproteomic data utilization while building a machine learning model. In this review work, we present the contribution of artificial intelligence and machine learning approaches in prediction of protein toxicity using proteomics data., (© 2020 John Wiley & Sons A/S.)

This review aims to provide a comprehensive recapitulation of the evolution in the field of cardiac rhythm monitoring, shedding light in recent progress made in multilead ECG systems and wearable devices, with emphasis on the promising role of the artificial intelligence and computational techniques in the detection of cardiac abnormalities., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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]

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.

Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully.

With the widespread popularity of intelligent education, artificial intelligence plays an important role in the field of education. Currently, there are issues such as low accuracy and low adaptability. By comparing algorithms such as logistic regression, decision tree, random forest (RF), support vector machine, and long short-term memory (LSTM) recurrent neural network (RNN), this article adopted a multi-classification fusion strategy and fully considered the adaptability of the algorithm to evaluate and grade students in two scenarios with different grades and teaching quality. By encoding and normalizing student grades, six evaluation parameters were selected for the evaluation criteria of teaching quality through principal component analysis feature selection. Multi-classifier models were used to fuse the five models in pairs, improving the accuracy of the experimental evaluation. Finally, the experimental data of the six fused multi-classification models in the scenarios of student performance estimation and teaching quality estimation were compared, and the experimental effects of education evaluation and grading under different models were analyzed. The experimental results showed that the LSTM RNN-RF model had the strongest adaptability in the scenario of student performance estimation, with an estimation accuracy of 98.5%, which was 12.9% higher than a single RF model. This experiment was closely related to educational scenarios and fully considered the adaptability of different machine learning algorithms to different scenarios, improving the prediction and classification accuracy of the model.

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: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully. [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]

Forecasting the state of charge (SOC) using battery control systems is laborious because of their longevity and reliability. Since battery degradation is typically nonlinear, predicting SOC estimation with significantly less degradation is laborious. So, the estimation of SOC is an increasingly major problem in ensuring the effectiveness and safety of the battery. To overcome these issues in SOC estimation, we found many methods in the scientific literature, with differing degrees of precision and intricacy. The SOC of lithium‐ion batteries can now be precisely predicted using supervised learning approaches. Reliable assessment of the SOC of a battery ensures safe operation, extends battery lifespan, and optimizes system performance. This work compares and studies the performance, benefits, and drawbacks of five supervised learning techniques for SOC estimates. Different SOC estimate methods are discussed, including both conventional and contemporary methods. These consist of techniques using voltage and current measurements and more complex algorithms using electrochemical models, impedance spectroscopy, and machine learning methods, incorporating the use of artificial intelligence and machine learning for flexible SOC estimation. In the future, SOC estimates will be a crucial component of a larger ecosystem for energy management, allowing for the seamless integration of energy storage into smart grids and adopting more environmentally friendly energy habits. The five methods we compare are random forest RF, gradient boosting machines, extra tree regressor, XG Boost, and DT. In these five methods, we are going to investigate, review, and discuss the current algorithms and overcome them to select one of the most precise and accurate algorithms to predict the accurate estimation of lithium‐ion battery SOC. [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]

The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtaining the mechanical parameters. During the work, the possibility of using the selected algorithm based on artificial intelligence like Random Forest, Decision Tree, K-Nearest Neighbors, Fuzzy K-Nearest Neighbors, Gradient Boosting, XGBoost, AdaBoost was also checked. During the work, tests were carried out to check which algorithm would be best for use in the decision support system being developed. Cross-validation was used, as well as hyperparameter tuning using different evaluation sets. In both cases, the best model turned out to be Random Forest, whose F1 metric score is 98.66% for cross-validation and 99.10% after tuning on the test set. This model can be considered the most promising in solving this problem. The first result is a more accurate estimate of how the model will behave for new data, while the second model talks about possible improvement after optimization or possible overtraining to the parameters. [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]

Maternal mortality is a major public health concern worldwide. It is the number of preventable deaths that occur each year due to pregnancy and childbirth. The research investigates how machine learning may be used to minimize maternal mortality. Historical data on maternal health is used to develop predictive models, early detection systems, and resource allocation techniques. Machine learning helps to identify risk factors, monitor vital signs, and improve access to care. This allows for targeted interventions and better healthcare delivery. The challenges of data accessibility and model interpretation are addressed, highlighting the ethical and equitable applications of machine learning in maternal healthcare. This study emphasizes the potential of machine learning to reduce maternal mortality rates and the pressing need for its incorporation into healthcare systems worldwide.

In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field. [ABSTRACT FROM AUTHOR]

Background: The success rate of clarithromycin‐containing Helicobacter pylori treatment had declined globally. This study aims to explore the role of different machine learning algorithms in predicting failure of H. pylori treatment. Materials and Methods: We included 84,609 adult patients who had received the first course of clarithromycin‐containing triple therapy for H. pylori in Hong Kong from 2003 to 2013 as training set. Results were validated in patients who had received similar triple therapy with 27,736 Hong Kong patients between 2014 and 2017 (internal cohort); and 18,050 UK patients between 2012 and 2017 (external cohort). The performance of 11 available machine learning algorithms were used to predict the failure of triple therapy. The performance was determined by the area under receiver operating characteristic curve (AUC). Results: The treatment failure rates in the training, internal and external validation cohort was 5.9%, 9.5%, and 6.1%, respectively. In the internal validation set, Extra‐Tree (ET) Classifier had the best AUC (0.88; 95% CI, 0.87–0.88), sensitivity (79.6%; 95% CI, 79.0–80.2) and specificity (79.4%; 95% CI, 79.0–79.8). In the external validation set, ET Classifier also had the best AUC (0.85; 95% CI, 0.85–0.86), sensitivity (80.1%; 95% CI, 79.5–80.9), and specificity (80.2%; 95% CI, 78.8–81.3). Top features of importance used by ET Classifier in predicting treatment failure included time interval between antibiotic use and triple therapy (48.8%), age (29.1%) and triple therapy regime (6.28%). Conclusions: Machine learning algorithm, based on simple baseline clinical parameters, could help to identify patients at high risk of failure from clarithromycin‐containing triple therapy for H. pylori. [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]

Machine learning (ML), a branch of artificial intelligence (AI), has been increasingly used in environmental engineering due to the ability to analyze complex nonlinear problems (such as ones connected with water quality management) through a data-driven approach. This study provides an overview of different ML algorithms applied for monitoring and predicting river water quality. Different parameters could be monitored or predicted, such as dissolved oxygen (DO), biological and chemical oxygen demand (BOD and COD), turbidity levels, the concentration of different ions (such as Mg2+ and Ca2+), heavy metal or other pollutant's concentration, pH, temperature, and many more. Although many algorithms have been investigated for the prediction of river water quality, there are several which are most commonly used in engineering practice. These models mostly include so-called supervised learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), random forest (RF), decision tree (DT), and deep learning (DL). To further enhance prediction power, novel hybrid algorithms, could be used. However, the quality of prediction is not only dependent on the applied algorithm but also on the availability of previously mentioned water quality parameters, their selection, and the combination of input data used to train the ML model. HIGHLIGHTS Classification, prediction, and anomaly detection algorithms were reviewed.; Hydrometeorology data can be used to compensate for missing parameter data.; Algorithms can struggle with generalization aspects important for real applications.; Covering critical sampling points and periods could enhance prediction accuracy.; Hybrid models could overcome the limitations of single models.;

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]

Hearing loss detection using automated audiometers and artificial intelligence methods has gained increasing attention in recent years. The proposed work aims: (a) to design an automated audiometer to diagnose hearing ability and to evaluate hearing intensity for healthy and profound hearing loss patients within 250 Hz to 8 kHz, (b) to compare the proposed automated audiometer with a conventional audiometer when estimating auditory perception level using pure tone and speech audiometers, and (c) to use the machine learning algorithms to classify hearing loss and normal subjects based on the selected features extracted from speech signals. Participants in the study included 50 healthy individuals and 50 patients with profound hearing loss. In the proposed hardware unit, the transmitted pure-tone signal and the speech signal stimulus are controlled automatically instead of being controlled manually. Using a digital potentiometer, a pure-tone audiometer can be automatically calibrated by varying the frequency and intensity of the generated tones according to the users' responses. During speech audiometric measurements, pre-recorded speech and repeated speech signals are analyzed to estimate speech recognition threshold (SRT) and word recognition score (WRS). The designed audiometer plots the audiogram automatically, estimating SRT and WRS, and classifying the subject as normal or hearing impaired. This study demonstrates the feasibility of using Machine Learning to predict hearing impairment in patients. A support vector machine, a random forest, and an AdaBoost model produced accuracy rates of 98%, 96%, and 96%, respectively, when identifying normal and hearing loss subjects. The proposed audiometer system is miniaturized, portable, and user-friendly in comparison to conventional audiometers. Consequently, the prototype would make it possible for subjects to conduct their own audiometric tests independently and send the results along with their audiogram to a trained medical professional to receive advice. [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]

A sophisticated system for predicting loan eligibility that takes credit scores and history into account is urgently needed due to the banking sector's rapid expansion. This study paper integrates the contributions of four separate components put forth by several researchers to present a comprehensive and new strategy. Using cutting-edge machine learning algorithms like Random Forest, Gradient Boosting, and Linear Regression, the first component predicts the best bank rate possibilities. The second component, which focuses on determining applicants' loan eligibility, presents a revolutionary technique that uses their credit histories and considers important factors including their gender, marital status, level of education, income, and credit history itself. The third element suggests a sophisticated Loan Eligibility Prediction System that includes several crucial subobjectives, including creditworthiness assessment, income and employment verification, collateral analysis, risk profiling, fraud detection, and regulatory compliance, to ensure thorough risk analysis. To forecast the applicant’s risk level efficiently and effectively, this component applies the power of logistic regression. The results are laid out in a simple tabular format for simple understanding. To help with mortgage estimation and analysis, the fourth component includes a sophisticated mortgage calculator tool that uses Decision Tree Regression. Using this tool, users may calculate and evaluate mortgage values based on important property characteristics like the number of bathrooms, beds, the size of the house, and the location. This research study suggests a novel and complete loan eligibility prediction system that combines these four elements and makes use of historical data, cutting-edge machine learning methods, and credit ratings. This approach facilitates rapid and successful lending processes while avoiding risks and maximizing outcomes, empowering both borrowers and lenders to make knowledgeable decisions. [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]

Dear Editor Traumatic brain injury (TBI) is a common cause of death and disability, with long-term consequences including cognitive impairment, emotional disturbances and physical disability. Prognosis prediction in TBI patients is critical for guiding treatment decisions and improving patient outcomes. The Glasgow coma scale (GCS) and Glasgow outcome scale (GOS) are useful tools to assess and monitor TBI patients, but other factors such as age, motor component of GCS, and type of injury also significantly influence clinical outcomes. With the advent of artificial intelligence (AI), there is growing interest in using machine learning algorithms to predict prognosis in TBI patients.[1] Here we discuss the harnessing of AI in prognosis prediction for TBI patients, as well as its potential benefits, disadvantages, challenges and predicted future directions. Current state of AI in prognosis prediction for TBI patientsReflecting on large bodies of data (clinical, radiographic and laboratory), AI has shown promise in predicting prognosis in TBI patients. Machine learning algorithms (MLA) can identify patterns in data that may not be immediately apparent to human clinicians, allowing for more accurate predictions of patient outcome. There are several studies that have used MLA to develop diagnostic systems for TBI and predict patient outcomes. These studies have used various clinical inputs such as vital signs, electroencephalography (EEG), hospital volume, Charlson comorbidity index and length of stay to develop machine learning (ML) models.[2] In a study conducted by Mawdsley et al, the effectiveness of ML models in predicting psychosocial aspects of TBI cases was systematically reviewed. The study included nine studies with eleven types of ML used to predict various outcomes of traumatic brain injury, concussion and psychosocial outcomes. The results indicated that while these models were able to develop predictive models successfully, there is insufficient evidence to consider ML algorithms as a dependable tool for clinical decision-making.[3] Alanazi et al., conducted a review in 2017 to assess the effectiveness of ML models in predicting patient outcomes for various disorders. The study revealed that AI has the potential to create several promising models that can predict outcomes using clinical, demographic, and imaging data. However, there are still limitations in applying these models in clinical settings. Therefore, further research is necessary to improve the reliability of these models in the future.[4] Potential benefits of AI in prognosis prediction for TBI patientsThe use of AI in prognosis prediction for TBI patients has several potential benefits. First and foremost, it can provide clinicians with more accurate and reliable predictions of patient outcomes, allowing for more informed treatment decisions. This can lead to improved patient outcomes and reduced healthcare costs. AI can help identify patients who are at high risk for poor outcomes, allowing for early interventions that may improve their prognosis, or early decision making around withdrawal of treatment. For example, if an algorithm predicts a patient is at high risk of adverse effects from an intracranial hemorrhage, clinicians can perform early surgical interventions or administer medications early to prevent further bleeding. In addition, AI can help identify patients who are likely to have favorable outcomes, or who do not require intervention, which allows for more efficient distribution of healthcare resources. For example, if an algorithm predicts a patient is likely to have a good outcome, they may not require intensive care unit (ICU) admission, or may not require prolonged rehabilitation services. Finally, AI can also help reduce the workload of medical professionals by performing repetitive tasks such as data entry and analysis. This can free up time for physicians and nurses to focus on providing high-quality care to patients.[1,5] Potential disadvantages of AI in prognosis prediction for TBI patientsThere are several potential disadvantages of using AI in predicting the clinical outcomes of patients with TBI. AI algorithms rely on large amounts of data to make accurate predictions. However, there may not be enough high-quality data available to train AI models effectively. Data entry relies on human factors and may be poorly or incorrectly entered, making errors possible. Also, AI algorithms may be biased if the data used to train them is not representative of the population being studied. For example, if the data used to train an AI model on TBI outcomes comes from a specific hospital or region, the model may not generalize well to other populations. AI models can be complex and difficult to interpret. Clinicians may struggle to understand how the model arrived at a particular prediction, which could lead to skepticism or mistrust of the technology. Finally, there are ethical considerations around the use of AI in healthcare. If an AI model predicts that a patient has a poor prognosis, this could lead to decisions about end-of-life care, which may go against the patient's wishes or values. Clinicians must carefully consider the potential risks and benefits of using AI in clinical decision-making.[1,5] Future DirectionsThe future of prognosis prediction in TBI patients using AI is promising. However, there are several challenges that must be addressed. One challenge is the lack of standardized data collection and reporting. To develop accurate algorithms, large amounts of high-quality data are required. However, there is currently no standardized way to collect and report TBI data, making it difficult to develop accurate, widely transferable algorithms. Another challenge is the need for validation studies. While initial research has shown promising results, further validation studies are needed to confirm the accuracy and reliability of AI algorithms in predicting TBI prognosis,[3,4] as well as ensuring safety in clinical practice.[1,6]With the increasing availability of electronic health records and the development of advanced AI algorithms, there is potential for more accurate and reliable predictions of TBI outcomes. AI can help identify patterns and correlations in patient data that may not be easily recognizable by human clinicians, leading to more personalized and effective treatment plans, however, there is the challenge of integrating AI into clinical practice. Clinicians must be trained on how to interpret and use AI predictions in their decision-making processes. This will require collaboration between clinicians and AI experts to develop user-friendly interfaces that can be integrated into clinical workflows. ConclusionThe reliability of prognosis prediction in TBI patients using AI depends on the quality and completeness of the data, the type of algorithm used and the level of validation testing performed. Conclusions surrounding the selection of optimal clinical or para-clinical features and the most precise machine learning model for predicting outcomes in TBI patients is still inconsistent. However, early studies have shown that AI algorithms can accurately predict mortality and functional outcomes in TBI patients by analyzing large amounts of patient data, including clinical, radiographic, and laboratory data. Therefore, further validation studies are needed to confirm the accuracy and reliability of AI algorithms in predicting TBI prognosis before they can be integrated into clinical practice. With continued research and collaboration between clinicians and AI experts, the use of AI in prognosis prediction for TBI patients has the potential to revolutionize TBI care and improve patient outcomes.

Copyright of Turkiye Klinikleri Journal of Forensic Medicine & Forensic Sciences / Türkiye Klinikleri Adli Tıp ve Adli Bilimler Dergisi is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent times, artificial intelligence (AI) has been generating a significant impact in various industry sectors, which implies that companies must be ready to adjust to this promising start and progress in the direction of sustainability. The objective of this paper was to analyze the industrial sectors impacted by artificial intelligence during the period 2018–2022. The methodology consisted of applying a quantitative and bibliometric approach to a collection of 164 manuscripts indexed in Scopus with the help of statistical packages such as RStudio version 4.3.0, VOSviewer version 1.6.19, and Microsoft Excel 365. The results indicate that artificial intelligence is having a growing impact in sectors such as technology, finance, healthcare, the environment, and construction. Geographically, the most impacted sectors are in Europe and Asia, while the least impacted are in the Americas, Africa, and Oceania. It is proposed to conduct future research using AI in power quality (PQ), energy storage systems (ESSs) and hydrogen fuel cell (HFC) systems to contribute, firstly, in the transition to a more sustainable economy, followed by a decrease in dependence on fossil fuels. This research contributes to existing knowledge and paves the way for future exploration of qualitative aspects and emerging trends in the field of artificial intelligence influence in industrial sectors. [ABSTRACT FROM AUTHOR]

Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learningbased suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems. [ABSTRACT FROM AUTHOR]

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]

The first death caused by the novel coronavirus in the USA was declared on February 29, 2020, in the Seattle area in Washington state. Forecasting the number of deaths has great importance in terms of public psychology and strategic decisions to be taken by the government. There are several data-driven models in the literature to predict the deaths in the USA caused by COVID-19. However, most of them are based on a few variables of the data for forecasting. From this point of view, this study provides an artificial neural network (ANN)-based approach by considering 12 different variables for forecasting the cumulative deaths caused by COVID-19 in the USA. The proposed ANN structure was trained with three algorithms, namely scaled conjugate gradient algorithm, Levenberg-Marquardt algorithm and Bayesian regularization algorithm. These three forecasting models were constructed on 13 parameters such as 12 inputs and one output. The sensitivity and performance of the proposed forecasting models were analyzed and compared by using indices mean absolute error, mean absolute percentage error, correlation coefficient (R-value), sum square error, variance account for, mean square error and root-mean-square error. Results show that the forecasting model with Bayesian regularization performs better than other models for forecasting the cumulative deaths due to COVID-19 in the USA. [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]