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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 105 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. [ABSTRACT FROM AUTHOR]

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]

Background: Bipolar disorder (BPD) is a kind of manic and depressive phase alternate episodes of serious mental illness, and it is correlated with well-documented cortical brain abnormalities. Emerging evidence supports that autophagy dysfunction in neuronal system contributes to pathophysiological changes in neurological disease. However, the role of autophagy in bipolar disorder has rarely been elucidated. This study aimed to identify the autophagy-related gene as a potential biomarker Correlated to immune infiltration in BPD. Methods: The microarray dataset GSE23848 and autophagy-related genes (ARGs) were downloaded. Differentially expressed genes (DEGs) between normal and BPD samples were screened using the R software. Machine learning algorithms were performed to screen the significant candidate biomarker from autophagy-related differentially expressed genes (ARDEGs). The correlation between the screened ARDEGs and infiltrating immune cells was explored through correlation analysis. Results: In this study, the autophagy pathway was abundantly enriched and activated in BPD, as indicated by Pathway enrichment analysis. We identified 16 ARDEGs in BPD compared to the normal group. A signature of 4 ARDEGs (ERN1, ATG3, CTSB, and EIF2AK3) was screened. ROC analysis showed that the above genes have good diagnostic performance. In addition, immune correlation analysis considered that the above four genes significantly correlated with immune cells in BPD. Conclusions: Autophagy - immune cell axis mediates pathophysiological changes in BPD. Four important ARDEGs are prospective to be potential biomarkers associated with immune infiltration in BPD and helpful for the prediction or diagnosis of BPD. [ABSTRACT FROM AUTHOR]

Summary Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder, and chemokines have been shown to be dysregulated in autoimmune disorders. We conducted a prospective analysis to identify potential chemokines that could enhance the diagnostic accuracy and bleeding evaluation in ITP patients. In the discovery cohort, a Luminex‐based assay was employed to quantify concentrations of plasma multiple chemokines. These levels were subjected to comparative analysis using a cohort of 60 ITP patients and 17 patients with thrombocytopenia other than ITP (non‐ITP). Additionally, comparative evaluation was conducted between a subgroup of 12 ITP patients characterised by bleeding episodes (ITP‐B, as defined by an ITP‐2016 bleeding grade ≥2) and 33 ITP patients without bleeding episodes (ITP‐NB, as defined by an ITP‐2016 bleeding grade ≤1). Machine learning algorithms further identified CCL20, interleukin‐2, CCL26, CCL25, and CXCL1 as promising indicators for accurate diagnosis of ITP and CCL21, CXCL8, CXCL10, CCL8, CCL3, and CCL15 as biomarkers for assessing bleeding risk in ITP patients. The results were confirmed using enzyme‐linked immunosorbent assays in a validation cohort (43 ITP patients and 19 non‐ITP patients). Overall, the findings suggest that specific chemokines show promise as potential biomarkers for diagnosis and bleeding evaluation in ITP patients. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and effective treatment of thyroid conditions, such as hypothyroidism and hyperthyroidism, are crucial due to their wide-ranging symptoms and consequences. However, conventional back-propagation neural networks have limitations, including slow convergence and susceptibility to local minima. To deal with these problems, the paper suggests an integrated strategy that includes back-propagation (BP) and genetic algorithms (GA). The suggested method uses evolutionary algorithms to investigate various weight combinations and back-propagation to modify weights in response to the discrepancy between expected and actual results. The study evaluated the method using three steps of experimentation, including network building, local search and optimization, and evaluation, on a real-world dataset of thyroid illnesses. The obtained results are very satisfying and promising, indicating that the MLP-GA/BP model is robust enough to detect and categorize thyroid disorders effectively and efficiently. This makes it a reliable diagnostic tool for medical practitioners, allowing them to effectively diagnose and treat patients with thyroid diseases. [ABSTRACT FROM AUTHOR]

In order to easily and effectively detect the common problem of drainage pipeline blockage in cities, a device of drainage pipeline was designed and a test of blockage was completed, water level data was monitored online, after optimizing machine learning algorithms, a diagnostic program for pipeline blockage was developed based on the best algorithm in Python code. The results show that when the drainage pipeline is blocked meanwhile the flow rate is the same, the more severe the blockage is, the higher the upstream water level line rises. The closer the blockage is to the position of upstream monitoring point, the higher the upstream water level rises. The downstream water level is almost only related to the flow rate. Through comparing machine learning algorithms, it is found that the K-nearest neighbor algorithm exhibites high accuracy in classifying data set. After optimized by the grid search crossing validation method, the model accuracy is around 98%. It is concluded that this diagnostic method can realize rapid diagnosis of pipeline blockage, optimize the process of dispatch scheduling, and reduce the costs of pipeline operation and maintenance. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats. [ABSTRACT FROM AUTHOR]

Analyzing lecturers' and students' satisfaction with using e-learning is important to improve the teaching-learning processes. The existing approaches have been widely employing machine learning algorithms, usage-based, and System Usability Scale (SUS) metrics based on users' opinions, activities, and usability testing, respectively. However, the usage-based and SUS metrics fail to cover users' opinions about e-learning systems and they involve manual features engineering. Whereas, the machine learning classifiers do not analyze satisfaction based on activities and usability. Toward this end, we propose a machine learning model that employs CNN and BiLSTM algorithms to concatenate the features extracted from users' activities, usability testing, and users' opinions. The proposed model is coined as E-learning Users' Satisfaction Detection (El-USD). Experimental results suggest that there is a significant correlation between satisfaction analysis by achieving an average r = 0.778. The evaluation results further suggest that our proposed approach can analyze users' satisfaction accurately. [ABSTRACT FROM AUTHOR]

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases. [ABSTRACT FROM AUTHOR]

Climate changes and global warming increase risk of recurrent extreme and complex climatic features. It necessitates accurate modeling and forecasting of climate phenomena for sustainable development goals. However, machine learning algorithms and advanced statistical models are extensively employed to analyze complex data and make predictions related to climate phenomena. It is important to have comprehensive knowledge to use these models and consider their potential implications. This study aims to evaluate and compare some popular machine learning and probabilistic methods by analyzing various time series indices associated with precipitation and temperature. For application, time series data of Standardized Precipitation Temperature Index (SPTI), Standardized Temperature Index (STI), Standardized Compound Drought and Heat Index (SCDHI), and Biased Diminished Weighted Regional Drought Index (BDWRDI) are used from various meteorological regions of Pakistan. The performance of each algorithm is compared using Residual Mean Square Error (RMSE) and Mean Average Error (MAE). The outcomes associated with this research indicate a higher preference of neural networks over machine learning methods in the training sets. However, the efficiency varies from model to model, indicator to indicator, time scale to time scale, and location to location during the testing phase. The most appropriate models are found by considering a list of candidates forecasting models and investigating the performance of each model. [ABSTRACT FROM AUTHOR]

Epilepsy is a persistent health condition marked by unusual and highly synchronized electrical activity in the brain cells, resulting in recurring seizures. This paper proposes a novel real-time method to improve the detection of seizures using the spectral features of non-stationary electroencephalogram (EEG) signals. It is observed that the discrete wavelet transform (DWT)-based features do not consider the interrelationship among EEG signal components. This interrelationship has been well captured by the novel representation of EEG in the form of graph signals. Here, the spectral analysis of the graph signals is investigated by the graph-based Fourier transform (GFT). Then, GFT-based features have been selected and fed into different classifiers for analysis. The seizure detection rate in two publicly available EEG-based datasets, the University of Bonn (UB) and the Neurology Sleep Clinic New Delhi (NSC-ND), have been achieved with accuracy of 98.68% and 96.84%, respectively. The accuracy achieved is significantly better than the existing state-of-the-art techniques. This approach demonstrates the impact of utilizing the interrelationship among the EEG components, followed by enhanced feature selection based on GFT for the improved detection of seizures. [ABSTRACT FROM AUTHOR]

Visible Light Communication (VLC) technology offers a promising alternative for high-speed data transfer due to its ability to simultaneously illuminate and transport data. This research studies the application of Machine Learning (ML) algorithms for the purpose of addressing several issues associated with VLC technology. These challenges include the reduction of non-linearity, channel estimation, detection of modulation format, localization, enhancement of security, optimization of mobility, and management of resources. ML algorithms have the potential to enhance the efficiency and reliability of VLC systems, ensuring that data flow is uninterrupted. Additionally, the study analyzes the challenges that are involved with the application of ML algorithms in VLC technology as well as the potential future research topics that could be pursued. [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]

The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors. [ABSTRACT FROM AUTHOR]

Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development. [ABSTRACT FROM AUTHOR]

• FFS maps were produced using CTREE, KNN, SVM, RF, GBM and XGBoost algorithms. • The performance of six machine learning techniques was evaluated. • This study will be the first to use the CTREE algorithm for FFS mapping. • XGBoost gave the best results compared to other models. • The most important factor for producing FFS maps in XGBoost models was annual temperature. Antalya is one of the provinces with the highest number of forest fires in Türkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector Machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) represent a significant component of air pollution. However, studies evaluating the impact of VOC exposure on chronic obstructive pulmonary disease (COPD) have predominantly focused on single pollutant models. This study aims to comprehensively assess the relationship between multiple VOC exposures and COPD. A large cross-sectional study was conducted on 4983 participants from the National Health and Nutrition Examination Survey. Four models, including weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum regression (WQS), and the dual-pollution model, were used to explore the association between blood VOC levels and the prevalence of COPD in the U.S. general population. Additionally, six machine learning algorithms were employed to develop a predictive model for COPD risk, with the model's predictive capacity assessed using the area under the curve (AUC) indices. Elevated blood concentrations of benzene, toluene, ortho-xylene, and para-xylene were significantly associated with the incidence of COPD. RCS analysis further revealed a non-linear and non-monotonic relationship between blood levels of toluene and m-p-xylene with COPD prevalence. WQS regression indicated that different VOCs had varying effects on COPD, with benzene and ortho-xylene having the greatest weights. Among the six models, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest predictive power, with an AUC value of 0.781. Increased blood concentrations of benzene and toluene are significantly correlated with a higher prevalence of COPD in the U.S. population, demonstrating a non-linear relationship. Exposure to environmental VOCs may represent a new risk factor in the etiology of COPD. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and implement semiautomated screening for meta-analyses (MA) in urology under consideration of class imbalance. Methods: Machine learning algorithms were trained on data from three MA with detailed information of the screening process. Different methods to account for class imbalance (Sampling (up- and downsampling, weighting and cost-sensitive learning), thresholding) were implemented in different machine learning (ML) algorithms (Random Forest, Logistic Regression with Elastic Net Regularization, Support Vector Machines). Models were optimized for sensitivity. Besides metrics such as specificity, receiver operating curves, total missed studies, and work saved over sampling were calculated. Results: During training, models trained after downsampling achieved the best results consistently among all algorithms. Computing time ranged between 251 and 5834 s. However, when evaluated on the final test data set, the weighting approach performed best. In addition, thresholding helped to improve results as compared to the standard of 0.5. However, due to heterogeneity of results no clear recommendation can be made for a universal sample size. Misses of relevant studies were 0 for the optimized models except for one review. Conclusion: It will be necessary to design a holistic methodology that implements the presented methods in a practical manner, but also takes into account other algorithms and the most sophisticated methods for text preprocessing. In addition, the different methods of a cost-sensitive learning approach can be the subject of further investigations. [ABSTRACT FROM AUTHOR]

Background: Influenza viruses are etiological agents which cause contagious respiratory, seasonal epidemics and, for influenza A subtypes, pandemics. The clinical picture of influenza has undergone continuous change over the years, due to intrinsic viral evolution as well as "reassortment" of its genomic segments. The history of influenza highlights its ability to adapt and to rapidly evolve, without specific circumstances. This reflects the complexity of this pathology and poses the fundamental question about its assumption as a "common illness" and its impact on public health. Summary: The global influenza epidemics and pandemics claimed millions of deaths, leaving an indelible mark on public health and showing the need for a better comprehension of the influenza virus. The clear understanding of genetic variations during the influenza seasonal epidemics is a crucial point for developing effective strategies for prevention, treatment, and vaccine design. The recent advance in next-generation sequencing approaches, model systems to virus culture, and bioinformatics pipeline played a key role in the rapid characterization of circulating influenza strains. In particular, the increase in computational power allowed the performance of complex tasks in healthcare settings through machine learning algorithms, which analyze different variables, such as medical and laboratory outputs, to optimize medical research and improve public health systems. The early detection of emerging and reemerging pathogens is a matter of importance to prevent future pandemics. Key Messages: The perception of influenza as a "trivial flu" or a more serious public health concern is a subject of ongoing debate, reflecting the multifaceted nature of this infectious disease. The variability in the severity of influenza sheds light on the unpredictability of the viral characteristics, coupled with the challenges in accurately predicting circulating strains. This adds complexity to the public health burden of influenza and highlights the need for targeted interventions. [ABSTRACT FROM AUTHOR]

Purpose: To identify tidal volume (VT) and positive end-expiratory pressure (PEEP) associated with the lowest incidence and severity of postoperative pulmonary complications (PPCs) for each phenotype based on preoperative characteristics. Methods: The subjects of this retrospective observational cohort study were 34,910 adults who underwent surgery, using general anesthesia with mechanical ventilation. Initially, the least absolute shrinkage and selection operator regression was employed to select relevant preoperative characteristics. Then, the classification and regression tree (CART) was built to identify phenotypes. Finally, we computed the area under the receiver operating characteristic curves from logistic regressions to identify VT and PEEP associated with the lowest incidence and severity of PPCs for each phenotype. Results: CARTs classified seven phenotypes for each outcome. A probability of the development of PPCs ranged from the lowest (3.51%) to the highest (68.57%), whereas the probability of the development of the highest level of PPC severity ranged from 3.3% to 91.0%. Across all phenotypes, the VT and PEEP associated with the most desirable outcomes were within a small range of VT 7–8 ml/kg predicted body weight with PEEP of between 6 and 8 cmH2O. Conclusions: The ranges of optimal VT and PEEP were small, regardless of the phenotypes, which had a wide range of risk profiles. [ABSTRACT FROM AUTHOR]

Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference crop evapotranspiration (ET0) is essential for water resources planning and irrigation scheduling. However, the absence of solar radiation (Rs) data is a common problem affecting the estimation of ET0. This study investigates the feasibility of employing temperature-based models to estimate Rs and proposes effective methodologies for obtaining more convenient and accurate ET0 estimates. To evaluate the effectiveness of different approaches, authors compared nine empirical models (M1-M9) and three machine learning algorithms (RF, GRNN and ANN) for daily Rs estimation. This analysis utilized data from 339 national basic meteorological stations in China, spanning the period from 2001 to 2018. Subsequently, authors proposed two strategies for estimating daily ET0 in regions where solar radiation data is limited or unavailable. The results showed that (1) temperature-based models exhibited satisfactory accuracy (R² > 0.6) for daily Rs estimation, with machine learning algorithms outperforming their empirical counterparts. The machine learning accuracies are ranked as follows: Artificial Neural Network (ANN) > Generalized Regression Neural Network (GRNN) > Random Forest (RF). And empirical models are ranked in descending order of accuracy: M9 > M8 > M6 > M7 > M5 > M2 > M3 > M1 > M4. The accuracies of twelve models in the four climatic zones are indicated as follows: the temperate continental zone (TCZ) > the temperate monsoon zone (TMZ) > the subtropical monsoon zone (SMZ) > the mountain plateau zone (MPZ). (2) The comprehensive assessment for nine empirical models indicates that the Hargreaves-Samani model (M1) is the most reliable for solar radiation estimation. Its estimated results are close to those of the other models, and the coefficient of variation of the parameters (0.10) is much lower than that of the other empirical models. Thus, combining the model with the nationally calibrated parameters computed by the Kriging interpolation method allows for reliable values of the daily solar radiation. (3) Machine learning techniques show variations in estimating daily ET0 across different climate zones. The machine learning accuracies are ranked as ANN>GRNN>RF, and TCZ>TMZ>MPZ>SMZ in the four climate zones. (4) The accuracies of the two daily ET0 estimation strategies, with or without actual Rs calibration, are very close. Both strategies provide accurate daily ET0 estimates (R² >0.95) with an average R² improvement of only 0.39% for strategy I compared to strategy II. In conclusion, this study provides new ideas to address the scarcity of solar radiation data and highlight the potential of machine learning in ET0 estimation. This approach can be effectively applied to reference crop evapotranspiration estimates in regions where solar radiation data is scarce. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms. Methods: mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments. Results: A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients. Conclusion: This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis. [ABSTRACT FROM AUTHOR]

The growth of the world population is leading to an increased demand for food production. Consequently, there is a need to update agricultural processes to enhance production. Smart farming has evolved as a farm management concept that leverages real-time, dependable, and site-specific agricultural information to ensure consistent and sustainable yields. This study focuses on soil nutrient analysis, specifically nitrogen (N), phosphorus (P), and potassium (K) for enhancing crop production. Maintaining an appropriate balance of NPK is crucial for the well-rounded nutrition of crops. Various environmental factors such as temperature, rainfall, humidity, and soil pH affect the NPK composition of agricultural land. The optimal usage of NPK levels can help to improve plant growth, increase crop yields, and enhance overall farm productivity. Hence, we have proposed a stack regressor learning algorithm comprising of two layers for accurate, reliable, and context-sensitive predictions of NPK levels. The proposed approach considers the combined impact of these environmental factors and predicts the optimal NPK requirements tailored to specific crops and agricultural land. The analysis of graphs demonstrates that the predictive accuracy of the proposed stacked regressor model far surpasses that of preexisting built-in machine learning models. This study helps farmers make informed decisions, leading to productivity and sustainability. [ABSTRACT FROM AUTHOR]

This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms. Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP). A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774–0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis. The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a two-stage hybrid approach that uses machine learning techniques and meta-heuristic algorithms. The first step, known as data preparation, involves converting string values to numeric format and subsequently normalizing the data. To increase the performance beyond the limitations of traditional methods, we use population-based meta-heuristic algorithms, namely Atom Search Optimization (ASO) and Equilibrium Optimization (EO), for feature selection, aiming to achieve global optimization. The second step, called attack detection, focuses on distinguishing normal traffic from malicious traffic. To improve the performance of this step, we use K-means clustering and firefly algorithm (FA). In addition, an elitism method is randomly integrated. The resulting approach is called ASO-EO-FA-K-means. We evaluate the performance of our proposed method using two datasets, namely NSL-KDD, UNSW_NB15, and KDD_CUP99. To establish a benchmark, we compare our method with other approaches including Particles Swarm Optimization (PSO), Genetic, Grey Wolf Optimization (GWO), Ant colony optimization (ACO), Harris Hawk Optimization (HHO), NSGA-2, Multi-objective PSO, Multi-objective GWO, learning vector quantization (LVQ), XGBoost, particle swarm optimization based on C4.5 (PSO-C4.5) and genetic algorithm based on multilayer perceptron (GA-MLP)) we compare. The evaluation results show that the proposed method achieves the highest accuracy and the lowest error rate in three datasets NSL-KDD and UNSW_NB15 KDD-CUP99 with accuracy values of 0.998, 0.995 and 0.995, respectively. In addition, our method shows superior efficiency in terms of computation time. In general, our research shows the effectiveness of the ASO-EO-FA-K-means method in intrusion detection and provides better accuracy and efficiency compared to alternative approaches. In all three data sets, the results have shown that NSL-KDD data set with MSE 0.012, accuracy value 0.998 has obtained better results than other data sets. [ABSTRACT FROM AUTHOR]

Summary Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy‐based phenotypic trade‐offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non‐pleiotropy‐induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large‐scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability. [ABSTRACT FROM AUTHOR]

Summary: Immune thrombocytopenia (ITP) is an autoimmune disease characterized by antibody‐mediated platelet destruction and impaired platelet production. The mechanisms underlying ITP and biomarkers predicting the response of drug treatments are elusive. We performed a metabolomic profiling of bone marrow biopsy samples collected from ITP patients admission in a prospective study of the National Longitudinal Cohort of Hematological Diseases. Machine learning algorithms were conducted to discover novel biomarkers to predict ITP patient treatment responses. From the bone marrow biopsies of 91 ITP patients, we quantified a total of 4494 metabolites, including 1456 metabolites in the positive mode and 3038 metabolites in the negative mode. Metabolic patterns varied significantly between groups of newly diagnosed and chronic ITP, with a total of 876 differential metabolites involved in 181 unique metabolic pathways. Enrichment factors and p‐values revealed the top metabolically enriched pathways to be sphingolipid metabolism, the sphingolipid signalling pathway, ubiquinone and other terpenoid–quinone biosynthesis, thiamine metabolism, tryptophan metabolism and cofactors biosynthesis, the phospholipase D signalling pathway and the phosphatidylinositol signalling system. Based on patient responses to five treatment options, we screened several metabolites using the Boruta algorithm and ranked their importance using the random forest algorithm. Lipids and their metabolism, including long‐chain fatty acids, oxidized lipids, glycerophospholipids, phosphatidylcholine and phosphatidylethanolamine biosynthesis, helped differentiate drug treatment responses. In conclusion, this study revealed metabolic alterations associated with ITP in bone marrow supernatants and a potential biomarker predicting the response to ITP. [ABSTRACT FROM AUTHOR]

Ultra-high-performance concrete (UHPC) has been used in building joints due to its increased strength, crack resistance, and durability, serving as a repair material. However, efficient repair depends on whether the interfacial substrate can provide adequate bond strength under various loading scenarios. The objective of this study is to investigate the bonding behavior of composite U-shaped normal strength concrete–ultra-high-performance fiber reinforced concrete (NSC-UHPFRC) specimens using multiple drop-weight impact testing techniques. The composite interface was treated using grooving (Gst), natural fracture (Nst), and smoothing (Sst) techniques. Ensemble machine learning (ML) algorithms comprising XGBoost and CatBoost, support vector machine (SVM), and generalized linear machine (GLM) were employed to train and test the simulation dataset to forecast the impact failure strength (N2) composite U-shaped NSC-UHPFRC specimen. The results indicate that the reference NSC samples had the highest impact strength and surface treatment played a substantial role in ensuring the adequate bond strength of NSC-UHPFRC. NSC-UHPFRC-Nst can provide sufficient bond strength at the interface, resulting in a monolithic structure that can resist repeated drop-weight impact loads. NSC-UHPFRC-Sst and NSC-UHPFRC-Gst exhibit significant reductions in impact strength properties. The ensemble ML correctly predicts the failure strength of the NSC-UHPFRC composite. The XGBoost ensemble model gave coefficient of determination (R2) values of approximately 0.99 and 0.9643 at the training and testing stages. The highest predictions were obtained using the GLM model, with an R2 value of 0.9805 at the testing stage. [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]

In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value. [ABSTRACT FROM AUTHOR]

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition. [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]

Background: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment of procedures with protocols. This is due to the challenge of identifying longitudinal patterns of service utilization in the cross-continuum data to assess adherence to the CPGs. Method: This paper proposes a new methodology for identifying patients' patterns of service utilization (PSUs) within sparse high-dimensional cross-continuum health datasets using graph community detection. Result: The result has shown that by using iterative graph community detections, and graph metrics combined with input from clinical and operational subject matter experts, it is possible to extract meaningful functionally integrated PSUs. Conclusions: This introduces the possibility of influencing the reorganization of some services to provide better care for patients with complex problems. Additionally, this introduces a novel analytical framework relying on patients' service pathways as a foundation to generate the basic entities required to evaluate conformance of interventions to cohort-specific clinical practice guidelines, which will be further explored in our future research. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut. The study tests the performance of multiple machine learning regression algorithms (MLRAs) and empirical vegetation indices (VIs) in retrieving groundnut's LAI using freely available Sentinel-2 data. The bands at 665 nm, 705 nm, 842 nm, and 2190 nm are the most sensitive for retrieving groundnut's LAI, according to an analysis of its band spectrum. Results suggest that VIs computed with wavebands centered at red (665 nm), red edge (705 nm), and near-infrared (842 nm) exhibited optimal R2 with Sentinel-2 data. Normalized difference vegetation index (NDVI), red edge normalized difference vegetation index (NDVIre), simple ratio (SR), red edge simple ratio (SRre), and green normalized difference vegetation index (gNDVI) were utilized as predictors for LAI. Regarding the results of the validation between estimated and measured LAI, SR demonstrated the highest accuracy for groundnut LAI prediction (r2 = 0.67, RMSE = 0.89). Ten MLRAs were tested, and results indicate from the perspective of the accuracy of models, the Gaussian processes regression, GPR (r2 = 0.73 and RMSE = 0.81), Kernel ridge regression, KRR (r2 = 0.72 and RMSE = 0.82) and Support vector regression, SVR (r2 = 0.70 and RMSE = 0.85) demonstrated to be the most suitable for LAI estimation for rainfed groundnut at the seedling stage. The systematic analysis based on the regression approaches tested here revealed that the GPR outperformed other models combined, therefore, most suitable for estimating rainfed groundnut LAI at the seedling stage. These findings serve as a benchmark for obtaining crop biophysical parameters in the framework of groundnut traits monitoring in a tropical West Africa. [ABSTRACT FROM AUTHOR]

Rice stands as the paramount food crop worldwide, catering to more than half of the global populace as staple sustenance. Accurately and non-destructively predicting rice yield on a large scale assumes paramount importance for assessing rice growth, market planning and food security monitoring. Nonetheless, the pivotal factors that influence the final yield remain inadequately understood. In this study, we evaluated the variation patterns of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Ratio Vegetation Index, Red Edge Ratio Vegetation Index and Normalized Difference Red Edge during crucial growth stages of long, medium and short-grain rice cultivars (YX054, DF018 and LF203) from 2019 to 2021. We investigated the correlation between vegetation index (VI) combinations at different growth stages and rice yield for these three cultivars. To establish predictive models, we deployed multi-seasonal VIs from multi-year dataset and three regression algorithms: partial least squares regression (PLSR), random forest regression (RFR) and support vector regression (SVR). The outcomes evinced a lack of significant correlation between single-season VIs and rice yield. The PLSR algorithm was deemed optimal for YX054, while the RFR was adjudged most suitable for DF018 and LF203. Moreover, the triple-growth and quadruple-growth period VIs models evinced superior robustness compared to the penta-growth period VIs models for all three cultivars, attaining the highest R2 value of 0.86 and the lowest RMSE of 88.17 kg/ha. This paper underscores the criticality of multi-seasonal VIs in bolstering the performance of rice yield prediction. [ABSTRACT FROM AUTHOR]

Pavement markings are essential traffic control devices that enhance safety for motorists during nighttime. Numerous statistical learning models have been developed in prior studies to predict the retroreflectivity of the markings, but the applicability of these models is questionable in terms of accuracy. The key objective of this study was to develop a machine learning–based framework that can be used by US transportation agencies to reliably predict the retroreflectivity of their pavement markings over a period of 3 years utilizing the initially measured retroreflectivity and other key project conditions. The random forest (RF) algorithm was used in this study to develop the proposed framework considering seven types of marking materials in three different US climate zones. A total of 49,632 transverse skip retroreflectivity measurements were retrieved from the National Transportation Product Evaluation Program (NTPEP) and 11 RF models were developed to sequentially predict retroreflectivity at different prediction horizons. The models were trained with randomly selected 80% of the total data points, and the remaining 20% data points were utilized for testing the predictive performance of the developed models. The RF models predicted the retroreflectivity with a superior level of accuracy (R2 ranging between 0.88 and 0.99) than the models proposed in prior studies. These models are expected to aid transportation agencies in reliably determining the effective service lives of their marking products. [ABSTRACT FROM AUTHOR]

Background: Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. Methods: We extracted structural data from control and glaucoma patients' electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. Results: The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. Conclusions: This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD. [ABSTRACT FROM AUTHOR]

The rapid growth of Android devices has led to increased security concerns, especially from malicious software. This study extensively compares machinelearning algorithms for effective Android malware detection. Traditional models, such as random forest (RF) and support vector machines (SVM), alongside advanced approaches, such as convolutional neural networks (CNN) and XGBoost, were evaluated. Leveraging the NATICUSdroid dataset containing 29,332 records and 86 traces, the results highlight the superiority of RF with 97.1% and XGBoost with 97.2% accuracy. However, evolving malware and real-world unpredictability require a cautious interpretation. Promising as they are, our findings stress the need for continuous innovation in malware detection to ensure robust Android user security and data integrity. [ABSTRACT FROM AUTHOR]

Introduction Water quality assessment is paramount for various sectors, including environmental planning, public health, and industrial operations. With the increasing importance of ensuring safe water sources, especially for drinking and irrigation purposes, modern methodologies like data mining offer valuable tools for predictive analysis and classification of water quality. Knowledge of water quality is considered one of the most important needs in planning, developing, and protecting water resources. Determining the quality of water for different uses, including irrigation and drinking in different areas of life. The use of modern data mining methods can be beneficial for predicting and classifying the quality of provider water. In the current study, the water quality of the Qizil-Uzen River was evaluated at Qara Gunei stations. In this regard, the drinking water quality index (WQI) using the chemical compounds of glass hardness, alkalinity (PH), electrical conductivity, total dissolved substances, calcium, sodium, magnesium, potassium, chlorine, carbonate, bicarbonate and sulfate in the statistical period of 21 years (2000-2020) was estimated. Water quality assessment is paramount for various sectors, including environmental planning, public health, and industrial operations. With the increasing importance of ensuring safe water sources, especially for drinking and irrigation purposes, modern methodologies like data mining offer valuable tools for predictive analysis and classification of water quality. Materials and Methods Due to the relatively large number of variables, principal component analysis and independent component analysis methods were used to reduce dimensions, and then different machine learning algorithms including decision tree, logistic regression, and multi-layer perceptron artificial neural network were used to model the water quality index. By using these methods, the number of parameters needed to calculate the quality index was reduced from 12 to 2. Reducing the dimensions of the data saves the time of sampling, monitoring the samples, and determining the quality of the water and reduces the costs required for modeling to a significant amount. The results showed that among the dimensionality reduction methods, the principal component analysis method can perform better than the independent component analysis method. In the current research, the WQI index was modeled using machine learning algorithms including decision tree, logistic regression, and artificial neural network method. The quality of water in the Qizil-Uzen Qara Gunei river station has been evaluated. Then, to estimate the numerical values of the WQI index, TH, pH, EC, TDS, Ca, Na, Mg, K, Cl, CO3, HCO3, and SO4 parameters of the mentioned station in the statistical period of 21 years (1378-1398) were used. PCA and ICA methods have been used to select different input parameters. Modeling has been done in a Python programming environment. Among the available samples, 75% are considered for training and 25% for testing. Results and Discussion In the present research, to model the water quality index in the first stage, different dimensionality reduction methods such as PCA and ICA were used to reduce the time and cost of implementation. In the second stage, machine learning methods such as decision tree, linear regression, and multilayer perceptron were used. In the method used by Tripathi and his colleagues, by using the principal component analysis method, they reduced the number of parameters needed to calculate the quality index from 28 to 9 and calculated the water quality index with the number of 9 parameters. Examining the two methods of PCA and ICA has reduced the dimensions of the problem from 12 dimensions to 2 dimensions. The results show that the PCA method can help us improve performance with little cost and high accuracy. Because of the PCA dimensions. The comparison of the results of the models was done using different numerical and graphical evaluation criteria, including R2, RMSE, and modified Wilmot coefficient as numerical criteria and Taylor diagram as graphical criteria. Because the PCA algorithm can help reduce noise in data, feature selection, and generate independent and unrelated features from data. The results show that multi-layer perceptron, decision tree, and logistic regression methods accurately perform the water quality index. In this research, for the first time, using the ICA dimension reduction algorithm, while reducing the dimensions of the problem, the water quality index is predicted with an accuracy of over 90%. Conclusion Water quality index modeling holds significant relevance in agricultural practices, where access to clean water is crucial for irrigation and crop growth. Surprisingly, only a limited number of studies have explored variable reduction methods in water quality index modeling, with none incorporating the relatively novel Independent Component Analysis (ICA) method for dimensionality reduction. Thus, the current research fills this gap by employing PCA and ICA techniques to reduce the dimensionality of large datasets in water quality index modeling. By utilizing these advanced methods, the study aims to enhance efficiency and accuracy in assessing water quality, thereby offering valuable insights for agricultural water management. Following dimensionality reduction, the dataset is then subjected to modeling using various machine learning algorithms. This approach not only optimizes computational resources but also facilitates a deeper understanding of the complex interrelationships among water quality parameters. Through this pioneering research endeavor, the efficacy of ICA alongside PCA in addressing water quality index modeling challenges is evaluated. By integrating these techniques with machine learning methodologies, the study endeavors to provide actionable intelligence for agricultural stakeholders, aiding in informed decision-making and resource allocation. Moreover, by venturing into unexplored territory with the inclusion of ICA, the research contributes to expanding the methodological toolkit available for water quality assessment. As agriculture faces increasing pressure from climate change and resource scarcity, such innovative approaches hold promise in ensuring sustainable water management practices. [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]

The object of research is a bracelet that uses the electromyography (EMG) method to control a bionic prosthesis. In the conditions of the development of modern biomedical technologies and robotics, such a system becomes key to improving the quality of life of people with disabilities, providing efficient and accurate control of prostheses. The problem addressed in the research is the development and analysis of a bionic prosthesis control system using a bracelet using the EMG method. The main focus is on the optimization of data collection and processing processes, as well as the development of machine learning algorithms for gesture recognition in order to improve the accuracy and efficiency of prosthetic control. The essence of the obtained results is the development and testing of a new bionic prosthesis control system that uses EMG signals obtained with the help of a bracelet. The study showed that the classifier based on the support vector method outperforms other algorithms such as neural networks and decision trees, achieving an average accuracy of 90 %. The obtained data were successfully filtered and subjected to feature extraction, which allowed to create effective gesture recognition algorithms. The system was tested in real time, which confirmed its high accuracy and efficiency. The proposed system includes an innovative bracelet for collecting EMG data, which are then processed and analyzed using modern machine learning algorithms. The innovativeness of the proposed approach lies not only in the high accuracy of gesture recognition, but also in the possibility of adapting the system to different types of bionic prostheses and operating conditions. This is achieved by using a classifier based on the support vector method, which demonstrates significantly higher accuracy compared to other algorithms such as neural networks and decision trees. The test results show an average accuracy of 92.5 %, which confirms the high efficiency of the system. The use of this system involves the intensive use of EMG sensors, which allows more accurate determination of the user’s intentions regarding the control of the prosthesis. This, in turn, contributes to the improvement of the quality of life of users, providing them with greater functionality and convenience in the use of bionic prostheses. [ABSTRACT FROM AUTHOR]

The damage assessment of numerous buildings after the earthquake is still a challenge by traditional methods as it requires a significant amount of time and resources for carrying out building-by-building seismic damage assessment. This study presents the machine learning (ML)-based damage assessment of buildings after an earthquake. The applicability of the machine learning model will be limited considering only structural properties or ground motion characteristics. Thus, in this study, the applicability of different machine learning techniques is explored using real-world earthquake damage datasets considering both the structural properties and ground motion characteristics. Initially, the entire dataset is used to train the ML models to classify the building's damage into five categories ranging from null to slight damage to collapse. Later, the dataset is divided into three traffic light damage tags: green, yellow, and red. The performance of the ML model to classify the building's damage into three damage tags is found better than that of classifying it into five damage categories. This research aims to be beneficial in planning and decision-making for emergency response and recovery following an earthquake. [ABSTRACT FROM AUTHOR]

Background: The present study was conducted to estimate gender from 2D:4D ratio and hand morphometry taken from participants by using machine learning (ML) algorithms. Materials and Methods: The study was conducted retrospectively on 88 men and 96 women between the ages of 18 and 30 who did not have any pathology, deformity or surgical interventions on their hands. Hand width (HW), hand length (HL), second digit length (2D), and fourth digit length (4D) of the individuals were measured as the right (R) and left (L) side by using digital calliper and recorded in Excel. In addition, the ratio between the second digit and fourth digit (2D:4D) of each individual was also recorded. Results: As a result of ML modelling, 0.92 accuracy was obtained with Random forest (RF) algorithm. With RF algorithm, all of the 16 women and 18 of the 21 men in the test set were estimated accurately. With SHAP analyzer of RF algorithm, HW-L parameter was found to have the highest contribution in estimating gender. The accuracy rates of the other ML models used in the study were found to vary between 0.78 and 0.89. Conclusions: It was found that 2D:4D ratio and hand morphometry measurements, which are frequently preferred in gender determination, have higher accuracy rate when examined with ML algorithms. In our study, we concluded that using 2D:4D ratio and hand morphometry in estimating gender provides accurate and reliable data. [ABSTRACT FROM AUTHOR]

Current measurement systems based on the IEEE-1159 standard have some limitations and robustness problems under noisy and fast-changing conditions. Besides, applying different methods for each Power Quality Disturbance (PQD) to every window is required but time-consuming and not feasible. Therefore, different kinds of two-stage methods, Detection and Classification (D&C), have been improved in many studies. Then, the required measurement can be performed to define disturbance. For this purpose, a new approach based on features of subcomponents with Machine Learning Algorithms (MLAs) to detect and classify PQDs is proposed. 21-class dataset including single and multiple PQDs under different noisy conditions was prepared randomly. Of this dataset, determined features were extracted and some of these were selected. Then, selected features were trained and tested with some MLAs in a workstation. Results obtained from comparative MLAs and the other classification methods show that the best MLA with related features is Random Forest with 96.97% while LightGBM, k-Nearest Neighbors, and XGBoost 96.85%, 96.73%, and 92.82% accuracy, respectively. Because the selected features, optimized parameters, and the related MLA were obtained by investigating for features provided from the PQDs in the whole parameter space, this approach brings the advantages of high accuracy, low D&C complexity, and computing load. [ABSTRACT FROM AUTHOR]

The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation. [ABSTRACT FROM AUTHOR]