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Introduction: Osteoporosis is a significant and growing global public health problem, projected to increase in the next decade. The Singh Index (SI) is a simple, semi-quantitative evaluation tool for diagnosing osteoporosis with plain hip radiographs based on the visibility of the trabecular pattern in the proximal femur. This work aims to develop an automated tool to diagnose osteoporosis using SI of hip radiograph images with the help of machine learning algorithms. Methods: We used 830 hip X-ray images collected from Indian men and women aged between 20 and 70 which were annotated and labeled for appropriate SI. We employed three state-of-the-art machine learning algorithms—Vision Transformer (ViT), MobileNet-V3, and a Stacked Convolutional Neural Network (CNN)—for image pre-processing, feature extraction, and automation. Each algorithm was evaluated and compared for accuracy, precision, recall, and generalization capabilities to diagnose osteoporosis. Results: The ViT model achieved an overall accuracy of 62.6% with macro-averages of 0.672, 0.597, and 0.622 for precision, recall, and F1 score, respectively. MobileNet-V3 presented a more encouraging accuracy of 69.6% with macro-averages for precision, recall, and F1 score of 0.845, 0.636, and 0.652, respectively. The stacked CNN model demonstrated the strongest performance, achieving an accuracy of 93.6% with well-balanced precision, recall, and F1-score metrics. Conclusion: The superior accuracy, precision-recall balance, and high F1-scores of the stacked CNN model make it the most reliable tool for screening radiographs and diagnosing osteoporosis using the SI. [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]

Background: β-thalassemia (β-TH) is a hereditary hemolytic anemia that results in deficient hemoglobin (Hb) synthesis. It is characterized by ineffective erythropoiesis, anemia, splenomegaly, and systemic iron overload. Exploration new potential biomarkers and drug candidates is important to facilitate the prevention and treatment of β-TH. Methods: We applied quasi-targeted metabolomics between wild type (Wt) and heterozygous β-TH mice (Th3/+), a model of non-transfusion-dependent β-TH intermedia, in plasma and peripheral blood (PB) cells. Futher data was deeply mined by Kyoto Encyclopedia of Genomes (KEGG) and machine algorithms methods. Results: Using KEGG enrichment analysis, we found that taurine and hypotaurine metabolism disorders in plasma and alanine, aspartate and glutamate metabolism disorders in PB cells. After systematically anatomize the metabolites by machine algorithms, we confirmed that alpha-muricholic acidUP and N-acetyl-DL-phenylalanineUP in plasma and Dl-3-hydroxynorvalineUP, O-acetyl-L-serineUP, H-abu-OHUP, S-(Methyl) glutathioneUP, sepiapterinDOWN, and imidazoleacetic acidDOWN in PB cells play key roles in predicting the occurrence of β-TH. Furthermore, Sepiapterin, Imidazoleacetic acid, Methyl alpha-Dglucopyranoside and alpha-ketoglutaric acid have a good binding capacity to hemoglobin E through molecular docking and are considered to be potential drug candidates for β-TH. Conclusion: Those results may help in identify useful molecular targets in the diagnosis and treatment of β-TH and lays a strong foundation for further research. [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]

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings. [ABSTRACT FROM AUTHOR]

The tourism and hospitality (T&H) industry can significantly impact the environment by using natural resources and waste generation. Measuring and improving firms' emission performance in this industry is becoming increasingly important. However, the prediction of financial indicators and governance scores on firms' emission performance is still poorly established. Drawing on the agency and stakeholders' theory, our research explores whether financial indicators and governance scores predict firms' emission performance in the T&H industry. Data on all companies that are publicly traded was acquired from Thomson Reuters Eikon using 485 publicly traded tourism firms as of the end of the fiscal year 2021. Our findings suggest that governance pillar score (GP), management score (MS), board size (BS), and market capitalization (MC) are the best four predictors of carbon emission reduction scores in T&H companies. Theoretical and practical implications are presented, and directions for further research are provided. [ABSTRACT FROM AUTHOR]

Automated analysis of microplastics is essential due to the labor-intensive, time-consuming, and error-prone nature of manual methods. Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy offers valuable molecular information about microplastic composition. However, efficient data analysis tools are required to effectively differentiate between various types of microplastics due to the large volume of spectral data generated by ATR-FTIR. In this study, we propose a machine learning (ML) approach utilizing ATR-FTIR spectroscopy data for accurate and efficient classification of undegraded and aged polyethylene terephthalate (PET) microplastics (MPs). We evaluate seven ML algorithms, including Random Forest (RF), Gradient Boosting (GB), Decision Tree (DT), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), to assess their performance. The models were optimized using fivefold cross-validation and evaluated using multiple metrics such as confusion matrix, accuracy, precision, recall (sensitivity), and F1-score. The experimental results demonstrate exceptional performance by RF, GB, DT, and k-NN models, achieving an accuracy of 99% in correctly classifying undegraded and aged PET MPs. The proposed approach capitalizes on the potential of ATR-FTIR spectra to discern distinct chemical signatures of undegraded and aged PET particles, enabling precise and reliable classification. Furthermore, the method offers the benefit of automating the classification process, streamlining the analysis of environmental samples. It also presents the advantage of providing an effective means for method standardization, facilitating more automated and optimized extraction of information from spectral data. The method's versatility and potential for large-scale application make it a valuable contribution to the field of MP environmental research. [ABSTRACT FROM AUTHOR]

In order to integrate the training of athletes with information technology, this paper proposes a method for evaluating the performance of athletes using training technology. HR, O2, and hemoglobin were selected as input vectors of SVM, and the corresponding values were used as outputs to generate the training model. Adjust the support vector machine to minimize measurement error based on the learning objective. Support vector machines are used to study training patterns and develop models to evaluate athletes' performance. College athletes were taken as research subjects and the effects of training were examined in five sports: football, basketball, basketball, swimming, and running. The results show that the relative error of this type is less than 1 when measuring the performance of various sports subjects; Relative errors in measuring the academic performance of athletes in various sports using physical fitness standards and sport-specific skills 1. The proposed model proves that the athlete's training index is incorrect and has a useful application. [ABSTRACT FROM AUTHOR]

Background: Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, genetic risk assessment remains challenging due to population-specific SNP variants. Therefore, we aimed to conducted a genetic analysis and developed a machine-learning-based predictive model to examine the association between previously reported SNPs and hypodontia in the Saudi Arabian population. Our case–control study included 106 participants (aged 8–50 years; 64 females and 42 males), comprising 54 hypodontia cases and 52 controls. We utilized TaqManTM Real-Time Polymerase Chain Reaction and allelic genotyping to analyze three selected SNPs (AXIN2: rs2240308, PAX9: rs61754301, and MSX1: rs12532) in unstimulated whole saliva samples. The chi-square test, multinomial logistic regression, and machine-learning techniques were used to assess genetic risk by using odds ratios (ORs) for multiple target variables. Results: Multivariate logistic regression indicated a significant association between homozygous AXIN2 rs2240308 and the hypodontia phenotype (ORs [95% confidence interval] 2.893 [1.28–6.53]). Machine-learning algorithms revealed that the AXIN2 homozygous (A/A) genotype is a genetic risk factor for hypodontia of teeth #12, #22, and #35, whereas the AXIN2 homozygous (G/G) genotype increases the risk for hypodontia of teeth #22, #35, and #45. The PAX9 homozygous (C/C) genotype is associated with an increased risk for hypodontia of teeth #22 and #35. Conclusions: Our study confirms a link between AXIN2 and hypodontia in Saudi orthodontic patients and suggests that combining machine-learning models with SNP analysis of saliva samples can effectively identify individuals with non-syndromic hypodontia. [ABSTRACT FROM AUTHOR]

Accurately predicting the spatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection. [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]

Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The long short-term memory algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy. [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]

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]

Today, fast-paced technology plays an important role in financial transactions, especially in payment-related digital habits. As fraud is a major concern in online payments, many machine-learning approaches have been proposed to detect and prevent fraudulent payment transactions. This study aimed to evaluate Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, and Random Forest in detecting fraudulent payment transactions. The results show that Random Forest, K-Nearest Neighbor, Decision Tree, and Logistic regression achieved total accuracy rates exceeding 99%. However, such impressive results do not necessarily indicate satisfactory performance. The results highlight the need to detect fraudulent transactions and investigate specific improvements to effectively manage and minimize unexpected financial transaction fraud. [ABSTRACT FROM AUTHOR]

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow's value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance. [ABSTRACT FROM AUTHOR]

Based on forest management inventory data, this study applies machine learning algorithms to explore the relationships between forest water conservation capacity and forest management inventory factors, thus providing more extensive insights into forest water conservation services. By integrating the InVEST model and machine learning algorithms, this study identifies the key factors related to water conservation services based on forest management inventory factors and investigates the differences in and accuracy of forest water conservation models using the random forest algorithm. The results are as follows: (1) The determination coefficients (R2) of the three machine learning models range from 0.508 to 0.869, with root mean square errors (RMSEs) ranging from 28.380 to 69.339. The performance of these models is generally satisfactory, with the random forest algorithm showing superior results. (2) By leveraging the advantages of the three machine learning algorithms in handling categorical data, this study analyzes the contributions of forest management inventory factors, revealing the impact mechanisms of forest-type water conservation services. (3) The integration of machine learning algorithms allows for better processing of the scale and correlation of independent variables, providing more objective information on the main controlling factors of forest water conservation. (4) Predictions of water conservation capacity using machine learning are consistent with that of the InVEST model. The water conservation per unit area shows a variation trend as follows: slow-growing broadleaf forests > shrub forests > middle-growing broadleaf forests > cunninghamia lanceolata forests > fast-growing broadleaf forests > pine forests > bamboo forests. (5) Since this study considers only the factors available in the forest management inventory, which does not encompass all relevant influencing factors, it is difficult to fully address the complexities of how forest water conservation services interact with forest structure. Therefore, further research is needed to investigate the intrinsic mechanisms underlying the interactions between water conservation and forest management inventory factors. [ABSTRACT FROM AUTHOR]

Droughts have a substantial impact on water supplies, agriculture, and ecosystems worldwide. Agricultural sustainability and production in the Mekong Delta of Vietnam are being jeopardized by droughts caused by climate change. Conventional forecasting methods frequently struggle to comprehend the intricate dynamics of meteorological occurrences connected to drought, necessitating the use of sophisticated prediction techniques. This study assesses the effectiveness of various statistical models (ARIMA), machine learning, and deep learning models (Gradient Boosting, XGBoost, RNN, and LSTM) in forecasting the SPEI over different time periods (1, 3, 6, and 12 months) across six prediction intervals. The models were developed and evaluated using data from 11 meteorological stations spanning from 1985 to 2022. These models incorporated various climatic variables, including precipitation, temperature, humidity, potential evapotranspiration (PET), Southern Oscillation Index (SOI) Anomaly, and sea surface temperature in the NINO4 region (SST_NINO4). The results demonstrate that XGBoost and LSTM models exhibit outstanding performance, showcasing lower error metrics and higher R² values compared to Gradient Boosting and RNN. The performance of the model fluctuated depending on the forecast step, with error metrics often increasing with longer prediction horizons. The use of climatic indices improved the accuracy of the model. These findings are consistent with earlier research on drought episodes in the Mekong Delta and support studies from other areas that show the effectiveness of advanced modeling tools for predicting droughts. The work emphasizes the capacity of machine learning and deep learning models to enhance the precision of drought forecasting, which is vital for efficient water resource management and agricultural planning in places prone to drought. [ABSTRACT FROM AUTHOR]

Fatigue and fracture prediction in composite materials using cohesive zone models depends on accurately characterizing the core and facesheet interface in advanced composite sandwich structures. This study investigates the use of machine learning algorithms to identify cohesive zone parameters used in the fracture analysis of advanced composite sandwich structures. Experimental results often yield non-unique solutions, complicating the determination of cohesive parameters. Numerical determination can be time-consuming due to fine mesh requirements near the crack tip. This research evaluates the performance of Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN) machine learning methods. The study uses features extracted from load–displacement responses during the fracture of the Asymmetric Double-Cantilever Beam (ADCB) specimen. The inputs include the displacement at the maximum load (δ*), the maximum load ( P m a x ), the total area under the load–displacement curve ( A t ), and the initial slope of the linear region of the load–displacement curve (m). There are two objectives in this research: the first is to investigate which method performs best in identifying the interfacial cohesive parameters between the honeycomb core and carbon-epoxy facesheets, while the second objective is to reduce the dimensionality of the dataset by reducing the number of input features. Reducing the number of inputs can simplify the models and potentially improve the performance and interpretability. The results show that the ANN method produced the best results, with a mean absolute percentage error (MAPE) of 0.9578% and an R-squared (R²) value of 0.7932. These values indicate a high level of accuracy in predicting the four cohesive zone parameters: maximum normal contact stress ( σ I ), critical fracture energy for normal separation ( G I ), maximum equivalent tangential contact stress ( σ I I ), and critical fracture energy for tangential slip ( G I I ). [ABSTRACT FROM AUTHOR]

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity. Antenna defects, ranging from manufacturing imperfections to environmental wear, pose significant challenges to the reliability and performance of communication systems. This review paper navigates the landscape of antenna defect detection, emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection. This review paper serves as a valuable resource for researchers, engineers, and practitioners engaged in the design and maintenance of communication systems. The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures. In this study, a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented. The PRISMA principles will be followed throughout the review, and its goals are to provide a summary of recent research, identify relevant computer vision techniques, and evaluate how effective these techniques are in discovering defects during inspections. It contains articles from scholarly journals as well as papers presented at conferences up until June 2023. This research utilized search phrases that were relevant, and papers were chosen based on whether or not they met certain inclusion and exclusion criteria. In this study, several different computer vision approaches, such as feature extraction and defect classification, are broken down and analyzed. Additionally, their applicability and performance are discussed. The review highlights the significance of utilizing a wide variety of datasets and measurement criteria. The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation, such as real-time inspection systems and multispectral imaging. This review, on its whole, offers a complete study of computer vision approaches for quality control in antenna parts. It does so by providing helpful insights and drawing attention to areas that require additional exploration. [ABSTRACT FROM AUTHOR]

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables. [ABSTRACT FROM AUTHOR]

Heart sounds and murmur provide crucial diagnosis information for valvular heart diseases (VHD). A phonocardiogram (PCG) combined with modern digital processing techniques provides a complementary tool for clinicians. This article proposes a benchmark different time–frequency representations, which are spectograms, mel-spectograms and cochleagrams for obtaining images, in addition to the use of two interpolation techniques to improve the quality of the images, which are bicubic and Lanczos. Deep features are extracted from a pretrained model called VGG16, and for feature reduction, the Boruta algorithm is applied. To evaluate the models and obtain more precise results, nested cross-validation is used. The best results achieved in this study were for the cochleagram with 99.2% accuracy and mel-spectogram representation with the bicubic interpolation technique, which reached 99.4% accuracy, both having a support vector machine (SVM) as a classifier algorithm. Overall, this study highlights the potential of time–frequency representations of PCG signals combined with modern digital processing techniques and machine learning algorithms for accurate diagnosis of VHD. [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]

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]

Adopting innovations in educational practice is a challenging task. In order to promote the use of technological innovations, acceptance of the technology by potential users is a prerequisite. Indeed, understanding the various factors that influence technology acceptance is critical for technology acceptance research. The use and acceptance of chatbots in education as a technological innovation is a topic that needs to be investigated. Chatbots, which offer close to human interaction between the user and technology through text and voice, can provide significant benefits in educational environments. The UTAUT2 model (extending UTAUT), which is widely used to evaluate technology acceptance, can serve as a framework for evaluating the acceptance and use of chatbots. This study aims to predict factors influencing students' use of chatbots in education within the UTAUT2 framework. PLS-SEM and machine learning tested the model, involving 926 students. According to the findings of the study, behavioral intentions were influenced by various factors including performance expectations and attitudes. Facilitating conditions and intentions significantly impacted chatbot usage time. Moderator effects were observed with age, gender, and usage experience affecting behavioral intentions. Support vector machine and logistic regression showed high prediction accuracies for behavioral intentions and usage time, respectively. These results provide insights for chatbot designers to meet user needs in educational settings. [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]

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality. [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]

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 research paper investigates the efficacy of various machine learning algorithms in predicting stress levels. By employing a diverse set of algorithms, including [List of Algorithms], we aim to identify the most accurate and reliable model for stress prediction. The study utilizes [Dataset Information] to train and test the algorithms, evaluating their performance based on metrics such as accuracy, precision, recall, and F1 score. The findings will contribute to the development of more effective stress prediction models, with potential applications in healthcare, workplace wellness, and personal well-being. [ABSTRACT FROM AUTHOR]

Early detection of critical diseases is a pivotal aspect of modern healthcare, significantly impacting patient outcomes and healthcare costs. This research paper provides a comprehensive review and analysis of various machine learning algorithms employed in the realm of early disease detection. The study explores the strengths, limitations, and overall efficacy of prominent algorithms, including Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, K-Nearest Neighbors, and Ensemble Learning. Each algorithm's suitability for early detection is assessed based on factors such as interpretability, scalability, and performance in handling diverse data types. Furthermore, the review discusses the specific applications of these algorithms in different medical contexts, highlighting their contributions to the early identification of critical diseases. By synthesizing the current state of research, this paper aims to provide valuable insights for researchers, and policymakers working towards advancing the field of early disease detection through machine learning. [ABSTRACT FROM AUTHOR]

In recent decades, various pollutants have been released into the environment in substantial amounts due to human activities. Oil pollution is one of these pollutants and can be harmful to both humans and the environment, making its detection and cleanup highly important. Considering the applicability of the electronic nose methods in various domains, including the food industry, environment, etc, this research aims to measure the feasibility of using electronic nose technology to detect soil contaminated with crude oil. An electronic nose system with eight metal oxide semiconductor sensors was used in this research. The soil used in this research was from one of the farms surrounding Karaj province, which, in the laboratory of the University of Tehran, was contaminated with crude oil at four concentrations: 200 ppm, 500 ppm, 1000 ppm, and 5000 ppm, and then the samples evaluated. This system was examined along with pattern recognition methods such as principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM). The results from the pattern recognition methods showed an accuracy of 92% for principal component analysis, 100% for linear discriminant analysis, and 100% for the support vector machine method. Moreover, by employing statistical data analysis using a factorial experiment method within a randomized block design and using decision tree regression, support vector regression, gradient boosted regression, and random forest regression, which has the pollutant detection rate with an average accuracy of 95.5% for the MQ3 sensor, showed the best performance, and detect the difference between the contaminated concentrations. In conclusion, by comparing the results of selected electronic nose sensors with the results obtained by the gas chromatography system on the soil contaminated with crude oil, they have had an acceptable sensitivity. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region's ecosystems and human communities. [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]

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness. [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]