Malware Detection and Classification using Shapley Additive Explanations Values in Machine Learning

One of the most serious and alarming problems facing humanity is the degradation of natural water resources such as lakes as well as rivers is one of the most serious and vexing problems we are facing today. The long-term effects of polluted water affect all areas of life. Therefore, it is essential to manage water resources if you want tomaximize the quality of your water. If data are examinedand water quality can be predicted, the effects of watercontamination can be dealt with effectively. The purpose of this study is to use machine learning to make a water qualityprediction model based on water quality measurements. Machine learning can be used for building models fromalgorithms with some data gathered from the sick ones. For a better examination of parametric findings, the acquired data will be pre-processed, separated into training and testing parts, and subjected to machine learning classification techniques. Decision tree, Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbor are some of the classification-type algorithms employed in this work. All the model's performance indicators are calculated, and they change for each model. A technique for improving machine learning model performance metrics is hyperparameter tuning.

ObjectiveAnalyze the psychological and clinical factors of clinically significant tinnitus (THI score ≥38) in patients with hearing loss, construct predictive models based on four machine learning (ML) algorithms, and compare the predictive performance of different models.MethodsPatients with hearing loss who visited the Department of Otolaryngology at Qinghai University between August 2024 and May 2025 were enrolled in this study. Clinical data were retrieved from the hospital’s electronic medical record system. The study outcome was the occurrence of clinically significant tinnitus. Predictive variables were screened using univariate analysis, the least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Four ML algorithms—logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—were applied to construct and validate predictive models. The area under the receiver operating characteristic curve (AUC) of each model in the validation set was compared using the DeLong test. Additionally, model performance metrics in the validation set were compared to identify the optimal model. Finally, the Shapley additive explanations (SHAP) algorithm was employed to interpret the best-performing model.ResultsNine key variables—age, hypertension, sleep disorder, anxiety, hearing loss severity, depression, noise exposure history, hearing side, and ototoxic drug use—were retained after LASSO and Boruta feature selection. Among the four ML models, the RF algorithm achieved the best predictive performance, with an AUC of 0.973 in the training set and 0.977 in the validation set, followed by XGBoost (AUC = 0.962 and 0.961, respectively). DeLong tests confirmed that RF significantly outperformed LR and SVM models (p < 0.001), while its difference from XGBoost was not significant. In the validation set, the RF model yielded the highest accuracy (0.923), sensitivity (0.929), specificity (0.914), precision (0.945), and F1-score (0.937). SHAP analysis indicated that hearing loss severity, age, and sleep disorder were the most influential predictors, suggesting that both auditory and non-auditory factors contribute substantially to the risk of clinically significant tinnitus.ConclusionThe RF model showed the best performance in predicting clinically significant tinnitus, with hearing loss severity, age, and sleep disorder identified as major predictors. Integrating auditory and psychological factors can improve early risk identification in patients with hearing loss.

Hepatitis C is a viral infectious disease caused that affects 58 million people globally making it a public health crisis, and it is reported that 20-30% of cases develop to severe liver disease. This creates the need for data-driven based method that such as Machine learning that depends on data inputs in making accurate outputs with high precision. This study explores the use of Machine learning algorithms in optimizing the detection of hepatitis C status of patients. This research utilizes a publicly available dataset that contains 12 explanatory features and one target feature that addresses the patient’s diagnosis status. Data preprocessing techniques such as checking of missing values, assessment for possible outliers and Exploratory data analysis was carried on the dataset to improve the quality of the dataset for predictive analysis. The EDA results reviews that majority of the features had significant relationship (p-value &lt; 0.05) with the target variable. Also, we observed the distribution of the disease diagnosis by gender reviews that females showed higher prevalence, and when we consider age, older people are more likely to contract the HCV. This result aligns with research findings from recent studies. The dataset was scaled and separated using a ratio split of 80:20 (80% of data for training). We applied SMOTE algorithm to the training data to balance the diagnosis distribution of the target variable. The model was trained using a wide parameter space using a cross-validation fold of 10 to select the best parameters. The ML models evaluated in this research include Logistic Regression, Support Vector Machine, Random Forest, XGBOOST, Decision Tree, and Extra Trees. The model performance metrics shows that XGBOOST was superior with an accuracy score of 98%, followed by Random Forest (97%), Decision Tree (94%), Extra Trees (93%), SVM (92%), and Logistic Regression (87%). The Kappa statistics for XGBOOST which was estimated as 0.8827 shows that difference between the predicted instances and the actual instances was minimal. The performance metrics of the XGBOOST was also contrast with ML algorithms trained by recent researchers in detecting HCV and it shows our model was more precise underscoring the significance of datapreprocessing, feature-scaling, SMOTE and hyperparameter tuning in predictive analysis in healthcare.

Objective: Thalassemia is a hereditary hemoglobinopathy and remains a significant public health problem, particularly in Mediterranean regions. Although genetic testing represents the gold standard for subtype classification, access to such testing is limited in many clinical settings. This pilot study aimed to explore the feasibility of using machine learning models based on routinely available clinical and laboratory parameters to support the differentiation of thalassemia subtypes in the absence of genetic testing.Methods: This retrospective cross-sectional study included 83 individuals (57 thalassemia major, 11 thalassemia intermedia, and 15 healthy controls). Demographic, clinical, and laboratory variables were analyzed using the R programming language. A supervised Random Forest algorithm was applied for multiclass classification. Model performance was assessed using accuracy, class-specific sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). To further evaluate the distinction between thalassemia major and intermedia, a simplified logistic regression model was constructed, and Firth logistic regression was applied to address the small sample size and class imbalance.Results: The Random Forest model demonstrated an overall test-set accuracy of 85.7%. Sensitivity was 80% for thalassemia major and 100% for both thalassemia intermedia and healthy controls. Variable importance analysis identified red cell distribution width (RDW), hematocrit, ferritin, and hemoglobin as the most influential predictors. In the simplified logistic regression model distinguishing thalassemia major from intermedia, RDW was the only variable reaching statistical significance (p = 0.0476). Model performance metrics, including high AUC values, should be interpreted cautiously given the limited sample size.Conclusion: The Random Forest model demonstrated an overall test-set accuracy of 85.7%. Sensitivity was 80% for thalassemia major and 100% for both thalassemia intermedia and healthy controls. Variable importance analysis identified red cell distribution width (RDW), hematocrit, ferritin, and hemoglobin as the most influential predictors. In the simplified logistic regression model distinguishing thalassemia major from intermedia, RDW was the only variable reaching statistical significance (p = 0.0476). Model performance metrics, including high AUC values, should be interpreted cautiously given the limited sample size.

The article explores the technical and theoretical aspects of machine learning (ML) in addressing the escalating complexities of cybersecurity threats in the digital age since the ever-growing rise in cybercrime has prompted users to utilize newer approaches to raise the bar on cybersecurity. Research considers the adoption of machine learning (ML) technology as a cornerstone of virtually any contemporary problem in cyber security, particularly processes and techniques involved in problem analysis, detection, attack prediction, and even behavioral profiling. Elaborated on how ML makes a better response compared to traditional methods like signature-based detection by explaining how real-time analysis of massive data becomes possible. An overview of the important features of supervised and unsupervised learning is provided in the context of anomaly detection and malicious activity recognition with a focus on Support Vector Machine and Isolation Forests algorithms as well as a detailed look at the LSTM model for phishing URL evolution analysis. Also, those algorithms have been highlighted from the technical implementation side: supervised learning with Support Vector Machines using Scikit-Learn to classify network traffic trained on features like IP addresses and ports, unsupervised learning with Isolation Forests for anomaly detection in multidimensional data, and deep learning with Long Short-Term Memory (LSTM) networks for phishing URL analysis. This paper investigates significantly important difficulties in carrying out ML algorithms, such as class imbalance, adversarial attacks, and lack of model transparency. Such techniques as SMOTE (Synthetic Minority Over-sampling Technique) are proposed for developing training datasets, whereas model adversarial training and robust optimization methods are suggested for defense against malicious model exploitation. Also, the role of explainability methods such as SHAP and LIME are emphasized to build the trust and acceptance of automated ML systems in cybersecurity. Identified research opportunities and suggested that further testing be done on improving model robustness and performance metrics in constrained environments.

Purpose of ReviewThis review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water.Recent FindingsWe found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions.SummaryWe reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality.

Data science techniques such as machine learning are rapidly becoming available to engineers building models from system data, such as aircraft operations data. These techniques require validation for use in fielded systems providing recommendations to operators or maintainers. The methods for validating and testing machine learned algorithms generally focus on model performance metrics such as accuracy or F1-score. Many aviation datasets are highly imbalanced, which can invalidate some underlying assumptions of machine learning models. Two simulations are performed to show how some common performance metrics respond to imbalanced populations. The results show that each performance metric responds differently to a sample depending on the imbalance ratio between two classes. The results indicate that traditional methods for repairing underlying imbalance in the sample may not provide the rigorous validation necessary in safety critical applications. The two simulations indicate that authorities must be cautious when mandating metrics for model acceptance criteria because they can significantly influence the model parameters.

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.

Prediction models are essential in clinical decision-making for estimating the probability of current (diagnosis, screening) or future (prognosis) outcomes. Network meta-analysis (NMA) serves as a powerful tool to compare the performance of multiple prediction models simultaneously. However, there is hardly any guidance on methods and reporting for studies employing NMA to evaluate prediction models. To provide an overview of NMAs assessing prediction model (external validation) performance, regardless of whether they use aggregate data (AD) or individual participant data (IPD). In addition, we offer recommendations for improving the reporting and conduct of NMAs in prediction model research. We searched PubMed and Embase up to September 1, 2025, to identify studies that addressed the evaluation of diagnostic or prognostic prediction model performance using NMA. We included articles that employed NMA to compare and assess at least three prediction models. We summarized the identified studies based on, eg, their application (diagnostic vs prognostic), data use (AD vs IPD), medical contexts in which the models were assessed, and evaluation metrics applied (eg, discrimination, calibration, and (re)classification). In addition, we examined the statistical approaches employed, the NMA assumptions (such as consistency, transitivity, and exchangeability), and the ranking methods used for model comparison. After screening 2436 articles, 28 were included. Twenty-six studies (92.9%) used AD, while two (7.1%) used IPD. Hospital care was the most common setting (n = 22; 78.6%), with respirology (n = 7; 25.0%) and cardiology (n = 5; 17.9%) as the most frequently studied clinical domains. Key NMA assumptions were addressed differently across the 28 NMAs: 14.3% (n = 4) discussed transitivity, similarity, or exchangeability, and 53.6% (n = 15) tested for consistency. The statistical approach also varied, with 60.7% of studies (n = 17) reporting Bayesian methods and 17.9% (n = 5) reporting frequentist approaches. Surface under the cumulative ranking was the predominant ranking method (n = 18). Most NMAs included 5 to 10 models in the network, with 5 NMAs analyzing more than 20 models. Performance metrics varied, with 39.3% of studies (n = 11) reporting discrimination measures, such as C statistics, while none reported calibration metrics. Sensitivity or specificity was provided in 64.3% of studies (n = 18), and no articles reported advanced decision-analytic metrics like the decision curve analysis. This scoping review highlights the limited and diverse use of NMA methods in evaluating prediction models, with a predominant reliance on aggregate rather than IPD, and inconsistent consideration of key NMA assumptions and model performance metrics. We provide recommendations for the reporting and conduct of an NMA of prediction model validation performance. Prediction models are tools used in medicine to estimate a patient's risk of developing a disease or experiencing a health outcome. Many different prediction models exist for the same condition, and it can be difficult for doctors and researchers to know which model performs best. One way to compare multiple models is through a statistical method called network meta-analysis (NMA), which is commonly used to compare treatments but has rarely been applied to prediction models. In our study, we reviewed all published NMAs that evaluated prediction models to see how they were conducted and reported. We looked at whether the studies reported important performance measures, such as how well the models could distinguish between patients with and without the outcome (discrimination), and how well the predictions matched actual outcomes (calibration). We also checked if key NMA assumptions were considered and how analyses were conducted. We found that most studies used summary data instead of patient-level data. Many did not report crucial performance measures and rarely checked NMA assumptions. There was also limited transparency in how the models were analyzed, making it difficult for others to reproduce the results. Our findings show that while NMA has great potential to help compare prediction models and identify the most reliable ones, current practice often lacks the detailed reporting needed to make these comparisons fully trustworthy. We recommend better reporting, sharing of data and analysis code, and careful checking of assumptions to help researchers and doctors choose the most reliable models, ultimately improving patient care.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

Multivariate image analysis (MIA) aims to extract useful information from multivariate image structures such as hypercubes. Multivariate image regression is particularly useful for the development of calibration models for quantitative analysis of hypercubes. The key step in multivariate calibration is model selection; this includes the selection of method (e.g. partial least squares or ridge regression) and setting of parameters within the selected method (e.g. number of latent variables or ridge parameter). Sub-optimal selection of method and or parameters generally leads to poor predictive performance on future samples. While conventional methods such as cross-validation are indispensible in this task, the inherent nature of hyperspectral images may also be exploited. Application of a given calibration model to each pixel in a hypercube results in a prediction map whose features may be used to evaluate the model itself. This paper explores the application of various image statistics extracted from prediction images as model performance metrics for MIA.

Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques

Summary: The potential of deep learning (DL) in genomic selection (GS) is constrained by the significant technical expertise required to design and implement neural networks. While DL has revolutionized fields like language processing and structural biology, its application in GS has not yet consistently outperformed traditional models like mixed linear models. The key to unlocking DL's power in GS lies in the exploration of network architectures tailored to genomic data, a process that demands intensive programming and poses a barrier for many researchers. To overcome this challenge, we developed Artificial Intelligence for Efficient and Versatile Evaluation and Representation (AI4EVER), a freely available graphical software platform that enables users to explore and apply machine learning (ML) models without any coding. AI4EVER integrates a graphical user interface (GUI) with a Python-based ML backend. The platform currently supports five models: Ridge Regression, Random Forest, Gradient Boosted Decision Trees, Multi-Layer Perceptron, and a customizable Keras-based neural network that can simultaneously predict multiple traits in a single model. A key feature of AI4EVER is optional incorporation of genome-wide association study (GWAS) results (p-values) as feature weights during model training, enabling biologically informed DL workflows. The platform further provides real-time visualization of model performance metrics and automated feature-importance outputs to enhance interpretability. AI4EVER also separates model training and prediction workflows, allowing trained models to be reused for independent prediction datasets. Using a representative maize dataset, we demonstrate that AI4EVER enables access to advanced AI, empowers genomic researchers to accelerate data-driven decision-making in breeding programs, ultimately lowering the barrier to artificial intelligence-enabled genetic improvement in crops and animals and human health management.