Landslide prediction is a critical task for ensuring public safety and preventing economic loss in regions prone to such natural disasters. Traditional models for landslide prediction often lack accuracy and precision because of the intricate interactions between various factors that lead to landslide events. To tackle this issue, we introduce an innovative hybrid approach for landslide prediction that combines Time Series Analysis (TSA), Genetic Algorithm (GA), and Support Vector Machine (SVM). TSA decomposes landslide displacement data into trend, seasonal, and residual components, improving the clarity of the data. GA optimizes the hyperparameters of SVM, ensuring the most effective application of the SVM. Finally, the SVM is trained on detrended data, producing a model capable of accurately predicting future landslides. Our experimental outcomes manifest that the TSA-GA-SVM model we advanced performs far better than the individual TSA and SVM models when it comes to forecasting landslide displacement. The hybrid model achieved a mean absolute error of 0.15 m compared to 0.42 m for TSA and 0.38 m for SVM alone. Sensitivity analysis revealed that increasing GA population size improved model stability, while higher mutation rates led to more variable predictions. The model showed good generalization ability, performing well across different regions and under various geological and hydrological conditions. This research not only advances the state of the art in landslide prediction but also provides a practical tool for authorities to implement in their disaster prevention and management strategies.

This study provides a comprehensive analysis of supply chain management practices based on survey responses from a sample of enterprises. Through descriptive statistics, hypothesis testing, predictive modeling, advanced analytics techniques such as classification, clustering, and association rule mining, the research offers valuable insights into key areas of collaboration, quality management, technology adoption, agility, risk management, and customer responsiveness within supply chains. The findings highlight the importance of strategic integration, proactive problem-solving, customer-centric practices, and agility in meeting changing demands. The study also identifies distinct profiles of practice adoption and reveals intricate relationships between different supply chain practices. Overall, the research contributes to a deeper understanding of supply chain dynamics and offers actionable insights for improving operational performance and strategic decision-making.

The COVID-19 pandemic highlighted humanity's vulnerability to emerging infectious diseases and the critical need for proactive strategies. While COVID-19's immediate crisis recedes, it's crucial not to underestimate other threats like Hantavirus, which, despite lower transmissibility, carries high fatality rates and lacks specific treatments or vaccines. This paper argues that the lessons and technological advancements from the COVID-19 response offer a vital blueprint for Hantavirus mitigation. We advocate for a rapid, multi-faceted approach, mirroring COVID-19's urgency. This includes implementing personal protective measures (e.g., masks, gloves) proven effective in reducing viral transmission. Crucially, we propose leveraging the significant advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) that matured during the pandemic. These technologies can revolutionize Hantavirus drug discovery by accelerating drug repurposing from existing libraries, executing rapid virtual screening and molecular docking of vast chemical databases to identify inhibitors, and informing de novo drug design. This accelerated process demands a 'Human-in-the-Loop' (HITL) paradigm. While AI/ML/DL offer unparalleled speed in hypothesis generation, human expertise from virologists, clinicians, and public health officials remains indispensable for validating models, interpreting complex biological data, guiding experimental design, and translating insights into effective interventions. By synergizing advanced computational power with invaluable human intuition, we can proactively develop robust therapeutic and preventive strategies for Hantavirus, shifting from reactive crisis management to a data-driven defense against future viral threats.

This study examines the role of artificial intelligence and blockchain adoption in enhancing sustainable financial performance among Jordanian financial institutions (FIs). Based on the resource-based view, the study explores the mediating role of data analytics capability through predictive analytics, intelligent data processing, and data-driven decision-making processes. A deductive quantitative approach employed and data were collected using structured questionnaires distributed to employees working in Jordanian commercial and Islamic FIs. A total of 300 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM). The results of this study reveal that artificial intelligence (AI) and blockchain adoption positively and significantly influenced sustainable financial performance through improving operational efficiency, transaction transparency, intelligent financial decision-making, and institutional adaptability. The results also confirm that data analytics capability played a significant mediating role between AI, blockchain adoption, and sustainable financial performance. FIs with stronger analytical capabilities were better able to transform technological investments into strategic financial outcomes, thereby improving financial resilience, organizational efficiency, and long-term sustainability. The study is limited to Jordanian FIs, which may restrict the generalizability of the findings. In addition, the cross-sectional design does not allow examination of long-term technological changes. Future studies may utilize longitudinal approaches, cross-country samples, and additional technological dimensions, including cloud computing integration, analytics, and intelligent automation systems. The study contributes to the growing literature on digital finance by emphasizing AI, blockchain adoption, and data analytics capability as strategic organizational resources that strengthen sustainable financial performance within emerging digital financial ecosystems.

Variable selection is an important step in data envelopment analysis (DEA) when the number of decision making units (DMUs) is insufficient. This research thus proposes a two-stage variable selection method integrating a well-known supervised machine learning technique, the random forest algorithm. In the first stage, a baseline DEA model with full input and output variables is implemented and each DMU can be determined as being efficient or inefficient. In the second stage, a random forest is trained to learn how to classify efficient or inefficient DMUs well in high dimensions. Accordingly, the importance of each variable can be calculated based on permutation importance indices in random forest. This paper further discusses two issues about data augmentation in order to improve the robustness of permutation importance when the number of DMUs is quite small.

The challenge presented by simultaneous buffer and service rate allocation in manufacturing systems represents a difficult non-deterministic polynomial problem. Previous studies solved this problem by iteratively utilizing a generative method and an evaluative method. However, it typically takes a long computation time for the evaluative method to achieve high evaluation accuracy, while the satisfactory solution quality realized by the generative method requires a certain number of iterations. In this study, a data-driven hybrid approach is developed by integrating a tabu search–non-dominated sorting genetic algorithm II with a whale optimization algorithm–gradient boosting regression tree to maximize the throughput and minimize the average buffer level of a manufacturing system subject to a total buffer capacity and total service rate. The former algorithm effectively searches for candidate simultaneous allocation solutions by integrating global and local search strategies. The prediction models built by the latter algorithm efficiently evaluate the candidate solutions. Numerical examples demonstrate the efficacy of the proposed approach. The proposed approach improves the solution efficiency of simultaneous allocation, contributing to dynamic production resource reconfiguration of manufacturing systems.

Natural fiber reinforced composites (NFRCs) have gained recognition as sustainable and environmentally friendly alternatives to synthetic composites. However, estimating their fatigue durability remains a major challenge due to their complex and heterogeneous behaviour. This study develops a machine learning framework to predict the fatigue life of hemp fiber-reinforced HDPE composites from experimental data, addressing the specific limitations of conventional empirical models. The dataset is divided into training and testing subsets following a preprocessing phase that includes logarithmic transformation, normalization, and outlier removal. Four regression models are compared: Multiple Linear Regression, Random Forest, Gradient Boosting Regressor, and Support Vector Regression. Grid Search with 5-fold cross-validation is used to optimize hyperparameters and improve predictive accuracy. Model performance is evaluated using the coefficient of determination (R²), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Results show that ensemble methods, particularly Random Forest (R² = 99.91%, MAPE = 1.11%) and Gradient Boosting (R² = 99.87%, MAPE = 1.52%), substantially outperform linear models and traditional S-N curve fitting. Feature importance analysis reveals that maximum stress accounts for the majority of prediction variance (approximately 68%–81% depending on the dataset), offering actionable insights for material design. The proposed framework demonstrates strong generalization potential and provides a reproducible template for data-driven fatigue modeling in sustainable composite materials.

A thorough analysis of developments in machine learning (ML) and deep learning (DL) technologies for skin cancer diagnosis is provided in this research. It investigates how ML and DL could improve the precision and effectiveness of melanoma, basal cell carcinoma, and squamous cell carcinoma detection. By looking at current studies, the study emphasizes the use of neural networks, convolutional neural networks (CNNs), support vector machines (SVM), random forests, and k-nearest neighbors (KNN) in the diagnosis of skin cancer. Key findings show that DL models, including VGG, ResNet, and Inception benefit from huge datasets and sophisticated data augmentation strategies to attain high accuracy, sensitivity, and specificity. The paper also discusses the challenges and limitations associated with these technologies, such as the requirement for extensive annotated datasets. The study concludes with a call for collaboration to overcome current challenges and enhance the practical application of ML and DL in skin cancer detection.

Conventionally, the deep beam shear strength is analyzed with codes (mechanics and empirical models). The purpose of this investigation is to provide an alternative way of accurately estimating the shear capacity of Reinforced Concrete Deep Beams (RCDBs), including those with and without shear reinforcements (WOR and WWR), by adopting machine learning models. Four machine learning algorithms: k-Nearest Neighbor (kNN), Random Forest, M5Rules, and Sequential Minimal Optimization for Regression (SMOReg), were considered, and the selection was based on their performance in previous related studies. A database of 733 samples for WWR and 378 samples for WOR was compiled, utilizing 14 and 8 input features, respectively, in each case. WEKA, an open-source software suite, was used in preprocessing the data and also tuning the hyperparameters. SMOReg beat other models for WOR with an R² value of 0.9607, while Random Forest did best for WWR with an R² value of 0.9667 in the testing sets. The shear strengths predicted by the machine learning models were compared to four traditional standard codes. The results show that the machine learning models beat conventional methods by a large margin, while also being consistent with earlier models generated using machine learning. This demonstrates the model's prediction accuracy and robustness.

While the number of AIDS-related deaths continues to rise, efforts have been made to transform the disease into a manageable chronic condition. HIV protease inhibitors have become central to combination therapy. As a result, these inhibitors have become a major focus of anti-HIV drug development. This research takes a data-driven approach to drug development through the use of quantitative structure-activity relationship (QSAR) analysis. A dataset of 450 anti-HIV drugs was used to construct and validate models. Using extensive validation methods and various machine learning algorithms, the results clearly showed that the "ET" regression outperformed the other models (“XGB”, “LGBM”, “DT”, “RF”, “GB”, “Bag”, and “HGB”) in terms of goodness of fit, predictivity, generalizability, and model robustness. Promising compounds were subjected to molecular docking and molecular dynamics simulation, resulting in drugs with favourable pharmacokinetic and pharmacodynamic properties that consistently interact with the therapeutic target.