Advancing soil-structure interaction (SSI): a comprehensive review of current practices, challenges, and future directions by Imtiyaz Akbar Najar, Raudhah Ahmadi, Akeem Gbenga Amuda, Raghad Mourad, Neveen El Bendary, Idawati Ismail, Nabilah Abu Bakar, Shanshan Tang

“…Additionally, the review discusses recent innovations, including the application of machine learning and advanced computational tools, and their potential to enhance the accuracy and efficiency of SSI analysis. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. …”
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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

“…In horticulture crop loss reduction, AI plays a vital role when coupled with machine learning algorithms. By analyzing extensive volumes of data encompassing weather patterns, soil conditions, and occurrences of pests and diseases, AI systems can provide farmers with real-time insights and predictive models. …”
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Estimation of Prevalence of Hospital-Acquired Blood Infections among Patients Admitted at a Tertiary Hospital in Oman over a Period of Five Years: A Cross-Sectional Study by Marah El-Beeli, Yahya Al-Farsi, Abdullah Balkhair, Zakariya Al-Muharrmi, Mansoor Al-Jabri, Samir Al-Adawi

“…The study calls for the timely formulation and adoption of national HA-BSI screening and management programs centered on surveillance systems based on real-time analytics and machine learning.…”
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A Heterogeneous Ensemble Learning Method Combining Spectral, Terrain, and Texture Features for Landslide Mapping by Yi He, Hesheng Chen, Qing Zhu, Qing Zhang, Lifeng Zhang, Tao Liu, Wende Li, Huaiyuan Chen

“…The existing landslide recognition methods mainly focus on the use of spectral bands of optical remote sensing and machine learning base classifiers, which are insufficient in landslide characterization in complex scenes, resulting in a high missed and false detection of landslides. …”
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Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions by Zhong Zhuang, Hao Qi, Siyu Huang, Qiqi Wang, Yanan Wan, Huafen Li

“…This study highlights the potential of hybrid data-driven approaches, combining machine learning, mechanistic model and stochastic prediction to simplify the complex environmental process, allowing for integrated risk evaluations.…”
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Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach by Mohamed Khalid AlOmar, Mohammed Majeed Hameed, Nadhir Al-Ansari, Mohammed Abdulhakim AlSaadi

“…The accurate and more reliable prediction of TDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths. Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have been applied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. …”
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Strategies to Improve the Quality and Freshness of Human Bone Marrow-Derived Mesenchymal Stem Cells for Neurological Diseases by Da Yeon Lee, Sung Eun Lee, Do Hyeon Kwon, Saraswathy Nithiyanandam, Mi Ha Lee, Ji Su Hwang, Shaherin Basith, Jung Hwan Ahn, Tae Hwan Shin, Gwang Lee

“…As studies on the traditional characteristics of hBM-MSCs before transplantation into the brain are very limited, omics and machine learning approaches are needed to evaluate cell conditions with indepth and comprehensive analyses. …”
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Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement by Johannes Gruen, Tobias Bauer, Theodor Rüber, Thomas Schultz

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Psychological distress in adolescence and later economic and health outcomes in the United States population: A retrospective and modeling study. by Nathaniel Z Counts, Noemi Kreif, Timothy B Creedon, David E Bloom

“…<h4>Methods and findings</h4>This analysis estimated the relationship between psychological distress in those aged 15 to 17 years in 2000 and economic and health outcomes approximately 10 years later, accounting for an array of explanatory variables using machine learning-enabled methods. The cohort was from the National Longitudinal Study of Youth 1997 and nationally representative of those aged 12 to 18 years in 1997. …”
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In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review by Michael Dorosan, Ya-Lin Chen, Qingyuan Zhuang, Shao Wei Sean Lam

“…The study’s findings will be published and presented in forums combining artificial intelligence and machine learning, clinical decision-making, and health technology impact analysis. …”
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Deep Learning-Based Feature Extraction Technique for Single Document Summarization Using Hybrid Optimization Technique by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak, Premananda Sahu, Kaushik Mishra

“…The proposed approach&#x2019;s results were compared with existing methods, including CSO, QABC, PSO, GJO, FF, and machine learning techniques like SVM and RF. The hybrid CSO-HHO algorithm achieved an accuracy of 99.56%, demonstrating its superiority in text summarization tasks.…”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. …”
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Feature selection enhances peptide binding predictions for TCR-specific interactions by Hamid Teimouri, Hamid Teimouri, Zahra S. Ghoreyshi, Zahra S. Ghoreyshi, Anatoly B. Kolomeisky, Anatoly B. Kolomeisky, Anatoly B. Kolomeisky, Jason T. George, Jason T. George, Jason T. George, Jason T. George

“…A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key properties contributing to binding affinity.ResultsOur analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR peptide interactions. …”
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Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study by Ping Zheng, Ting Pan, Ya Gao, Juan Chen, Liren Li, Yan Chen, Dandan Fang, Xuechun Li, Fei Gao, Yilei Li

“…This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. …”
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Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder by Chun-Chuan Chen, Meng-Chang Tsai, Eric Hsiao-Kuang Wu, Shao-Rong Sheng, Jia-Jeng Lee, Yung-En Lu, Shih-Ching Yeh

“…Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. …”
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Environmental exposures related to gut microbiota among children with asthma: a pioneer study in Taiwan by Aji Kusumaning Asri, Tsunglin Liu, Hui-Ju Tsai, Jiu-Yao Wang, Chih-Da Wu

“…Air pollution was estimated using an ensemble learning model that combined regression and machine-learning algorithms, while greenspace was quantified using the normalized difference vegetation index (NDVI) and green land-cover data. …”
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Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy by Qianqian Yan, Zihao Zhao, Dongwei Liu, Jia Li, Shaokang Pan, Jiayu Duan, Zhangsuo Liu

“…Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. …”
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Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications by Romaniyanto Romaniyanto, Muhana Fawwazy Ilyas, Aldebaran Lado, Daffa Sadewa, Daffa Sadewa, Dykall Naf'an Dzikri, Enrico Ananda Budiono, Enrico Ananda Budiono

“…The recent phase reflects a shift towards technology-driven approaches, including minimally invasive techniques, artificial intelligence, and machine learning. China emerged as the leading country with the most contributions based on author, affiliations, funding sponsors, and countries. …”
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A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD by Saeedeh Komijani, Dipak Ghosal, Manpreet K. Singh, Julie B. Schweitzer, Julie B. Schweitzer, Prerona Mukherjee, Prerona Mukherjee

“…We utilized a hierarchical clustering technique to mitigate these collinearity issues and implemented a non-parametric machine learning (ML) model to predict the significance of symptom relations over time. …”
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