Biomolecular Detection Using AlN/<italic>&#x03B2;</italic>-Ga₂O₃ MOSHEMT: A Machine Learning-Assisted Analytical and Simulation Framework by Aishwarya Tomar, Apurba Chakraborty, Rahul Kumar

“…In comparison to the AlGaN/GaN MOSHEMT, the AlN/<inline-formula> <tex-math notation="LaTeX">$\beta -Ga_{2}O_{3}$ </tex-math></inline-formula> MOSHEMT showed improved sensitivity in terms of drain current. Additionally, the machine learning (ML) model created for this investigation correlates strongly with the simulation results. …”
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Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 by Jinhui Zheng, Shuai Zhang

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Advancing Spike Sorting Through Gradient‐Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering by Mohammad Amin Lotfi, Fatemeh Zareayan Jahromy, Mohammad Reza Daliri

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A lightweight neural attention-based model for service chatbots by Sinarwati Mohamad Suhaili, Mohamad Nazim Jambli

“…Further experiments investigated activation functions and weight initializers integrated into the proposed model to identify optimal configurations that optimize the model’s performance. …”
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Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning... by Feiyang Xia, Tingting Fan, Mengjie Wang, Lu Yang, Da Ding, Jing Wei, Yan Zhou, Dengdeng Jiang, Shaopo Deng

“…However, the interpretation of the diverse microbial communities in relation to complex pollutants is still challenging, and there is limited research in multi-polluted groundwater. Advanced machine learning (ML) algorithms help identify key microbial indicators for different pollution types (CAHs, BTEX plumes, and mixed plumes). …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. …”
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Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning by Qawsar Gulzar, Khurram Mustafa

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Pre-Routing Slack Prediction Based on Graph Attention Network by Jinke Li, Jiahui Hu, Yue Wu, Xiaoyan Yang

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Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

“…The issue of insufficient multi-scale feature extraction and difficulty in accurately classifying fault features in rolling bearing fault diagnosis is addressed by proposing a novel diagnostic method that integrates stochastic convolutional neural networks (SCNNs) and a hybrid kernel extreme learning machine (HKELM). First, the convolutional layers of the CNN were designed as multi-branch parallel layers to extract richer features. …”
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Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Ma... by Lin Luo, Junfeng Yuan, Chen Xu, Huilin Xu, Haojie Tan, Yinhao Shi, Haiping Zhang, Haijun Xi

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Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures by Eslam Deef-Allah, Magdy Abdelrahman

“…The framework predicted the rheological resistance of the binders to rutting and cracking using linear and nonlinear machine learning models. The nonlinear models outperformed the linear models for the three rheological parameters. …”
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Deeper Effects of fiscal multidimensional poverty reduction: household characteristics, financial lags and elite capture. by Wenjie Jiang, Hong Yang, Chunyu Liu

“…The research results indicate that fiscal investment in agriculture can effectively alleviate multidimensional relative poverty among rural households, and this conclusion still holds after the robustness and endogeneity tests of traditional measurement and Double Machine Learning. However, differences in household characteristics affect the performance of fiscal poverty alleviation. …”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…Through comprehensive evaluation using both synthetic and real-world traces from deep learning training workloads, our method demonstrates substantial improvements over state-of-the-art algorithms: achieving up to 45.1% reduction in average I/O latency, 32.5% improvement in throughput for critical applications, and 28.8% reduction in storage costs. …”
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Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics by Lorenzo Fantechi, Federico Barbarossa, Sara Cecchini, Lorenzo Zoppi, Giulio Amabili, Mirko Di Rosa, Enrico Paci, Daniela Fornarelli, Anna Rita Bonfigli, Fabrizia Lattanzio, Elvira Maranesi, Roberta Bevilacqua

“…Radiomics, combined with machine learning (ML), offers a promising approach by extracting quantitative imaging features from CT scans. …”
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Combining single-cell ATAC and RNA sequencing for supervised cell annotation by Jaidip Gill, Abhijit Dasgupta, Brychan Manry, Natasha Markuzon

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Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing by Tianchu Wang, Wentao Yang, Ziteng Xu, Wenwen Qi, Liam Taylor, Ming Wang, Wei Wu

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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, <i>n</i> = 14,863) and patients hospitalized with heart failure (patients’ dataset, <i>n</i> = 8220). …”
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Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks by Jun Niu, Peng Liu, Chunhui Huang, Yangming Zhang, Moxuan Zeng, Kuo Shen, Yangzhong Wang, Suyu An, Yulong Shen, Xiaohong Jiang, Jianfeng Ma, He Wang, Gaofei Wu, Anmin Fu, Chunjie Cao, Xiaoyan Zhu, Yuqing Zhang

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AI Roles in 4R Crop Pest Management—A Review by Hengyuan Yang, Yuexia Jin, Lili Jiang, Jia Lu, Guoqi Wen

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A Systematic Review on the Use of Big Data in Tourism by Jafar Ahangaran, Abbas Sadeghnia

“…Results By examining the results obtained from the research questions, it was found that, firstly, the use of big data analysis in scientific articles has grown significantly, and secondly, in most of the researches conducted around the topic of tourism using big data analysis, the most source of big data collection is the data produced. by users (UGC) with the approach of statistical analysis and then analysis by artificial intelligence and machine learning. This means that social networks, which are responsible for the dissemination of user-generated data, can play a more prominent role in scientific research, and the traditional method of collecting questionnaires will give way to examining the real opinions of users on social networks about a specific topic. …”
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