Opposition-based learning techniques in metaheuristics: classification, comparison, and convergence analysis by Rihab Lakbichi, Farouq Zitouni, Saad Harous, Aridj Ferhat, Abdelhadi Limane, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

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A novel method based on improved SFLA for IP information extraction from TEM signals by Ruiyou Li, Ruiheng Li, Guang Li, Yong Zhang, Xiaohui Ding, Long Zhang

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Chaos-enhanced metaheuristics: classification, comparison, and convergence analysis by Abdelhadi Limane, Farouq Zitouni, Saad Harous, Rihab Lakbichi, Aridj Ferhat, Abdulaziz S. Almazyad, Pradeep Jangir, Ali Wagdy Mohamed

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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

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Finding high posterior density phylogenies by systematically extending a directed acyclic graph by Chris Jennings-Shaffer, David H. Rich, Matthew Macaulay, Michael D. Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A. Suchard, Frederick A. Matsen

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Generation of a Social Network Graph by Using Apache Spark by Y. A. Belov, S. I. Vovchok

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RAPO: An Automated Performance Optimization Tool for Redis Clusters in Distributed Storage Metadata Management by Yunkai Zhu, Tian Xia, Te Zhu, Zhoujie Zhao, Kexin Li, Xingbo Hu

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Efficient black-box attack with surrogate models and multiple universal adversarial perturbations by Tao Ma, Hong Zhao, Ling Tang, Mingsheng Xue, Jing Liu

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Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning by Yusuf Alaca

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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

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Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques by Sahas V. Swamy, Bijay Mihir Kunar, Karra Ram Chandar, Mamdooh Alwetaishi, Shashikumar Krishnan, Sudhakar Reddy

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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

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Novel neoadjuvant therapies for muscle‐invasive bladder cancer: Systematic review and meta‐analysis by Shugo Yajima, Naoki Imasato, Hitoshi Masuda

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DREAM(LoAX): Simultaneous Calibration and Diagnosis for Tracer‐Aided Ecohydrological Models Under the Equifinality Thesis by Songjun Wu, Doerthe Tetzlaff, Keith Beven, Chris Soulsby

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Method and experimental verification of spatial attitude prediction for an advanced hydraulic support system under mining influence by Zhuang Yin, Kun Zhang, ZengBao Zhang, Hongyue Chen, Lingyu Meng, Zhen Wang, Mingchao Du, Xiangpeng Hu, Defu Zhao, Dan Tian

“…Based on this, the WOA algorithm was utilized to search for the optimal number of neurons in the hidden layer and the learning rate. …”
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Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning by Jing Zhang, Yong-feng Fu, Hao Shen, Quan Liu, Li-ning Sun, Li-guo Chen

“…The experimental results demonstrate that the proposed liquid lens microscope with DRLAF exhibits high robustness, achieving a 79% increase in speed compared to traditional search algorithms, a 97.2% success rate, and enhanced generalization compared to other deep learning methods.…”
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Frequency Regulation Provided by Doubly Fed Induction Generator Based Variable-Speed Wind Turbines Using Inertial Emulation and Droop Control in Hybrid Wind–Diesel Power Systems by Muhammad Asad, José Ángel Sánchez-Fernández

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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Advancing Mental Health Care: A Comprehensive Review of Digital Tools and Technologies for Enhancing Diagnosis, Treatment, and Wellness by Muhammad Khalid Anser, Agha Amad Nabi, Ishfaq Ahmad, Muhammad Moinuddin Qazi Abro, Khalid Zaman

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Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy by Moustaq Karim Khan Rony, Dipak Chandra Das, Most. Tahmina Khatun, Silvia Ferdousi, Mosammat Ruma Akter, Mst. Amena Khatun, Most. Hasina Begum, Md Ibrahim Khalil, Mst. Rina Parvin, Daifallah M Alrazeeni, Fazila Akter

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