Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

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RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability by Harel Kotler, Luca Bergamin, Fabio Aiolli, Elena Scagliori, Angela Grassi, Giulia Pasello, Alessandra Ferro, Francesca Caumo, Gisella Gennaro

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A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning by Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le, Nhu-Tung Nguyen

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Population status and impact of climate change on the distribution of vulnerable multipurpose plant Hippophae rhamnoides ssp. turkestanica for conservation in Trans-Himalaya, India by Shiv Paul, Shiv Paul, Khilendra Singh Kanwal, Anil Kumar, Sher Singh Samant, Sher Singh Samant, Indra Dutt Bhatt, Rakesh Chandra Sundriyal, Rakesh Chandra Sundriyal, Swaran Lata

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Local power flow control by L. M. Chetochnikova, N. I. Smolentsev, N. V. Tomskich, D. A. Gimgina, D. A. Lavrentieva

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Research on Fracture Energy Prediction and Size Effect of Concrete Based on Deep Learning with SHAP Interpretability Method by Huiming Wang, Weiqi Zhang, Jie Lin, Shengpin Guo

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Coordinated Jamming and Poisoning Attack Detection and Mitigation in Wireless Federated Learning Networks by Sofia Barkatsa, Maria Diamanti, Panagiotis Charatsaris, Stefanos Voikos, Eirini Eleni Tsiropoulou, Symeon Papavassiliou

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Modeling the Relationship between Financial Stability and Banking Risks: Artificial Intelligence Approach by Hakeem Faraj Gumar, Parviz Piri, Mehdi Heydari

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Bayesian Optimization of insect trap distribution for pest monitoring efficiency in agroecosystems by Eric Yanchenko, Thomas M. Chappell, Anders S. Huseth

“…Monitoring for invasive pests often requires expedient decisions based on dispersal potential and ecology to inform trap placement. …”
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Review: the application of deep reinforcement learning to quantitative trading in financial market by XU Bo, HE Yijun, WEN Jiancheng, LI Xiangxia

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Closed-Loop Fault Diagnosis of SDR Using Gap Metric, PCA, and Kalman’s Principle by Hongfu Wang, Qinghua Zeng, An Wang, Zongyu Zhang, Weide Liu

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Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods by Younes Nouri, Farzad Shahabian, Hashem Shariatmadar, Alireza Entezami

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Impact of Long-term Stability on Mental Health in Adults with Atopic Dermatitis: A Cohort Study with Unsupervised Cluster Analysis by Lea K. Nymand, Mia-Louise Nielsen, Maria Oberländer Christensen, Simon F. Thomsen, Jacob P. Thyssen, Alexander Egeberg

“…Clusters 2 and 3 were characterized by annual flares ranging from 4 to 16 and anxiety scores of 6.0 (3.0–8.5) and 4.0 (2.0–7.0) and depression scores of 3.0 (1.0–6.0) and 2.0 (1.0–5.0), respectively. …”
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Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China by Lei-Lei Liu, Aasim Danish, Xiao-Mi Wang, Wen-Qing Zhu

“…In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. …”
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Comparative Analysis of Single‐ and Dual‐Marker Strategies for Rapid Non–ST‐Segment–Elevation Myocardial Infarction Rule‐Out Using Cardiac Myosin‐Binding Protein C, Copeptin, and H... by Mustafa Yildirim, Christian Salbach, Matthias Mueller‐Hennessen, Norbert Frey, Evangelos Giannitsis

“…We evaluated biomarker algorithms including cMyBP‐C (<10 ng/L, <2.3 ng/L), hs‐cTnT (limit of blank [<3 ng/L], limit of detection [<5 ng/L], 99th percentile [≤14 ng/L]), and DMS combinations of copeptin (<10 pmol/L) with hs‐cTnT, cMyBP‐C with hs‐cTnT, and copeptin with cMyBP‐C. …”
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Leader-Follower Game Mechanism and Strategy of Industrial Park Demand Response with User Aggregator by Zhangyi LI, Xin MA, Wei PEI, Liang ZHANG, Mingshuang LIU, Qiuhong LIANG

“…Besides, two stage-optimization algorithms are used to solve this model. By the comparison and analysis of optimization results, it is verified that the proposed mechanism could guarantee the economic interests of the three parties as well as improve the weak positions of aggregators and enterprise users during gaming mechanism. …”
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Optimization of Adaptive Observation Strategies for Multi-AUVs in Complex Marine Environments Using Deep Reinforcement Learning by Jingjing Zhang, Weidong Zhou, Xiong Deng, Shuo Yang, Chunwang Yang, Hongliang Yin

“…However, traditional D3QN faces slow convergence and weak action–decision correlation in partially observable, dynamic marine settings. …”
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Priority-aware task offloading for LEO satellite edge computing network: a multi-agent deep reinforcement learning-based approach by Juan Chen, Jie Zhong, Zongling Wu, Di Tian, Yujie Chen

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Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

“…By preprocessing sensor data and by combining machine learning algorithms (MobileNet, EfficientNet, Random Forest, SVM, and Decision Trees) where each ML processes different sensor data types, this supports providing more accurate data to DST. …”
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Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin by Gil Marcio Avelino Silva, Fernando Pellon de Miranda, Jarbas Vicente Poley Guzzo, Wagner Leonel Bastos, Ygor Rocha, Igor Viegas Alves Fernandes de Souza, Italo Oliveira Matias, Sarah Barron Torres, Francisco Fabio de Araujo Ponte

“…Seven machine learning algorithms were evaluated, with the random forest model achieving the highest classification accuracy of 91%. …”
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