Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review by Pierpaolo Dini, Davide Paolini

“…The versatility of ML enables its application to material discovery, model development, quality control, real-time monitoring, charge optimization, and fault detection, positioning it as an essential technology for modern battery management systems. …”
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Spatial patterning of chloroplasts and stomata in developing cacao leaves by Insuck Baek, Seunghyun Lim, Visna Weerarathne, Dongho Lee, Jacob Botkin, Silvas Kirubakaran, Sunchung Park, Moon S. Kim, Lyndel W. Meinhardt, Ezekiel Ahn

“…These findings suggest a coordinated developmental sequence between chloroplasts, stomata, and leaf ontogeny. A Support Vector Machine (SVM) model successfully classified distinct leaf regions based on these morphological features (>80% accuracy), highlighting the potential of machine learning applications in this area. …”
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Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm. by Zeonlung Pun, Qiaoyun Xue, Yichi Zhang

“…These results underscore the potential of leveraging advanced machine learning and optimization techniques to accelerate the discovery of effective cancer therapies.…”
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Instagram fake profile detection using an ensemble learning method by Bharti Goyal, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Shreyas J

“…To find these profiles, we use a number of analytical parameters. Using machine learning is one of the main reasons for developing a model to effectively combat these false accounts. …”
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Strategic innovations and future directions in deep learning for engineering applications: a systematic literature review by Arianna G. Tobias, Javeed Kittur

“…Key trends include strategic deep learning model development, practical evaluation frameworks, and the optimization of efficiency. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. Therefore, this study proposes a Spatial–Temporal Graph Attention Network (STGAT) that integrates STL decomposition components and graph structures to model both temporal patterns and asset correlations. …”
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Estimation of Elasticity of Porous Rock Based on Mineral Composition and Microstructure by Zaobao Liu, Jianfu Shao, Weiya Xu, Chong Shi

“…The SVM is trained with 30 samples to search for optimal parameters using the PSO, and thus the estimation model is established. …”
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Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches by Franciszek Seredynski, Miroslaw Szaban, Jaroslaw Skaruz, Piotr Switalski, Michal Seredynski

“…We perform an extensive experimental study of both models and show that the proposed learning automata-based model significantly outperforms the cellular automata-based model.…”
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Utilizing UAVs in Wireless Networks: Advantages, Challenges, Objectives, and Solution Methods by Mohammad Javad Sobouti, Amirhossein Mohajerzadeh, Haitham Y. Adarbah, Zahra Rahimi, Hamed Ahmadi

“…We also examine the primary models and assumptions employed for UAV placement and trajectory optimization, including channel models, mobility models, network architectures, and constraints. …”
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Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum). by Rajermani Thinakaran, Ecenur Korkmaz, Başak Ünver, Seyid Amjad Ali, Zeshan Iqbal, Muhammad Aasim

“…Heatmap and network plot analyses further illustrated strong positive correlations between sucrose, BAP, and tuber formation, whereas auxins exhibited comparatively weaker effects. Results analyzed by Machine learning (ML) models revealed maximum predictive accuracy for tuberization by Random Forest (RF) model with an R2 of 0.379. …”
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Research on filter-based adversarial feature selection against evasion attacks by Qimeng HUANG, Miaomiao WU, Yun LI

“…With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification, resulting in serious consequences in various domains such as spam detection, traffic signal recognition, and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed, based on the minimum redundancy and maximum relevance (mRMR) method, while considering security metrics against evasion attacks.Additionally, a robust adversarial feature selection algorithm was introduced, named SDPOSS, which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases, the runtime of SDPOSS decreases linearly, while achieving excellent classification performance.Moreover, SDPOSS exhibits strong robustness against evasion attacks, providing new insights for adversarial machine learning.…”
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Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle by Keshu Zhang, Jiandong Jia, Xiaodan Shangguan, Jing Dong

“…Traditional EMS methods, such as rule-based approaches and optimization-based methods like model predictive control (MPC), either lack flexibility or are computationally complex and rely on prior driving experience. …”
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A data-driven state identification method for intelligent control of the joint station export system by Guangli Xu, Yifu Wang, Zhihao Zhou, Yifeng Lu, Liangxue Cai

“…In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. …”
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Weather Classification in West Java using Ensemble Learning on Meteorological Data by Cynthia Nur Azzahra, Yulison Herry Chrisnanto, Gunawan Abdillah

“…To address the class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, while model optimization was conducted using GridSearchCV. …”
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Preamble Allocation and Access Delay Reduction for Improved Massive Random Access in 5G Beamforming Networks by Gangwoo Lee, Jaewook Jung, Jong-Moon Chung

“…Based on the model, an optimization problem is proposed to derive the optimal preamble allocation and PRACH occasion duration considering the access rate in each region under the uniform traffic model. …”
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Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems by Payman Goodarzi, Julian Schauer, Andreas Schütze

“…Additionally, the integration of HP optimization and model selection significantly reduces the training cost of ensemble models. …”
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Exploring Process Heterogeneity in Environmental Statistics: Examples and Methodological Advances by Gregor Laaha, Johannes Laimighofer, Nur Banu Özcelik, Svenja Fischer

“…A tree-based machine learning model shows that prediction performance and model parameters vary with quantile loss optimization, suggesting the need for different or combined models for full time series in the presence of process heterogeneity. …”
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A Fault Diagnosis Method for Planetary Gearboxes Based on IFMD by Fengfeng Bie, Xueping Ding, Qianqian Li, Yuting Zhang, Xinyue Huang

“…Subsequently, a convolutional neural network integrated with the support vector machine model (CNN-SVM) is established, leveraging the convolutional neural network for feature extraction. …”
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Conjugation of Hypersoft Set and Triangular Neutrosophic to Evaluate Cognitive Digital Twins' Contribution to Smart Manufacturing by Mona Mohamed, Nurhan Alaa

“…Multi-Criteria Decision Making (MCDM) as CRiteria Importance Through Intercriteria Correlation and multi-objective optimization based on simple ratio analysis (MOOSRA) are leveraged to construct soft decision models. …”
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USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE MANAGEMENT OF MULTIPLE SCLEROSIS by Fatima Aliyeva, Aytan Mammadbayli, Rana Shiraliyeva

“…The MindGlide platform accelerates MRI analysis, while CDSS facilitates the optimization of personalized treatment decisions. Biomarker-based models offer new avenues for early detection of the disease at the subclinical stage. …”
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