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

“…Key parameters of the HKELM were dynamically adjusted using a novel optimization algorithm, significantly enhancing fault diagnosis accuracy and system stability. …”
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Simulation and Prediction of Springback in Sheet Metal Bending Process Based on Embedded Control System by Jinhan Xu, Jun Yan, Yan Huang, Dawei Ding

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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were evaluated using performance metrics such as Receiver Operating Characteristic-Area Under the Curve (ROC AUC), accuracy, sensitivity, specificity, and F1 score. …”
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Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development by Jung-Hoon Sul, Lasitha Piyathilaka, Diluka Moratuwage, Sanura Dunu Arachchige, Amal Jayawardena, Gayan Kahandawa, D. M. G. Preethichandra

“…By focusing on EMG-driven strategies through signal processing, machine learning, and sensor fusion innovations, this review bridges gaps in human–machine interaction, offering insights into improving the precision, adaptability, and robustness of next generation exoskeletons.…”
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Prediction of ball-on-plate friction and wear by ANN with data-driven optimization by Alexander Kovalev, Yu Tian, Yonggang Meng

“…After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.…”
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FLIP: A Novel Feedback Learning-Based Intelligent Plugin Towards Accuracy Enhancement of Chinese OCR by Xinyue Tao, Yueyue Han, Yakai Jin, Yunzhi Wu

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TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations by Hamad Almujibah, Abdulrazak H. Almaliki, Caroline Mongina Matara, Adil Abdallah Mohammed Elhassan, Khalaf Alla Adam Mohamed, Mudthir Bakri, Afaq Khattak

“…The TPE-tuned LCE model outperformed benchmark algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multiple Linear Regression (MLR) achieved the lowest Mean Absolute Error (MAE) of 1.94, Mean Squared Error (MSE) of 21.50, Root Mean Squared Error (RMSE) of 4.64, Residual Standard Ratio (RSR) of 0.38, and the highest Coefficient of Determination (R2) of 0.87. …”
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Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests by Markus Siebert, Michael Fister, Christian Spieker, Daniel Stengler

“…The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. …”
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A Motion‐Sensing Integrated Soft Robot with Triboelectric Nanogenerator for Pipeline Inspection by Rui Chen, Huigang Wang, Haoji Wang, Huijiang Wang, Li Bai, Xinpei Ai, Lifu Liu, Zhihao Hu, Zean Yuan

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Cost-Effective Autonomous Drone Navigation Using Reinforcement Learning: Simulation and Real-World Validation by Tomasz Czarnecki, Marek Stawowy, Adam Kadłubowski

“…The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. …”
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Intelligent Data Reduction for IoT: A Context-Driven Framework by Laercio Pioli, Douglas D. J. De Macedo, Daniel G. Costa, Mario A. R. Dantas

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Prediction of Electrotactile Stimulus Threshold in Real Time Using Voltage Waveforms Between Electrodes by Vibol Yem, Yasushi Ikei, Hiroyuki Kajimoto

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An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection by Adam Kadi, Aymene Selamnia, Zakaria Abou El Houda, Hajar Moudoud, Bouziane Brik, Lyes Khoukhi

“…Traditional Intrusion Detection Systems (IDSs) often struggle with the complexity and high dimensionality of modern cyber threats. Quantum Machine Learning (QML) seamlessly integrates the computational power of quantum computing with the adaptability of machine learning, offering an innovative approach to solving intricate and high-dimensional challenges. …”
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Research on power data security full-link monitoring technology based on alternative evolutionary graph neural architecture search and multimodal data fusion by Zhenwan Zou, Bin Wang, Tao Chen, Jia Chen

“…By using Particle Swarm Optimization-Genetic Algorithm (PSO-GA) for optimal architecture search and combining the dynamic adaptability of Deep Q-Network (DQN) algorithm, this method can automatically identify the most suitable GNN architecture for power data monitoring, thereby improving the adaptive detection and defense efficiency of the system. …”
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Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data by Erli Wu, Xuan Yin, Feng Liang, Xianqing Zhou, Jiamin Hu, Wanting Yuan, Feihan Gu, Jingxin Zhao, Ziyang Gao, Ming Cheng, Shouxiang Yang, Lei Zhang, Qingqing Wang, Qingqing Wang, Xiaoyu Sun, Xiaoyu Sun, Wei Shao, Wei Shao

“…Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. …”
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Short-Term Power Load Forecasting Based on DPSO-LSSVM Model by Shujun Ji, Linhao Zhang, Jinteng Wang, Tao Wei, Jiadong Li, Bu Ling, Jinglong Xu, Zuoping Wu

“…The dynamic particle swarm optimization algorithm is utilized to dynamically adjust the parameters to achieve higher accuracy in load forecasting. …”
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Multi-Agent Mapping and Tracking-Based Electrical Vehicles with Unknown Environment Exploration by Chafaa Hamrouni, Aarif Alutaybi, Ghofrane Ouerfelli

“…Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. …”
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AMNED: An Efficient Framework for Spiking Neuron Coding in AirComp Federated Learning by Juncheng Ji, Chan-Tong Lam, Ke Wang, Benjamin K. Ng

“…This paper advances ACFL technologies by proposing the Adaptive Memristor Neuron Encoding-Decoding (AMNED) framework for AirComp Federated Learning (ACFL), enabling efficient, privacy-preserving model aggregation optimized for resource-constrained wireless environments. …”
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EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application by Jiyong Hu, Hongfei Yang, Jiatang He, Dongxu Bai, Hongda Chen

“…This framework, designed specifically for the unique challenges presented by load-bearing rails, integrates advanced machine vision and deep learning technologies. Initially, a Multi-Scale Pyramid Pooling (MSPP) module, incorporating the concept of residual stacking, is introduced to effectively enhance the extraction of complex features; Subsequently, the coordinate attention mechanism is optimized, leading to the development of a novel Spatial Coordinate Attention Mechanism (DAM), focused on detecting small-sized defects; Thereafter, a Dual Sampling Transition Module (DSTM) is applied to enhance information retention during the down-sampling process; Finally, the DBDAMN clustering algorithm is utilized to optimize anchor sizes, allowing for more precise adaptation to the diversity of defect sizes. …”
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Mapping the landscape of Artificial intelligence for serious games in Health: An enhanced meta review by Xiya Tao, Nicolás Sáenz-Lechón, Martina Eckert

“…Game control algorithms adapt the game environment and difficulty, while user assessment algorithms gather information about the player's state, such as performance, mood, or physiological data, to evaluate the treatment progress. …”
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