State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun, Haitao Liu

“…A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. …”
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Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering by Thommas K. S. Flores, Morsinaldo Medeiros, Marianne Silva, Daniel G. Costa, Ivanovitch Silva

“…While VQ is a technique that reduces model size by grouping similar parameters, incremental clustering, implemented via the AutoCloud K-Fixed algorithm, preserves accuracy during compression. …”
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Investigation of Load Sharing and Dynamic Load Characteristics of a Split Torque Transmission System with Double-Helical Gear Modification by Xuan Liu, Zongde Fang, Haitao Jia, Ning Zhao, Yunbo Shen, Hui Guo, Xijin Zhang

“…The modified tooth surface of a third-stage double-helical gear is obtained by optimizing the amplitude of static loaded transmission error and meshing-in impact via nondominated sorting genetic algorithm-II (NSGA-II). …”
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Development of Automatic Balancing Application forFashion Company Using Artificial Intelligence by May Alrasheed, Mohamed Jmali, Thouraya Hamdi

“…It focuses on utilising ant colony algorithms for optimal balancing. The results show the significance of these algorithms in attaining optimal balancing in production systems. …”
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Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control by Ebunle Akupan Rene, Willy Stephen Tounsi Fokui

“…The findings were compared with those derived from an optimal proportional integral derivative (PID) controller designed using the artificial bee colony (ABC) algorithm. …”
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Dynamic Adaptation for Independent Task Scheduling Using Dynamic Programming in Multiprocessor Systems by Lotfi BENDIAF, Ahmed HARBOUCHE, Mohammed Amin TAHRAOUI

“…DyTAg leverages dynamic programming to minimize makespan while maximizing resource utilization, ensuring that tasks are allocated optimally even in complex, heterogeneous environments. …”
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Land Use Transition and Regional Development Patterns Under Shared Socioeconomic Pathways: Evidence from Prefecture-Level Cities in China by Xiaodong Zhang, Mingjie Yang, Rui Guo, Yaolong Li, Fanglei Zhong

“…This study integrates the population–development–environment model with back propagation (BP) neural networks, a supervised learning algorithm, to analyze how differentiated development trajectories reshape land systems. …”
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Impact of rainy season on approach trajectories in high-altitude airport terminal maneuvering area: a clustering analysis by Jianxiong Chen, Jingtao Wang, Fan Li, Lin Zou

“…After data preprocessing, a clustering algorithm was used to identify trajectory patterns and detect outlier trajectories. …”
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Performance Analysis of Three-Phase Interleaved Buck-Boost Converter in Wind Energy Maximum Power Point Tracking by Muhammad Qasim Nawaz Sciences, Wei Jiang Sciences, Aimal Khan Sciences

“… This paper presents a performance analysis of a three-phase interleaved buck-boost converter integrated with a Maximum Power Point Tracking (MPPT) algorithm using the Perturb and Observe (P&O) method for an independent wind energy generation system. …”
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Laser-induced Breakdown Spectroscopy Based on Pre-classification Strategy for Quantitative Analysis of Rock Samples by Weiheng KONG, Lingwei ZENG, Yu RAO, Sha CHEN, Xu WANG, Yanting YANG, Yixiang DUAN, Qingwen FAN

“…Different element quantitative models were constructed for each rock type. The kNN algorithm was selected using cross-validation to determine the optimal k value, and the key punishment parameter C and RBF width parameter γ of the SVM algorithm were determined using a grid search method. …”
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DQN-Based Shaped Reward Function Mold for UAV Emergency Communication by Chenhao Ye, Wei Zhu, Shiluo Guo, Jinyin Bai

“…The experimental outcomes underscore the prowess of our methodology in effectively curtailing training time while augmenting convergence rates. In summary, our work underscores the potential of leveraging sophisticated virtual environments and refined reinforcement learning techniques to optimize UAVs deployment in emergency communication contexts.…”
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Simplified Model Predictive for Controlling Circulating and Output Currents of a Modular Multilevel Converter by Abolfazl Sheybanifar, Seyed Masoud Barakati

“…In addition, a bilinear mathematical model of the MMC is derived and discretized to predict the states of the MMC for one step ahead. A sorting algorithm is used to retain the balancing capacitor voltage in each SM, while the cost function guarantees the regulation of the output current, and MMC circulating current. …”
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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…One of the challenges in this field is using optimal datasets and classification models that meet real-time applications on low-end hardware devices. …”
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Energy Optimisation in Aquaponics—Integrating Renewable Source and Water as Energy Buffer for Sustainable Food Production by Abdul Aziz Channa, Kamran Munir, Mark Hansen, Muhammad Fahim Tariq

“…We employed a dynamic control algorithm to intelligently adjust water temperature based on solar forecasts. …”
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Estimation of Current RMS for DC Link Capacitor of S-PMSM Drive System by ZHANG Zhigang, CHANG Jiamian, ZHANG Pengcheng

“…The proposed technique simplifies the tedious calculation process of traditional algorithms and guarantees high calculation accuracy, providing guidance for optimizing the selection of DC link capacitors and the design of life monitoring controllers. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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Deep Reinforcement Learning Based Transferable EMS for Hybrid Electric Trains by Yogesh Wankhede, Sheetal Rana, Faruk Kazi

“…The DDPG+TL agent consumes up to 3.9% less energy than conventional rule-based EMS while maintaining the battery's charge level within a predetermined range. …”
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Sentiment Analysis of Public Satisfaction with the 'INFO BMKG' Application using Naive Bayes, SVM, and KNN by Natasya Aditiya, Pratomo Setiaji, Supriyono Supriyono

“…This research employs three classification algorithms—Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—to categorize user reviews into positive, neutral, or negative sentiments. …”
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Memory consolidation from a reinforcement learning perspective by Jong Won Lee, Min Whan Jung, Min Whan Jung

“…Based on these findings, we propose that the CA3 region of the hippocampus generates diverse activity patterns, while the CA1 region evaluates and reinforces those patterns most likely to maximize rewards. …”
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Fast and Efficient Drone Path Planning Using Riemannian Manifold in Indoor Environment by Rohit Dujari, Brijesh Patel, Bhumeshwar K. Patle

“…This paper introduces an innovative dual-path planning algorithm rooted in a topological three-dimensional Riemannian manifold (T3DRM) to optimize drone navigation in complex environments. …”
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