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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Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review by Arnick Abdollahi, Marta Yebra

“…We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. …”
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Real-time and Cost-efficient Indoor Localization and Mapping Solution for Emergency Response Applications by H. Elsayed, A. Shaker

“…It utilizes the Google Cartographer SLAM engine to generate real-time 2D raster maps and stream position and orientation data at 200 poses per second, ensuring continuous poses feed while minimizing latency. The resulting maps are continuously being optimized using a loop-closing algorithm to reduce drift and maintain the trajectory integrity. …”
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Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network by Bin Luo, Susan Halabi

“…To capture potential nonlinear effects, we further extend the approach to a sparse-input neural network model with structured group penalties on input-layer weights. Both methods are optimized using a composite gradient descent algorithm combining standard gradient steps with proximal updates. …”
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Initialization Methods for FPGA-Based EMT Simulations by Xin Ma, Xiao-Ping Zhang

“…To accelerate initialization, software-to-hardware algorithm and structure are developed to automate initialization data sources for different topologies. …”
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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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