A Multi-Parameter Calibration Method Based on the Newton Method and the Genetic Algorithm in Airborne Array Synthetic Aperture Radar by Dawei Wang, Zhenhua Li, Fubo Zhang, Longyong Chen

“…The genetic algorithm is utilized to locate a sub-optimal solution in proximity to the optimal one, subsequently converging swiftly to the optimal solution via the Newton method, which incorporates second-order information. …”
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Modeling and Numerical Prediction on Mechanical Behaviors of Hybrid Fiber Reinforced Polymer Bio Composites Using Fuzzy Logic Algorithm by Vinoth Viswanathan, Sathiyamurthy Subbarayan, Ananthi Narayanaswamy, Devi Panneerselvam, Prabhakaran Jayasankar

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Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI by Myung-Joo Park, Hyo-Sik Yang

“…This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. …”
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Advancements in Optimization Techniques for Active Magnetic Bearing Systems: Current Trends and Future Directions by Tasnemul Hasan Nehal, Waleed M. Hamanah, Mohammad Ali Abido

“…This review delves into cutting-edge optimization techniques for AMBs, from time-tested methods like PID control to innovative approaches such as metaheuristic algorithms, multi-objective optimization, and AI-powered strategies including reinforcement learning and iterative learning control. …”
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Implementation of personalized customization and enhanced experiences for cultural tourism resources using genetic algorithm-based virtual reality technology by Huiya Xing, Xiangyi Li, Min Liu, Yicong Zhong

“…By adjusting to these individual parameters, the algorithm cleverly optimizes tourist itineraries. The DDE-GA-powered VR system works better than current methods, according to experimental data, with improvements in reaction time (1.1 s), accuracy (98 %), precision (97 %), and modeling error (0.10). …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…Publicly available multi-sensor satellite data, such as Landsat, Sentinel-1, and Sentinel-2, from 2018 to 2020 were employed, providing temporal observation frequencies of up to five days, which represents a 25% increase compared to traditional monitoring approaches. Sophisticated algorithms were developed and implemented to improve the accuracy of fire detection while minimizing false alarms. …”
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Optimized deep learning models for stress-based stroke prediction from EEG signals by Sivasankaran Pichandi, Gomathy Balasubramanian, Venkatesh Chakrapani, J. Samuel Manoharan

“…The proposed research aims to classify stress-induced emotions and predict stroke risk using advanced deep learning algorithms. The study utilizes EEG signals to categorize stress-related emotions, subsequently assessing stroke risk via an optimized deep learning model. …”
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Risk-Adjusted Deep Reinforcement Learning for Portfolio Optimization: A Multi-reward Approach by Himanshu Choudhary, Arishi Orra, Kartik Sahoo, Manoj Thakur

“…Recent advances in portfolio optimization have shown promising capabilities of deep reinforcement learning algorithms to dynamically allocate funds across various potential assets to meet the objectives of prospective investors. …”
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A study of parameter aggregation algorithms for virtual power plant terminal decentralized resource scheduling characteristics in alpine regions by Yan Wang, Ruizhi Zhang, Ying Wang, Wen Xiang, Lu Wang

“…An improved ant colony algorithm based on continuous optimization was used to solve the aggregation parameters. …”
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A New Approach Combining RSA and ElGamal Algorithms: Advancements in Encryption and Digital Signatures Using Gaussian Integers by Yahia Awad, Douaa Jomaa, Yousuf Alkhezi, Ramiz Hindi

“…This article introduces a novel approach that integrates the ElGamal and RSA algorithms to advance the security and efficiency of public-key cryptosystems. …”
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RE-BPFT: An Improved PBFT Consensus Algorithm for Consortium Blockchain Based on Node Credibility and ID3-Based Classification by Junwen Ding, Xu Wu, Jie Tian, Yuanpeng Li

“…Experimental results indicate that RE-BPFT markedly reduces consensus latency and communication costs while achieving higher throughput and better scalability than classical PBFT, RBFT, and PPoR algorithms. …”
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A Servo Control Algorithm Based on an Explicit Model Predictive Control and Extended State Observer with a Differential Compensator by Zhuobo Dong, Shuai Chen, Zheng Sun, Benyi Tang, Wenjun Wang

“…By employing an offline optimization approach, a control law is explicitly formulated to handle system constraints while minimizing online computational overhead. …”
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A Near-Real-Time Imaging Algorithm for Focusing Spaceborne SAR Data in Multiple Modes Based on an Embedded GPU by Yunju Zhang, Mingyang Shang, Yini Lv, Xiaolan Qiu

“…Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. …”
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ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network by Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah

“…The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. …”
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Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid by Josue N. Otshwe, Bin Li, Songsong Chen, Feixiang Gong, Bing Qi, Ngouokoua J. Chabrol

“…System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. …”
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Delivering data: A real-world dataset for last-mile delivery optimizationZenodo by Anna Vrani, Savvas D. Apostolidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos

“…The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. …”
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Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach by Hassan S. Ahmed, Ahmed J. Abid, Adel A. Obed, Ameer L. Saleh, Reheel J. Hassoon

“…This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. …”
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Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

“…To develop medium-Mn steels with an ultimate tensile strength (UTS) exceeding 2 GPa and excellent ductility, we created a highly accurate UTS prediction machine learning (ML) model using a boosted decision tree model and 1520 dataset of tensile properties of medium-Mn steels with micro-alloying elements. We also optimized the hyper-parameters of a genetic algorithm (GA) using the Shannon diversity index to enhance search efficiency while retaining diversity. …”
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Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms by Sherly Ardhya Garini, Ary Mazharuddin Shiddiqi, Widya Utama, Alif Nurdien Fitrah Insani

“…However, the ANN can suffer from overfitting and requires large datasets for optimal performance. In contrast, KNN struggled with missing-not-at-random (MNAR) data due to its reliance on the k parameter and distance metric, making it less effective in mapping missing data relationships. • Missing values in well-log data can hinder lithology classification accuracy for efficient resource exploration in the oil and gas industry. • This research aims to address the problem of missing values in well-log datasets by applying machine learning algorithms such as XGBoost, ANN, and KNN to enhance classification performance. • XGBoost demonstrated superior performance in handling extreme missing data (30 %) in well-log datasets. …”
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