A new Hybrid Metaheuristic Model for Image Edge Detection by Adil Khalil, Adel Abed Al Wahaab, Ahmed Abbas

“…These techniques may have the potential to provide advanced capabilities to improve the image edge detection process. A metaheuristic model such as the Bat algorithm may offer appropriate parameters for the edge detection algorithm. …”
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Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization by Sivakumar Palanıswamy, Vijayakumar Vaıthyam Rengarajan, Sandhya Devi Ramıah Subburaj

“…It is presumed that the implementation of deep learning algorithms demands a large amount of data to learn complex features automatically and this can pose a challenge for applications with lesser data to achieve generalization. …”
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Optimization of Offshore Saline Aquifer CO₂ Storage in Smeaheia Using Surrogate Reservoir Models by Behzad Amiri, Ashkan Jahanbani Ghahfarokhi, Vera Rocca, Cuthbert Shang Wui Ng

“…This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. …”
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Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning by Yi Qin, Junyan Chen, Lei Jin, Rui Yao, Zidan Gong

“…Secondly, DCEDRL utilizes an optimized density clustering method to identify and classify computing tasks with similar characteristics to improve subsequent task scheduling and resource allocation efficiency. …”
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Optimal parameter extraction of equivalent circuits for single- and three- phase Power transformers based on arctic puffin algorithm accomplished with experimental verification by Amlak Abaza, Ragab A. El-Sehiemy, Mohamed Assaad Hamida, Raphael Chenouard, Ahmed Bayomyi

“…This paper addresses one of the major problems which hopes to enhance the accuracy of estimation of parameters, which is critical in power transformer modeling, maintenance, and operating efficiency. In that context, this work estimates the parameters of single- and three-phase power transformers by a new optimizer called Arctic Puffin Optimization Algorithm (APO). …”
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Optimization of trusted wireless sensing models based on deep reinforcement learning for ISAC systems by Hao Zhang, Yi Jing, Wenhui Xu, Ronghui Zhang

“…Abstract This paper investigates using deep reinforcement learning (DRL) methods for optimizing trustworthy federated learning models, with a focus on integrated sensing and communication in practical wireless sensing scenarios. …”
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Voltage Quality Enhancement of Low-Voltage Smart Distribution System Using Robust and Optimized DVR Controllers: Application of the Harris Hawks Algorithm by Mohamed Metwally Mahmoud, Yahia M. Esmail, Basiony Shehata Atia, Omar Makram Kamel, Kareem M. AboRas, Mohit Bajaj, Syed Sabir Hussain Bukhari, Daniel Eutyche Mbadjoun Wapet

“…To enhance the DVR’s control system, for surpassing nonstandard voltage with a quick response and harmonics reduction at LVL under harsh operating events, an optimal controller design using the Harris Hawks algorithm (HHA) is proposed. …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. …”
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Integrated production-distribution planning optimization models: A review in collaborative networks context by Beatriz Andres, Raquel Sanchis, Jaques Lamothe, Leila Saari, Frederic Hauser

“…In this regard, new optimisation algorithms are required in order to improve the collaboration within the supply network partners. …”
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Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input by Jooyoung Kim, Wonkyung Lee, Jungwoon Park

“…This paper introduces the League of Legends Match Data Compactor (LoL-MDC), a tool designed to transform extensive match data into a concise and structured format optimized for LLM processing. By systematically summarizing structured match information—including match overviews, player and team statistics, timeline summaries, and algorithmically selected key events—the LoL-MDC significantly reduces the data size from approximately 80,000 tokens to under 2000 tokens while retaining analytical value. …”
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Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation by Boyang Fan, Yuming Bo, Xiang Wu

“…Specifically, a Transformer-based model is proposed to learn design NeuroSelect Heuristic for DPSO to improve the evolutionary process. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…The LSKRX model demonstrated significant improvements in prediction accuracy compared to single-model approaches, with the most notable enhancement in BIAS. …”
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Rapid Path Planning Algorithm for Percutaneous Rigid Needle Biopsy Based on Optical Illumination Principles by Jian Liu, Shuai Kang, Juan Ren, Dongxia Zhang, Bing Niu, Kai Xu

“…Traditional methods relying on clinician expertise or slow algorithms struggle with complex anatomical modeling for structures such as blood vessels. …”
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Efficient cooling capability in microchannel heat sink reinforced with Y-shaped fins: Based on artificial neural network, genetic algorithm, Pareto front, and numerical simulation by Xiang Ma, Ali Basem, Pradeep Kumar Singh, Rebwar Nasir Dara, Ahmad Almadhor, Amira K. Hajri, Raymond Ghandour, Barno Abdullaeva, H. Elhosiny Ali, Samah G. Babiker

“…The applied cost functions demonstrated the high accuracy of the models in predicting system performance. A genetic algorithm was employed for single-objective optimization targeting three criteria: maximizing total efficiency, minimizing pressure drop, and maximizing the Nusselt number. …”
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Utilization of a bioinspired algorithm for optimum reservoir operation in an altering climate a case study of China by Qiong Li, Hang Zhou, Ali Shirzad

“…Modified Seagull Optimization Algorithm (MSOA) improves reservoir Rule Curves for changing hydrology and reduced operational risks. …”
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Optimized deep neural network architectures for energy consumption and PV production forecasting by Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy

“…This paper introduces a novel hybrid optimization approach that integrates Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance the DNN architecture for more accurate energy forecasting. …”
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Modeling peak ground acceleration for earthquake hazard safety evaluation by Fatima Khalid, Milad Razbin

“…The ANN architecture comprises 4 nodes in the input layer, two hidden layers each containing 25 nodes, and a single-node output layer, resulting in 750 unknown weight and bias values that the model must optimize. Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. …”
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A Novel Overload Control Algorithm for Distributed Control Systems to Enhance Reliability in Industrial Automation by Taikyeong Jeong

“…The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. …”
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Energy-Efficient Optimization Method for Timetable Adjusting in Urban Rail Transit by Lianbo Deng, Shiyu Tang, Ming Chen, Ying Zhang, Yuanyuan Tian, Qun Chen

“…An energy efficiency optimization model is then established to minimize the total net energy consumption of the timetable, and a solution algorithm based on a genetic algorithm is proposed. …”
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The Hub Location and Flow Assignment Problem in the Intermodal Express Network of High-Speed Railways and Highways by Xiaoting Shang, Zhenghang Wang, Xin Cheng, Xiaoyun Tian

“…Owing to the NP-hard computational complexity, an improved genetic algorithm with local search is designed by combining the genetic operators and two optimization strategies to solve the problem effectively. …”
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