Research on soybean leaf disease recognition in natural environment based on improved Yolov8 by Chen Chen, Chen Chen, Xiaolei Lu, Lei He, Lei He, Ruoxue Xu, Ruoxue Xu, Yi Yang, Yi Yang, Jing Qiu, Jing Qiu

“…Experimental results demonstrate that YOLOv8-DML achieves a mAP50 of 96.9%, marking a 1.8% improvement over the original YOLOv8 algorithm, while also achieving an 18.6% reduction in parameters. …”
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Synergistic Effect of <i>Bacillus</i> and Chitosan: From Flocculation to Enhanced Antimicrobial Activity by Selena Dmitrović, Nataša Lukić, Ivana Danilov, Vanja Vlajkov, Jovana Grahovac, Aleksandar Jokić

“…The response surface approach, followed by desirability function optimization and the genetic algorithm were applied. …”
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Collaborative Federated Learning of Unmanned Aerial Vehicles in Space–Air–Ground Integrated Network by Huibo Li, Peng Gong, Siqi Li, Weidong Wang, Yu Liu, Xiang Gao, Dapeng Oliver Wu, Duk Kyung Kim, Guangwei Zhang, Jihao Zhang

“…In order to solve the mixed integer nonlinear problem (MINLP), a data offloading selection strategy based on proximity discovery and an iterative method-based resource allocation algorithm (IRA) are proposed. In addition, the closed-form solutions of the optimized variables are obtained. …”
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Robust SAR Change Detection Using Hierarchical Clustering With Adaptive Parameter Tuning by Ahidjo Abdoulaye, Alejandro C. Frery, Mingyang Ma, Shaohui Mei

“…The method automatically identifies clusters of varying densities while filtering out noise, ensuring a more precise change detection process. …”
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Integrated Energy Management in Small-Scale Smart Grids Considering the Emergency Load Conditions: A Combined Battery Energy Storage, Solar PV, and Power-to-Hydrogen System by Hossein Jokar, Taher Niknam, Moslem Dehghani, Pierluigi Siano, Khmaies Ouahada, Mokhtar Aly

“…Compared to conventional evolutionary methods like particle swarm optimization, non-dominated sorting genetic algorithm III, and biogeography-based optimization, the proposed model exhibits remarkable improvements, outperforming them by 11.4%, 5.6%, and 11.6%, respectively. …”
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Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities by Weronika Jarych, Elżbieta Tokarczyk, Patryk Iglewski, Daria Ziemińska, Karina Motolko, Rafał Burczyk, Konrad Duszyński, Michał Kociński, Jan Reinald Wendt

“…In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential. …”
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Reinforcement Learning-Driven Secrecy Energy Efficiency Maximization in RIS-Enabled Communication Systems by Akram Y. Sarhan, Osamah A. Abdullah, Hayder Al-Hraishawi, Faisal S. Alsubaei

“…We introduce an innovative framework that utilizes the deep deterministic policy gradient (DDPG) algorithm to address the challenges of the MDP. Our extensive simulations show that our method works much better than current techniques using deterministic policy gradients, like the proximal policy optimization (PPO) framework, in improving SEE. …”
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An in-depth exploration of supervised and semi-supervised learning on face recognition by Purnawansyah, Wibawa Aji Prasetya, Widiyaningtyas Triyanna, Haviluddin, Darwis Herdianti, Azis Huzain

“…Although there are some instances of sub-optimal performance in certain algorithms like CNN on specific datasets, this research provides comprehensive insights into the effectiveness of various models in contexts of limited-label learning. …”
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EEG-Based Engagement Monitoring in Cognitive Games by Yusuf Ahmed, Martin Ferguson-Pell, Kim Adams, Adriana Ríos Rincón

“…Twenty-seven participants (nine older adults) played a stunt plane video game while their EEG signals were recorded using EPOCX. …”
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Multi-Agent Deep Reinforcement Learning Cooperative Control Model for Autonomous Vehicle Merging into Platoon in Highway by Jiajia Chen, Bingqing Zhu, Mengyu Zhang, Xiang Ling, Xiaobo Ruan, Yifan Deng, Ning Guo

“…To enhance training efficiency, we develop a dual-layer multi-agent maximum Q-value proximal policy optimization (MAMQPPO) method, which extends the multi-agent PPO algorithm (a policy gradient method ensuring stable policy updates) by incorporating maximum Q-value action selection for platoon gap control and discrete command generation. …”
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Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association by Jessica Giovagnola, Manuel Pegalajar Cuéllar, Diego Pedro Morales Santos

“…Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. …”
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Investigating the impact of fatty acid profiles on biodiesel lubricity using artificial intelligence techniques by Atthaphon Maneedaeng, Attasit Wiangkham, Atthaphon Ariyarit, Anupap Pumpuang, Ekarong Sukjit

“…Feature importance analysis and sensitivity evaluation reveal that polyunsaturated fatty acids significantly enhance lubricity, while an optimal balance between saturated and unsaturated fatty acids is necessary to achieve stable frictional behavior. …”
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Cross-modal adaptive reconstruction of open education resources by Tang Shengju, Feng Li, Zhan Wang, Xie Zhaoyuan

“…., text, video, user behavior logs). (2) Maintaining the knowledge graph’s real-time relevance. (3) Reducing the cognitive demand of recommendations (optimizing cognitive efficiency). Our approach employs contrastive learning (a technique for similarity learning) with an enhanced algorithm. …”
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Eliminating Effect of Moisture Content in Prediction of Lower Heating Value and Ash Content in Sugarcane Leaves Biomass by Kanvisit Maraphum, Kantisa Phoomwarin, Nirattisak Khongthon, Jetsada Posom

“…Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. …”
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RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 by Wenjie Tang, Yangjun Deng, Xu Luo

“…This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. …”
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Line-Structured Light-Based Three-Dimensional Reconstruction Measurement System with an Improved Scanning-Direction Calibration Method by Jia Chen, Shantao Ping, Xiaowei Liang, Xulong Ma, Shiyan Pang, Ying He

“…In this study, we propose an improved method to calibrate the sensor’s scanning direction that iteratively optimizes control points via plane transformation while leveraging the rotational invariance of the rotation matrix during translation. …”
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IoT-driven smart assistive communication system for the hearing impaired with hybrid deep learning models for sign language recognition by Mashael Maashi, Huda G. Iskandar, Mohammed Rizwanullah

“…Finally, the attraction-repulsion optimization algorithm (AROA) adjusts the hyperparameter values of the CNN-BiGRU-A method optimally, resulting in more excellent classification performance. …”
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Integrated Bidding and Battery Scheduling in a Microgrid for Sealed-Bid Double Auction Power Trading With Peer Microgrids Under Uncertainty and Its Blockchain-Based Implementation by Zubin J. B., Sunitha R., Gopakumar Pathirikkat

“…In parallel, battery operations are optimized using a hybrid method that combines Genetic Algorithm (GA) and Simulated Annealing (SA), explicitly incorporating the bid buffer capacity to align scheduling with market commitments. …”
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A Three-Dimensional Feature Space Model for Soil Salinity Inversion in Arid Oases: Polarimetric SAR and Multispectral Data Synergy by Ilyas Nurmemet, Yilizhati Aili, Yang Xiang, Aihepa Aihaiti, Yu Qin, Bilali Aizezi

“…The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). …”
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A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks by Qing An, Santiago Segarra, Chris Dick, Ashutosh Sabharwal, Rahman Doost-Mohammady

“…In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. …”
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