Provide a Solution Based on Teacher and Student Learning Algorithm to Reduce Regression Test Cases by Mahmood Deypir, Amirhossein Mohammadpour

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A Bilevel Programming Approach for Optimizing Multi-Satellite Collaborative Mission Planning by Yi Wang, Desheng Liu

“…Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. …”
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Optimal Modeling of Wireless LANs: A Decision-Making Multiobjective Approach by Tomás de Jesús Mateo Sanguino, Jhon Carlos Mendoza Betancourt

“…To reduce this gap, this paper describes an optimization algorithm—based on evolutionary strategy—created as an aid for decision-making prior to the real deployment of wireless LANs. …”
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A classification model for power corridors based on the improved PointNet++ network by Li Bo, Liu Siyuan, Wang Xiangfeng, Zou Cunyu

“…An improved classification model based on PointNet++ is proposed. …”
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Multi-objective Optimization Design for a Kind of Three-translation Parallel Robot Mechanism by Zhiyuan Ma, Wei Zhu, Hanbing Li, Kanrong Gu, Chuanwei Wang, Jiuwei Shi

“…A kind of 2RRPaR+PPaP three translational space parallel mechanism is designed, and the forward and inverse position solutions equation and Jacobi matrix of the mechanism are derived. The optimization mathematical model is set up with the optimization goal of workspace and global dexterity. …”
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Metaheuristic Algorithm and Laser Projection for Adjusting the Model of the Last Lower Surface to a Footprint by J. Apolinar Muñoz Rodríguez

“…Nowadays, metaheuristic algorithms have been applied to optimize last lower-surface models. …”
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Explainable Model of Fusion Network With Enhanced Optimization Approach for Tuberculosis Diagnosis by C. R. Dhivyaa, K. Nithya, C. Sathiya Kumar, R. Sudhakar

“…The second approach utilizes the EAMSO (Ensemble of AMS Optimization model) for feature selection. It combines the results from AEO (Artificial Ecosystem-based Optimization), MBO (Monarch butterfly optimization), and Seagull-Algorithm to form three optimized feature subsets. …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…To enhance predictive performance by transforming features, an improved Owl Search Optimization (IOSO) algorithm is used. …”
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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements by Tianyu Li, Xuanrui Xiong, Yuan Zhang, Xiaolin Fan, Yushu Zhang, Haihong Huang, Dan Hu, Mengting He, Zhanjun Liu

“…The proposed method achieves a mean average precision (mAP) of 82.04% on KITTI, representing an improvement of 2.34% over the baseline model. For heavily occluded objects on CityPersons, the Log Average Miss Rate (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>MR</mi><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula>) is reduced to 40.31%, which is a decrease of 9.65% compared to the baseline. …”
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Optimizing the Scheduling of Teaching Activities in a Faculty by Francis Patrick Diallo, Cătălin Tudose

“…This study provides an optimized automated schedule creation technique. The system generates schedules that aim to be conflict-free and efficient, utilizing evolutionary algorithms along with multi-objective optimization. …”
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A Talent Cultivation and Performance Evaluation Model Based on a Fuzzy Control Algorithm by Shanshan Jiang

“…The algorithm combines an adaptive neuro-Fuzzy Inference System with Adaptive Hybrid Particle Swarm Optimization. …”
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A New Non Quadratic Model For Unconstrained Non Linear Optimization by Basim Hassan, Nidhal Al-Assady

“…A new non-quadratic model is proposed for solving unconstrained optimization problems, which modifies and develops the classical conjugate gradient methods. …”
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ATHOS: A Hybrid Accelerator for PQC CRYSTALS-Algorithms Exploiting New CV-X-IF Interface by Alessandra Dolmeta, Maurizio Martina, Guido Masera

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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. We hypothesize that incorporating soil moisture and temperature data from land surface models into a ML framework will enhance accuracy of corn yield predictions. …”
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UAV Path Optimization for WSN in Smart Agriculture by Katia Karam, Ali Mansour, Mohamad Khaldi, Benoit Clement, Mohammad Ammad-Uddin

“…The optimization problem is formulated as a Nonlinear Programming (NLP) model and solved using an appropriate solver. …”
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DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models by Yirong Luo, Yanping Du, Zhaohua Wang, Jingtian Mo, Wenxuan Yu, Shuihai Dou

“…For this reason, an improved target detection model, DScanNet, is proposed in this paper. …”
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Educational resources recommendation algorithm based on GMF-MLP-NeuMF prediction model by Rui Wang, Qiusheng Cui, Yezhi Xiao, Jinfeng Du, Ruicheng Yue

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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Moreover, This study develops the proposed EffiXNet, a more refined version of EfficientNet augmented with self-attention mechanisms, dynamic convolutions, improved normalization methods, and Sparrow Search Optimization Algorithm for hyperparameter optimization. …”
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Research on the Intelligent Bus Scheduling System Based on Genetic Algorithm by LIU Jiguo

“…Aiming at the problem of bus scheduling optimization, it analyzed the bus line model and the hierarchical characteristics of different levels of bus lines, proposed a bus scheduling model based on genetic algorithm and established the objective function and constraints. …”
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Evaluating Task Optimization and Reinforcement Learning Models in Robotic Task Parameterization by Michele Delledonne, Enrico Villagrossi, Manuel Beschi, Alireza Rastegarpanah

“…The results demonstrate that although RL models improve task optimization efficiency by 95%, they still need more flexibility. …”
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