Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network by Jing Li, Huimin Jiang, Moyao Gao, Shuang Li, Zhanli Wang, Zaixiang Pang, Yang Zhang, Yang Jiao

“…MATLAB 2021 simulations demonstrate the proposed algorithm’s superior trajectory tracking performance compared to conventional sliding mode control, effectively eliminating control chattering. …”
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Automated speech therapy through personalized pronunciation correction using reinforcement learning and large language models by Ritika Lakshminarayanan, Ayesha Shaik, Ananthakrishnan Balasundaram

“…The system employs a custom Proximal Policy Optimization (PPO) algorithm for precise pronunciation evaluation and an Large Language Models to deliver detailed, encouraging, and user-specific feedback. …”
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Efficient Positive Semidefinite Matrix Approximation by Iterative Optimisations and Gradient Descent Method by Vali Asimit, Runshi Wang, Feng Zhou, Rui Zhu

“…Experiments on randomly generated data show that this method enjoys better resilience to noise while maintaining good accuracy. For example, in our experiments with noised data, the iterative quadratic programming algorithm performs best in more than 41% to 67% of the samples when the standard deviation of noise is 0.02, and the gradient descent method performs best in more than 70% of the samples when the standard deviation of noise is 0.2. …”
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A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding by Thaweesak Trongtirakul, Karen Panetta, Artyom M. Grigoryan, Sos S. Agaian

“…This paper introduces a novel adaptive unsupervised entropy algorithm (A-Entropy) to enhance multilevel thresholding for thermal image segmentation. …”
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Power-Yeoh: A Yeoh-Type Hyperelastic Model with Invariant I₂ for Rubber-like Materials by Subraya Krishna Bhat, Keerthan A.

“…Also, the interesting phenomenon of thin balloon expansion is investigated by applying the model to relevant experimental data on elastomeric balloons available in the literature. A genetic algorithm-based least squares optimization routine is carried out to determine the material constants while applying the reported experimental data. …”
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A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet by Kaizhe Chen, Jin Liu, Wenjing Lyu, Tianyuan Wang, Jinxi Wen

“…The novelty of this study lies in its novel combination of deep learning algorithms with large-scale real-world charging data, proposing a new method for optimizing EV ride-hailing charging behavior, and providing practical solutions for promoting electric vehicle adoption and achieving low-carbon transportation.…”
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Regularized K-Means Clustering via Fully Corrective Frank-Wolfe Optimization by Ahmed Yacoub Yousif, Basad Al-Sarray

“…The research presents a robust clustering system which combines the Fully Corrective Frank-Wolfe (FCFW) algorithm with k-means objective that uses Frobenius norm regularization. …”
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Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…These results suggest the algorithm's potential for early detection to decrease the number of stunting children. …”
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A Target Near-Field Scattering Measurement Technique Utilizing 3D Near-Field Imaging via Cylindrical Scanning by Zongkai Yang, Jingcheng Zhao, Weikang Si, Changyu Lou, Xin Zhao, Jungang Miao

“…Ultimately, accurate calculations are performed utilizing backpropagation (BP) in time domain algorithms. This method significantly reduces the computational burden while maintaining imaging accuracy by integrating rapid scattering source extraction with precise computation for critical regions. …”
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Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing by Salman Bari, Xiagong Wang, Ahmad Schoha Haidari, Dirk Wollherr

“…Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research.…”
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Changes and factors of thermal discharge from 2013 to 2023: A case study of the Tianwan nuclear power plant by Xiangle Jiang, Wenjin Zhu, Yang Zhang, Qing Xu, Xiaotian Dong

“…Results showed: (1) The SST retrieval algorithm we modified had high accuracy and applicability; (2) Between 2013 and 2023, the increase in nuclear units had intensified thermal discharge, expanding the affected marine areas; (3) The thermal discharge in spring and summer had a more pronounced impact on the temperature rise areas; (4) Tidal dynamics and seasonal variations, especially spring tides, significantly influenced thermal discharge and mixing; (5) Tidal currents improved vertical mixing, with spring tides weakening stratification and raising bottom temperatures; (6) Planktonic microorganism diversity peaked in spring, autumn, and winter, while summer discharge reduced abundance. …”
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Identifying the Peak Flowering Dates of Winter Rapeseed with a NBYVI Index Using Sentinel-1/2 by Fazhe Wu, Peng Lu, Shengbo Chen, Yucheng Xu, Zibo Wang, Rui Dai, Shuya Zhang

“…The study uses phenological characteristics and the random forest classification algorithm to create a map of winter rapeseed in parts of the middle and lower reaches of the Yangtze River Basin, achieving a Kappa coefficient of 90.57%. …”
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Design of a fully autonomous oil sampling collection robot system for substation oil-filled equipment by Shaotong Pei, Haichao Sun, Weiqi Wang, Bing Xiao, Hengjiang Zhu, Mianxiao Wu, Bo Lan

“…Furthermore, this paper introduces a path planning algorithm based on Deep Attention Q-Network to address the issue of robot path planning in partially observable environments. …”
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A Tri-Level Transaction Method for Microgrid Clusters Considering Uncertainties and Dynamic Hydrogen Prices by Hui Xiang, Xiao Liao, Yanjie Wang, Hui Cao, Xianjing Zhong, Qingshu Guan, Weiyun Ru

“…A polygonal uncertainty set method quantifies wind and solar energy uncertainties, while an enhanced interval optimization technique refines the model. …”
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Genetic Diversity of Apis spp. in Thailand Inferred from 28S rRNA Nuclear and Cytochrome b Mitochondrial Gene Sequences by Thitipan Meemongkolkiat, Atsalek Rattanawannee, Chanpen Chanchao

“…For 28S rRNA, a low genetic variation was detected within species (haplotype diversity ranging from 0.212 to 0.394), while 19 polymorphic sites were detected between species. …”
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Myocarditis Detection Using Proximal Policy Optimization and Mutual Learning by Asadi Srinivasulu, Sivaram Rajeyyagari

“…Myocarditis poses a significant health threat, often leading to severe outcomes such as heart failure or death. While endomyocardial biopsy (EMB) is a reliable diagnostic method, its invasive nature limits its use to severe cases. …”
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The Parabolic Variational Inequalities for Variably Saturated Water Flow in Heterogeneous Fracture Networks by Zuyang Ye, Qinghui Jiang, Chi Yao, Yanzhang Liu, Aiping Cheng, Shibing Huang, Yaning Liu

“…Fractures are ubiquitous in geological formations and have a substantial influence on water seepage flow in unsaturated fractured rocks. While the matrix permeability is small enough to be ignored during the partially saturated flow process, water seepage in heterogeneous fracture systems may occur in a non-volume-average manner as distinguished from a macroscale continuum model. …”
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Multiple-level thresholding for breast mass detection by Xiang Yu, Shui-Hua Wang, Yu-Dong Zhang

“…To reduce the false positive rate in the detection results, we applied the non-maximum suppression algorithm to combine the overlapped detection results. …”
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Artificial intelligence based analysis for magnetized casson fluid in partially heated cavity rooted with heated fin by Khalil Ur Rehman, Wasfi Shatanawi, Lok Yian Yian

“…The lower wall is hot while the top wall is considered an adiabatic. Left and right walls are taken cold. …”
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Using Segmentation to Boost Classification Performance and Explainability in CapsNets by Dominik Vranay, Maroš Hliboký, László Kovács, Peter Sinčák

“…This integration helps in better feature extraction by focusing on significant image parts while reducing the number of parameters required for accurate classification. …”
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