Analyzing Momentum Shifts in Tennis: A Machine-Learning Approach to Predicting Match Outcomes by Yuean Xia, Changfeng Li, Tanran Zhang

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Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems by Sarucha Yanyong, Somyot Kaitwanidvilai

“…The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. …”
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Automated Measurement of Air Bubbles Dispersion in Ice Cream Using Machine Learning Methods by Igor A. Korolev

“…It can be found in public domain, and researchers are free to adapt it to solve various computer vision issues.…”
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Machine learning-based cotton yield forecasting under climate change for precision agriculture by Muhammad Umair Shahzad, Sana Tahir, Javed Rashid, Osama A. Khashan, Rashid Ahmad, Sheikh Mansoor, Anwar Ghani

“…This study employs a diverse range of machine learning (ML) methods, including multiple regression, k-nearest neighbors (KNN), boosted tree algorithms, and various types of artificial neural networks (ANNs), to investigate the intricate relationship between climate factors and cotton yields. …”
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Research on Control Strategy Technology of Upper Limb Exoskeleton Robots: Review by Libing Song, Chen Ju, Hengrui Cui, Yonggang Qu, Xin Xu, Changbing Chen

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Adaptive zero velocity correction method for fiber optic inertial navigation system in coal mining roadheader by Qinghua MAO, Qing ZHOU, Jianquan CHAI, Yanzhang CHEN, Wenjuan YANG, Xusheng XUE

“…Finally, the accuracy of zero-speed detection is improved by introducing the sandcat swarm optimization algorithm to optimize the kernel function and penalty parameters.Aiming at the problem of high cost and error accumulation with time of high-precision fiber-optic inertial navigation, an adaptive zero-speed correction method is proposed. …”
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Adaptive ensemble techniques leveraging BERT based models for multilingual hate speech detection in Korean and english by Seohyun Yoo, Eunbae Jeon, Joonseo Hyeon, Jaehyuk Cho

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Wasserstein GAN for moving differential privacy protection by Enze Liu, Zhiguang Chu, Xing Zhang

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Adaptive Sliding Mode Control of DC–DC Buck Converter with Load Fluctuations for Renewable Energy Systems by Haris Sheh Zad, Abasin Ulasyar, Adil Zohaib, Muhammad Irfan, Samid Ali Haider, Zeeshan Yaqoob

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Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review by Claire R. van Genugten, Claire R. van Genugten, Melissa S. Y. Thong, Melissa S. Y. Thong, Melissa S. Y. Thong, Wouter van Ballegooijen, Wouter van Ballegooijen, Wouter van Ballegooijen, Annet M. Kleiboer, Annet M. Kleiboer, Donna Spruijt-Metz, Arnout C. Smit, Mirjam A. G. Sprangers, Mirjam A. G. Sprangers, Yannik Terhorst, Yannik Terhorst, Heleen Riper, Heleen Riper, Heleen Riper

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”
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MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection by Chuantao Li, Chen Huang, Ruihan Chen, Zhuohong Yu, Sheng Li

“…Abstract Feature selection constitutes a fundamental component of machine learning and Genetic Algorithms (GAs) are extensively employed in feature selection. …”
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Federated Learning-Based State of Charge Estimation in Electric Vehicles Using Federated Adaptive Client Momentum by Metin Yilmaz, Eyup Cinar, Ahmet Yazici

“…Centralized machine learning methods are used to improve SoC estimation. …”
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Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems by Ruchika Bhakhar, Rajender Singh Chhillar

“…Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. …”
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Design and optimization of distributed energy management system based on edge computing and machine learning by Nan Feng, Conglin Ran

“…Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.…”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. …”
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Detection of offensive content in the Kazakh language using machine learning and deep learning approaches by Milana Bolatbek, Moldir Sagynay, Shynar Mussiraliyeva, Zhastay Yeltay

“…This article demonstrates the effectiveness of combining n-gram and stemming methods with machine learning algorithms, achieving high accuracy in content classification. …”
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Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

“…However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.MethodsA novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. …”
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Multi-Strategy Fusion RRT-Based Algorithm for Optimizing Path Planning in Continuous Cherry Picking by Yi Zhang, Xinying Miao, Yifei Sun, Zhipeng He, Tianwen Hou, Zhenghan Wang, Qiuyan Wang

“…This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. …”
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Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. …”
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Trade-Space Exploration With Data Preprocessing and Machine Learning for Satellite Anomalies Reliability Classification by Abdul Mutholib, Nadirah Abdul Rahim, Teddy Surya Gunawan, Mira Kartiwi

“…Existing anomaly classification methods often lack scalability, interpretability, and adaptability to diverse datasets. This study introduces the Trade-Space Exploration Machine Learning (TSE-ML) framework, a comprehensive pipeline for satellite anomaly classification that optimizes preprocessing, transformation, normalization, and machine learning stages. …”
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