EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…Firstly, a conventional machine learning model was developed post-pre-processing, and feature extraction from the power spectral density was done using a Random Forest classifier. …”
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A Structured Data Model for Asset Health Index Integration in Digital Twins of Energy Converters by Juan F. Gómez Fernández, Eduardo Candón Fernández, Adolfo Crespo Márquez

“…This design and development has been realized from a scalable point of view and for future integration of Machine-Learning improvements. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, showing the model’s effective assistance in anticipating failures, optimizing maintenance strategies, and improving asset resilience. …”
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A new temporal locality-based workload prediction approach for SaaS services in a cloud environment by Wiem Matoussi, Tarek Hamrouni

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Students' performance prediction employing Decision Tree by Abtahi Ahmed, Farzana Akter Nipa, Wasi Uddin Bhuyian, Khaled Md Mushfique, Kamrul Islam Shahin, Huu-Hoa Nguyen, Dewan Md. Farid

“…This study uses the vast power and analytical domain of EDM, combining it with machine learning models, upholding an accurate prediction of students' academic performance. …”
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An Evidential Solar Irradiance Forecasting Method Using Multiple Sources of Information by Mohamed Mroueh, Moustapha Doumiati, Clovis Francis, Mohamed Machmoum

“…This study proposes an innovative approach to solar irradiance forecasting based on the theory of belief functions, introducing a novel and flexible evidential method for short-to-medium-term predictions. The proposed machine learning model is designed to effectively handle missing data and make optimal use of available information. …”
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A Systematic Review on the Use of Big Data in Tourism by Jafar Ahangaran, Abbas Sadeghnia

“…By describing the need for demand forecasting and price optimization in tourism, Jiang et al. believe that accurate demand forecasting helps tourism businesses to optimize pricing and resource allocation. …”
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Students' performance prediction employing Decision Tree by Abtahi Ahmed, Farzana Akter Nipa, Wasi Uddin Bhuyian, Khaled Md Mushfique, Kamrul Islam Shahin, Huu-Hoa Nguyen, Dewan Md. Farid

“…This study uses the vast power and analytical domain of EDM, combining it with machine learning models, upholding an accurate prediction of students' academic performance. …”
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3D Human Motion Capture Method Based on Computer Vision by A. D. Obukhov, D. L. Dedov, E. O. Surkova, I. L. Korobova

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Rethinking the 'best method' paradigm: The effectiveness of hybrid and multidisciplinary approaches in chemoinformatics by José L. Medina-Franco, Johny R. Rodríguez-Pérez, Héctor F. Cortés-Hernández, Edgar López-López

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Flexible imputation toolkit for electronic health records by Alireza Vafaei Sadr, Jiang Li, Wenke Hwang, Mohammed Yeasin, Ming Wang, Harold Lehmann, Ramin Zand, Vida Abedi

“…Pympute’s core algorithm, Flexible, intelligently selects the optimal imputation method for each variable based on its characteristics. …”
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Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization by Hua Huang, Haiyan Shao, Yifan Wang, Lili Ge

“…The DRPS model, optimized by the StepCox[backward] algorithm, demonstrated robust prognostic accuracy across four validation cohorts (mean C-index = 0.658). …”
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AI Roles in 4R Crop Pest Management—A Review by Hengyuan Yang, Yuexia Jin, Lili Jiang, Jia Lu, Guoqi Wen

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Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery by Haneen Alzamer, Russlan Jaafreh, Jung-Gu Kim, Kotiba Hamad

“…Among these, materials science has seen a significant transformation due to the availability of vast datasets, through which AI techniques, such as machine learning (ML) and deep learning (DL), can solve complex problems. …”
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Indian SUMO traffic scenario-based misbehaviour detection dataset for connected vehicles by Umesh Bodkhe, Sudeep Tanwar

“…Finally, we compare the AhmST dataset with recent datasets, assess the proposed dataset using various machine learning techniques and present an optimized model with improved accuracy.…”
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Changes in the Distribution of Thermokarst Lakes on the Qinghai-Tibet Plateau from 2015 to 2020 by Rongrong Wei, Xia Hu, Shaojie Zhao

“…The results indicated that the RF model performed optimally in the extraction of thermokarst lakes, followed by GBDT, CART, and SVM. …”
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Research on Shale Oil Well Productivity Prediction Model Based on CNN-BiGRU Algorithm by Yuan Pan, Xuewei Liu, Fuchun Tian, Liyong Yang, Xiaoting Gou, Yunpeng Jia, Quan Wang, Yingxi Zhang

“…Comparative analysis revealed a mean relative error (MRE) of 16.11% between predicted and observed production values, substantiating the model’s predictive competence. This methodology establishes a novel paradigm for machine learning-driven productivity prediction in unconventional reservoir engineering.…”
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WirelessNet: An Efficient Radio Access Network Model Based on Heterogeneous Graph Neural Networks by Jose Perdomo, M. A. Gutierrez-Estevez, Chan Zhou, Jose F. Monserrat

“…A network digital twin can enable the ability to safely and rapidly recreate what-if scenarios of mobile networks for more cost effective and intelligent network optimization. Towards enabling a network digital twin of mobile networks, accurate and efficient radio access network models are needed. …”
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D2: An LLM agent driving end-to-end visual AI modeling in energy platforms by Yu Li, Qiaoqiao Zhao, Min Hou, Quansheng Bai, Xiyan Zou, Changle Xie, Chang Shu, Boyang Ma, Zhijin Li

“…X-AI integrates advanced Machine Learning (ML) and Deep Learning (DL) capabilities into a workflow-driven environment that enables energy engineers to construct and deploy predictive models without prior AI expertise. …”
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Deep Learning and Methods Based on Large Language Models Applied to Stellar Light Curve Classification by Yu-Yang Li, Yu Bai, Cunshi Wang, Mengwei Qu, Ziteng Lu, Roberto Soria, Jifeng Liu

“…We unveil StarWhisper LightCurve (LC), a series of 3 LLM models based on an LLM, a multimodal large language model (MLLM), and a large audio language model (LALM). …”
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Application of Artificial Intelligence Algorithms in the Field of Antimicrobial Peptide Prediction by QIAN Yuchen, NIE Ting, HUA Yanming, XU Shiying, GUO Sheng, ZHANG Xin, LUO Xiaohu, LIU Yanan

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