Global MPPT optimization for partially shaded photovoltaic systems by T. Nagadurga, V. Dhana Raju, Abdulwasa Bakr Barnawi, Javed Khan Bhutto, Abdul Razak, Anteneh Wogasso Wodajo

“…From the simulation results, it was found that ChOA have shown higher conversion efficiency of 99.63% with maximum power output of 525.13 W when compared to other optimization algorithms for the given shading pattern condition. …”
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Learning Analytics in a Non-Linear Virtual Course by Jhon Mercado, Carlos Mendoza-Cardenas, Luis Fletscher, Natalia Gaviria-Gomez

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Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games by Jun Zhao, Jintian Ji, Robail Yasrab, Shuxin Wang, Liang Yu, Lingzhen Zhao

“…To address these challenges, this study proposes an interpretable reinforcement learning framework based on an Enhanced Deep Deterministic Policy Gradient (Enhanced-DDPG) algorithm. By leveraging a custom simulation environment and integrating key linguistic features word frequency, letter frequency, and repeated letter patterns (rep) the model dynamically predicts the number of attempts needed to solve <b>Wordle</b> puzzles. …”
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A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

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Masked Convolutions Within Skip Connections for Video Anomaly Detection by Demetris Lappas, Vasileios Argyriou, Dimitrios Makris

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Enhanced Viral Genome Classification Using Large Language Models by Hemalatha Gunasekaran, Nesaian Reginal Wilfred Blessing, Umar Sathic, Mohammad Shahid Husain

“…The classification of genomic sequences is a crucial area of research in the field of virology. This is due to the increasing number of outbreaks we have faced in recent times. …”
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AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection by Fujiang Yuan, Zihao Zuo, Yang Jiang, Wenzhou Shu, Zhen Tian, Chenxi Ye, Junye Yang, Zebing Mao, Xia Huang, Shaojie Gu, Yanhong Peng

“…Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. …”
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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

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Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model by Zachary Barbre, Gang Li

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Measuring the Inferential Values of Relations in Knowledge Graphs by Xu Zhang, Xiaojun Kang, Hong Yao, Lijun Dong

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BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization by Danqi Zheng, Jiyun Qin, Zhen Liu, Qinglei Zhang, Jianguo Duan, Ying Zhou

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AI Under Attack: Metric-Driven Analysis of Cybersecurity Threats in Deep Learning Models for Healthcare Applications by Sarfraz Brohi, Qurat-ul-ain Mastoi

“…The confusion matrices also revealed a higher rate of false positives in the adversarial FGSM and evasion attacks than more balanced misclassification patterns observed in data poisoning. By proposing a unified framework for quantifying and analyzing these post-attack vulnerabilities, this research contributes to formulating resilient AI models for critical domains where accuracy and reliability are important.…”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…For the present study, it is a major drawback to the deployment of deep learning methods mandated with detecting patterns and prognosticating Alzheimer’s disease. Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. …”
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Construction and scheduling optimization of renewable energy consumption forecasting system for twisted tire porcelain manufacturing industry based on deep learning by Fangrong Liao, Runlin Ran, Jiayi Zhang

“…It has become possible to realize the intelligent classification algorithm of twisted tire porcelain patterns. Taking twisted tire porcelain in Dangyangyu, Jiaozuo City as the research object, combining with the needs of twisted tire porcelain manufacturers in Dangyangyu, twisted tire porcelain is divided into four kinds of patterns: natural pattern, geometric pattern, ancient pattern and bionic pattern, and a large number of twisted tire porcelain pattern pictures are collected, and the classification data set of twisted tire porcelain pattern is constructed by manual labelling and labelling. …”
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Lensless camera: Unraveling the breakthroughs and prospects by Shuowen Li, Yunhui Gao, Jiachen Wu, Mingjie Wang, Zhangcheng Huang, Shumei Chen, Liangcai Cao

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Circadian Regulator-Mediated Molecular Subtypes Depict the Features of Tumor Microenvironment and Indicate Prognosis in Head and Neck Squamous Cell Carcinoma by Ling Aye, Zhanying Wang, Fanghua Chen, Yujun Xiong, Jiaying Zhou, Feizhen Wu, Li Hu, Dehui Wang

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E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances by Ali Agheli, Kayvan Aghabayk, Matin Sadeghi, Subasish Das

“…The increasing use of e-scooters in urban areas has raised safety concerns, necessitating research for effective safety interventions. This study analyzes three years of e-scooter crash data from the United Kingdom using statistical and machine learning methods to identify key factors influencing crash severity. …”
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Diagnostic and prognostic implications of family history of fibrotic interstitial lung diseases by D. Duminy-Luppi, A. Alcaide-Aldeano, L. Planas-Cerezales, G. Bermudo, V. Vicens-Zygmunt, P. Luburich, B. Del Río-Carrero, R. Llatjós, L. Pijuan, I. Escobar, F. Rivas, A. Montes-Worboys, Y. Gutiérrez-Rodríguez, D. Rodríguez-Plaza, A. Padró-Miquel, A. Esteve-Garcia, B. Fernández-Varas, C. Flores, M. Fuentes, J. Dorca, S. Santos, R. Perona, A. Günther, J. Shull, M. Molina-Molina

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Transcriptome Classification Reveals Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Yuxia Liu, Chang Xu, Wenxin Gao, Huaqiong Liu, Chenglong Li, Mingwei Chen

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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The presented study aims to fill these gaps and demonstrate the efficiency of using open, accessible data and known algorithms. The research objective is to describe a pattern of appropriate selection of the least resource-intensive delivery forecasting model based on the analysis of machine learning algorithms.Materials and Methods. …”
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