Learnings from COVID-19 Forced Lockdown on Regional Air Quality and Mitigation Potential for South Asia by Abhishek Upadhyay, Parth Sarathi Mahapatra, Praveen Kumar Singh, Sishir Dahal, Suresh Pokhrel, Amit Bhujel, Indu Bikram Joshi, Shankar Prasad Paudel, Siva Praveen Puppala, Bhupesh Adhikary

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Exploration of the Needs and Development of Arabic Learning Content Based on Google Sites by Muhamad Muzaki Kurnia Ilahi Zaki, Ruhamaul Waro, Wardiyatul Husna

“…Based on the results of the needs analysis, a prototype of Arabic language learning content based on Google Sites was produced consisting of several types, including; vocabulary content, grammar, and four language skills (listening, speaking, reading, writing) which are presented in the form of text, animated images, audio, video and also equipped with interactive quizzes.' …”
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An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data by Naiwen Zhang, Xiaolong Guo, Xiaxia Yu, Zhen Tan, Zhen Tan, Feiyue Cai, Feiyue Cai, Ping Dai, Jing Guo, Guo Dan

“…This study aims to use ensemble technology to establish a machine learning model for the prediction of dyslipidemia.MethodsThis study included three consecutive years of physical examination data of 2,479 participants, and used the physical examination data of the first two years to predict whether the participants would develop dyslipidemia in the third year. …”
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CLDP-pFedAvg: Safeguarding Client Data Privacy in Personalized Federated Averaging by Wenquan Shen, Shuhui Wu, Yuanhong Tao

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A critical review of SkELL (Sketch Engine for Language Learning) – V1.11.8 by Ibrahim Halil Topal

“… In an era of technology transforming education, data-driven learning (DDL) has gained recognition as a powerful approach in language education. …”
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Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolit... by Sungwook Jung

“…Performance differences across data sets and between deep learning models were tested to be statistically significant. …”
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Data Mining Techniques in a Virtual Workshop for the Digitalization of Education by E. V. Timoschenko, A. F. Razhkov

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Data Mining Techniques in a Virtual Workshop for the Digitalization of Education by E. V. Timoschenko, A. F. Razhkov

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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

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Multi-Source Satellite Imagery and Machine Learning for Detecting Geological Formations in Cameroon’s Western Highlands by Kacoutchy Jean Ayikpa, Valère-Carin Jofack Sokeng, Abou Bakary Ballo, Pierre Gouton, Koffi Fernand Kouamé

“…The data were preprocessed to remove atmospheric biases and harmonize spatial resolutions. …”
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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. …”
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Integration of UAV Multi-Source Data for Accurate Plant Height and SPAD Estimation in Peanut by Ning He, Bo Chen, Xianju Lu, Bo Bai, Jiangchuan Fan, Yongjiang Zhang, Guowei Li, Xinyu Guo

“…This study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains. …”
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Personalized learning path planning for higher education based on deep generative models and quantum machine learning: a multimodal learning analysis method integrating transformer... by Changzhi Sun, Shijie Huang, Banghui Sun, Shiwei Chu

“…Abstract In the field of personalized learning path planning for higher education, traditional methods lack in-depth analysis of students' dynamically changing learning status and interests, resulting in insufficient personalization, and focus on the analysis of a single data type, making it difficult to integrate data of different modalities. …”
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Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme by Abdul Majeed, Seong Oun Hwang

“…Machine learning (ML) practitioners are always in pursuit of refined data to develop robust and generalizable ML models to solve real-world problems. …”
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Synthetic data generation in genomic cancer medicine: a review of global research trends in the last ten years by Valentina De Nicoló, Maria Frasca, Agnese Graziosi, Gianluca Gazzaniga, Davide La Torre, Arianna Pani

“…Abstract This paper reviews the global evolution of synthetic data (SD) generation in the field of genomic cancer medicine, with an analysis of research trends from the past decade. …”
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ARTIFICIAL INTELLIGENCE IN LANGUAGE EDUCATION IN TÜRKİYE: POTENTIAL CHALLENGES by S. Doğruer, Y. Tuğlu

“…METHOD: The study uses the document analysis method to examine possible problems with AI-supported language learning in English and German languages in Türkiye. …”
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MQTTEEB-D: A Real-World IoT Cybersecurity Dataset for AI-Powered Threat Detection in MQTT NetworksMendeley Data by Abderrahmane Aqachtoul, Khaoula Karam, Abderrahmane Elamrani, Mehdi Najib, Najat Rafalia, Mohamed Bakhouya

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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method by Ankit Kumar, Kamred Udham Singh, Manish Kumar

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Burden of mental disorders by gender in Pakistan: analysis of Global Burden of Disease Study data for 1990–2019 by Mohsin Hassan Alvi, Tehmina Ashraf, Farah Naz, Asif Sardar, Akbar Ullah, Anita Patel, Tayyeba Kiran, Anil Gumber, Nusrat Husain

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UA-HSD-2025: Multi-Lingual Hate Speech Detection from Tweets Using Pre-Trained Transformers by Muhammad Ahmad, Muhammad Waqas, Ameer Hamza, Sardar Usman, Ildar Batyrshin, Grigori Sidorov

“…Based on the analysis of the results, our language-based model (XLM-R) outperformed traditional supervised learning approaches, achieving 0.99 accuracy in binary classification for Arabic, Urdu, and joint-multilingual datasets, and 0.95, 0.94, and 0.94 accuracy in multi-class classification for joint-multilingual, Arabic, and Urdu datasets, respectively.…”
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