EVALUATING THE EFFICACY OF DISCORD-BASED BLENDED LEARNING ON ENGLISH SPEAKING SKILLS AND STUDENT ENGAGEMENT by Ahmad Zaki Munibi, Endry Boeriswati, Novi Anoegrajekti, Aceng Rahmat

“…The findings indicate that Discord significantly enhances interactivity, collaboration, and multimedia integration in learning activities. Educators utilize Discord's functionalities to host engaging discussions, deliver diverse learning materials, and conduct both formative and summative assessments effectively. …”
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Identification of Ferroptosis‐Related Gene in Age‐Related Macular Degeneration Using Machine Learning by Meijiang Zhu, Jing Yu

“…Machine learning algorithms were employed to screen diagnostic genes. …”
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The Digital Assessment of Learning: Current Situation and Perspectives: Case of Teachers of Life and Earth Sciences by Mohammed Rehhali, Abderrazzak Mazouak, Said Belaaouad

“…Necessary for the conduct of learning activities or to design and implement its assessment tool. …”
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geodl: An R package for geospatial deep learning semantic segmentation using torch and terra. by Aaron E Maxwell, Sarah Farhadpour, Srinjoy Das, Yalin Yang

“…Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. …”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…Anomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. …”
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All together now: building resilient citizen energy communities through collaborative learning by Paul Tobin, Breffní Lennon

“…Findings indicate that togetherness—expressed through social and functional relationships—is a foundational component of EC, supported by key factors such as context, values, and needs. …”
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Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau by Junpo Yu, Yajun Si, Wen Zhao, Zeyu Zhou, Jiming Jin, Wenjun Yan, Xiangyu Shao, Zhixiang Xu, Junwei Gan

“…As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. …”
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Diminished reward circuit response underlies pain avoidance learning deficits in problem drinkers by Thang M. Le, Takeyuki Oba, Chiang-Shan R. Li

“…Here, we combined functional imaging and a probabilistic learning go/nogo task to examine the neural processes underlying proactive pain avoidance learning in 103 adult drinkers. …”
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How does students' perception of ChatGPT shape online learning engagement and performance? by Mahboobeh Mehrvarz, Ghasem Salimi, Samaneh Abdoli, Bruce M. McLaren

“…Artiicial intelligence technologies, such as ChatGPT, Bert, LaMDA, and T5 have become deeply integrated into universities, influencing numerous functions within higher education and various aspects of students' lives. …”
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Chemical Space Exploration and Machine Learning-Based Screening of PDE7A Inhibitors by Yuze Li, Zhe Wang, Shengyao Ma, Xiaowen Tang, Hanting Zhang

“…The specific substructures that significantly enhance the activity of PDE7A inhibitors, including benzenesulfonamido, acylamino, and phenoxyl, were identified by an interpretable machine learning analysis. Subsequently, a machine learning model employing the Random Forest–Morgan pattern was constructed for the qualitative and quantitative prediction of PDE7A inhibitors. …”
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Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis by Xiao Chen, Zhiyun Shen, Tingyu Guan, Yuchen Tao, Yichen Kang, Yuxia Zhang

“…We applied a supervised machine learning approach including human annotation and machine learning–based models for topic modeling and sentiment analysis of the public discourse. …”
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The Model of Fundamental Chemical Training of Bachelors of Technical and Technological Directions in the Conditions of Blended Learning by N. M. Vostrikova

“…The concept and the structural-functional model of the fundamental chemical training of bachelors in the information-and-activity-based educational environment of blended learning have been proposed. …”
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Trust-driven approach to enhance early forest fire detection using machine learning by Tayyab Khan, Karan Singh, Bhoopesh Singh Bhati, Khaleel Ahmad, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Furthermore, a machine learning regression model is deployed at the base station to augment the precision of fire detection. …”
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Impact of the COVID-19 Pandemic and Lockdown on Non-COVID Neurosurgical Patients: Lessons Learned by Rajnish Kumar Arora, Jitendra Shakya, Rajkumar Pannem, Suresh Sharma, Saravanan Sadhasivam, Vikram Singh Rawat, Rajashekhar Rekhapalli, Rakesh Kumar Sihag, Radhey Shyam Mittal

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Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence by Taghi Riahi, Bahareh Shateri‐Amiri, Amirhossein Hajialiasgary Najafabadi, Sina Garazhian, Hanieh Radkhah, Diar Zooravar, Sahar Mansouri, Roya Aghazadeh, Mohammadreza Bordbar, Shirin Raiszadeh

“…Traditional machine learning (ML) models have shown limited generalizability in clinical settings. …”
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Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method by Fangzhou Lin, Shang Gao, Yichuan Tang, Xihan Ma, Ryo Murakami, Ziming Zhang, John D. Obayemi, Winston O. Soboyejo, Haichong K. Zhang

“…This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserving spectral integrity, providing noise reduction, and ensuring that functional information is maintained. …”
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Unsupervised Learning for Distributed Downlink Power Allocation in Cell-Free mMIMO Networks by Mattia Fabiani, Asmaa Abdallah, Abdulkadir Celik, Omer Haliloglu, Ahmed M. Eltawil

“…For both learning frameworks, the proposed approach is utilized to maximize three well-known power control objectives under maximum-ratio and regularized zero-forcing precoding schemes: 1) sum of spectral efficiency, 2) minimum signal-to-interference-plus-noise ratio (SINR) for max-min fairness, and 3) product of SINRs for proportional fairness, for each of which customized loss functions are formulated. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…The ML-based modeling method developed overcomes the difficulty of the determination of the functional relationship between nitrate loss intensity and its influencing factors, providing a data-driven solution for estimating nitrate–nitrogen loss in farmlands in North China and strengthening sustainable agricultural practices.…”
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Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey by Yong Han Kim, Wei Ye, Ritbik Kumar, Finn Bail, Julia Dvorak, Yanchao Tan, Marvin Carl May, Qing Chang, Ragu Athinarayanan, Gisela Lanza, John W. Sutherland, Xingyu Li, Chandra Nath

“…These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. …”
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Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling by Hongbo Wang, Yan Hu

“…In order to explore the application of AI technology in construction management system modeling, the author proposed the application of a deep learning-based AI technology in construction management system modeling. …”
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