Design and analysis of antenna through machine learning for next-generation IoT system by Rachit Jain, R. Ramya, Vandana Vikas Thakare, P. K. Singhal

“…Abstract This paper presents a novel machine learning-driven approach for designing and optimizing multi-band patch antennas tailored for next-generation Internet of Things applications in 5G and 6G wireless communication systems. …”
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Challenges in machine learning-based social bot detection: a systematic review by Nasser Alkathiri, Khaled Slhoub

“…As a result, detecting and mitigating the influence of social bots has become a critical area of research. Machine learning-based detection techniques have shown promise in distinguishing bots from human users; however, their implementation presents several challenges, including issues related to data quality, model generalizability, feature selection, adversarial resistance, and computational efficiency. …”
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Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis by Liang Xu, Jing Li, Wei Gong

“…Nonetheless, the diversity of tumor types and the complexity of their pathogenesis present significant challenges in the diagnostic process. …”
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Applications of Deep Reinforcement Learning for Home Energy Management Systems: A Review by Dominik Latoń, Jakub Grela, Andrzej Ożadowicz

“…This review examines the role of reinforcement learning (RL) in the advancement of HEMS, presenting it as a powerful tool for the adaptive management of complex, real-time energy demands. …”
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Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning by Youpeng Yang, Jiaying Liu, Yutong He, Yingjie Yang, Tao Jiang, Jia Tang, Xin Li

“…The primary challenge lies in identifying the extremely low abundance of ctDNA in cell-free blood plasma (cfDNA). In this study, we present Oncoder, an interpretable deep learning-based tool for economical and accurate non-invasive tumor monitoring and diagnosis. …”
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An Efficient and Automated Classification System for Rocks Based on Visually Explainable Deep Learning by Shan Lin, Quanke Hu, Hongwei Guo, Miao Dong, Kaiyang Zhao, Hong Zheng, Zhijun Liu

“…Although current machine learning models have mitigated some drawbacks by classifying rock images, their generalization and predictive performance are limited by suboptimal network structures, image data quality, and quantity. …”
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Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis by L. Lakshmi, Ali B.M. Ali, K Dhana Sree Devi, Muhammad Rafiq, Iskandar Shernazarov, Nashwan Adnan Othman, M. Ijaz Khan

“…We employ three machine learning algorithms—stacking, random forest, and LogitBoost—to evaluate the performance of these approaches. …”
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ChatGPT in ESL Higher Education: Enhancing Writing, Engagement, and Learning Outcomes by Promethi Das Deep, Nara Martirosyan, Nitu Ghosh, Md. Shiblur Rahaman

“…Artificial intelligence (AI) in education has become increasingly common in higher education, particularly in learning English as a second language (ESL). ChatGPT is a conversational AI model frequently used to support language acquisition by creating personalized, interactive learning experiences. …”
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Efficient and Explainable Bearing Condition Monitoring with Decision Tree-Based Feature Learning by Trong-Du Nguyen, Thanh-Hai Nguyen, Danh-Thanh-Binh Do, Thai-Hung Pham, Jin-Wei Liang, Phong-Dien Nguyen

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Augmented Reality Interfaces in Education Enhancing Learning Experiences Through Interactive Technologies by Endla Purushotham, N Kirthiga, Prasad J Laxmi, P Chellammal, C R Bharathi, G Durgadevi

“…Together, these recent studies (2020–2024) compile a literature review, presenting collective evidence for utility across a wide range of disciplines including STEM education, medical training, and collaborative learning environments, and beyond. …”
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ACE2: accurately learning subseasonal to decadal atmospheric variability and forced responses by Oliver Watt-Meyer, Brian Henn, Jeremy McGibbon, Spencer K. Clark, Anna Kwa, W. Andre Perkins, Elynn Wu, Lucas Harris, Christopher S. Bretherton

“…Abstract Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. …”
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Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives by Nouman Ali, Nimra Hanif, Hassan Abbas Khan, Muhammad Abdullah Waseem, Afshan Saeed, Sadia Zakir, Abeeha Khan, Mejerrah Aamir, Adeeba Ali, Aamir Ali, Amna Saleem

“…In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. …”
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Fostering the Interdisciplinary Learning of Contemporary Physics Through Digital Technologies: The “Gravitas” Project by Matteo Tuveri, Arianna Steri, Daniela Fadda, Riccardo Stefanizzi, Viviana Fanti, Walter Marcello Bonivento

“…This paper discusses the learning outcomes, highlighting the potential of integrating formal high school curricula with innovative educational and digital tools.…”
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Maximising contributions of midwives in Africa towards achieving MNH targets: Lessons learned by Doreen K. Kaura, Jemima A. Dennis-Antwi, Frances D. Ganges, Sarah N. Ngoma

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Unravelling the tapestry of M-learning in English language teaching: a bibliometric analysis by S. Hema, S. N. S. Gandhimathi

“…As a result, the demand for cost-effective solutions has increased the importance of Mobile-Assisted Language Learning (MALL), which provides accessible tools like language learning apps, audio and video resources for language acquisition. …”
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Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms by Ana I. Oviedo, Jorge M. Londoño, John F. Vargas, Carolina Zuluaga, Ana Gómez

“…This study presents a methodology to optimize concrete mixtures by integrating machine learning (ML) and genetic algorithms. …”
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Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…Leveraging advanced Deep Learning (DL) methodologies, including Convolutional Neural Networks (CNN) and Transfer Learning, a robust predictive model has been developed to analyze arc behavior and identify defective cells based on image data. …”
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Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools by Alfredo Cuzzocrea, Abderraouf Hafsaoui, Carmine Gallo

“…In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. …”
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FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning by Wei Huang, Junling Chen, Dexian Wang, Pengfei Zhang, Jia Liu, Tianrui Li

“…Also, in the case of multiple knowledge graphs distributed across different clients, it is of interest to ensure that the knowledge graph embedding representations are learned without exposing data and collaboratively. …”
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Data-driven protease engineering by DNA-recording and epistasis-aware machine learning by Lukas Huber, Tim Kucera, Simon Höllerer, Karsten Borgwardt, Sven Panke, Markus Jeschek

“…Abstract Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. …”
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