A Dilemma-Based Learning-to-Rank Approach for Generative Design in Urban Architectural Regeneration by Ioannis Kavouras, Ioannis Rallis, Dimitra Zouli, Emmanuel Sardis, Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis

“…Nature-based solutions in architectural and urban design offer promising remedies but are often hindered by time, cost, and early design phase challenges. To address this, we present a Generative Design System framework utilizing AI-generated images and learning-to-rank algorithms. …”
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Learning Analytics: A Case Study on Exploring Social Interaction and Problem-Solving Strategies by Vladimir Grubelnik, Nataša Rizman Herga, Andrej Flogie, Mihaela Brumen, Marko Marhl, Dejan Dinevski

“…Originality/value: This study presents CRA as a novel integration of learning analytics and domain-specific visualisations in the science classroom. …”
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Machine learning-based cotton yield forecasting under climate change for precision agriculture by Muhammad Umair Shahzad, Sana Tahir, Javed Rashid, Osama A. Khashan, Rashid Ahmad, Sheikh Mansoor, Anwar Ghani

“…The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. …”
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Automated snow cover detection on mountain glaciers using spaceborne imagery and machine learning by R. Aberle, E. Enderlin, S. O'Neel, C. Florentine, L. Sass, A. Dickson, H.-P. Marshall, A. Flores

“…We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. …”
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A State-of-the-Art Survey on Advanced Electromagnetic Design: A Machine-Learning Perspective by Masoud Salmani Arani, Reza Shahidi, Lihong Zhang

“…Limitations of conventional optimization methods are discussed, while the need for novel machine learning techniques capable of handling multiple objectives and large design spaces is highlighted. …”
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Enhancing network slice security with Deep Reinforcement Learning and Moving Target Defense strategies by Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos Markakis, Athina Bourdena, George Mastorakis

“…Therefore, this article presents a novel framework integrating Deep Reinforcement Learning (DRL) with Moving Target Defense (MTD) strategies to create a dynamic, multi-layered security system. …”
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Combining Physics and Mathematics Learning: A Taylor Series Analysis of an Oscillating Magnetic Field by S. Ortuño-Molina, A. Garmendía-Martínez, P. Fernández de Córdoba, J. C. Castro-Palacio, J. A. Monsoriu, F. M. Muñoz-Pérez

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Identifying Catastrophic Outlier Photometric Redshift Estimates in the COSMOS Field with Machine Learning Methods by Mitchell T. Dennis, Esther M. Hu, Lennox L. Cowie

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Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors by Xhesila Xhafa, Ali Dogus Gungordu, Mustafa Berke Yelten

“…This paper presents a machine-learning-based approach for the degradation modeling of hot carrier injection in metal-oxide-semiconductor field-effect transistors (MOSFETs). …”
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MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging by Yunhe Li, Mei Yang, Tao Bian, Haitao Wu

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An Improved Particle Swarm Optimization with Biogeography-Based Learning Strategy for Economic Dispatch Problems by Xu Chen, Bin Xu, Wenli Du

“…Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeography-based learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. …”
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Protocol for cellular age prediction in yeast and human single cells using transfer learning by Subhadeep Duari, Vishakha Gautam, Gaurav Ahuja

“…Summary: Here, we present a protocol for predicting cellular age via computer vision analysis of cellular morphology and aging-related bioactivities from phase contrast microscopy images. …”
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Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis by W. Eltayeb Ahmed, Muhammad Farhan Hanif, Muhammad Kamran Siddiqui, Brima Gegbe

“…Abstract In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. …”
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Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction by BingKun Yu, PengHao Tian, XiangHui Xue, Christopher J. Scott, HaiLun Ye, JianFei Wu, Wen Yi, TingDi Chen, XianKang Dou

“…Because they can significantly influence radio communications and navigation systems, accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems. In this study, we present Es predictions made by an empirical model and by a deep learning model, and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations. …”
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Identifying learner strategies for second/foreign language learning and use: The choice of assessment tools by Małgorzata Dąbrowska

“…This, in turn, may encourage learners to stretch their individual learning preferences, develop and enrich language learning strategy repertoires, and facilitate future autonomous or self-regulated language learning, and continued lifelong learning.…”
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Various Deep Learning Techniques for the Applications in Polymer, Polymer Composite Chemistry, Structures and Processing by Sanaz Mohammadzadeh Koumleh, Hamid Hassanpour, Masoumeh Esmaeili, Akram Gholami

“…This paper aims to categorize various deep learning approaches used in the field of polymer science. …”
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A Survey of Unsupervised Learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems. by Sunkanmi Oluwadare, Zag ElSayed

“…This has facilitated the need to have intelligent-based IDS that could detect anomalies without relying on a detailed signature repository. In this paper, we present a literature-based survey of popular deep learning algorithms and evaluated their capabilities, strengths, limitations, and resource requirements for detecting anomalies and Zero-Day attacks. …”
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Collaborative and Cross-Modal Accomplishment of Practical Accord in a Video-Mediated Learning Situation by tuire

“…This study uses screen recorded data and multimodal conversation analysis (CA) to present a case analysis of an extended sequence in interaction where a group of learners has been divided into two groups, and they encounter a difficulty in sharing materials with each other during a momentary breakdown of the learning platform. …”
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Enhancing grade eight students’ creative thinking in the water stem education learning unit by Sukaya Sutaphan, Chokchai Yuenyong

“…Water issues, a commonly discussed issue in water STEM education, present students with a challenge to put their engineering design skills to the test in a STEM project. …”
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Deephive: A Reinforcement Learning Approach for Automated Discovery of Swarm-Based Optimization Policies by Eloghosa Ikponmwoba, Opeoluwa Owoyele

“…We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. …”
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