Learning Interactions between Rydberg Atoms by Olivier Simard, Anna Dawid, Joseph Tindall, Michel Ferrero, Anirvan M. Sengupta, Antoine Georges

“…We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain, regarding both the size and the shape of the system, yielding an accurate determination of the Hamiltonian parameters with a minimal set of measurements. …”
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Learning and cognition in highspeed decision making by Martin Krause, Wolfram Schulze, Stefan Schuster

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Does digital learning stimulate creativity? by Marcel Pikhart, Liqaa Habeb Al-Obaydi, Blanka Klimova

“…E-learning, or digital learning, has transformed education, raising questions about its impact on creativity. …”
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Care farming, learning and young people: An exploration into the possible contribution of care farming to young people’s engagement with learning by Rachael Fell-Chambers

“…Unstructured interviews, photo elicitation and semi-structured interviews were all triangulated with observational fieldwork notes. Data yielded in this study found that care farms provide a nurturing and enabling learning environment for young people to self-discover and freedom from the humiliation and frustration experienced by some in the traditional schooling system. …”
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Comparative Analysis of Automated Machine Learning and Optimized Conventional Machine Learning for Concrete’s Uniaxial Compressive Strength Prediction by Chukwuemeka Daniel

“…This study pioneers the application of automated machine learning (AutoML) and conventional ML techniques to unravel the intricate relationships between the UCS and six factors. …”
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Development of Problem-Based Learning Model Learning Module To Improve Mathematical Communication and Curiosity of Grade 5 Students by Aunurrokhim Munawir, YL Sukestiyarno M.S., Sugilar Sugilar

“… This study aimed to examine the development process, outcomes, and impact of Problem-Based Learning (PBL) modules designed to enhance fifth-grade elementary students’ mathematical communication skills and curiosity in arithmetic. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Classification and Prediction of Liver Disease in Patients with Hepatitis C by Shalem Preetham Gandu, M. Roshni Thanka, E. Bijolin Edwin, Ebenezer Veemaraj, S. Stewart Kirubakaran

“…This study used the Electronic Health Records dataset from the UCI Machine Learning Repository, which included 615 individuals, of which 540 were healthy, and 75 had hepatitis C. …”
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LEARNING INNOVATION: APPLICATION OF DISCOVERY LEARNING-BASED WORKSHEETS TO SHARPEN STUDENTS’ COLLABORATION SKILLS ON HYDROELECTRIC POWER PLANTS by Ihza Rizky Winedar, Habibatul Unayah, Muhammad Rasyid, Jumadi Jumadi, Sabar Nurohman

“…This study aims to analyze the effect of discovery learning on improving students' collaboration skills in studying alternative energy sources, mainly hydropower. …”
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Developing Engaging IPAS Lessons : Google Sites Learning Media Based on Banten Culture to Boost Interest and Learning Outcomes by Neni Yuliani, Ratna Sari Dewi, Suroso Mukti Leksono

“…The results indicate that the validation of the Banten culture-based Google Sites learning material yields a score of 96%, categorized as 'Very Good'. …”
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Comparative analysis of different PV technologies under the tropical environments by V. Femin, R. Veena, M. I. Petra, S. Mathew

“…The field performances of these cells were initially assessed using standard performance indices such as Array Yield, Reference Yield, Capture Loss, Performance Ratio, and Efficiency Ratio. …”
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Investigating Transfer Learning in Noisy Environments: A Study of Predecessor and Successor Features in Spatial Learning Using a T-Maze by Incheol Seo, Hyunsu Lee

“…In discussing these results, we emphasize the critical role of hyperparameter optimization in refining the performance and transfer learning efficacy of learning algorithms. This research advances our understanding of the functionality of PF and SF algorithms, particularly in navigating the inherent uncertainty of transfer learning tasks. …”
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Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic by Md. Abdul Kaium, Md. Sharif Ahmed, Muhammad Habib-Ur-Rahman, Md. Saidul Islam, Yeasmin Akter Ratry, Md Mostofa Uddin Helal, Muhammad Ali Fardoush Siddquy, Most. Moslema Haque, Ahsan Raza, Fatma Mansour, Majed Alotaibi, Ayman El Sabagh, Reimund P. Roetter

“…Utilizing a machine learning approach, future yield patterns were predicted for these crops under various climate change scenarios. …”
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Deep Learning Methods Used in Precision Agriculture by Wang Yuchu

“…Our primary objectives are to provide a comprehensive overview of artificial neural networks (ANNs), including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and to explore their applications in precision agriculture. Deep learning has shown great potential in accurately predicting crop yields and quality, and in optimizing the use of resources in farming. …”
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Early detection of human Mpox: A comparative study by using machine learning and deep learning models with ensemble approach by Madhumita Pal, Francesco Branda, Adel Qlayel Alkhedaide, Ashish K Sarangi, Himansu Bhusan Samal, Lizaranee Tripathy, Binapani Barik, Salah M El-Bahy, Alok Patel, Ranjan K Mohapatra, Lawrence Sena Tuglo, Mona Youssef

“…Objective This study aims to enhance the early diagnosis of Mpox through machine learning (ML) and deep learning (DL) models, integrating an ensemble approach to improve classification accuracy. …”
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EnsembleNPPred: A Robust Approach to Neuropeptide Prediction and Recognition Using Ensemble Machine Learning and Deep Learning Methods by Supatcha Lertampaiporn, Warin Wattanapornprom, Chinae Thammarongtham, Apiradee Hongsthong

“…In this study, we propose EnsembleNPPred, an ensemble learning framework that integrates traditional machine learning (ML) models with a deep learning (DL) component. …”
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Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk by Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa, Federica Pediconi

“…This study aimed to develop and evaluate two deep learning (DL) models based on transfer learning for the binary classification of breast lesions (benign vs. malignant) using DBT images to support clinical decision-making and risk stratification. …”
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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

“…PurposeIndividuals with learning disabilities (LD) often face higher rates of premature mortality and prolonged hospital stays compared to the general population. …”
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DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants by Yixin Guo, Jinchao Pan, Xueying Wang, Hong Deng, Hong Deng, Mingliang Yang, Mingliang Yang, Enliang Liu, Qingshan Chen, Qingshan Chen, Rongsheng Zhu, Rongsheng Zhu

“…By improving the Siamese Network in metric learning, the optimal Siamese Network model was obtained. …”
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Neural network compression for reinforcement learning tasks by Dmitry A. Ivanov, Denis A. Larionov, Oleg V. Maslennikov, Vladimir V. Voevodin

“…Abstract In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. …”
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The problem with two-event sequence learning by pigeons by Thomas R. Zentall, Daniel N. Peng

“…Abstract Bonobos appear to show little evidence of learning to make one response (R1) to an AB sequence and a different response (R2) to sequences BB, AA, and BA (Lind et al. …”
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