A systematic literature review of mobile learning trends in biology education over ten years by Kristin Sangur, Siti Zubaidah, Sulisetijono

“…However, there is a dearth of literature studies related to the application of mobile learning in biology education. Thus, this literature review was conducted to present a mapping of empirical findings from the literature on the use of mobile learning in biology learning from 2013 to 2024. …”
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Visualizing Plant Disease Distribution and Evaluating Model Performance for Deep Learning Classification with YOLOv8 by Abdul Ghafar, Caikou Chen, Syed Atif Ali Shah, Zia Ur Rehman, Gul Rahman

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Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects by Adedibu Sunny Akingboye

“…The case studies presented in this review evaluate how supervised and unsupervised ML techniques enhance ERT and SRT by improving data interpretation, refining inversion accuracy, automating lithological differentiation, and predicting seismic velocity from resistivity data. …”
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Efficient extraction of experimental data from line charts using advanced machine learning techniques by Wenjin Yang, Jie He, Xiaotong Zhang

“…This research provides an efficient foundation for large-scale scientific data analysis and machine learning model development, advancing the field of automated data extraction technology.…”
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Embracing Linguistic Diversity: Pre-Service Teachers’ Lesson Planning for English Language Learning in Sweden by BethAnne Paulsrud, Parvin Gheitasi

“…Pre-service teachers today face challenges and opportunities in increasingly linguistically diverse Swedish schools. The present study aims to understand how pre-service English teachers of Years 4–6 (ages 10–12) plan lessons while considering this linguistic diversity. …”
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Privacy-Preserving Deep Learning: A Survey on Theoretical Foundations, Software Frameworks, and Hardware Accelerators by Eric Jahns, Milan Stojkov, Michel A. Kinsy

“…Deep Learning as a Service (DLaaS) has become a cornerstone in enabling access to deep learning capabilities, allowing users to train models or leverage pre-trained ones through APIs. …”
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Factors influencing student learning performance and continuous use of artificial intelligence in online higher education by Rashmi Singh, Shailendra Kumar Singh, Niraj Mishra

“…Abstract Students in higher education increasingly integrate emerging technologies to enrich their learning experiences. Universal tools such as virtual classrooms, multimedia presentations, and learning management systems are now widely employed in teaching and learning activities. …”
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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…Second, higher breast density is associated with an increased risk of breast cancer, making accurate assessments vital. This paper presents a comprehensive review of the mammographic density estimation literature, with an emphasis on machine-learning-based approaches. …”
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PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads by Chaiwat Punyammaree, Somyot Kaitwanidvilai

“…This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. …”
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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Ayman Nabil

“…Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. …”
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Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control by Denis Pompon, Luis F. Garcia-Alles, Philippe Urban

“…A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the present work, caffeine oxidation to alternative bioactive compounds was used to decipher this pluripotency. …”
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Identification of system models from potential-stream equations on the basis of deep learning on experimental data by I. E. Starostin, S. P. Khalyutin

“…As the potential flow equations describing the physicochemical processes are generally quite complicated for analytical transformations, the aforementioned relationship must be obtained by numerical methods. The present work is devoted to the use of deep learning as a universal approximator for obtaining the described connection between the characteristics of arbitrary systems. …”
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Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. by Bo Han, Sudan Bai, Yang Liu, Jiezhang Wu, Xin Feng, Ruihao Xin

“…Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. …”
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Reimagining Engagement in Dementia Care: Insight From a Community-Based Ukulele Learning Program by Jason Jincheng Fu, Joey Oi Yee Wong, Mario Gregorio, Lillian Hung

“…As recognition grows around the need for equitable, person-centered engagement for people living with neurocognitive disorders or dementia, accessible community programs that challenge stigma and foster inclusion remain limited. This program presents a 12-week ukulele learning program co-led by a person living with dementia that advances health equity by promoting inclusion, empowerment, and social connection through music. …”
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SOCIAL AND EMOTIONAL LEARNING AS A FACTOR OF SELECTION OF NON-VIOLENT BEHAVIOR STRATEGIES AMONG STUDENTS by Mykhailo М. Alokhin

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Development of a High‐Latitude Convection Model by Application of Machine Learning to SuperDARN Observations by W. A. Bristow, C. A. Topliff, M. B. Cohen

“…Abstract A new model of northern hemisphere high‐latitude convection derived using machine learning (ML) is presented. The ML algorithm random forests regression was applied to a database of velocities derived from the Super Dual Auroral Radar Network (SuperDARN) observations processed with the potential mapping technique, Map‐Potential (Ruohoniemi & Baker, 1998, https://doi.org/10.1029/98ja01288). …”
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Transfer learning strategies for neural networks: A case study in amine gas treating units by Daniela Galatro, Manoj Machavolu, Gladys Navas

“…This work presents a framework where strategies are applied within a workflow created to enhance the accuracy and transferability of the transfer learning process. …”
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Developing a Dynamic Simulation Model for Point-of-Care Ultrasound Assessment and Learning Curve Analysis by Sandra Usaquén-Perilla, Laura Valentina Bocanegra-Villegas, Jose Isidro García-Melo

“…This study presents an evaluation model incorporating the learning curve, developed from the domains of the AdHopHTA project. …”
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Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source by Parisa Sadeghli Dizaji, Hamidreza Habibiyan

“…However, the current design flow is hindered by computational challenges and a complex, high-dimensional parameter space with interdependent variables. In this work, we present a knowledge-integrated machine learning framework based on Bayesian Optimization for designing squeezed light sources using microring resonators. …”
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Leveraging machine learning and molecular docking techniques to predict novel melanocortin-4 receptor agonists by Stavroula Giakoumopoulou, Paris Christodoulou, Minos-Timotheos Matsoukas, Vasilis Panagiotopoulos, Dionisis Cavouras, Eftichia Kritsi

“…The MC4R has emerged as a promising target for drug development due to its role in energy homeostasis and adipose tissue formation. Aim: The present study explores the hybridization of machine learning and in silico techniques to propose natural compounds that potentially act as agonists against the obesity-related MC4R. …”
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