Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques by S. Vijayalakshmi, Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas Lelisho

“…Abstract Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. …”
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Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils by Mao Zhenxuan, Chen Kun, Liu Qiang, Xu Mengjiao

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Predictive value of machine learning model based on CT values for urinary tract infection stones by Jiaxin Li, Yao Du, Gaoming Huang, Chiyu Zhang, Zhenfeng Ye, Jinghui Zhong, Xiaoqing Xi, Yawei Huang

“…Summary: Preoperative diagnosis of infection stones presents a significant clinical challenge. We developed a machine learning model to predict urinary infection stones using computed tomography (CT) values, enabling in vivo preoperative identification. …”
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Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment by Vijendra Kumar, Kul Vaibhav Sharma, Nikunj K. Mangukiya, Deepak Kumar Tiwari, Preeti Vijay Ramkar, Upaka Rathnayake

“…Finally, recommendations and future directions of ML models in flood analysis are presented.…”
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Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping by Lung‐Fai Tuen, Ching‐Lieh Li, Yu‐Jen Chi, Chien‐Ching Chiu, Po Hsiang Chen

“…Abstract This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. …”
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Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data by Christoph Herbert, Adriano Camps, Florian Wellmann, Mercedes Vall‐llossera

“…In this work, Arctic sea ice is segmented using a Bayesian unsupervised learning algorithm aiming to recognize spatial patterns by harnessing multi‐incidence angle brightness temperature observations. …”
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Real-Time Based Big Data and E-Learning: A Survey and Open Research Issues by Wael Hadeed, Dhuha Abdullah

“…Hence, the objective of this survey is to provide a comprehensive overview of the integration between real-time and big data fields of study with the field of E-learning. Finally, this survey also presents the colorful aspects of big data and their relationship to E-learning domains such that e-learning platforms, big data frameworks, and datasets used. …”
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Students' Attitudes Toward the Clinical Learning Environment and Clinical Supervision in Nursing College at the University of Mosul by Hanaa Hussein Mukhlif

“…Objectives: Assessing students' attitudes toward the clinical learning environment and clinical supervision. Also, identifying if student class and the patient’s number in the wards had an effect on attitudes.    …”
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Antimatter annihilation vertex reconstruction with deep learning for ALPHA-g radial Time Projection Chamber by A. Ferreira, M. Singh, Y. Saito, A. Capra, I. Carli, D. Duque Quiceno, W. Fedorko, M. C. Fujiwara, M. Li, L. Martin, G. Smith, A. Xu

“…The standard approach requires identifying the trajectories of the ionizing particles in the rTPC from the location of their interaction in the gas (spacepoints), and inferring the vertex positions by finding the point where those trajectories (helices) pass closest to one another. In this work, we present a novel approach to vertex reconstruction using an ensemble of models based on the PointNet deep learning architecture. …”
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Academic Emotions, Emotion Regulation, Academic Motivation, and Approaches to Learning: A Person-Centered Approach by Christos Rentzios, Evangelia Karagiannopoulou, Georgios Ntritsos

“…Contemporary educational literature suggests that academic emotions and emotion regulation should be explored in tandem, while academic motivation has been discussed both as a self-regulation metacognitive construct and as a construct inherently tied to motivation. The present study uses a person-centered approach to explore profiles of university students based on academic emotions, emotion regulation, academic self-regulation, and approaches to learning. …”
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Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing by Than Le, Le Quang Vinh, Van Huy Pham

“…Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. …”
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Explainable AI in early autism detection: a literature review of interpretable machine learning approaches by Renuka Agrawal, Rucha Agrawal

“…The present study delves into the extensive applications of XAI in diverse fields including healthcare, emphasizing its significance in guaranteeing an ethical and dependable implementation of AI. …”
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Construction of a Web Game for the Teaching-Learning Process of Electronics during the COVID-19 Pandemic by Ricardo-Adán Salas-Rueda, Clara Alvarado-Zamorano, Jesús Ramírez-Ortega

“…This web game consists of a simulator that presents the contents of the output function for two variables and their representation through logic gates. …”
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The Role of Mobile-assisted Language Learning on EFL Learners’ Development of Writing Accuracy, Fluency, and Complexity by Leila Ahmadpour, Mohammad Hossein Yousefi

“…The present study was an attempt to investigate the effectiveness of mobile-assisted language learning (MALL) in comparison to lack of it in improving learners’ writing accuracy, fluency, and complexity. …”
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High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study by Fátima Rodrigues, Miguel Machado

“…The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. …”
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Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning by Wafaa Al-Kahla, Eyad Taqieddin, Ahmed S. Shatnawi, Rami Al-Ouran

“…These vectors are then used to train different Machine Learning algorithms for detecting and classifying malware. …”
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Remote sensing for land cover mapping across Victoria, Australia – a machine learning application by Sabah Sabaghy, Mohammad Abuzar, Doug Crawford, Andy McAllister, Kathryn Sheffield

“…Abstract This paper presents a detailed land cover mapping study for the state of Victoria in Australia conducted through a machine learning approach (random forest algorithm) using Sentinel-2 imagery. …”
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Comparison Between Guided and Non-Guided Homework as a Tool for Learning Electric Circuit Theory by Rafael Diez, Arturo Fajardo, Jairo A. Hurtado

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Balancing Privacy and Accuracy: Exploring the Impact of Data Anonymization on Deep Learning Models in Computer Vision by Jun Ha Lee, Su Jeong You

“…Therefore, it is essential to understand how the anonymization of image datasets affects deep learning model performance. In this paper, we thoroughly analyze the effects of different anonymization techniques on the performance of deep learning-based models in computer vision tasks, with a particular emphasis on presenting a model-centric perspective, such as the type of deep learning model and the number of parameters. …”
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Towards a Retrieval-Augmented Generation Framework for Originality Evaluation in Projects-Based Learning Classrooms by Samuel Yanes Luis, Daniel Gutiérrez Reina, Sergio Toral Marín

“…Project-Based Learning is central to modern engineering education, but assessing the originality of student work poses significant challenges, particularly when previous project repositories are accessible. …”
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