Non-digital game-based learning: a powerful tool to teach theories of architecture by Alia Sameh Okasha

“…While the conclusion stresses the role of fun and active learning, it suggests a gradual shift from non-digital to digital game-based learning and highlights that these methods require significant further investigation.…”
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Enhancing Antimicrobial Peptide Functionality and Manufacturability Through Deep Learning-Based Sequence Design by Aysenur Soyturk Patat, Aycan Gundogdu, Ozkan Ufuk Nalbantoglu

“…Additionally, ''Nisin A,'' a bacterial peptide effective against Gram-positive bacteria widely used as a food preservative, and ''Plectasin,'' a fungal peptide noted for its activity against Gram-positive bacteria, were designed using this approach.We propose a deep learning-based method for optimizing complete protein design. …”
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Machine Learning-Based Model Used for Predicting the Risk of Hepatocellular Carcinoma in Patients with Chronic Hepatitis B by Wu T, Yan J, Xiong F, Liu X, Zhou Y, Ji X, Meng P, Jiang Y, Hou Y

“…The ANN model proficiently categorized patients into low-risk and high-risk groups based on their 10-year projections. In the training cohort, the positive predictive value (PPV) for the incidence of liver cancer in low-risk individuals was 92.5% (95% CI 0.921– 0.939), whereas the negative predictive value (NPV) stood at 88.2% (95% CI 0.870– 0.894). …”
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Inclusive learning innovation with mobile-based bilingual interactive games for slow learner students by Suastika Yulia Riska, Widya Adhariyanty Rahayu, Abdul Aziz Muslim

“…The results of the implementation show that the use of this game has a positive impact on the understanding of slow-learner students in bilingual learning, increase learning motivation, and assists teachers in integrating technology into teaching methods. …”
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Pearson Autocovariance Distinct Patterns and Attention-Based Deep Learning for Wind Power Prediction by W. G. Jency, J. E. Judith

“…This paper presents a wind power prediction method with feature selection and prediction called, Pearson Autocovariance Distinct Patterns and Attention-based Deep Learning (PACDP-ADL). …”
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The Impact of the Online Project-Based Learning on Students’ Communication, Engagement, Motivation, and Academic Achievement by Ханса Хассан Аль-Бахадлі, Лікаа Хабеб Аль-Обайді, Марсель Пікхарт

“…Purpose. Project-based learning (PBL) is a student-centered environment that emphasizes a dynamic classroom approach. …”
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The Impact of Team-Based Learning on Anxiety Among Graduate Students in a Data Science Master's Program by Andrea Marques, Maria Anastasiadou, Roberto Henriques

“…Objectives: This study aims to explore the impact of Team-Based Learning (TBL) on anxiety levels (AL) among master’s students in Data Science, particularly how TBL influences students' anxiety in academic settings and contributes to their engagement, skill development, and learning awareness. …”
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CNN Based Deep Learning Modeling with Explainability Analysis for Detecting Fraudulent Blockchain Transactions by Mohammad Hasan, Mohammad Shahriar Rahman, Mohammad Jabed Morshed Chowdhury, Iqbal H. Sarker

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The nature-based school curriculum: A solution to learning-teaching that promotes students’ freedom by Supriyoko Supriyoko, Ana Fitrotun Nisa, Novita Freshka Uktolseja

“…The positive impact of this research is that the innovation of natural curriculum has been in line with the implementation of independent curriculum by implementing four pillars in learning-teaching process and are carried out through experience-based learning and project-based learning methods. …”
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Research on detection and tracking methods of unmanned ship water targets based on light vision by LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao

“…This study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. …”
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MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children by Yasen Yimit, Yasen Yimit, Parhat Yasin, Yue Hao, Abudouresuli Tuersun, Abudouresuli Tuersun, Chencui Huang, Xiaoguang Zou, Xiaoguang Zou, Ya Qiu, Ya Qiu, Yunling Wang, Mayidili Nijiati, Mayidili Nijiati

“…BackgroundMedulloblastoma (MB) and ependymoma (EM) in children share similarities in terms of age group, tumor location, and clinical presentation, which makes it challenging to clinically diagnose and distinguish them.PurposeThe present study aims to explore the effectiveness of T2-weighted magnetic resonance imaging (MRI)-based deep learning (DL) combined with clinical imaging features for differentiating MB from EM.MethodsAxial T2-weighted MRI sequences obtained from 201 patients across three study centers were used for model training and testing. …”
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Determining AI-Based Learning Adoption Model for Students in Entrepreneurship Education: A Design Thinking Approach by Cep Abdul Baasith Wahpiyudin, Sabda Alam Muhammadan, Riska Amalia, Adelia Chrisanta, Asep Taryana

“…Findings: Results indicate that learning motivation significantly affects students' intentions to engage with AI-based systems, positively impacting attitudes toward AI. …”
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The Impact of Mnemonic Strategies and Inquiry-Based Learning on Memory Retention and Creativity in Biology Education by Nurul Azizah, Aditya Marianti, Wiwi Isnaeni, Endah Peniati

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Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification by Justin Sonntag, Lin Yu, Xilu Wang, Thomas Schack

“…To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.MethodsIn this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. …”
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MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma by Song Gao, Li Zhao, Nan Li, Xiaoming Zhou, Chongfeng Duan

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Virtual reality for experiential learning: enhancing agitation management skills, confidence, and empathy in healthcare students by Gabrielle Wann Nii Tay, Mian Mian Tong, John Yap, Hon Keat Mak, Shawn Yong Shian Goh, Cyrus Su Hui Ho

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Feasibility of face-to-face and online learning methods to provide nutrition education to midwives, general practice nurses and student nurses by Catherine Jane Lucas, Ellen Lyell, Britney Koch, Victoria Elder, Leanne Cummins, Sarah Lambert, Anne T McMahon, Karen E Charlton

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A blood test-based machine learning model for predicting lung cancer risk by Lihi Schwartz, Naor Matania, Matanel Levi, Teddy Lazebnik, Teddy Lazebnik, Shiri Kushnir, Noga Yosef, Assaf Hoogi, Dekel Shlomi, Dekel Shlomi

“…For lung cancer (LC), age and smoking history are the primary criteria for annual low-dose CT screening, leaving other populations at risk of being overlooked. Machine learning (ML) is a promising method to identify complex patterns in the data that can reveal personalized disease predictors.MethodsAn ML-based model was used on blood test data collected before the diagnosis of LC, and sociodemographic factors such as age and gender among LC patients and controls were incorporated to predict the risk for future LC diagnosis.ResultsIn addition to age and gender, we identified 22 blood tests that contributed to the model. …”
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A Novel Platform for Case-Based Learning in the Clinical Endodontics Training: Feasibility Study by Yuxiu Lin, Rui Zhang, Wei Zhang, Weiwei Qiao, Fushi Wang, Li Wang

“…Background Case-based learning (CBL) is currently used in multiple health-care settings around the world. …”
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Research on the Cultivation of Practical English Talents Based on a Big Data-Driven Model and Sentiment Dictionary Analysis by Qiuwei Fang

“…Notably, the precision of recognizing positive, negative, and neutral emotions reaches an impressive 92.5%, marking a notable improvement over methods devoid of dictionary feature integration. …”
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