Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

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The Effectiveness of Interactive Multimedia-Based E-Moral Games in Teaching and Learning Moral Education by Sakinah Salleh, Ain Syafia Qutreenie Alwi, Alwi Mohd Yunus

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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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Embedding-based pair generation for contrastive representation learning in audio-visual surveillance data by Wei-Cheng Wang, Sander De Coninck, Sam Leroux, Pieter Simoens

“…As data annotation is expensive, self-supervised methods such as contrastive learning are used to learn audio-visual representations for downstream tasks. …”
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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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Pregnancy probability prediction models based on 5 machine learning algorithms and comparison of their performance by REN Chao, REN Chao, YANG Huan, ZHOU Niya, ZHOU Niya

“…It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires. …”
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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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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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The influence of game-based learning on tactical awareness and skill development in golf training programs. by Chenghui Jin

“…<h4>Objective</h4>To investigate the effects of game-based learning on tactical awareness and skill development in golf training programs.…”
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Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations by Seung Yeob Ryu, Seon Min Lee, Young Jae Kim, Kwang Gi Kim

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Virtual Physical Education: Google Meet as an alternative platform for learning skill-based concepts by Joseph Lobo

“…The said videoconferencing platform is highly efficient based on previously published scholarly works. To further assess these claims in the current study’s situation, this paper is designed to explore the factors linked with students’ acceptance and observation of Google Meet as an alternative educational platform to learn concepts in various Physical Education courses which are skill-based by adopting the Technology Acceptance Model. …”
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Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms by Jianmin Zhang, Yuliang Li, Mingxing Zhou, Sixuan Chu

“…This ambiguity often results in suboptimal market positioning and design decisions. Existing methods, primarily limited to single product categories, rely on labor-intensive user surveys and computationally expensive data processing techniques, thus failing to support cross-category collaboration. …”
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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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Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms by Youness Khourdifi, Alae El Alami, Mounia Zaydi, Yassine Maleh, Omar Er-Remyly

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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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Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning by LI Xin, LI Shuhua, CHEN Hao, SI Lei, WEI Dong, ZOU Xiaoyu

“…The vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues, leading to frequent false positives in traditional machine learning-based fault diagnosis methods. …”
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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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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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