Examining the Impact of Construction Field Trips on Learning Outcomes: Perspectives from Structural Architecture Courses by Wafa Labib, Amal Abdelsattar

“…An experimental design was implemented to assess the impact of this method on achieving core Course Learning Outcomes (CLOs). …”
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Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images by V. S. Rozova, C. Russo, V. Y. Lekarev, V. V. Kazantseva, A. M. Dymov, A. S. Rzhevskiy, A. V. Zvyagin

“…The results of the study can be a starting point for developing new methods to diagnose PBC using deep learning technologies.…”
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Detection of <i>Aspergillus flavus</i> in Figs by Means of Hyperspectral Images and Deep Learning Algorithms by Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas Cortez

“…These toxins, produced by fungi like <i>Aspergillus flavus</i> in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. …”
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Detection of formal thought disorders in child and adolescent psychosis using machine learning and neuropsychometric data by Przemysław T. Zakowicz, Maksymilian A. Brzezicki, Charalampos Levidiotis, Sojeong Kim, Oskar Wejkuć, Zuzanna Wisniewska, Dominika Biernaczyk, Barbara Remberk

“…Participants underwent neuropsychological assessment using the Iowa Gambling Task (IGT) and Simple Reaction Time (SRT) tasks. A range of machine learning models (Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)) were employed to classify participants into FTD-positive and FTD-negative groups based on these neuropsychological measures and their antipsychotic regimen (medication load in chlorpromazine equivalents).ResultsThe best performing machine learning model was LR with mean +/- standard deviation of cross validation Receiver Operating Characteristic Area Under Curve (ROC AUC) score of 0.850 (+/- 0.133), indicating moderate-to-good discriminatory performance. …”
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Identifying and analyzing drivers of innovative behavior based on strategic management of human resources in knowledge-based organizations by Mitra Sadoughi

“…On the other hand, knowledge-based leadership and human resource development have had a positive and constructive impact on the sustainable competitive advantage of companies. …”
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Adaptive intelligent tutoring systems for STEM education: analysis of the learning impact and effectiveness of personalized feedback by William Villegas-Ch, Diego Buenano-Fernandez, Alexandra Maldonado Navarro, Aracely Mera-Navarrete

“…Traditional teaching methods, characterized by their standardized approach, fail to adapt to individual needs, limiting learning potential. …”
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The impact of exercising self-efficacy on physical education learning engagement: the mediating role of flow experience by Wenzhe Huang, Ruikai Wei, Bojin Cheng

“…IntroductionThe study aims to examine the direct and indirect effects of exercising self-efficacy on physical education learning engagement through mediator flow experience.MethodsBased on a questionnaire survey, using the Exercise Self-efficacy Scale, Flow Experience Scale, and Physical Education Learning Engagement Scale, a survey was conducted among 568 university students from five universities in Guangdong Province, China.Results① There were significant positive correlations between exercise self-efficacy, flow experience, and physical education learning engagement; ② Exercise self-efficacy positively influenced physical education learning engagement; ③ Flow experience played a mediating role between exercise self-efficacy and physical education learning engagement.DiscussionThis study fills the theoretical gap of flow experience application in the context of physical education, and uses the variable of exercise self-efficacy, which combines individual self-efficacy with the field of physical exercise, to enhance the depth and strength of explanation and prediction. …”
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Analysis of Hybrid Learning Sentiment among Information Systems Students using The Naïve Bayes Classifier by Dolly Indra, Ramdaniah Ramdaniah, Widianti Sukur

“…Hybrid, or blended learning, integrates various teaching methods, such as face-to-face, computer-based, and mobile learning, and offers advantages by reducing the time required for meetings and information delivery. …”
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Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization by Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu

“…The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy. …”
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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

“…Experimental evaluations on benchmark financial datasets and real-world transactional data reveal that our approach improves fraud detection accuracy by 15–30% while reducing false positives compared to existing machine learning-based solutions. …”
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Detecting Potential Investors in Crypto Assets: Insights from Machine Learning Models and Explainable AI by Timotej Jagrič, Davor Luetić, Damijan Mumel, Aljaž Herman

“…This study explores the characteristics of individual investors in crypto asset markets using machine learning and explainable artificial intelligence (XAI) methods. …”
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Nurturing bonds that empower learning: a systematic review of the significance of teacher-student relationship in education by Giulia Di Lisio, Antonio Milá Roa, Amaia Halty, Ana Berástegui, Alba Couso Losada, Carlos Pitillas

“…This systematic review aims to map the relational dimensions of the TSR and its impact on academic (dis)engagement, (under)achievement and early school leaving (ESL), focusing on Secure Base and Safe Haven attachment dimensions and the influence of vulnerability factors.MethodsThe review followed the PRISMA guidelines and included 45 empirical quantitative studies (2018–2022) sourced from Academic Search Complete, ERIC, Scopus and Web of Science. …”
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Turkish medical and pharmacy students’ attitudes towards physician-pharmacist collaboration and readiness for interprofessional learning by Elif Ulutaş Deniz, Rumeysa Eren, Betül Zeybek, Esra Çınar Tanrıverdi

“…Moreover, the willingness of healthcare students to participate in collaborative learning is crucial for the positive impact of interprofessional education on fostering effective collaboration between healthcare professionals. …”
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The role of parental involvement and mathematics self-concept of elementary school students in online mathematics learning by Y. W. Purnomo, N. Apriyanti, S. A. Mubarokah, . Susilowati, W. A. Anggraheni

“…This study is aimed to analyse parental involvement and students’ mathematical self-concept, the relationship between the two, and their effect on students’ mathematical performance in online learning.Methodology and research methods. A cross-sectional survey was used to achieve the research objectives. …”
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Sentences, entities, and keyphrases extraction from consumer health forums using multi-task learning by Tsaqif Naufal, Rahmad Mahendra, Alfan Farizki Wicaksono

“…Methods This work contributes to two key aspects related to these three tasks. …”
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Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles by Lin Ma, Yi Qiao, Runqiu Wang, Hualin Chen, Guanghua Liu, He Xiao, Ran Dai

“…Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. <b>Results:</b> We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. …”
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Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). …”
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Objective assessment of gait and posture symptoms in Parkinson’s disease using wearable sensors and machine learning by Lingyan Ma, Lingyan Ma, Shinuan Lin, Shinuan Lin, Jianing Jin, Jianing Jin, Zhan Wang, Zhan Wang, Xuemei Wang, Xuemei Wang, Zhonglue Chen, Zhonglue Chen, Yun Ling, Yun Ling, Fei Zhang, Fei Zhang, Kang Ren, Kang Ren, Tao Feng, Tao Feng, Tao Feng

“…This study aims to predict the severity of gait and posture symptoms using data collected from wearable sensors during a single laboratory-based walking assessment, providing an objective, efficient, and automated evaluation approach.MethodsSensor-based gait parameters were collected from 225 PD participants (mean age 63.15 ± 10.46 years) through a standardized walking assessment. …”
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Integrated machine learning and bioinformatic analysis of mitochondrial-related signature in chronic rhinosinusitis with nasal polyps by Bo Yang, MMed, Min Gu, MMed, Chen Hong, MMed, Xin-Yuan Zou, MMed, Jia-Qi Zhang, MBBS, Ye Yuan, MBBS, Chang-Yu Qiu, MD, MSc, Mei-Ping Lu, MD, PhD, Lei Cheng, MD, PhD

“…Subsequently, the hub DEMRGs were selected using 4 integrated machine learning algorithms. Immune and mitochondrial characteristics were estimated based on CIBERSORT and ssGSEA algorithms. …”
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Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video by Qin Wu, Chengyi Wu, Maoliang Zhang, Jie Yang, Junxiang Zhang, Yun Jin, Yanhong Du, Xingbo Sun, Liyuan Jin1, Kai Wang, Zhengbiao Hu, Xiaoyang Qi1, Jincao Yao, Zhengping Wang, Dong Xu

“…Background: We developed a machine learning-based predictive model for diagnosing prostate cancer within the gray zone of prostate-specific antigen (PSA) levels, leveraging transrectal prostate ultrasound video clips. …”
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