CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application by Yu-Wen Chen, Kuo-Hsuan Hung, You-Jin Li, Alexander Chao-Fu Kang, Ya-Hsin Lai, Kai-Chun Liu, Szu-Wei Fu, Syu-Siang Wang, Yu Tsao

“…This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. …”
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Dynamic selectout and voting-based federated learning for enhanced medical image analysis by Saeed Iqbal, Adnan N Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Atif Mahmood, Saaidal Razalli Bin Azzuhri

“…Federated learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. …”
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Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches by Xuemin Fu, Luling Wu, Luling Wu, Jingna Xun, Benno Pütz, Zhihang Zheng, Yanpeng Li, Yanpeng Li, Yinzhong Shen, Hongzhou Lu, Jun Chen, Bertram Müller-Myhsok

“…These findings suggest promising directions for individualized healthcare solutions, leveraging machine learning to enhance survival predictions in HIV-related cryptococcosis.…”
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Cumulative sum analysis for evaluating learning curve of endoscopic lateral neck dissection by Zhen-Xin Chen, Xin-Ran Zhao, Jie-Min Deng, Ying Cao, Jing-Bao Chen, Feng-Shun Pang, Zhan-Hong Lin, Xiao-Bo Zhang, Bo Xu, You Qin

“…This approach may be an alternative for patients who desire cosmesis. Furthermore, the present data and experience insights regarding endoscopic LND via chest approach may help other surgeons to pass the learning phase more safely.…”
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Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis by Xun Zhao, MD, Maryam Naghibzadeh, MD, Yingji Sun, MSc, Arya Rahmani, BSc, Leslie Lilly, MD, Nazia Selzner, MD, PhD, Cynthia Tsien, MD, MPH, Elmar Jaeckel, MD, Mary Pressley Vyas, Rahul Krishnan, PhD, Gideon Hirschfield, MD, PhD, MB, Mamatha Bhat, MD, PhD

“…The significant performance improvement with the inclusion of PSC-specific variables highlights the importance of disease-specific variables for predicting trajectories of clinically distinct presentations.…”
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Born Too Soon: learning from the past to accelerate action in the next decade by Anna Gruending, Joy E. Lawn, Amy Reid, Etienne V. Langlois, Bo Jacobsson, Lori McDougall, Rajat Khosla, Zulfiqar A. Bhutta, Ashok K. Deorari, Lily Kak, Judith Robb-McCord, Nabila Zaka, Ditas Duque Decena, Helga Fogstad, Anshu Banerjee, Mary V. Kinney, Queen Dube

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Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis by Lingjiang Liu, Zhiyuan Li, Yaxin Hu, Chunyou Li, Shuhan He, Shibei Zhang, Jie Gao, Huaiyi Zhu, Guoping Huang

“…Recent advancements in machine learning (ML) present promising solutions for risk prediction. …”
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Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification by Haniyeh Rafiepoor, Mohammad M. Banoei, Alireza Ghorbankhanloo, Ahad Muhammadnejad, Amirhossein Razavirad, Saeed Soleymanjahi, Saeid Amanpour

“…Abstract Background Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most important predictors and unravel their interactions to classify patients, which might guide prioritized treatment decisions. …”
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Analysis of the Inquiry-Infusion learning model to develop students' critical thinking ability by Beni Asyhar

“…With the modifications in these two steps, it is hoped that students can independently learn to practice not to easily believe the information presented before knowing the truth of the information and it is hoped that students get used to finding out the truth of information before making conclusions on the problems at hand so as to maximize students' critical thinking skills. …”
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data by Maksym A. Jopek, Krzysztof Pastuszak, Sebastian Cygert, Myron G. Best, Thomas Wurdinger, Jacek Jassem, Anna J. Zaczek, Anna Supernat

“…Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. …”
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The Effectiveness of Self-Directed Learning on The Professional Development of Pre-Registration Nursing Students: by Yusra Al-Nasseri

“…SDL does not only provide a means for effective learning of knowledge and skills but also serves as a vehicle for enabling nursing students to acquire and develop independent learning skills; as well as develop a sense of accountability and responsibility that equips them with the essential attributes they require for their future nursing career. …”
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Generative artificial intelligence and machine learning methods to screen social media content by Kellen Sharp, Rachel R. Ouellette, Rujula Singh Rajendra Singh, Elise E. DeVito, Neil Kamdar, Amanda de la Noval, Dhiraj Murthy, Grace Kong

“…The primary objective of this article is to present generative artificial intelligence (AI; e.g., ChatGPT) and machine learning methods to screen content from social media platforms. …”
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Applying machine learning to assist in the morphometric assessment of brain arteriolosclerosis through automation by Jerry J. Lou, Peter Chang, Kiana D. Nava, Chanon Chantaduly, Hsin-Pei Wang, William H. Yong, Viharkumar Patel, Ajinkya J. Chaudhari, La Rissa Vasquez, Edwin Monuki, Elizabeth Head, Harry V. Vinters, Shino Magaki, Danielle J. Harvey, Chen-Nee Chuah, Charles S. DeCarli, Christopher K. Williams, Michael Keiser, Brittany N. Dugger

“…To address this gap, we present a prototype end-to-end machine learning (ML)-based algorithm, Arteriolosclerosis Segmentation (ArtSeg), followed by Vascular Morphometry (VasMorph) – to assist persons in the morphometric analysis of arteriolosclerotic vessels on whole slide images (WSIs). …”
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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…The LightGBM model presented the best predictive power regarding the surgical outcomes of DCM patients. …”
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Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis by Alessio Gerussi, Oliver Lester Saldanha, Giorgio Cazzaniga, Damiano Verda, Zunamys I. Carrero, Bastian Engel, Richard Taubert, Francesca Bolis, Laura Cristoferi, Federica Malinverno, Francesca Colapietro, Reha Akpinar, Luca Di Tommaso, Luigi Terracciano, Ana Lleo, Mauro Viganó, Cristina Rigamonti, Daniela Cabibi, Vincenza Calvaruso, Fabio Gibilisco, Nicoló Caldonazzi, Alessandro Valentino, Stefano Ceola, Valentina Canini, Eugenia Nofit, Marco Muselli, Julien Calderaro, Dina Tiniakos, Vincenzo L’Imperio, Fabio Pagni, Nicola Zucchini, Pietro Invernizzi, Marco Carbone, Jakob Nikolas Kather

“…This study investigates the use of a deep learning (DL)-based pipeline for the diagnosis of AIH and PBC to aid differential diagnosis. …”
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ENHANCING CRITICAL THINKING IN FIQH LEARNING: THE ROLE OF STRATEGIES AND MEDIA INTEGRATION IN ISLAMIC HIGHER EDUCATION by Saiful Jazil, Ahmad Zahro, Bassam Abul A'la, Moh. Rifqi Rahman, Muh. Sholihuddin, Syamsun Ni’am, Anin Nurhayati

“…Qualitative data were gathered through observations and interviews and analyzed through data condensation, presentation, and conclusion drawing. Findings – The results indicate that students’ critical thinking practices in fiqh learning are generally neutral and somewhat inconsistent. …”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…Compared with traditional fruit detection algorithms, the fruit detection algorithm based on deep learning can extract and learn rich features from a large amount of data, and has higher accuracy and robustness when processing noisy data. …”
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Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning by A. Olivelli, A. Olivelli, A. Olivelli, R. Arcucci, M. Rehkämper, T. van de Flierdt

“…However, the scarcity and sparsity of in situ measurements of Pb concentrations and isotope compositions do not allow for a comprehensive understanding of Pb pollution pathways and biogeochemical cycling on a global scale. Here, we present three machine learning models developed to map seawater Pb concentrations and isotope compositions, leveraging the global GEOTRACES dataset as well as historical data. …”
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ENHANCING CRITICAL THINKING IN FIQH LEARNING: THE ROLE OF STRATEGIES AND MEDIA INTEGRATION IN ISLAMIC HIGHER EDUCATION by Saiful Jazil, Ahmad Zahro, Bassam Abul A'la, Moh. Rifqi Rahman, Muh. Sholihuddin, Syamsun Ni’am, Anin Nurhayati

“…Qualitative data were gathered through observations and interviews and analyzed through data condensation, presentation, and conclusion drawing. Findings – The results indicate that students’ critical thinking practices in fiqh learning are generally neutral and somewhat inconsistent. …”
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The City as the Classroom: The Case of “Start ‘em Young Advocacy” at Silliman University, Philippines by Novee Elarmo Maestrecampo Jr., Giovanni Tuballa Macahig

“…Overall, this paper presents urban environments as active learning spaces that support student development and provide opportunities to contribute to the United Nations Sustainable Development Goals (UN SDGs).…”
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