Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms by Norhan Khallaf, Osama Abd-El Rouf, Abeer D. Algarni, Mohy Hadhoud, Ahmed Kafafy

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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. …”
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Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms by Qiao Chang, Yuxing Bai, Shaofeng Wang, Fan Wang, Shuang Liang, Xianju Xie

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Safety-Critical Oracles for Metamorphic Testing of Deep Learning LiDAR Point Cloud Object Detectors by Simon Speth, Maximilian Trien, Dominik Kufer, Alexander Pretschner

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A Hybrid Machine Learning Framework for Soccer Match Outcome Prediction: Incorporating Bivariate Poisson Distribution by Chen Zhong An

“…This study explores the application of mathematical and machine learning models to predict results of soccer matches, with the dual aim of academic advancement and profitable betting. …”
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Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania by Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar

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Student Engagement and Feedback on Online Learning and Facilitation: A Case Study of the National Open University of Nigeria by Adewale Adesina, Dorothy Ofoha, Olufemi A. Peters, Isaac S. Butswat

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Immune cell profiles and predictive modeling in osteoporotic vertebral fractures using XGBoost machine learning algorithms by Yi-Chou Chen, Hui-Chen Su, Shih-Ming Huang, Ching-Hsiao Yu, Jen-Huei Chang, Yi-Lin Chiu

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ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction. by Yunyun Dong, Yuanrong Zhang, Yuhua Qian, Yiming Zhao, Ziting Yang, Xiufang Feng

“…In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs. …”
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Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review by Md Mohshin Ali, Md. Arif Hossen, Azrina Abd Aziz

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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

Subjects: “…Machine learning…”
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Effectiveness of project-based learning in improving science literacy and collaborative skills of Muhammadiyah middle school students by Iin Hindun, N. Nurwidodo, Sri Wahyuni, Nur Fauziah

“…The research results show that the average Science literacy and collaboration in PjBL learning is higher than conventional learning. The results of the analysis can be concluded that the implementation of PjBL effectively influences students' Science literacy and students' collaboration skills. …”
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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

Subjects: “…Machine learning…”
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Implementing adaptive e-learning for newborn care in Tanzania: an observational study of provider engagement and knowledge gains by Peter Andrew Meaney, Christine Lynn Joyce, Ambrose Agweyu, Joseph R Mwanga, Marc Berg, Adolfine Hokororo, Hanston Ndosi, Alex Dahlen, Theopista Jacob, Florence Salvatory Kalabamu, Rishi Mediratta, Boris Rozenfeld, Zachary Haines Smith, Neema Chami, Namala Mkopi, Castory Mwanga, Enock Diocles

“…Providers were often unaware of knowledge gaps, and lower baseline knowledge may decrease initial learning completion or activity. Further study to identify barriers to adaptive e-learning normalisation is needed.…”
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Enhancing Monkeypox Detection through Data Analytics: A Comparative Study of Machine and Deep Learning Techniques by Kinjal A. Patel, Asadi Srinivasulu, Kuntesh Jani, Goddindla Sreenivasulu

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HEAL-Dengue: Hospital- based Exploration, Analysis and Learning about Dengue Outcomes (HEAL-D-ALDO) by Aijaz Zeeshan Khan, Omer Sabir, Adnan Hussain Shahid, Muhammad Bilal Basit, Syeda Arzinda Fatima Shah, Qamar Sajad

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Development of a respiratory virus risk model with environmental data based on interpretable machine learning methods by Shuting Shi, Haowen Lin, Leiming Jiang, Zhiqi Zeng, ChuiXu Lin, Pei Li, Yinghua Li, Zifeng Yang

“…This study aimed to develop a nationwide respiratory virus infection risk prediction model through machine learning approach. We utilized the CRFC algorithm, a random forest-based method for multi-label classification, to predict the presence of various respiratory viruses. …”
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The The Pluralistic Method Exploration on Mathematics Learning: A Case Study of Students with Diverse Academic Skill Levels by Mewa Zabeta, Nikmah Nurvicalesti, Ratnasari Ratnasari, Isania Marsel Viliyati

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Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects by Kim Junhyeong, Kim Jae-Yong, Kim Jungmin, Hyeong Yun, Neseli Berkay, You Jong-Bum, Shim Joonsup, Shin Jonghwa, Park Hyo-Hoon, Kurt Hamza

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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. …”
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