Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…This study underscores the potential of machine learning in addressing educational challenges. …”
Get full text

Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

Get full text

Intelligent Virtual Reality and Augmented Reality Technologies: An Overview by Georgios Lampropoulos

“…Through content analysis and topic modeling, the main topics and areas in which this combination is mostly being researched and applied are as follows: (1) “Education/Learning/Training”, (2) “Healthcare and Medicine”, (3) “Generative artificial intelligence/Large language models”, (4) “Virtual worlds/Virtual avatars/Virtual assistants”, (5) “Human-computer interaction”, (6) “Machine learning/Deep learning/Neural networks”, (7) “Communication networks”, (8) “Industry”, (9) “Manufacturing”, (10) “E-commerce”, (11) “Entertainment”, (12) “Smart cities”, and (13) “New technologies” (e.g., digital twins, blockchain, internet of things, etc.). …”
Get full text

Predictors of community-based health insurance enrollment among reproductive-age women in Ethiopia based on the EDHS 2019 dataset: a study using SHAP analysis technique, 2024 by Sisay Yitayih Kassie, Solomon Abuhay Abebe, Mekdes Wondirad, Samrawit Fantaw Muket, Ayantu Melke, Alex Ayenew Chereka, Adamu Ambachew Shibabaw, Abiy Tasew Dubale, Yitayish Damtie, Habtamu Setegn Ngusie, Agmasie Damtew Walle

“…The SHAP analysis, based on this superior random forest model, indicated that residence, wealth, the age of the household head, the husband’s education level, media exposure, family size, and the number of children under five were the most influential factors affecting enrollment in community-based health insurance.ConclusionThis study highlights the significance of machine learning in predicting community-based health insurance enrollment and identifying the factors that hinder it. …”
Get full text

DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system by G. Usha, H. Karthikeyan, Kumar Gautam, Nikhil Pachauri

Get full text

Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review. by Donnie Mategula, Judy Gichuki, Karen I Barnes, Emanuele Giorgi, Dianne Janette Terlouw

“…The studies employed various statistical and machine-learning models to forecast malaria cases, often incorporating environmental covariates such as rainfall and temperature. …”
Get full text

The prognostic and therapeutic significance of polyunsaturated fatty acid‐derived oxylipins in ST‐segment elevation myocardial infarction by Zhiyong Du, Yingyuan Lu, Ying Ma, Yunxiao Yang, Wei Luo, Sheng Liu, Ming Zhang, Yong Wang, Lei Li, Chun Li, Wei Wang, Hai Gao

Get full text

End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality by Tingting Zhang, Jing Li, Jinpeng Tong, Yihu Song, Li Wang, Renye Wu, Xuan Wei, Yuanyuan Song, Rensen Zeng

Get full text

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer by Ayan Chatterjee, Michael A. Riegler, K. Ganesh, Pål Halvorsen

Get full text

Utilisation of Deep Neural Networks for Estimation of Cajal Cells in the Anal Canal Wall of Patients with Advanced Haemorrhoidal Disease Treated by LigaSure Surgery by Inese Fišere, Edgars Edelmers, Šimons Svirskis, Valērija Groma

Get full text

PM2.5 concentration 7-day prediction in the Beijing–Tianjin–Hebei region using a novel stacking framework by Xintong Gao, Xiaohong Wang, Fuping Li, Wenhao Jiang, Meng Zhe, Jiaxing Sun, Ao Zhang, Linlin Jiao

Get full text

Combined Analysis of Transcriptome and Mendelian Randomization Reveals AKT1 and PPARG as Biomarkers Related to Glucose Metabolism in Sepsis by Ma J, Li W, Ma Q, Ding L, Wang Z, Wang R, Huang Y, Ma G, Gao J

“…Eleven hub genes were identified from the PPI network, and six biomarkers were selected through machine learning and area under the curve (AUC) validation. …”
Get full text

Understanding students’ sentiment from feedback with a new feature selection and semantics networks by Tran Anh Tuan, Dao Thi Thanh Loan, Nichnan Kittiphattanabawon

“…The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.…”
Get full text

A Structured Dataset for Automated Grading: From Raw Data to Processed Dataset by Ibidapo Dare Dada, Adio T. Akinwale, Ti-Jesu Tunde-Adeleke

“…To assess the dataset’s potential for automated grading applications, several machine learning and transformer-based models were tested, including TF-IDF with Linear Regression, TF-IDF with Cosine Similarity, BERT, SBERT, RoBERTa, and Longformer. …”
Get full text

Evaluating the level of digitalization of the innovation process with artificial intelligence approach in the digital transformation of knowledge-based companies by ali Bagheri, reza radfar, sepehr ghazinoory

“…As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). …”
Get full text

Mechatronic device of AI systems by A. Y. Kulsha, M. A. Klimovich, M. V. Sterjanov, V. N. Tesluk, N. G. Egorova

“…The robot is an experimental model that can be used in further research in the field of artificial intelligence, machine learning, automation systems, and is also a potential platform for teaching robotics at the level of specialized secondary and higher education.…”
Get full text

SelfCode 2.0: An Annotated Corpus of Student and Expert Line-by-Line Explanations of Code Examples for Automated Assessment by Jeevan Chapagain, Arun Balajiee Lekshmi Narayanan, Kamil Akhuseyinoglu, Peter Brusilovsky, Vasile Rus

“…To facilitate the development of Artificial Intelligence (AI) and Machine Learning models for automated assessment of learners' self-explanations, annotated datasets are essential. …”
Get full text

Process Development for the Continuous Manufacturing of Carbamazepine-Nicotinamide Co-Crystals Utilizing Hot-Melt Extrusion Technology by Lianghao Huang, Wen Ni, Yaru Jia, Minqing Zhu, Tiantian Yang, Mingchao Yu, Jiaxiang Zhang

Get full text

Natural language access point to digital metal–organic polyhedra chemistry in The World Avatar by Simon D. Rihm, Dan N. Tran, Aleksandar Kondinski, Laura Pascazio, Fabio Saluz, Xinhong Deng, Sebastian Mosbach, Jethro Akroyd, Markus Kraft

Get full text

Smart Grid Intrusion Detection for IEC 60870-5-104 With Feature Optimization, Privacy Protection, and Honeypot-Firewall Integration by Pedamallu Sai Mrudula, Rayappa David Amar Raj, Archana Pallakonda, Yanamala Rama Muni Reddy, K. Krishna Prakasha, V. Anandkumar

Get full text