A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics by Jia Wang, Zitao Liu, Chenxi Zhang, Yu Cao, Benyuan Liu, Yimin Shu, Yau Thum, John Zhang

“…Abstract Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or female reproductive system, presents a global challenge due to its impact on one in six individuals worldwide. …”
Get full text

Machine learning insights for predicting density and hardness in centrifugal SHS synthesized ceramic coatings by N. Radhika, M. Sabarinathan, S. Sivaraman

“….% of additives, influence the ceramic layer's properties. In the present work, several Machine Learning (ML) regressors, such as Categorical Boosting (CatBoost), Decision Tree (DT), Polynomial Regression (PR), Stacking Regression (SR), Extreme Gradient Boosting Regression (XGBoost), and Bagging Regression (BR), were employed to effectively predict the density and hardness of ceramic coating based on the influencing parameters. …”
Get full text

Development of argument-driven inquiry model with blended learning approach in environmental science course by Rizqa Devi Anazifa, P. Paidi, Anggi Tias Pratama, Atik Kurniawati

“…In addition, as we navigate the complexity of a digital age, learning process should align with the demands of the digitally driven learning activities. …”
Get full text

A robust, deep learning-based analysis of time-domain signals for NMR spectroscopy by Kyungdoe Han, Eunhee Kim, Kyoung-Seok Ryu, Donghan Lee

“…As a proof of concept, we present the resulting spectra, along with peak lists predicted by supplying only FID input to the deep learning algorithm. …”
Get full text

Combating electricity fraud: Employing hybrid learning and computer vision for sustainable energy management by Jui-Sheng Chou, Nader Anwar Charaf, Dani Nugraha Limantono, Hoang-Minh Nguyen

“…The study compares the classification performance of convolutional neural networks (CNNs), traditional machine learning models, and metaheuristic optimization algorithms in identifying fraudulent versus genuine users through detailed analysis and experimentation. …”
Get full text

Dynamic Impact-Based Heavy Rainfall Warning with Multi-classification Machine Learning Approaches by Anand Shankar

“…Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes are some of the machine learning methods used in the study to find out how dynamically vulnerable affected districts are to flooding in different compound scenarios. …”
Get full text

Using e-scaffolding to develop students’ scientific reasoning through inquiry-based learning by S. Koes Handayanto, S. Fawaiz, A. Taufiq

“…Through inquiry-based learning (IBL), scaffolding is provided to help students develop their scientific reasoning (SR). …”
Get full text

Heart Disease Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective by Enoch Sakyi-Yeboah, Edmund Fosu Agyemang, Vincent Agbenyeavu, Akua Osei-Nkwantabisa, Priscilla Kissi-Appiah, Lateef Moshood, Lawrence Agbota, Ezekiel N. N. Nortey

“…Future work should explore the integration of ensemble learning algorithms with real-time patient monitoring systems. …”
Get full text

Revolutionizing clinical decision making through deep learning and topic modeling for pathway optimization by Liu Tianzhao, He Jinzhi, Zhou Rong, Song Jun, Liu Hailong, Liang Yan

Get full text

Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease by Chang Hu, Yihong Dong, Shoubo Peng, Yuehan Wu

“…In the context of graph-based mental disorder classification, open-world semi-supervised learning for node classification aims to classify unlabeled nodes into known classes or potentially new classes, presenting a practical yet underexplored issue within the graph community. …”
Get full text

Potential of Graphic Organizers in Developing Communicative Competence in Learning Russian as a Foreign Language by Sharofat T. Chorieva

“…As a result of their use, students learn to transform information into a plan, algorithm, table, diagram; skillfully present educational material in visual and verbal form; use a dialogical form of communication; transform information into a more accessible form for further use in life situations. …”
Get full text

Ensemble deep learning models for tropical cyclone intensity prediction using heterogeneous datasets by Dikshant Gupta, Menaka Pushpa Arthur

“…This paper presents a thorough examination of several deep-learning models such as CNN, Recurrent Neural Networks (RNN) and transfer learning models (AlexNet and VGG) to determine their effectiveness in forecasting TC intensity. …”
Get full text

Emerging Trends in Machine Learning Assisted Optimization Techniques Across Intelligent Transportation Systems by Blessing Itoro Afolayan, Arka Ghosh, Jenny Fajardo Calderin, Antonio D. Masegosa

“…Model-based optimization approaches, reinforcement learning techniques, model-predictive control techniques, and generative AI techniques are the four areas into which this study divides AI optimization techniques for the sake of structure, clarity, and comparative analysis. …”
Get full text

Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing by Övünç Öztürk, Tuğba Özacar, Bora Canbula

“…Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. …”
Get full text

Empowering machine learning for robust cyber-attack prevention in online retail: an integrative analysis by Kamran Razzaq, Mahmood Shah, Mohammad Fattahi, Jing Tang

“…Abstract Cyber-attack prevention in online retailing has proven to be a challenging task. Machine learning (ML) algorithms play a significant role in preventing cyber-attacks. …”
Get full text

An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…The classifying network traffic method using machine learning shows to perform well in detecting malware. …”
Get full text

Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

“…Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. …”
Get full text

Machine learning in psychiatric health records: A gold standard approach to trauma annotation by Eben Holderness, Bruce Atwood, Marc Verhagen, Ann K. Shinn, Philip Cawkwell, Hudson Cerruti, James Pustejovsky, Mei-Hua Hall

“…Abstract Psychiatric electronic health records present unique challenges for machine learning due to their unstructured, complex, and variable nature. …”
Get full text

Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms by Li Qinsheng, Li Ming, Li Yuening, Zhao Xiufeng

“…Traditional experimental and computational approaches often fail to capture this dynamic and multi-scale nature, particularly when influenced by pharmacological agents, which can have both therapeutic and adverse effects.MethodsIn this work, we present a novel deep learning-based framework for action recognition, specifically designed to analyze drug-induced bone remodeling mechanisms. …”
Get full text

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity by Hui Zheng, Eric Sivonxay, Rasmus Christensen, Max Gallant, Ziyao Luo, Matthew McDermott, Patrick Huck, Morten M. Smedskjær, Kristin A. Persson

“…We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. …”
Get full text