Traffic Incident Duration Prediction: A Systematic Review of Techniques by Artur Grigorev, Adriana-Simona Mihaita, Fang Chen

“…Key challenges identified include the following: data quality issues, model interpretability, and the complexities associated with high-dimensional datasets. Future research directions proposed include the following: (1) development of data fusion models that integrate heterogeneous datasets of incident reports for enhanced predictive modeling; (2) utilization of natural language processing (NLP) to extract contextual information from textual incident reports; and (3) implementation of advanced ML pipelines that incorporate anomaly detection, hyperparameter optimization, and sophisticated feature selection techniques. …”
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Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions by VIJAI M. MOORTHY, BHUPAL N. DHARAMSOTH, VIJAYALAKSHMI MUTHUKARUPPAN, ARUL ELANGO, KALAIARASI GANESAN

“…Abstract This research focuses on predicting cardiovascular disease using machine learning classification strategies. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…Bayesian optimization is employed to systematically fine-tune the model’s hyperparameters, thereby improving accuracy and efficiency by concentrating on promising areas of the hyperparameter space and avoiding unnecessary evaluations. …”
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Unsupervised Classification and Remaining Useful Life Prediction for Turbofan Engines Using Autoencoders and Gaussian Mixture Models: A Comprehensive Framework for Predictive Maint... by Tomasz Lodygowski, Slawomir Szrama

“…The article concludes with practical insights into industrial applications and future research directions.…”
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Evaluating and implementing machine learning models for personalised mobile health app recommendations. by Hafsat Morenigbade, Tareq Al Jaber, Neil Gordon, Gregory Eke

“…The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this study, a data set including app descriptions, ratings, reviews, and other relevant attributes from various health app platforms was selected. …”
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Lightweight Models for Real-Time Steganalysis: A Comparison of MobileNet, ShuffleNet, and EfficientNet by Achmad Bauravindah, Dhomas Hatta Fudholi

“…This research evaluates MobileNet, ShuffleNet, and EfficientNet for such tasks, using the BOSSbase-1.01 dataset. …”
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OPTIMIZING LONG TEXT CLASSIFICATION PERFORMANCE THROUGH KEYWORD-BASED SENTENCE SELECTION: A CASE STUDY ON ONLINE NEWS CLASSIFICATION FOR INDONESIAN GDP GROWTH-RATE DETECTION by Dinda Pusparahmi Sholawatunnisa, Lya Hulliyyatus Suadaa

“…These tailored approaches ensure that selected sentences align precisely with specific keywords relevant to the research case, such as GDP growth rate detection. The study emphasizes the necessity of adapting summarization methods to capture information in unique research contexts effectively. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. …”
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Simplifying Masked Image Modeling With Symmetric Masking and Contrastive Learning by Khanh-Binh Nguyen, Chae Jung Park

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Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Sani I. Abba, Jamil Hussain, Soo-Mi Choi

“…This research addresses the critical gap in determining optimal hyperparameters for machine learning models in FSM. …”
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Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement by Pradnyawant M. Gote, Praveen Kumar, Hemant Kumar, Prateek Verma, Moses Makuei Jiet

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A Hybrid LSTM-GRU Model for Stock Price Prediction by Amirfarhad Farhadi, Azadeh Zamanifar, Amir Alipour, Alireza Taheri, Mohammad Asadolahi

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Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive–Motor Rehabilitation by Kathya P. Acuña Luna, Edgar Rafael Hernandez-Rios, Victor Valencia, Carlos Trenado, Christian Peñaloza

“…This research explored the integration of the real-time machine learning classification of motor imagery data with a brain–machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). …”
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Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods by JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan

“…Finally， in view of the challenges of machine learning methods in electricity price prediction research， this study outlined future research directions to provide constructive references for the development of the spot market under the construction of a unified national electricity market.…”
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The Enterprise School Readiness Prediction System (ESRPS) Uses Machine Learning to Assess Children's Readiness for Entering Elementary School by Muhammad Choerul Umam, Cicilia Dyah Sulistyaningrum I., Dydik Kurniawan, Priyono Tri Febrianto

“…This study aims to develop and evaluate the Enterprise School Readiness Prediction System (ESRPS) to predict children's readiness for elementary school using machine learning algorithms.  This research employs the Research and Development (R&D) method using Borg and Gall’s model and Instruments include questionnaires, programming tools, performance evaluation metrics, and web/database development tools to ensure the system's validity, reliability, and practical applicability.The research analyzes data from 300 students in various Indonesian cities, focusing on attributes like age, gender, and parental education. …”
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ECG-based transfer learning for cardiovascular disease: A scoping review by Sharifah Noor Masidayu Sayed Ismail, Siti Fatimah Abdul Razak, Nor Azlina Ab Aziz

“…Our study also identifies several issues requiring further investigation and recommends areas for future research, including hyperparameter tuning to enhance model performance.…”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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Deep learning-based profiling side-channel attacks in SPECK cipher by Faisal Hameed, Hoda Alkhzaimi

“…Abstract Over recent years, deep learning-based profiling side-channel analysis has garnered significant attention in both academia and industry. Research in this field has progressed to address challenges such as hyperparameter tuning in deep learning models, with ensemble methods emerging as a noteworthy approach. …”
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Rice leaf disease classification using a fusion vision approach by B. Naresh kumar, S. Sakthivel

“…Detecting these diseases at an early stage is very important for effective management of these risks. This research introduces a novel approach for rice disease detection using the fusion vision boosted classifier (FVBC), integrating VGG19 for feature extraction and LightGBM for classification. …”
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Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market by Moumita Barua, Teerath Kumar, Kislay Raj, Arunabha M. Roy

“…This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. …”
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