A Survey of Machine Learning Methods for Time Series Prediction by Timothy Hall, Khaled Rasheed

“…This study provides a comprehensive survey of the top-performing research papers in the field of time series prediction, offering insights into the most effective machine learning techniques, including tree-based, deep learning, and hybrid methods. …”
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Plant leaf disease detection using vision transformers for precision agriculture by Murugavalli S, Gopi R

“…The model may be adjusted by hyperparameter tuning and adaptive learning rate scheduling for robust performance with minimal overfitting. …”
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Predicting the Evolution of Capacity Degradation Histograms of Rechargeable Batteries Under Dynamic Loads via Latent Gaussian Processes by Daocan Wang, Xinggang Li, Jiahuan Lu

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Real Time 3D Internal Building Directory Map by Zi Yang Chia, Pey Yun Goh

“…Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithms have become popular and utilized in wireless indoor positioning to achieve better performance. …”
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Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

“…By building actionable predictive models, our research provides a unique treatment for key hyperparameter tuning, scalability, and real-time resource allocation challenges. …”
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A Comparative Study of XBORE and XBOREOPT Hybrid Models for Ore Production Forecasting in the Mining Industry by Morris Mkokweza, Santhi Kumaran, George Mufungulwa

“…Through empirical evaluation using real-world data, this research aims to provide insights into the practical benefits of hybrid modeling approaches in industrial predictive analytics. …”
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A comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification by Jack Breen, Katie Allen, Kieran Zucker, Lucy Godson, Nicolas M. Orsi, Nishant Ravikumar

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Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images by Resky Adhyaksa, Bedy Purnama

“…The study's findings underscore the potential of VGG16 in improving diagnostic accuracy and reliability in medical imaging, providing a robust tool for fracture detection. Future research should continue exploring hyperparameter optimization to further enhance model performance while balancing computational efficiency.…”
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Deep Learning-Enhanced Spectrogram Analysis for Anatomical Region Classification in Biomedical Signals by Abdul Karim, Semin Ryu, In cheol Jeong

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Automation of Predictive Maintenance: An Experimental Framework for Aircraft Landing Gear by Izaak Stanton, Kamran Munir, Ahsan Ikram, Murad El‐Bakry

“…This research presents an experimental analysis comparing industry‐selected machine learning models and a hand‐picked selection of automated machine‐learning tools. …”
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Penerapan Decision Tree dan Random Forest dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur by Sza Sza Amulya Larasati, Elok Nuraida Kusuma Dewi, Brahma Hanif Farhansyah, Fitra Abdurrachman Bachtiar, Fajar Pradana

“…The results of this research are expected to provide insights for other researchers in the same field and can serve as a reference for detecting ongoing stress. …”
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Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting by G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray, K. N. Singh

“…The novelty of this research lies in the automated and adaptive tuning of these hyperparameters using MHAs, enabling improved generalization, faster convergence, and enhanced predictive accuracy. …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…Model performance is optimized through hyperparameter tuning, using grid search with cross-validation.Results: Logistic regression achieves the highest accuracy and precision, making it the top-performing model overall. …”
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Adaptive online modeling of ship maneuvering motion based on error monitoring by Yaohui YU, Suyang LIU, Zihao WANG, Wenbo XIE, Yan PENG

“…Taking a KCS container ship as the research object, the method is tested and validated under zigzag maneuvering and turning circle motion scenarios with variable speed, and the influence of the error monitoring mechanism’s hyperparameter selection on the online modeling is analyzed. …”
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Ensemble based high performance deep learning models for fake news detection by Mohammed E.Almandouh, Mohammed F. Alrahmawy, Mohamed Eisa, Mohamed Elhoseny, A. S. Tolba

“…We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. …”
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Unsupervised single-image super-resolution for infant brain MRI by Cheng Che Tsai, Xiaoyang Chen, Sahar Ahmad, Pew-Thian Yap

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Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy by Yagyanath Rimal, Navneet Sharma, Siddhartha Paudel, Abeer Alsadoon, Madhav Parsad Koirala, Sumeet Gill

“…Conversely, the logistic regression model exhibited the lowest accuracy of 84% among the four machine learning models. However, this research does not cover hyperparameter optimization, which could potentially improve model performance.…”
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Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults by Konstantinos Kofidis, Cătălina Lucia Cocianu

“…The ANOVA analyses substantiate that the disparities in model performance are statistically significant. The research acknowledges that both the LSTM and SVR models remain in the developmental stages, with ongoing initiatives aimed at refining these models through hyperparameter optimization and advanced architectural frameworks to enhance their predictive efficacy in practical applications.…”
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Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI by Myung-Joo Park, Hyo-Sik Yang

“…This study lays a solid foundation for future research on integrating these forecasting models into real-world AMI applications to support effective demand response and grid stability.…”
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Indoor fire and smoke detection based on optimized YOLOv5. by Md Shafak Shahriar Sozol, M Rubaiyat Hossain Mondal, Achmad Husni Thamrin

“…This study aimed to address these challenges in fire and smoke detection in indoor settings. It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. …”
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