Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach حسب Raza, Ali

"…The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents.…"
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Random vector functional link network: Recent developments, applications, and future directions حسب A.K. Malik (16003193)

"…Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. …"

Data-Efficient Wheat Disease Detection Using Shifted Window Transformer: Enhancing Accuracy, Sustainability, and Global Food Security حسب Muhammad Khubaib (22927822)

"…Using Bayesian hyperparameter optimization, we tuned key parameters such as learning rates, batch sizes, and dropout rates, and achieved an accuracy of 99.3%. …"

A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula حسب Ahmad Qadeib Alban (16855206)

"…Subsequently, hyperparameter optimization is performed on each model using grid search. …"

A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model حسب Md. Nazmul Islam Shuzan (16888827)

"…The best ML model and the best feature selection algorithm combination were fine-tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. …"

AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite حسب Reda, Mariam

"…Our method comprises a comparative analysis to maximize our multi-label classification model’s performance and determine the effects of varying the convolutional neural network (CNN) architectures, transfer learning approach, and hyperparameter optimizations. We tested four lightweight, mobile-optimized CNNs – MobileNet, MobileNetV2, NasNetMobile, and EfficientNetB0 – and tested four transfer learning scenarios (percentage of frozen-vs.…"
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