Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed (6488738)

“…This groundbreaking methodology offers a very efficient and effective solution for optimizing deep learning models, leading to substantial computational performance improvements across a wide range of applications.…”

RETRACTED_Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN by Irfan Ali Kandhro (17541876)

“…The performance of the model was evaluated using a variety of methodologies, including activation, optimization, and regularization, as well as other hyperparameters, as detailed in this study. …”

Toward AI-Native 6G: Unveiling Online Optimization and Deep Reinforcement Learning for Autonomous Network Slicing by Amr Abo-eleneen (17032284)

“…This paper presents the first unified framework that jointly optimizes communication resources, computation capacity, and AI hyperparameters to maximize the average accuracy of multiple concurrent AI services. …”

A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm by Amirsajjad Rahmani (17541453)

“…The SVM hyperparameters were optimized simultaneously with feature selection, and the model was tested with test data. …”

Adaptive PPO With Multi-Armed Bandit Clipping and Meta-Control for Robust Power Grid Operation Under Adversarial Attacks by Mohamed Massaoudi (16888710)

“…Specifically, our approach introduces three key innovations: 1) multi-armed bandit (MAB) mechanism for dynamic epsilon-clipping that adaptively adjusts exploration-exploitation trade-offs; 2) meta-controller framework that automatically tunes hyperparameters including the activation learning rate (ALR) penalties and exploration factors; and 3) integrated gradient-based optimization approach that combines policy gradients with environmental feedback. …”

AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite by 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. …”
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FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model by Tadesse G. Wakjira (14779165)

“…Grid search is combined with a 10-fold cross-validation to optimize the hyperparameters of the ML models. The prediction capability of the proposed super-learner ML model was benchmarked against boosting- and tree-based ML models, such as classification and regression trees, adaptive boosting, gradient boosted decision trees, and extreme gradient boosting. …”

Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification by Mudassir Iftikhar (21323900)

“…The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. …”

Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

“…The EfficientBiLSTMNet model, which integrates the EfficientNet and BiLSTM layers, is trained on the preprocessed datasets. The model’s hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). …”

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

“…The proposed model is trained on a dataset of 9,346 wheat leaf images, categorized into eight disease classes and one healthy class. Using Bayesian hyperparameter optimization, we tuned key parameters such as learning rates, batch sizes, and dropout rates, and achieved an accuracy of 99.3%. …”

GAN-Based Data Augmentation for Fault Diagnosis and Prognosis of Rolling Bearings: A Literature Review by Md. Sulyman Islam Sifat (22928983)

“…Our analysis reveals standardized hyperparameter configurations with learning rate 0.0001, Adam optimizer, and batch size 32 being most effective. …”

IoT empowered smart cybersecurity framework for intrusion detection in internet of drones by Syeda Nazia Ashraf (17541222)

“…We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. …”