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

“…<p dir="ltr">Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. While Particle Swarm Optimization (PSO) and Large Language Models (LLMs) have been individually applied in optimization and deep learning, their combined use for enhancing convergence in numerical optimization tasks remains underexplored. …”

HALOGrid—HyperAdaptive long short term memory model with intelligent grid optimization by Kamran Ahmad Awan (23643430)

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. …”

Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks by Mohamed Massaoudi (16888710)

“…This article introduces an efficient transient stability status prediction method based on deep temporal convolutional networks (TCNs). A grey wolf optimizer (GWO) is utilized to fine-tune the TCN hyperparameters to improve the proposed model's accuracy. …”

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. …”

AI-Aided Robotic Wide-Range Water Quality Monitoring System by Awwad, Ameen

“…To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. …”
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Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems by Tarek Berghout (16905132)

“…Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. …”

Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection by Alok Singh Chauhan (8264985)

“…The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. …”

Preventing Road Accidents through Early Detection of Driver Behavior using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach by 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 by 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 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%. …”

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

“…Preprocessing of the data involves inputting missing values, data deseasonalization, and data normalization. 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 by 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. …”

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. 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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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. …”

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. …”

Boosting the visibility of services in microservice architecture by Ahmet Vedat Tokmak (17773479)

“…We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. …”

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. …”