Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code حسب Goutami Sooda (20714729)

Data and code resources. حسب Sam Hall-McMaster (10343795)

The pipeline of the proposed CNN-Transformer model. حسب Zenglei Liu (20629838)

الموضوعات:

Schematic representation of Faster_R-CNN architecture. حسب Puneet Thapar (21464240)

الموضوعات:

Performance comparison of R-CNN and leading techniques. حسب Puneet Thapar (21464240)

الموضوعات:

CNN-LSTM parameters. حسب Saad Hammood Mohammed (20623506)

"…Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data’s spatial and temporal features. …"

The parameter setting of the evolutionary neural network. حسب Wenbing Shi (5806160)

الموضوعات:

The genome coding scheme. حسب Wenbing Shi (5806160)

الموضوعات:

Hyberparameters of CNN architectures. حسب Abdullah Sheneamer (19169236)

"…<div><p>The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. …"

Architecture of hybrid CNN-LSTM. حسب Yousef Sanjalawe (22216626)

The optimal number of clusters identified using the GMM clustering algorithm. حسب Dinh K. Tang (20469082)

Optimal number of clusters identified using the GMM clustering algorithm. حسب Dinh K. Tang (20469082)

Impact of multiunit merging on neural information encoding. حسب Daniel Suárez-Barrera (22676654)

الموضوعات:

Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" حسب Yize Wang (19535173)

"…<p dir="ltr">Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"</p>…"

Performance metrics of the CNN-LSTM model. حسب Yousef Sanjalawe (22216626)

"…The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, <i>SLADRO</i>, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. …"

Comparison of CNN-LSTM with baseline models. حسب Yousef Sanjalawe (22216626)

"…The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, <i>SLADRO</i>, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. …"

Training duration for CNN-LSTM and DQN. حسب Yousef Sanjalawe (22216626)

"…The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, <i>SLADRO</i>, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. …"

Hyperparameter tuning for CNN-LSTM model. حسب Yousef Sanjalawe (22216626)

"…The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, <i>SLADRO</i>, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. …"

code_4 حسب Olga Ovtsarenko (20432033)

The genetic algorithm. حسب Viktoria Zemliak (19500335)