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

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

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

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

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

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CNN-LSTM parameters. by 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. by Wenbing Shi (5806160)

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The genome coding scheme. by Wenbing Shi (5806160)

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Hyberparameters of CNN architectures. by 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. …”

Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements. by Martina Bukač (20161460)

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

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

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

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

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Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" by 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. by 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. by 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. by 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. by 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 by Olga Ovtsarenko (20432033)