The Roc for the training process of the CNN algorithm. by Yasser M. Ayid (21417798)

GA pseudo-code. by Jianpeng Zhang (528185)

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Pseudo-code for the study design model. by Jianpeng Zhang (528185)

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Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images by Navya K. T (20398103)

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

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

Features of CNN architectures. by Gülcan Gencer (20691782)

QRS detection in ECG signal using pan-tompkins algorithm with R-peak detection. by Rida Nayyab (21519512)

Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security by Nikola Savanovic (14146602)

Hyberparameters of CNN architectures. by Abdullah Sheneamer (19169236)

“…Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. …”

Action potential detection with high temporal accuracy using a convolutional neural network (CNN). by Tjitse van der Molen (20391106)

Action potential detection with high temporal accuracy using a convolutional neural network (CNN). by Tjitse van der Molen (20391106)

Visual representation of CNN architecture. by Gülcan Gencer (20691782)

Here is the extracted cattle’s image from the original image using YOLOv5 algorithm, which accurately detects and isolates the cattle for further processing. by Md Junayed Hossain (22615268)

CNN-GRU model. by Jianpeng Zhang (528185)

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

“…The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. 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. …”

DT Algorithm. by Nashmia Khalid (20496403)

Accuracy parameters for various CNN models. by Shakti Kundu (21445612)

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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