False alarm rate analysis of DDcGAN-GSOM with existing approaches. حسب Nanavath Kiran Singh Nayak (22184565)

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True positive rate (TPR) analysis of DDcGAN-GSOM with existing techniques. حسب Nanavath Kiran Singh Nayak (22184565)

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Flow diagram for the proposed methodology. حسب Nanavath Kiran Singh Nayak (22184565)

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Specificity comparison of DDcGAN-GSOM with prevailing methods. حسب Nanavath Kiran Singh Nayak (22184565)

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Discriminator loss of suggested technique. حسب Nanavath Kiran Singh Nayak (22184565)

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Comprehensive comparison of state-of-the-art approaches. حسب Nanavath Kiran Singh Nayak (22184565)

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Energy consumption analysis of DDcGAN-GSOM with existing methods. حسب Nanavath Kiran Singh Nayak (22184565)

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Analysis of DDcGAN-GSOM delay with existing techniques. حسب Nanavath Kiran Singh Nayak (22184565)

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Comparative analysis of DDcGAN-GSOM’s Energy Consumption, Throughput, and Dealy. حسب Nanavath Kiran Singh Nayak (22184565)

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System model of the proposed method. حسب Nanavath Kiran Singh Nayak (22184565)

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The robustness test results of the model. حسب Xini Fang (20861990)

"…Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. …"

Models’ performance without optimization. حسب Muhammad Usman Tariq (11022141)

"…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …"

DataSheet1_Development of an Intelligent Power Management System for Solar PV-Wind-Battery-Fuel-Cell Integrated System.docx حسب Vincent Anayochukwu Ani (10322801)

RNN performance comparison with/out optimization. حسب Muhammad Usman Tariq (11022141)

"…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …"

Performance metrics for BrC. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

Proposed CVAE model. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

Proposed methodology. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

Loss vs. Epoch. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

Sample images from the BreakHis dataset. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"

Accuracy vs. Epoch. حسب Afnan M. Alhassan (18349378)

"…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …"