Accuracy vs. Epoch. by 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. …”

Segmentation results of the proposed model. by 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. …”

S1 Dataset - by 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. …”

CSCO’s flowchart. by 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. …”

Models’ performance without optimization. by 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. …”

Data_Sheet_1_Adaptive restraint design for a diverse population through machine learning.pdf by Wenbo Sun (531308)

RNN performance comparison with/out optimization. by 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. …”

Comparative analysis of DDcGAN-GSOM’s True Positive Rate, False Alarm Rate, and Specificity. by Nanavath Kiran Singh Nayak (22184565)

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Architecture of proposed system model. by Nanavath Kiran Singh Nayak (22184565)

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F1-score analysis of DDcGAN-GSOM with existing methods. by Nanavath Kiran Singh Nayak (22184565)

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Accuracy analysis of DDcGAN-GSOM with existing techniques. by Nanavath Kiran Singh Nayak (22184565)

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Network simulation of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

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Simulation parameters of SDN network. by Nanavath Kiran Singh Nayak (22184565)

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Generator loss of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

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Simulation parameters of DDcGAN. by Nanavath Kiran Singh Nayak (22184565)

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Throughput analysis of DDcGAN-GSOM with prevailing approaches. by Nanavath Kiran Singh Nayak (22184565)

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Performance metrics. by Nanavath Kiran Singh Nayak (22184565)

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Performance comparison of proposed system with state-of-the-art approaches. by Nanavath Kiran Singh Nayak (22184565)

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Schematic representation of univariate ensemble-based feature selection. by Nanavath Kiran Singh Nayak (22184565)

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

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