Comparative analysis of DDcGAN-GSOM’s Accuracy, Precision, and F1-score. by Nanavath Kiran Singh Nayak (22184565)

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

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

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

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

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

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

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

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

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

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

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Reducing positional uncertainty by He Wu (22584128)

Data_Sheet_1_A hybrid energy-based and AI-based screening approach for the discovery of novel inhibitors of JAK3.pdf by Juying Wei (13815403)

DataSheet1_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

DataSheet3_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

DataSheet2_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

DataSheet4_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

Performance metrics for BrC. 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. …”

Proposed CVAE 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. …”

Proposed methodology. 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. …”