CSCO’s flowchart. 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. …"

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. …"

Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures حسب Hongpeng He (348094)

"…This study is devoted to develop a quantitative structure–property relationship model for predicting the <i>T</i>_d,_5%onset of binary imidazolium IL mixtures. …"

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

Model performance and benchmarking for multiclass MOA and 30S/50S protein-synthesis classifiers. حسب Josh L. Espinoza (10492448)

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. …"

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

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

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

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

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

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

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

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

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

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Performance metrics. حسب Nanavath Kiran Singh Nayak (22184565)

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

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

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

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Precision analysis of DDcGAN-GSOM with current approaches. حسب Nanavath Kiran Singh Nayak (22184565)

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