Hyperparameter settings for the optimization algorithms. by Ahmed M. Elshewey (21463867)

Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms. by Ahmed M. Elshewey (21463867)

Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

Performance of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Total computational cost of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

Features selected by optimization algorithms. 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. …”

Adaptive dynamic algorithm for Australia rainfall. by Ahmed M. Elshewey (21463867)

The outcomes of the ANOVA results for the proposed AD-PSO-Guided WOA-LSTM algorithm for rainfall prediction. by Ahmed M. Elshewey (21463867)

Analysis plots of the obtained results using the proposed AD-PSO-Guided WOA LSTM algorithm. by Ahmed M. Elshewey (21463867)

The AD-PSO-Guided WOA LSTM framework. by Ahmed M. Elshewey (21463867)

“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”

Principle of the SVM algorithm. by Anfeng Zhu (17817144)

Hyperparameters of the LSTM Model. by Ahmed M. Elshewey (21463867)

“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”

Prediction results of individual models. by Ahmed M. Elshewey (21463867)

“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”

Metrics for evaluating the performance of the proposed model. by Ahmed M. Elshewey (21463867)

Hybrid feature selection algorithm of CSCO-ROA. 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. …”

Sample images from the BreakHis 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. …”

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)