MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. …”

Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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

Subjects:

Initial parameter settings for the optimization algorithms. by Doaa Sami Khafaga (21463870)

Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

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

Subjects:

The confidence curve of a binary image. by Dingming Yang (8505990)

The confidence change of the binary image after iteration. by Dingming Yang (8505990)

Numerical example for determining the optimal number of clusters in Algorithm 2. by Yi Wei Tye (22208735)

Binary contingency table. by Lu Xiao (314030)

The schematic diagram of benefit-form binary utility measure functions and. by Chao Yu (562804)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

Subjects:

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. …”