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

“…<p>Comparison between ILSTM and machine learning-based algorithms in binary classification.…”

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

“…<p>Comparison between ILSTM and deep learning-based algorithms in binary classification.…”

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

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

“…The model further showed superior results on binary classification compared with existing methods. …”

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

“…The model further showed superior results on binary classification compared with existing methods. …”

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

Subjects:

The scheme of CARS-Logistic algorithm. by Maryam Sadiq (7046903)

Effect of image resolution on the performance of the binary classifier algorithm in predicting occurrence of a bird target(s) on an individual image using the validation imagery se... by Tsung-Wei Ke (18297551)

Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification. by Sebastião Rogério da Silva Neto (11944944)

Subjects:

Model 1: All Variables for binary classification. by Toheeb Salahudeen (21368040)

“…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …”

The results of the ten performance metrics for NQE+SVC vs NQE+QSVC based on classifying miRNA expression profiles. by Aninda Astuti (21736334)

Applying the proposed image enhancement algorithm. by Nicodemus Songose Awarayi (18414494)

AUC score comparison table for all the deep learning algorithms. by Khandaker Mohammad Mohi Uddin (13124356)

Effect of training regions on the performance of the Mask R-CNN detection algorithm based on the refined annotation bounding boxes. by Tsung-Wei Ke (18297551)

Performance of the baseline SVM and Mask R-CNN detection algorithms based on the original, manually-derived annotated bounding boxes. by Tsung-Wei Ke (18297551)

Prediction AUC of Strokeformer and the competitor methods on the ten large-scale binary classification datasets. by Maocheng Cao (22127378)

Prediction AUC of Strokeformer and the competitor methods on the nine small-scale binary classification datasets. by Maocheng Cao (22127378)

Binary confusion matrix. by Chen Zhu (445894)