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)

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. …”

The classification algorithm flow chart for binary classification among NC, EMCI, LMCI and AD groups. by Qian Zhang (49333)

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

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

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

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

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

“…The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. …”

The workflow of SCARS-Logistic algorithm. by Maryam Sadiq (7046903)

Binary confusion matrix. by Chen Zhu (445894)

For each classification algorithm, the classification accuracies for binary subject groups NC versus EMCI, NC versus LEMC, EMCI versus LEMC, EMCI versus AD, LMCI versus AD, AD vers... by Qian Zhang (49333)

Structure of the proposed hybrid CNN-SVD-based ensemble model for lung cancer classification. by Md. Sabbir Hossain (9958939)

Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm by Ge Zhang (112487)

“…A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. …”

Processes in the bagged tree ensemble classification in our experiment. by Rinrada Tanthanathewin (18611171)

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)

“…In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. …”

Using the Accuracy metric, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for the binary classification of nor... by Amin Ullah (12015113)

Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary... by Amin Ullah (12015113)

Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary... by Amin Ullah (12015113)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)