Experimental results for the binary relevance classifier. by Antonio Fernando Lavareda Jacob Junior (17816730)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants... by Shifa Zhong (1685533)

Subjects:

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

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

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Image4_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Image1_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Image2_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Image3_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

EvoImp’s GA structure example. by Antonio Fernando Lavareda Jacob Junior (17816730)

Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”

Illustration of the workflow combining BayesFlow with missing data encoding. by Zijian Wang (481291)

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”

Comparison of accuracy and DR on UNSW-NB15. by Arshad Hashmi (13835488)

“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”

Comparison of DR and FPR of UNSW-NB15. by Arshad Hashmi (13835488)

“…This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. …”