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

MCL-FWA-BILSTM binary and multiclass accuracy comparison. by Arshad Hashmi (13835488)

Binary classification accuracy and DR comparison on NSL-KDD. by Arshad Hashmi (13835488)

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

Accuracy comparison with existing approaches for Binary Classification with state of art on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

Accuracy comparison with existing approaches for binary classification with state of art on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

The main workflow of the proposed algorithm. by Xin Li (51274)

Comparison of accuracy of MCL-FWA-BILSTM model with existing models for binary classification on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

The detected blocks. by Xin Li (51274)

Symmetry test result. by Jiyong Moon (16819302)

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

Parameter settings. by Yang Cao (53545)

“…Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. …”

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

“…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …”

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

“…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …”

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

“…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …”

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

“…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Fig 12 - by Nisha Yadav (366131)

MERs and their Fourier descriptor figure. by Xin Li (51274)