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

“…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

Subjects:

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

Subjects:

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

Subjects:

Datasets and their properties. by Olaide N. Oyelade (14047002)

Subjects:

Parameter settings. by Olaide N. Oyelade (14047002)

Subjects:

Fig 4 - by Olaide N. Oyelade (14047002)

Subjects:

Fig 7 - by Olaide N. Oyelade (14047002)

Subjects:

Fig 2 - by Olaide N. Oyelade (14047002)

Subjects:

An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

Subjects:

An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

Subjects:

Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

Subjects:

One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

Mean fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

Standard deviation of fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

Subjects:

a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

Subjects:

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. …”