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. …”

Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

List of selected mRNAs in the feature selection step. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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The performance of classifiers based on 60 selected mRNAs. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Confusion metrics using LR-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Confusion metrics using MNB-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Confusion metrics using DT-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Confusion metrics using SVM-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). …”

Selected mRNAs based on association rule mining analysis, for early and late stages of PTC. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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The overview of the proposed method. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Top mRNAs based on repeat count in early-stage and late-stage rules. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Fig 4 - by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Fig 3 - by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Twenty of the top early-stage and late-stage rules based on sorted lift value. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Graph network of ZNF518B (with lift score > 1.23) and its related association rules with other mRNAs. ZNF518B, its rules, and related mRNAs are represented by orange, yellow, and b... by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Pseudocode of NSGAII [23]. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Fig 2 - by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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