Evaluating Deep Neural Network Ensembles by Majority Voting Cum Meta-Learning Scheme

Jain, Anmol; Kumar, Aishwary; Susan, Seba

doi:10.1007/978-981-16-1249-7_4

Cited by 9 publications

(3 citation statements)

References 17 publications

(15 reference statements)

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“…Due to the lack of similar CSE recognition systems, we utilized a baseline system based on the majority voting [91] method, in which the predictions of all the classifiers are combined and the class label with the highest frequency is selected as the final prediction. By comparing the results of the two models, we aim to show the benefits of using a more sophisticated late fusion model over a simple majority voting ensemble model for CSE attack recognition.…”

Section: Cse-ars Evaluationmentioning

confidence: 99%

CSE-ARS: Deep Learning-Based Late Fusion of Multimodal Information for Chat-Based Social Engineering Attack Recognition

Tsinganos,

Fouliras,

Mavridis

et al. 2024

IEEE Access

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With the increasing prevalence of chat-based social engineering (CSE) attacks targeting unsuspecting users, the need for robust defenses has never been more critical. In this paper, we introduce Chat-based Social Engineering Attack Recognition System (CSE-ARS), an innovative and effective CSE defense system. CSE-ARS employs a late fusion strategy that integrates the findings of five specialized deep learning models, each focused on detecting distinct CSE attack enablers: critical information leakage recognizer (CRINL-R), personality traits recognizer (PERST-R), dialogue acts recognizer (DIACT-R), persuasion recognizer (PERSU-R), persistence recognizer (PERSI-R). The system harnesses weighted linear aggregation and employs simulated annealing with 10-fold cross-validation, ensuring optimal model performance. CSE-ARS is trained on the CSE-ARS Corpus, a carefully curated dataset tailored to the intricacies of CSE attacks. Extensive evaluation reveals that CSE-ARS achieves satisfactory results in identifying and neutralizing CSE threats, enhancing user security in online interactions.

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Section: Cse-ars Evaluationmentioning

confidence: 99%

CSE-ARS: Deep Learning-Based Late Fusion of Multimodal Information for Chat-Based Social Engineering Attack Recognition

Tsinganos,

Fouliras,

Mavridis

et al. 2024

IEEE Access

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“…In traditional ensemble learning, sometimes an objective function needs to be approximated by combining multiple models. Generally, the results of each model are combined by voting (majority wins), weighted voting (some classifiers are more authoritative than others), and averaging the results [40].…”

Section: Proposed Stacking Algorithm Based On Gcn (Sa-gcn)mentioning

confidence: 99%

Breast Cancer Drugs Screening Model Based on Graph Convolutional Network and Ensemble Method

Li,

Zhao,

Shi

et al. 2024

Mathematics

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Breast cancer is the first cancer incidence and the second cancer mortality in women. Therefore, for the life and health of breast cancer patients, the research and development of breast cancer drugs should be accelerated. In drug development, the search for compounds with good bioactivity, pharmacokinetics, and safety, including Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET ), has always been a time-consuming and labor-intensive process. In this paper, the relationship between the molecular descriptor and ADMET properties of compounds is studied. Aiming at the problem of composite ADMET attribute classification, a Stacking Algorithm based on Graph Convolutional Network (SA-GCN) was proposed. Firstly, feature selection was performed in the data of molecular descriptors. Then the SA-GCN is developed by integrating the advantages of ten classical classification algorithms. Finally, various performance indicators were used to conduct comparative experiments. Experiments show that the SA-GCN is superior to other classifiers in the classification performance of ADMET, and the classification accuracy is 97.6391%, 98.1450%, 94.4351%, 96.4587%, and 97.9764% compared to other classifiers. Therefore, this method can be well applied to the classification of ADMET properties of compounds and then could provide some help to screen out compounds with good biological activities.

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“…Previous ensemble learning studies have shown that the combination of multiple networks can learn more effective information from images due to a single network. Good results have been obtained for integration learning in many areas of research, such as [51][52][53]. One of the feature combinations for emotion recognition in [46] is fc5 VGG13 + fc7 VGG16 + pool ResNet, which has the disadvantage of the network and parameter size being too large and not easy to train.…”

Section: Implementation Detailsmentioning

confidence: 99%

Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention

Zhu

et al. 2022

Future Internet

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Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In recent years, with the rapid development of computer technology, especially the great progress of deep learning, more and more convolutional neural networks have been developed for FER research. Most of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise, due to expression-independent intra-class differences. In this paper, we propose a Dual Path Stacked Attention Network (DPSAN) to better cope with the above challenges. Firstly, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences. Secondly, by providing the global image and segmented local image regions as training data for the integrated dual path model, the overfitting problem of the deep network due to a lack of data can be effectively mitigated. Finally, this paper also designs a stacked attention module to weight the fused feature maps according to the importance of each part for expression recognition. For the cropping scheme, this paper chooses to adopt a cropping method based on the fixed four regions of the face image, to segment out the key image regions and to ignore the irrelevant regions, so as to improve the efficiency of the algorithm computation. The experimental results on the public datasets, CK+ and FERPLUS, demonstrate the effectiveness of DPSAN, and its accuracy reaches the level of current state-of-the-art methods on both CK+ and FERPLUS, with 93.2% and 87.63% accuracy on the CK+ dataset and FERPLUS dataset, respectively.

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Evaluating Deep Neural Network Ensembles by Majority Voting Cum Meta-Learning Scheme

Cited by 9 publications

References 17 publications

CSE-ARS: Deep Learning-Based Late Fusion of Multimodal Information for Chat-Based Social Engineering Attack Recognition

CSE-ARS: Deep Learning-Based Late Fusion of Multimodal Information for Chat-Based Social Engineering Attack Recognition

Breast Cancer Drugs Screening Model Based on Graph Convolutional Network and Ensemble Method

Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention

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