Antisocial Behavior Identification from Twitter Feeds   Using Traditional Machine Learning Algorithms and Deep Learning.

Singh, Ravinder; Subramani, Sudha; Du, Jiahua; Zhang, Yanchun; Wang, Hua; Miao, Yuan; Ahmed, Khandakar

doi:10.4108/eetsis.v10i3.3184

Cited by 36 publications

(3 citation statements)

References 89 publications

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“…During the same period, Yin et al [ 30 ] proposed a modality-aware graph convolutional network (MAGCN) module to embed multimodality entity attributes and topological graph connectivity features into a unified lower dimensional feature space to boost link prediction performance. In 2023, Ravinder et al [ 31 ] proposed a proactive approach based on natural language processing and deep learning that can enable online platforms to actively look for the signs of antisocial behaviour and intervene before it gets out of control. Ge et al proposed a distributed prediction-randomness framework for the evolutionary dynamic multiobjective partitioning optimization of databases [ 32 ] and a distributed cooperative coevolutionary genetic algorithm (DCCGA) to optimize the MODP problem [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Ma,

2024

PLoS ONE

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In an attribute-based strong designated verifier signature, a signer who satisfies the access structure signs the message and assigns it to a verifier who satisfies the access structure to verify it, which enables fine-grained access control for signers and verifiers. Such signatures are used in scenarios where the identity of the signer needs to be protected, or where the public verifiability of the signature is avoided and only the designated recipient can verify the validity of the signature. To address the problem that the overall overhead of the traditional attribute-based strong designated verifier signature scheme is relatively large, an efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography is proposed, as well as a security analysis of the new scheme given in the standard model under the difficulty of the elliptic curve discrete logarithm problem (ECDLP). On the one hand, the proposed scheme is based on elliptic curve cryptography and uses scalar multiplication on elliptic curves, which is computationally lighter, instead of bilinear pairing, which has a higher computational overhead in traditional attribute-based signature schemes. This reduces the computational overhead of signing and verification in the system, improves the efficiency of the system, and makes the scheme more suitable for resource-constrained cloud end-user scenarios. On the other hand, the proposed scheme uses LSSS (Linear Secret Sharing Schemes) access structure with stronger access policy expression, which is more efficient than the "And" gate or access tree access structure, making the computational efficiency of the proposed scheme meet the needs of resource-constrained cloud end-users.

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Section: Related Workmentioning

confidence: 99%

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Ma,

2024

PLoS ONE

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“…Some researchers devoted to identifying neurological disorders from EEG signals, early detection of mild cognitive impairment [11][12][13][14]. Some also attemped to identify antisocial behavior [15], and automatically detect autism spectrum disorder from EEG [16]. Mental health was analyzed based on emotion recognition from facial expressions and psychometric evaluations [17].…”

Section: Introductionmentioning

confidence: 99%

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

Hao,

Zhang,

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: A lightweght lung image segmentation model was explored. It was with fast speed and low resouces consumed while the accuracy was comparable to those SOAT models. OBJECTIVES: To improve the segmentation accuracy and computational efficiency of the model in extracting lung regions from chest X-ray images, a lightweight segmentation model enhanced with a visual attention mechanism called VAEL-Unet, was proposed. METHODS: Firstly, the bneck module from the MobileNetV3 network was employed to replace the convolutional and pooling operations at different positions in the U-Net encoder, enabling the model to extract deeper-level features while reducing complexity and parameters. Secondly, an attention module was introduced during feature fusion, where the processed feature maps were sequentially fused with the corresponding positions in the decoder to obtain the segmented image. RESULTS: On ChestXray, the accuracy of VAEL-Unet improves from 97.37% in the traditional U-Net network to 97.69%, while the F1-score increases by 0.67%, 0.77%, 0.61%, and 1.03% compared to U-Net, SegNet, ResUnet and DeepLabV3+ networks. respectively. On LUNA dataset. the F1-score demonstrates improvements of 0.51%, 0.48%, 0.22% and 0.46%, respectively, while the accuracy has increased from 97.78% in the traditional U-Net model to 98.08% in the VAEL-Unet model. The training time of the VAEL-Unet is much less compared to other models. The number of parameters of VAEL-Unet is only 1.1M, significantly less than 32M of U-Net, 29M of SegNet, 48M of Res-Unet, 5.8M of DeeplabV3+ and 41M of DeepLabV3Plus_ResNet50. CONCLUSION: These results indicate that VAEL-Unet’s segmentation performance is slightly better than other referenced models while its training time and parameters are much less.

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“…Additionally, in terms of input coding, certain studies focus exclusively on categorized fields. They neglect numeric fields [15] or resort to simplistic encoding techniques like one-hot coding for the categorized fields [16]. Consequently, crucial feature information is lost during the data processing stage, potentially compromising the effectiveness of IDS.…”

Section: Introductionmentioning

confidence: 99%

DTT: A Dual-domain Transformer model for Network Intrusion Detection

Xu,

Sun,

2024

ICST Transactions on Scalable Information Systems

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With the rapid evolution of network technologies, network attacks have become increasingly intricate and threatening. The escalating frequency of network intrusions has exerted a profound influence on both industrial settings and everyday activities. This underscores the urgent necessity for robust methods to detect malicious network traffic. While intrusion detection techniques employing Temporal Convolutional Networks (TCN) and Transformer architectures have exhibited commendable classification efficacy, most are confined to the temporal domain. These methods frequently fall short of encompassing the entirety of the frequency spectrum inherent in network data, thereby resulting in information loss. To mitigate this constraint, we present DTT, a novel dual-domain intrusion detection model that amalgamates TCN and Transformer architectures. DTT adeptly captures both high-frequency and low-frequency information, thereby facilitating the simultaneous extraction of local and global features. Specifically, we introduce a dual-domain feature extraction (DFE) block within the model. This block effectively extracts global frequency information and local temporal features through distinct branches, ensuring a comprehensive representation of the data. Moreover, we introduce an input encoding mechanism to transform the input into a format suitable for model training. Experiments conducted on two distinct datasets address concerns regarding data duplication and diverse attack types, respectively. Comparative experiments with recent intrusion detection models unequivocally demonstrate the superior performance of the proposed DTT model.

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Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.

Cited by 36 publications

References 89 publications

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Efficient attribute-based strong designated verifier signature scheme based on elliptic curve cryptography

Fast Lung Image Segmentation Using Lightweight VAEL-Unet

DTT: A Dual-domain Transformer model for Network Intrusion Detection

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