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Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

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International Journal of computers and Applications 2024年第12期46卷 1152-1165页

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Emotion Recognition

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年第21期84卷 23899-23933页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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A Local Differential Privacy Hybrid data Clustering Iterative Algorithm for Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1421-1434页

作者： Yousheng ZHOU Zhonghan WANG Yuanni LIU School of Computer Science and Technology Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications

As a new computing method, edge computing not only improves the computing efficiency and processing power of data, but also reduces the transmission delay of data. Due to the wide variety of edge devices and the increasing amount of terminal data, third-party data centers are unable to ensure no user privacy data leaked. To solve these problems, this paper proposes an iterative clustering algorithm named local differential privacy iterative aggregation(LDPIA) based on localized differential privacy, which implements local differential privacy. To address the problem of uncertainty in numerical types of mixed data, random perturbation is applied to the user data at the attribute category level. The server then performs clustering on the perturbed data, and density threshold and disturbance probability are introduced to update the cluster point set iteratively. In addition, a new distance calculation formula is defined in combination with attribute weights to ensure the availability of data. The experimental results show that LDPIA algorithm achieves better privacy protection and availability simultaneously.

关键词： Differential privacy Privacy Uncertainty Perturbation methods Clustering algorithms Wheels Iterative algorithms Servers Protection Edge computing

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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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CAED: A Comprehensive Android Emulator Detection Framework With data Augmentation

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IEEE Internet of Things Journal 2025年第13期12卷 22847-22861页

作者： Yan, Ran Niu, Weina Hou, Qinsheng Su, YuChi Gong, Jiacheng Zhang, Xiaosong University of Electronic Science and Technology of China School of Computer Science and Engineering Institute for Cyber Security Chengdu611731 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China

Anti-emulation is crucial for Android and IoT security as it helps apps determine whether they are running on a real mobile device or in an emulation environment. This prevents apps from being analyzed, debugged, or reverse-engineered in emulators, ultimately stopping criminals from making illegal profits. Current emulator detection methods cannot balance accuracy, universality, robustness, and compatibility. Their universality is often hindered by limited data diversity and accessibility. To address these issues, we propose the comprehensive Android emulator detection (CAED) framework. The Preprocessing Module of CAED collects and normalizes data from both phones and emulators. We propose the first data augmentation method for emulator detection, emulator detection augmentation generative adversarial network (EDA-GAN), which is tailored to the characteristics of our data and effectively enhances data diversity. The classifier module MFBoost employs an adaptive imputation algorithm and multiple classification and regression trees (CART) for precise classification. Experiments on 324 devices show that CAED improves detection rate by at least 12.5% and up to 44.71% over state-of-the-art (SOTA) methods. The EDA-GAN data augmentation method boosts classifier accuracy, achieving a performance of up to 99.62%. Additionally, CAED’s unique loss function and imputation algorithm enhance the robustness and compatibility of CAED, with a 24% smaller accuracy drop than other methods when features are modified or unavailable. This study presents the CAED framework as an effective solution for protecting apps against real-world security threats in Android and IoT environments. © 2014 IEEE.

关键词： Crime

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A Machine Learning Approach for Identifying Mental Health Disorders and Depression in Psychoactive Drug Users 8

A Machine Learning Approach for Identifying Mental Health Di...

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Srivardhan, Cherukuri Daniel, Esther Jebapriya, S. Mathu, T. Karunya Institute of Technology & Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology & Sciences Division of Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798331544607

Mental illnesses are among the most prevalent problems worldwide regardless of age. The current investigation examines ML (Machine Learning) methods to predict Depression and CMD (Common Mental Disorders) for people who utilize Psychoactive Substances. An assortment of techniques such as Support Vector Machine(SVM), Random Forest(RF), K-Nearest Neighbour(KNN), and Naive Bayes, as well as a voting classifier that combines Baging Classifier, along with a Decision Tree, are used. Utilizing Sequential Backward Selection (SBS), the findings demonstrated exceptional accuracy, with Support Vector Machine (SVM) achieving 84.33% for Common Mental Disorder (CMD) predictions as well as 83.93% for Depression. In an attempt to achieve higher accuracy, possibly exceeding 90%, ensemble techniques such as the voting classifier were investigated. © 2025 IEEE.

关键词： Support vector machines

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Adaptive Neuro Transformers for Stroke Lesion Segmentation: GatedNeuroTransUNet Implemented using CNNs with Bottleneck Residual Blocks

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SN computer science 2025年第5期6卷 1-17页

作者： Sulaiman, Sadiya Thanka, M. Roshni Edwin, E. Bijolin Salam, Nader Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

Accurate lesion segmentation is essential for diagnosing and treating strokes. However, the task of segmenting stroke lesions is difficult due to the intricate backgrounds and noise prevalent in medical images. To tackle this challenge, the proposed research introduces an enhanced U-Net architecture integrating pre-trained CNN blocks, bottleneck residuals, and an adaptive gated neuro transformer with a modified MLP. This novel architecture is structured to assimilate both local and global aspects, thereby improving segmentation accuracy. The pre-trained CNN blocks in the architecture take advantage of features learned through supervised tasks, which enhances the model's generalization abilities. Bottleneck residual blocks are incorporated to perform high-level feature extraction and dimensionality reduction, that is essential for managing the complexities inherent in medical imagery. The adaptive gated neuro transformer, equipped with a novel MLP, will efficiently captures long-range correlations by utilizing spatial data, further refining the segmentation process. To confirm our model's efficacy, we conducted trials with the ATLAS and ISLES2022 collections. Observations showed that our model markedly surpassed traditional approaches, highlighted by substantial enhancements in the Dice Similarity Coefficient (DSC) and decreases in the Hausdorff Distance (HD). Such indicators are vital for assessing the precision and reliability of segmentation methods. In addition to its quantitative success, the proposed method excels qualitatively by preserving edges and accurately detecting lesion boundaries. This indicates that the proposed improved U-Net transformer architecture is not only effective in segmenting stroke lesions but also holds considerable promise for clinical application. By enhancing the precision of stroke lesion segmentation, the proposed model is potentially contributing to better diagnosis and treatment planning, ultimately enhancing patient care. © The A

关键词： Bottleneck Residual Blocks CNN GatedNeuroTransUNet Lesion Neuro Transformers Segmentation Stroke lesions

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Effective Tool Augmented Multi-Agent Framework for data Analysis

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data Intelligence 2024年第4期6卷 923-945页

作者： Xilin Zhang Zhixin Mao Ziwen Chen Shen Gao School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering(School of Cyber Security) University of Electronic Science and Technology of ChinaChengdu 611731China

data analysis tasks aim to provide insightful analysis for given data by incorporating background knowledge of the represented phenomenon, which in turn supports decision-making. While existing large language models(LLMs) can describe data trends, they still lag behind human data analysts in terms of integrating external knowledge and in-depth data analysis. Therefore, we propose a multi-agent data analysis framework based on LLMs. The framework decomposes the data analysis task into subtasks by employing three different agents. By empowering agents with the ability to utilize data search tools, the framework enables them to search for arbitrary relevant knowledge during the analysis process, leading to more insightful analysis. Moreover, to enhance the quality of the analysis results, we propose a multi-stage iterative optimization method that iteratively performs data analysis to form more in-depth conclusions. To validate the performance of our framework, we apply it to three real-world problems in the research development of higher education in China data. Experimental results demonstrate that our approach can achieve more insightful data analysis results compared to directly using LLMs alone.

关键词： Exploratory data analysis Multi-agent Large language models

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DP-PartFIM: Frequent Itemset Mining using Differential Privacy and Partition

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IEEE Transactions on Emerging Topics in Computing 2024年 1-11页

作者： Liu, Xinyu Gan, Wensheng Yu, Lele Liu, Yining School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China College of Cyber Security Jinan University Guangzhou China

Itemset mining is a popular data mining technique for extracting interesting and valuable information from large datasets. However, since datasets contain sensitive private data, it is not permitted to directly mine the data or share the mining results. Previous privacy-preserving frequent itemset mining research was not efficient because of the use of privacy budgets or long transaction truncation strategies, which are impractical for large datasets. In this paper, we propose a more efficient partition mining technology, DP-PartFIM, based on differential privacy, which protects privacy while mining data. DP-PartFIM uses partition mining to mine frequent itemsets and constructs vertical data storage formats for each partition, which makes the algorithm equally efficient for large datasets. To protect data privacy, DP-PartFIM adds Laplace noise to support candidate itemsets. The experimental results show that, compared with the classical privacy-preserving itemset mining methods, DP-PartFIM better guarantees data utility and privacy. IEEE

关键词： Differential privacy

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