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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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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 IEEE.

关键词： Data integrity

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 914-930页

作者： Yutao Ma Xiao Geng Jian Wang Keqing He Dionysis Athanasopoulos School of Computer Science Wuhan UniversityWuhanChina School of Computer Science Central China Normal UniversityWuhanChina School of Electronics Electrical Engineering and Computer ScienceQueen's University BelfastBelfastUK

Recent years have witnessed the rapid development of service‐oriented computing *** boom of Web services increases software developers'selection burden in developing new service‐based systems such as *** recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software *** service recom-mendation approaches are mainly designed for mashup development in the single‐round *** is hard for them to effectively update recommendation results according to developers'requirements and behaviours(*** service selection).To address this issue,the authors propose a service bundle recommendation framework based on deep learning,DLISR,which aims to capture the interactions among the target mashup to build,selected(component)services,and the following service to ***,an attention mechanism is employed in DLISR to weigh selected services when rec-ommending a candidate *** authors also design two separate models for learning interactions from the perspectives of content and invocation history,respectively,and a hybrid model called *** on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.

关键词： attention deep learning mashup development recommender systems service bundle

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Attention-Based Speech Enhancement Using Human Quality Perception Modeling

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 250-260页

作者： Nayem, Khandokar Md. Williamson, Donald S. Indiana University Department of Computer Science BloomingtonIN47408 United States Ohio State University Department of Computer Science and Engineering ColumbusOH43210 United States

Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optimize performance of deep-learning-based speech enhancement algorithms. These objective functions, however, do not always strongly correlate with a listener's assessment of perceptual quality, so optimizing with these measures often results in poorer performance in real-world scenarios. In this work, we propose an attention-based enhancement approach that uses learned speech embedding vectors from a mean-opinion score (MOS) prediction model and a speech enhancement module to jointly enhance noisy speech. The MOS prediction model estimates the perceptual MOS of speech quality, as assessed by human listeners, directly from the audio signal. The enhancement module also employs a quantized language model that enforces spectral constraints for better speech realism and performance. We train the model using real-world noisy speech data that has been captured in everyday environments and test it using unseen corpora. The results show that our proposed approach significantly outperforms other approaches that are optimized with objective measures, where the predicted quality scores strongly correlate with human judgments. © 2014 IEEE.

关键词： Speech enhancement

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Block Chain Based Voting System 2

Block Chain Based Voting System

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Patil, Vijeeta Bagalkot, Suhasini Rohit Guru, K.M. Kle Institute of Technology Computer Science and Engineering India Rrce Bangalore Computer Science and Engineering India

ISBN: (纸本)9798350383287

As we as a whole realize that casting ballot extortion is basic in India and furthermore in many created nations as well. There have been a few activities to lessen casting a ballot misrepresentation, for instance, the utilization of EVM machines in India. In the event that we can utilize some innovation to ensure against something similar. Here comes the utilization of Blockchain, a blockchain is a protected conveyed record that is constant and utilizes cryptographic techniques to execute the given properties. In this task, we plan a proposition for setting up the best answer for a blockchain casting a ballot decentralized applications(DApp) that consolidates citizen self-sovereign-ID and undeniable voting form arrangement. We will utilize brilliant agreements to execute the standards set up by the Election commission of India. Our proposition tends to the essential standards and shows key capacities and contemplations for a completely supported drive and improved elector end up, progressing commitment, and tremendous expense investment funds by limiting blunders and manual information passage and framework organization for heritage frameworks. We accept that an electronic democratic DApp ought to be reason-driven, the local area situated, in view of open guidelines for blockchain advancements, citizen protection, and security as a goal, and autonomous check and straightforwardness for polling from the organization. © 2024 IEEE.

关键词： Smart contract

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Comprehensive Evaluation of Network Performance Monitoring Solutions

Comprehensive Evaluation of Network Performance Monitoring S...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Juneja, Sonam Arshdeep Maiti, Souvik Raweri, Sneha Bhati, Bhoopesh Singh Sharma, Harsh Chandigarh University Computer Science and Engineering Gharuan India Iiit Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350375237

The paper provides a thorough review of tools and strategies for network performance monitoring, highlighting their vital significance in preserving effective network operations. It talks on the need for improved traffic and internet performance measurement as well as the growing complexity of contemporary networks. The study highlights the advantages and disadvantages of several internet performance monitoring programmes and technologies by classifying them according to protocol levels. Various challenges related to internet performance measurement are discussed, such as reliability, precision, expandability, and security. Furthermore, the design and development of the POWWOW-NMS network management system are examined. The basic concepts of network performance monitoring-performance, fault, and account monitoring-as well as the functions of SNMP and RMON in network data collection, are covered. The article offers a categorization of network performance monitoring tools, outlining the features, architecture, and benefits of each tool. These products include integrated SNMP systems, wireless sensor network monitoring tools, Grid network monitoring tools, and WiMAX network monitoring solutions. All things considered, this abstract provides a thorough manual for comprehending and applying efficient network monitoring methods and instruments. © 2024 IEEE.

关键词： Internet protocols

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A Novel Identity Authentication Mechanism forUnmanned Maritime Vessels Communication Based on MITRE ATT&CK Framework

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Journal of Marine science and Technology (Taiwan) 2024年第2期32卷 170-180页

作者： He, Jun-Xian Chang, Shih-Hao Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Dept. of Computer Science and Information Engineering National Taipei University of Technology Taipei City Taiwan

With the development of the smart shipping industry, unmanned vessel technology is rapidly evolving, accompanied by a demand for robust Internet of Things (IoT) communication security practices. Key communication technologies for the operation of unmanned vessels include external vessel communication. One crucial aspect is the verification of the identities of the parties involved in these communication systems, as this ensures secure and trusted interactions between unmanned vessels and port management authorities. Identity authentication plays a vital role in ensuring the secure communication of unmanned vessels. This paper aims to analyze potential risks related to identity authentication technology in unmanned vessel communication systems. To effectively address these risks, we propose leveraging digital certificates and blockchain technology to achieve device non-repudiation and substitution. Based on this technology, we organize and implement defense strategies using the MITRE ATT&CK framework. This framework provides a comprehensive and systematic approach to understanding potential threats and designing appropriate defense measures. By utilizing this framework, security teams can better comprehend vulnerabilities and formulate effective countermeasures. To validate the effectiveness of the proposed defense framework, we conducted simulation experiments. These experiments helped us evaluate the efficiency and reliability of the strategies in real-world scenarios. Through these experiments, we could ascertain whether the countermeasures effectively mitigated the identified risks and ensured the secure communication of unmanned vessels. © 2024 National Taiwan Ocean University

关键词： Risk assessment

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Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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