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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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BIOPHYSICAL SIGNAL PROCESSING FOR AUTOMATIC ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Pandya, Kushagra Hathi, Heli Ravikumar, R.N. Nadarajan, Sivakumar Praveen, Manoj Pal, Vinod Kumar Tss Consultancy Pvt. Ltd. Rajkot India Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Velalar College of Engineering and Technology Dept. of Ai & Ds Erode India Marwadi University Dept. of Computer Science Rajkot India

ISBN: (纸本)9798350379716

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance. © 2024 IEEE.

关键词： Contrastive Learning

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Lightweight Authentication System Based on Zero Knowledge Proof for IoT Devices 2

Lightweight Authentication System Based on Zero Knowledge Pr...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Khandait, Ashish Umesh Pattanshetti, Tanuja R. Coep Technological University Pune Dept. of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350388916

In the rapidly expanding field of the Internet of Things, ensuring secure and efficient communication between IoT devices and servers is crucial. This paper henceforth designs a lightweight authentication system under the Zero Knowledge Proof (ZKP) protocol, utilizing the HMAC hashing algorithm for the improvement of security measures put in place. This system is designed to enable the authentication of IoT devices without sending their actual password, thus avoiding risks due to password interception at the point of authentication. For instance, if using the phrase "God is great"as the password, one can get it hashed into HMAC with this method and still be assured that it is secure even if proof is intercepted. Simulations in the simulator shall evaluate the system's effectiveness and performance. Performance metrics include accuracy, response time, memory consumption, latency, and throughput. The presented results intend to serve lightweight device authentication mechanisms for IoT devices such that the trustworthiness of IoT devices is achieved without hampering their performance. The presented results are also shown to outperform conventional methods. © 2024 IEEE.

关键词： Authentication Protocol

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Bauder, Chandler J. Fathy, Aly E. University of Tennessee Knoxville Dept. of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-depth metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases. © 2025 IEEE.

关键词： Electrocardiography

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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Resume Parser and Job Description Matcher 8

Resume Parser and Job Description Matcher

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Resume Parser and Job Description Matcher is an innovative project aimed at enhancing the efficiency and accuracy of job candidate screening and matching processes. Leveraging natural language processing (NLP) techniques, particularly using SpaCy and machine learning models, the system parses resumes and job descriptions to extract relevant skills, experience, and qualifications. By analyzing textual data and applying advanced algorithms, such as Support Vector Machines (SVM), the system learns to match candidates with job postings based on keyword relevance and contextual understanding. Our paper demonstrates improved matching results compared to traditional methods, facilitating streamlined recruitment processes, and enhancing workforce management strategies. © 2024 IEEE.

关键词： Support vector machines

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Local Closeness Gravity Model to Identify the Vital Nodes in Complex Networks 16

Local Closeness Gravity Model to Identify the Vital Nodes in...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Nandi, Suman Dutta, Animesh National Institute of Technology Durgapur Dept. of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350383119

In complex network analysis, identifying the viral nodes is a major concern of the research domain by which any kind of information or infection is controlled throughout the entire network. Several algorithms have been developed over the past few years to identify the viral nodes (influential spreaders) considering many properties of the network. Among them, some authors proposed gravity-based centrality to identify the vital nodes based on the law of gravity with certain limitations. The major limitation of existing gravity-based methods is the mass of the object (i.e. node) is considered as the degree or kshell index only, which does not always signify the spreading ability of the nodes. To address this research challenge, we propose an innovative Local Closeness Gravity method (named LCG) to measure the influential ability of individual nodes, facilitating the identification of the vital nodes in the network. To minimize the computational complexity of Closeness centrality, at first, we measure the local Closeness centrality of individual nodes considering all the nodes residing in the truncation radius. Thereafter we introduce a new parameter 'information sharing ability' based on connectivity strength to measure the distance between the nodes. Finally, the influential ability of each node is measured based on the gravity model considering the local closeness centrality, kshell index, and the distance. The efficiency of LCG is compared with the existing baseline centrality methods by using the Susceptible-Infectious-Recovered (SIR) simulator. The correlation between the LCG method and the baseline centrality methods with the SIR method is compared by Kendalls' tau method considering various infection probabilities and various percentages of seed nodes respectively. The ranking uniqueness of the LCG method and the baseline centrality methods are also measured by the monotonicity metrics. Through the obtained results and various analyses, it becomes evident that t

关键词： Complex Networks Information sharing ability Local Closeness centrality SIR epidemic simulator Vital nodes

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Strategies for Data Security and Privacy Protection in Cloud Infrastructure 3

Strategies for Data Security and Privacy Protection in Cloud...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Naikwade, Namrata Pathan, Shafi Mit Art Design and Technology University Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

In today's rapidly growing digital era, cloud computing has emerged as an important technology, providing the ability to scale resources efficiently, accessibility, and cost-effectiveness for storing and processing huge amounts of data. However, alongside its numerous benefits, the adoption of cloud computing has also introduced deep concerns regarding data protection and privacy. The current scenario of cloud computing is characterized by increase in usage of cloud services and an exponential growth of data. While organizations leverage the cloud to enhance agility and innovation, the data sharing ability of cloud and the outsourcing of data management raise significant challenges for ensuring the privacy, integrity, and availability of sensitive data. Moreover, the rising cyber threats, regulatory compliance requirements, and the possibility for unauthorized access bring out the critical importance of robust data protection mechanisms in the cloud *** paper presents a comprehensive survey of strategies for data protection and identity protection in the context of cloud based systems. It examines encryption techniques, access control mechanisms, identity management solutions, among other approaches, highlighting their strengths, limitations, and applicability in different cloud deployment models and use cases. Furthermore, the paper discusses emerging trends such as homomorphic encryption, Attributes Based Encryption (ABE) Schemes and Identity Based Encryption (IBE), which hold promise for enhancing data protection in the developing landscape of cloud technology. © 2024 IEEE.

关键词： Cloud security

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Adaptive Forward Error Correction for Voice over IP with Random Linear Network Coding 2

Adaptive Forward Error Correction for Voice over IP with Ran...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Su, Yi-Chiuan Wu, Quincy National Chi Nan University Dept. of Computer Science and Information Engineering Nantou Taiwan

ISBN: (纸本)9798331530099

This paper proposes the use of Random Linear Network Coding (RLNC) as an erasure code in the forward error correction (FEC) mechanism for Internet telephony. It also designs and implements a forward error correction algorithm applied to the Real-time Transport Protocol (RTP), which we call Adaptive Systematic RLNC (AS-RLNC). The algorithm learns the current packet loss rate of the network through the Real-time Transport Control Protocol (RTCP) and dynamically adjusts the number of redundant packets sent. Compared with traditional forward error correction methods based on block codes, this approach can enhance the reliability of data transmission and improve audio quality by 31%. © 2024 IEEE.

关键词： Internet telephony

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