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2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Jagendra Govindaraju, P. Kumar, Vinish Yamsani, Nagendar Umamaheswari, J. Munagala, Madhu Bennett University School of Computer Science Engineering & Technology Greater Noida India K S R Institute for Engineering and Technology Department of Electronics and Communication Engineering Tiruchengode India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India SR University School of Computer Science and Artificial Intelligence Warangal India Brainware University Department of Computer Science Kolkata India Koneru Lakshmaiah Education Foundation Department of of English Vaddeswaram India

ISBN: (纸本)9798350394412

The accelerating spread of junk food consumption is progressively putting the younger generations at risk of obesity. The concept of health prioritization is deeply rooted in the development of nurturing and general welfare in society. The job has further become more complex by the glaring statistics of people across the globe who are fighting obesity. The World Health Organization (WHO) puts a staggering estimate that approximately 1 billion people across the globe suffer from obesity-related issues, a statistic heavy in terms of impact primarily because obesity greatly affects one's health but also holds fatal risks. Diagnosing the early stages of obesity can just be simple and prevent one from being obese;however, several obstacles stand in the way of its early diagnosis. Symptoms or signs that one is in the trajectory of suffering from obesity generally go unnoticed by the individual, and the availability of specialists to diagnose such risks persistently on time is not always possible. In this regard, the current paper shall showcase an application hitherto unknown using cloud-based diagnosis, in collaboration with machine learning through Secure Access Internet of Things to predict obesity-related risks without having to depend on glaring physical parameter indicators - in this regard, redefining how such information could be sourced even without the need of regularly visiting health professional people. To ensure the best possible health predictability from this machine learning, we gathered and plotted the datasets consisting of obese and overweight people. Other than machine learning, we use a Secure Access IoT sensor network. This allows for real-time monitoring and diagnosis, making early detection achievable. Consequently, this system paves the way for mitigating and substantially reducing the complications associated with obesity. © 2024 IEEE.

关键词： Nutrition

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Deep Neural Networks for Detecting Insider Threats and Social engineering Attacks 4

Deep Neural Networks for Detecting Insider Threats and Socia...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Zewdie, Marshet Girma, Anteneh Sitote, Tilahun Melak Science and Technology University Department of Computer Science & Engineering Adama Adama Ethiopia WashingtonDC United States

ISBN: (纸本)9798350395914

Cybersecurity (CS) plays a crucial role in protecting valuable and sensitive organizational data, systems, computers, and networks from unauthorized access. However, the incressing prevalence of insider threats and social engineering attack (SEA) presents significant challenges in effectively detecting and mitigating of these risks. A yearly report from 2023 highlighted that despite 90% of companies implementing multiple security measures, they still experienced an average loss of 16 million per incident. The detection capabilities of existing detection methods, which are primarily network-based or host-based intrusion detection, have limitations. This article aims to enhance detection methods through a comprehensive analysis of network and host level insiders' behavior along with Deep Learning approaches. This proposed method of detection provide a unified and holistic detection. Insider threats, whether intentional or unintentional, also create vulnerabilities to external threats and attacks such as phishing and SEA attacks. By addressing the gap in insider threat detection, the proposed comprehensive analysis of insider network and host level activities will enhance detection performance and reduce security costs by compact the existing fragmented detection approaches. As a result the false positive and false negative alarms will reduce the cost of detection and mitigate business operation disturbances. Since insiders interact with network devices and computers as users, integrating their host and network behaviors' into the detection methods offer both enhanced detection capabilities and a unified detection. To evaluate the proposed detection method, an Auto-encoder Deep Learning model will be developed, and public network and host intrusion detesets will be utilized. Evaluation metrics such as Accuracy, precision, recall, and F1- score will be employed. Preliminary analysis results have shown the proposed compre-hensive behavior analysis with Deep Learning (DL)

关键词： Network intrusion

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Vision-Based Hand Gesture Recognition System for Assistive Communication Using Neural Networks and GSM Integration 2

Vision-Based Hand Gesture Recognition System for Assistive C...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Singhal, Pooja Verma, Sonia Gupta, Ritu Kumar, Rishi Arya, Rajneesh Kumar Department of Computer Science ABES Engineering College Uttar Pradesh Ghaziabad India Department of Computer Science Bhagwan Parshuram of Institute and Technology GGSIPU Delhi India Dehradun India

ISBN: (纸本)9798331530389

This paper explores the application of vision-based hand gesture recognition in addressing communication challenges faced by deaf and dumb individuals within the realm of human-computer interaction (HCI). Highlighting the barriers posed by conventional communication methods and sign language interpretation, the paper introduces the Dumb Aid Phone system as an innovative solution. This system, functioning as an interface, enables seamless interaction for non-speaking individuals with their surroundings. Utilizing image processing and neural networking, the system captures and interprets hand gestures, matching them with a database to generate corresponding speech sounds. Enhanced by a web camera and a GSM module, the Dumb Aid Phone system not only facilitates effective communication but also holds the potential to revolutionize accessibility, contributing to a more inclusive society for the deaf and dumb community. © 2025 IEEE.

关键词： Gesture recognition

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Code word Based Bi-Server Authentication and Key Exchange Encryption Technique 1

Code word Based Bi-Server Authentication and Key Exchange En...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Thangarasan, T. Karthikeyan, V. Arunkumar, G. Arul Prakash, A. Keerthana, R. Gouthami, Kamsala Madanapalle Institute of Technology & Science Department of Computer Science & Engineering AndhraPradesh Madanapalle India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Department of Computer Science & Engineering Tamilnadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilandu Chennai India K.S.R.College of Engineering Department of Computer Science and Engineering Tamilnadu Tiruchengode India

ISBN: (纸本)9798350328042

The data transfer and communication happening between the client and server or the source to destination should be made in some secure way. It must be protected and authenticated. the passwords related to that all will be stored in a centralized database. The majority of codeword-based user authentication systems assume complete reliance in the authentication server, this refers to the location where centralized databases store passwords in plain text or easily inferred code verification information. System defenses against server-side offline dictionary attacks are nonexistent. In the event that insiders or outsiders hack the authentication server, all user code words become public. which could have severe legal and financial ramifications for a business. A codeword-based client authentication and confidential key exchange system using cutting-edge bi-server architecture serves as the answer to the aforementioned problems. The sever providing the front-end service communicates directly with users in this setup;the control server remains in the background. The proposed system provides security against the offline dictionary attacks that can happen from any of the servers. The proposed work has been implemented by using java and netbeans IDE 6.1. © 2023 IEEE.

关键词： Data transfer

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Enhancing digital security using Signa-Deep for online signature verification and identity authentication

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第2期8卷 58-69页

作者： Ch, Ravikumar Sridevi, Mulagundla Ramchander, M. Ramesh, Vankudoth Kumar, Vadapally Praveen Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Applications Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Emerging Technologies CVR College of Engineering Hyderabad 500039 India

In the contemporary digital realm, the utilization of online services has surged, facilitated by the seamless integration of deep learning technology, which is paramount in applications demanding precision and efficiency. A pivotal use case in this context is online handwritten signature verification, where the need for exceptional accuracy is indisputable. This paper introduces ‘Signa-Deep, ’ an innovative approach designed to address the challenge of online signature verification and the determination of an individual’s authorization status. The study explores a range of methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), GoogleNet, and MobileNet, to discern the authenticity of signatures and affirm the identity of the signatory. The results of our proposed method are promising, showcasing its potential to significantly enhance the security of digital transactions and identity verification processes. In summary, ‘Signa-Deep’ harnesses deep learning technology to bolster the accuracy and reliability of online signature verification, thereby contributing to the overall robustness of digital interactions and identity validation processes. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Authorization Status Deep Learning Digital Transactions Security Identity Authentication Online Signature Verification

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Improving Security and Performance of Databases Through Single Cache System 4

Improving Security and Performance of Databases Through Sing...

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4th International Conference on Blockchain technology and Information Security, ICBCTIS 2024

作者： Ul Haq, Ikram Khan, Salabat Khan, Mansoor Tamsal, Saher School of Computer Science and Technology Southwest University of Science and Technology Mianyang China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331528324

In today's world, connectivity and performance-driven technology are rapidly advancing each year, and new platforms and mechanisms are being utilized to enhance the security and performance of databases and cloud computing, which are trending in the global market. This examination focused on customer information security in cloud computing, i.e., information stockpiling security issues and how to control unapproved access to data by proposing access control;valuable measures were characterized subsequently, and an entrance control show was prescribed. Cloud computing can be described as an innovation that depends on the web and the sharing of resources. It has an adaptable foundation that customers can use with much of a stretch. With its wide event relationship, there are numerous obstacles to continuing with cloud computing because of security issues, mostly considering customer data security. The proposed study helped hesitant clients sensibly continue forward in the cloud while understanding the dangers of cloud computing. The research objective is to examine the security techniques employed in cloud technology to improve its performance by utilizing data mining within a unified cache system. We will apply the Apriori algorithm as a proposed solution to be used in a single cache while considering the security measures in the cloud. The study expects to apply this method to all cloud providers and vendors by entering data into a single cache mechanism that can be extended into different components and implemented using SPSS modelers to improve the security of the database. © 2024 IEEE.

关键词： Cloud security

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Optimizing Vanet Communications: A Bibliometric Analysis of Energy-Efficient Strategies with Deep Learning

Optimizing Vanet Communications: A Bibliometric Analysis of ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Prakash, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Vehicular Ad Hoc Networks (VANETs) are important for intelligent transportation systems, permitting vehicles to communicate with one another and nearby infrastructure in order to enhance road safety, traffic management and infotainment services. But the VANET communication is a high energy consumption technology, which may become a big challenge of large-scale power efficiency being in some real scenarios where huge number vehicles are needed to implement such V2I or V2V services sustaining. This paper conducts a analysis of different transmission stratagems for energy-efficient communication strategies among vehicles in VANETs, along with the focus to incorporate deep learning accelerators into an optimized architecture framework for enhancing communication processes. The paper presents an overview of VANET architecture and communication protocols, and then reviews the techniques to save energy in a VANET including efficient mechanisms for Transmission Power Control (TPC), some energy-aware routing protocols, as well as data aggregation realizations. Next, the paper introduces deep learning models that can adaptively optimize VANET transmission by forecasting network states and updating strategies on-the-fly. This analysis emphasizes hybrid solutions where deep learning is combined with traditional communication strategies to improve the energy efficiency apparent on a system level. Finally addresses the current challenges in the field and the need for advanced AI-driven techniques. This bibliometric analysis provides a comprehensive overview of energy-efficient communication strategies in VANETs, offering insights into how deep learning can contribute to the development of more sustainable vehicular networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Empowering Biomedical Named Entity Recognition Through Multi-Tagger Collaboration 27

Empowering Biomedical Named Entity Recognition Through Multi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Jin Xie, Jian Zhu, Tinghui Guo, Qian Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781643685489

Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in BioNER. However, existing paradigms in the region-based methods suffer from inherent limitations, including the generation of negative samples, and the ignorance of token dependencies. To overcome these limitations, we propose a new paradigm, implemented as Token Cascade Tagger (TCT), which combines span identification and category classification. The TCT utilizes category information to enhance the correlation between the heads and tails of entities, effectively reducing the generation of negative samples. Additionally, we introduce a Token Dependency Tagger (TDT) that captures token dependencies within entity spans by identifying the longest span in a sentence. The TDT filters out incorrect spans and further improves the accuracy of span detection obtained from the TCT. Furthermore, we employ a multi-task learning framework to optimize both the TCT and TDT, leading to superior performance in BioNER. Extensive experiments on publicly available biomedical datasets demonstrate our method outperforms the previous state-of-the-art methods, achieving 92.44%, 92.54%, and 81.26% on NCBI-Disease, BC5CDR, and GENIA, respectively, in terms of F1 score. © 2024 The Authors.

关键词： Multi-task learning

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Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

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computers, Materials & Continua 2023年第6期75卷 4713-4733页

作者： Muhammad Bilal Sandile C.Showngwe Abid Bashir Yazeed Y.Ghadi College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Department of Mathematical Statistics and Actuarial Science Faculty of Natural and Agricultural SciencesUniversity of the Free StateBloemfontein9301South Africa Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAbu DhabiUAE

To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task *** this purpose,authen-tication protocol plays a vital role in web communication which securely transfers data from one party to *** authentication works via OpenID,Kerberos,password authentication protocols,***,there are still some limitations present in the reported security *** this paper,the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern *** proposed OpenID-based encrypted Email’s Authentication,Authorization,and Accounting(EAAA)protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String(SSEAS).This string is deployed on both the relying party and the email server,which is unique and *** first authentication,OpenID Uniform Resource Locator(URL)identity,is performed on the identity provider side.A second authentication is carried out by the hidden Email’s server side and receives a third authentication *** Email’s third SSEAS authentication link manages on the relying party(RP).Compared to existing cryptographic single sign-on protocols,the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing *** study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods,such as Single Sign-On(SSO)and *** experimental sites were examined by 72 information technology(IT)specialists,who found that 88.89%of respondents successfully validated the user authorization provided to them via *** proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.

关键词： Secure user authentication SSO OpenID phishing attack MITM attack

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