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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Glory, J. Sherine Nalina, E. Santhosh, Y. Prasad, d. Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai India

ISBN: (纸本)9798350305708

In our country, the youth play a pivotal role in bringing pride and accolades to our nation. However, it is essential to question the prevailing notion that everyone must aspire to become a doctor or an engineer. Instead, why not encourage them to pursue their passion for singing, dancing, or any other non-conventional path? by integrating embedded artificial intelligence and models into our educational system, we can revolutionize the way we nurture and support young talents. AI-driven platforms identify and nurture individual strengths, tailoring resources for creative pursuits. They analyze learning styles, interests, and aptitudes, providing a customized educational experience. Extracurriculars are crucial for talent development. AI organizes and ensures access to aligned opportunities. Smart scheduling balances academics and co-curriculars. Career counseling and mentorship, powered by AI, offer tailored guidance, boosting confidence and self-esteem. Feedback tools promote resilience and confidence. Interdisciplinary learning connects academic and creative interests, preparing students for the modern world. This well-rounded approach empowers youth to excel and contribute meaningfully. AI unlocks every student's potential, shaping a generation of accomplished individuals. © 2023 IEEE.

关键词： Career Counselling Educational Revolution Extracurricular Activities Feedback and Assessment Holistic development learning Styles Mentorship Programs Skill Set development Smart Scheduling Systems Student Guidance Talent Identification

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Security System for Services Access in Smart City System Using Internet of Everything and Cloud 2

Security System for Services Access in Smart City System Usi...

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2nd International Conference on disruptive Technologies, ICdT 2024

作者： Wadhwa, Amit Arora, Neerja G. L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350371055

With the advancement of technologies like cloud computing and Internet of things, there are enormous areas in which these can be utilized. One such domain is Smart City concept utilizing or based on the architecture of Internet of everything. In a smart city the distinguish concept is the utilization of internet of everything architecture to effectively manage the services provided to users. Moreover, integration of internet of everything with cloud computing architecture would provide a better ecosystem in the area of smart cities. Providing services to smart city users could pose certain challenges and security issues with the integration of cloud computing. As per previous studied literature there is scope for improving access and authentication-based security of services imparted to smart city users. In this work, initially the consolidation of internet of everything (IoE) and cloud computing in smart cities is put forward. Further, this work focusses upon issues and challenges encountered in leveraging IoE in smart city environments with analysis of existing techniques for authentication and access control of cloud-based services and later provides a proposed system focusing upon imparting access control and authentication-oriented security for services delivered over a cloud and IoE followed by its analysis. © 2024 IEEE.

关键词： Access Control Authentication Cloud Computing Services IOE (Internet of Everything) Smart Cities

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Securing MIMO Wiretap Channel With learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, diep N. linh Trung, Nguyen Nguyen, Van-dinh Van Huynh, Nguyen Thai Hoang, dinh Krunz, Marwan dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BlER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BlER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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Review Based on Blockchain and Financial Transactions Related to Cryptocurrencies Across Globe 11

Review Based on Blockchain and Financial Transactions Relate...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Sonekar, S.V. Bejjaniwar, Saikiran dandekar, Shejal Pal, Sarthak Karwade, Pratik J D College of Engineering and Management Department of Computer Science and Engineering Nagpur India J D College of Engineering and Management Department of Information Technology Nagpur India

ISBN: (纸本)9798350348422

Blockchain is one of the most interesting and exciting field of technology today it provides a decentralized network for anyone who is willing to store data on it as blockchain is relatively new there is still experiments going on it to reveal its true potential. One of the applications of blockchain which has gone incredibly popular is cryptocurrency and also blockchain payment system. In today's blockchain payment system there are few components which are required for a successful transaction such as nodes, ledger, wallet, nonce and hash. Payment gateways are one of the first blockchain application that provide a decentralized way to do transactions we will also discuss some of the ledger applications of blockchain. We carefully selected the most recent research papers in this review paper. We will be reviewing various research paper and they take on blockchain technology. © 2023 IEEE.

关键词： Smart contract

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Novel Face Mask detection Technique using Machine learning to control COVId’19 pandemic

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Materials Today: Proceedings 2023年 80卷 3714-3718页

作者： Gupta, Sandeep Sreenivasu, S.V.N. Chouhan, Kuldeep Shrivastava, Anurag Sahu, Bharti Manohar Potdar, Ravindra Department of Electrical Engineering JECRC University Jaipur India Department of Computer Science and Engineering Narasaraopeta Engineering College Andhra Pradesh Narasaraopet India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Lakshmi Narain College of Technology and Science Indore India Department of Computer Science and Engineering Dr. D.Y. Patil Institute of Technology Pimpri Pune India Department of Electronics and Telecommunication Engineering Bhilai Institute of Technology Chhattisgarh Durg India

The COVId-19 pandemic has been scattering speedily around the world since 2019. due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of antiviral drugs is one of the reasons for the spreading of COVId-19 virus. This disease is spreading continuously and easily due to some common mistakes by people, like breathing, coughing and sneezing by infected persons. The main symptom is the normal flu. Therefore, in the present condition, the best precaution for this disease is the face mask, which covers both areas of mouth & nose. According to the government and the World Health Organization, everyone should wear a face mask in busy places like hospitals and marketplaces. In today's environment, it's difficult to tell if someone is wearing a mask or not, and physical inspection is impractical since it adds to labour costs. In this research, we present a mask detector that uses a machine learning facial categorization system to determine whether a person is wearing a mask or not, so that it may be connected to a CCTV system to verify that only persons wearing masks are allowed in. © 2021

关键词： COVId-19

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A robustness assessment approach for transportation networks with cyber-physical interdependencies

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Resilient Cities and Structures 2025年第1期4卷 71-82页

作者： Ntafloukas, Konstantinos Pasquale, liliana Martinez-Pastor, Beatriz McCrum, daniel P. School of Civil Engineering University College Dublin Dublin D07 R2WY Ireland School of Computer Science University College Dublin Dublin D07 R2WY Ireland

While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case. Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependencies between the physical and cyber networks. As a result, the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks. Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems. Firstly, they rely on the assumption of a uniform and strong level of interdependency. That is, once a node within a network fails its counterpart fails immediately. Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure, that now serve as a cyber-physical system. The last is responsible not only for the physical operation (e.g., flow of vehicles) but also for the continuous data transmission and subsequently the cyber operation of the entire transportation network. Therefore, the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects. In this paper, we propose a new robustness assessment approach for modern transportation networks and their underlying interdependent physical and cyber network, subjected to earthquake events. The novelty relies on the modelling of interdependent networks, in the form of a graph, based on their interdependency levels. We associate the serviceability level of the coupled physical and cyber network with the damage states induced by earthquake events. Robustness is then measured as a degradation of the cyber-physical serviceability level. The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from rea

关键词： Seismic response

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Effective Segmentation and Classification of Brain Tumour MRI Images Using YOlOv8 Technique

Effective Segmentation and Classification of Brain Tumour MR...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Taradale, Aniket Prabhu Kattimani, Suvarna laxmikant Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura India

ISBN: (纸本)9798331505134

Brain tumour classification is crucial for prompt diagnosis and life-saving treatment. A novel approach employing You Only look Once (YOlO) technology for brain tumour segmentation and classification We used pre-existing data for tumour diagnosis and categorised in four main types: meningioma, glioma, pituitary, and no tumours. In diagnosis, segmentation is necessary to identify regions containing tumours seen in medical images such as MRI scans. Our method offers use of YOlO's real-time product search capabilities and makes them relevant for clinical use. The method achieves 95% F1-Score, 96.2% precision, 93.6% recall, and 97.2% mAP50 in identifying and segmenting tumour regions while using a large dataset of MRI scans as training. This method is superior to traditional segmentation methods as it provides a fast and accurate target detection method. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Novel Image Processing Technique Using Thresholding and deep learning Model to Identify Plant diseases in Complex Background

Novel Image Processing Technique Using Thresholding and Deep...

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International Conference on Next Generation Systems and Networks, BITS EEE CON 2022

作者： Khan, Saiqa Narvekar, Meera Computer Engineering Department M. H. Saboo Siddik College of Engineering Mumbai India Computer Engineering Department D. J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9789819904822

Every year tomato crops suffer huge losses due to the occurrence of disease outbreaks. Therefore, the identification of diseases has its importance in the research fraternity. However, this whole process is influenced by the presence of a complex natural environment and similarity in diseases in terms of disease symptom patterns, size of disease region, and similar color variations in leaves with its background. To counter all these issues, in this work we propose an Integrated model that helps to distinguish between various tomato diseases. The Region of Interest ROI identification process along with CNN architecture can capture discriminative features. To ensure the effectiveness of the proposed work, the model has been evaluated on a real-world dataset and Plant Village standard dataset and it is compared with several state-of-the-art CNN architectures. It is shown that the proposed model has achieved higher accuracy compared to other state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： deep learning

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Advancements in Quantum Error Mitigation Strategies for Reliable Quantum Computing on IBM Simulators 6

Advancements in Quantum Error Mitigation Strategies for Reli...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sherin Beevi, l. Joe Prathap, P.M. Sudhish Reddy, d. Agnes Joshy, S. Vinil dani, W. R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India Francis Xavier Engineering College Department of Information Technology Tirunelveli India St. Peter's College of Engineering and Technology Department of Electrical and Electronics Engineering India

ISBN: (纸本)9798350306613

We explore the field of quantum computing and specifically address the crucial problem of error reduction in the context of IBM simulators. Although quantum computing is a rapidly developing technology with great potential, its practical applications are severely hindered by its inherent error-proneness. We tackle this problem by introducing new developments in quantum error correction techniques designed for IBM simulators. In contrast to conventional methods that frequently depend on large matrices or pre-established error models;our technique presents a flexible and dynamic approach. By utilizing several variations of a main circuit, in which classically efficient gates are used in place of non-Clifford gates, we are able to discover the best compensation scheme from scratch for actual quantum systems. The suggested method exhibits great flexibility in handling different levels of noise and different kinds of noise, such as spatially and temporally correlated variations. It also effectively reduces errors on IBM quantum hardware as well as precisely emulated imperfect quantum computers. By offering a thorough investigation of sophisticated error mitigation techniques, this research advances the continuing quest for dependable quantum computations and opens up interesting new directions for improving both the accuracy and performance of quantum computing operations carried out on IBM simulators. © 2024 IEEE.

关键词： Adaptive Compensation Strategies IBM Quantum Simulators NISQ Quantum Computing Reliability Quantum Error Mitigation

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AI based Analysis of Hyperspectral Images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Jadhav, Samarth Yogesh More, Rutuja Rajatam Tingare, Bhagyashree Ashok lakshmi, G. Prasanna Kolse, Shivam darwante, N.K. Sanjivani College of Engineering Department of Information Technology MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani College of Engineering Department of Electronics and Computer Engineering MH Kopargaon India

ISBN: (纸本)9798350390049

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control. © 2024 IEEE.

关键词： Convolutional neural networks

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