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6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Gaur, Kashish Gaur, Kaamya Diwakar, Manoj Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India Dit University School of Computing Dehradun248009 India

ISBN: (纸本)9798350346961

With rising popularity of social platforms like Google, Amazon, Microsoft, etc., there have been several incidents of cloud cyber-attacks that have been reported in the past few years. End-to-end security have become one of the biggest challenges in the field of cloud computing in recent days. Although there have been several claimed advantages that come from cloud computing models, what needs to be noticed is the loss of control in security over the cloud-based assets [9]. In this paper, we have analyzed the models to deal with cloud-related attacks and with the ways of improving cloud security. We have studied and analyzed each one of the proposals and have finalized one of them as the one model, that is comparatively better than the rest.. © 2023 IEEE.

关键词： Cloud security

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Quantum-Secured E-Commerce with Blockchain, BB84 and BLAKE3 Encryption Using GNN

Quantum-Secured E-Commerce with Blockchain, BB84 and BLAKE3 ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Harikrishnan S Gopikrishnan R M. L. Sworna Kokila V. Bibin Christopher Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The rapid advancements in quantum computing present significant challenges to traditional cryptographic techniques, posing risks to the security of e-commerce transactions. Existing systems are vulnerable to quantumenabled cyberattacks, especially as classical encryption methods, such as RSA and ECC, become obsolete in the face of quantum algorithms. In response, the research proposes an innovative quantum-secure e-commerce fraud detection framework that integrates Graph Neural Networks (GNNs) with Quantum Key Distribution (BB84 QKD), Quantum-Proof-of-Work (QPoW), blockchain technology, and post-quantum cryptographic techniques, including BLAKE3 encryption. The key challenge addressed in the work is ensuring transaction security and fraud detection in a quantum-enabled environment, where classical cryptographic systems are no longer sufficient. Traditional fraud detection mechanisms also struggle to detect subtle anomalies in complex, quantumsecured transaction networks. This approach overcomes these issues by leveraging deep learning-based anomaly detection through GNNs, which model blockchain transactions as graphs to capture intricate relationships between entities. The combination of BB84 QKD ensures secure key distribution, while QPoW offers a resilient consensus mechanism, and BLAKE3 encryption provides fast and secure hashing. The framework aims to offer a robust, scalable solution for ecommerce platforms, financial institutions, and digital identity systems by combining quantum-secure encryption with advanced machine learning for fraud detection. By addressing the growing threat of quantum computing, the methodology provides enhanced security for sensitive transactions, ensuring long-term protection against future quantum-enabled attacks.

关键词： Quantum computing Scalability Graph neural networks Fraud Encryption Consensus protocol Security Electronic commerce Computer crime Anomaly detection

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Detecting and Diagnosing Alzheimer's Disease: A Transformer-Based Survey (DAD-T) 4

Detecting and Diagnosing Alzheimer's Disease: A Transformer-...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Selvam, Prabu Gunasundari, C. Tamilselvi, M. Haneef, Pinjari Suresh, T. Jini, R. School of Computing Srm Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India Roever Engineering College Department of Computer Science & Engineering Tiruchirapalli India India K. Ramakrishnan College of Engineering Department of Aiml Tiruchirapalli India Sri Muthukumaran Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

Alzheimer's Disease (AD) represents a significant health challenge in today's world. Applying deep learning across various disciplines has demonstrated remarkable potential and has garnered substantial interest. The Transformer model has been shown to have exceptional performance across various computer vision tasks, significantly enhancing its application in medical imaging. Compared to conventional CNN, RNNs, and traditional deep learning algorithms, the Transformer network has demonstrated encouraging results in numerous studies and tasks. This paper reviews multiple works for Mild Cognitive Impairment, early diagnosis, AD and related conditions in the Transformer network over the last half-decade. This paper also presents a comprehensive overview of the data sources, pre-processing, feature extraction, training protocols, and evaluation methods to serve as a resource for future research on AD. Additionally, this survey offers a performance comparison of Transformer models across different datasets, a concise examination of these challenges and suggests potential directions for future research. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Advancing ASR for Indian-Accented English: Dataset Creation and Whisper Fine-Tuning

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Procedia Computer science 2025年 258卷 2510-2519页

作者： Jaswanth Kunisetty Pranav Ramachandrula Sruthi S Susmitha Vekkot Deepa Gupta Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Electronics & Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

Despite advancements in Automatic Speech Recognition (ASR) technology, accurately transcribing Indian-accented English, remains a significant challenge. The main challenge associated with the transcription of Indian English is the lack of curated datasets covering a wide range of regional accents in the Indian sub-continent. Addressing this issue, this paper concentrates on building and testing a diverse dataset that captures the nuances of Indian-accented English, covering various regions and dialects across India. In Phase 1, data was collected from over 200 speakers, yielding 70 hours of speech data using custom-made healthcare transcripts in Telugu, Hindi, Kannada, Marathi, Tamil, and Malayalam. Phase 2 data collection include 100 hours of data from around 400 speakers, with transcripts derived from novels, newspapers, books, and online articles across Tamil Nadu, Karnataka, Andhra Pradesh, Maharashtra, Madhya Pradesh, Delhi, Assam, Manipur, and Rajasthan. The comprehensive dataset, spanning a total of 633 speakers, with around 170 hours of data was collected in two phases to understand the variations in pronunciation, intonation, and phonetic emphasis characteristic of the Indian accent. Further, the training was conducted by fine-tuning the existing Whisper ASR model to enhance its performance for Indian-accented English. Our results show that the fine-tuned Whisper-Tiny model achieved a Word Error Rate (WER) of 18.141%, Whisper-Small achieved 17.36%, and Whisper-Medium achieved 15.08%, demonstrating a significant improvement in recognizing and transcribing Indian-accented English.

关键词： Automatic speech recognition Indian accent Whisper Word Error Rate

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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Computer Modeling in engineering & sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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Exploration of the Impact of Remote work and Online Education on Visual Health during the COVID-19 using Machine Learning Approaches 14

Exploration of the Impact of Remote work and Online Educatio...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Shanmukha Aditya, G. Kruthika, B. Venkata Sneha, G. Gupta, Deepa Smitha, T.V. Manek, Asha S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Mathematics Bengaluru India T. John Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

The COVID-19 pandemic has been associated with several manifestations that affect an individual's visual health. To address these concerns, we propose a classification model to predict the impact of COVID-19 on vision. The model is built on a dataset corresponding to visual symptoms and other factors. The proposed classification model will classify people into three categories: those with no impact on vision, those with mild-to-moderate impact, and those with severe impact. Some machine learning algorithms, such as logistic regression, decision trees, random forests, etc., were applied by us to build the model to identify the optimal algorithm for the task. The results obtained from the model show that it has high accuracy, precision, and recall. The model can predict the severity of visual symptoms in people due to COVID-19 with an accuracy of over 84%. The study findings are entirely focused on the E-learning or online learning caused due to COVID-19 pandemic and their impact on vision of people from different age groups. © 2023 IEEE.

关键词： COVID-19

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A Review on Double Spending Problem in Blockchain

A Review on Double Spending Problem in Blockchain

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Abhishek Sah, Bashant Kumar Mehrotra, Tushar Rajput, Gaurav Kumar Sharda School of Engineering & Technology Sharda University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India College of Computing Sciences & It Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350323917

The double-spending problem in blockchain technology is a significant challenge that threatens the integrity and trustworthiness of decentralized systems. This problem occurs when a user attempts to spend the same cryptocurrency unit twice, leading to a situation where the blockchain network must decide which transaction to accept and which to reject. One of the most urgent problems with blockchain technology is the issue of double spending, as it undermines the fundamental principles of trust and transparency that underlie decentralized systems. Various factors can contribute to the double-spending problem, including network latency, malicious actors, and the consensus mechanism used to validate transactions. This study investigates the many approaches put out to solve the double-spending issue in blockchain technology. The proof-of-work consensus mechanism, which necessitates network users to carry out difficult calculations in order to validate transactions, is one of the most popular alternatives. The proof-of-stake consensus technique is an additional remedy, which relies on participants staking their cryptocurrency units to validate transactions. While both mechanisms have their advantages and disadvantages, they are not foolproof and can be vulnerable to attacks. Emerging technologies, like multi-party computation and zero-knowledge proofs, are being investigated in addition to current solutions to the double-spending issue. Overall, this paper highlights the critical nature of the double-spending problem in blockchain technology and evaluates the existing and emerging solutions to the issue. © 2023 IEEE.

关键词： Blockchain

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN)

Multi-Controller Architecture Design and Features in Softwar...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： V. Prabhu P. Balamurugan Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Networking paradigm known as “Software-Defined Networking” (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can't be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done.

关键词： Performance evaluation Scalability Process control Computer architecture Telecommunication traffic Coherence Software Reliability Software defined networking Mobile computing

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Comparison between the incremental conductivity and grey-wolf method for MPPT in photovoltaic system under irregular shadow conditions 1

Comparison between the incremental conductivity and grey-wol...

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1st International Conference on engineering and Advanced Technology, ICEEAT 2023

作者： Assala, Bouguerra Essalam, Badoud Abd Saad, Mekheilef University of Setif 1 Automatic Laboratory of Setif Department of Electrical Engineering Setif Algeria University of Technology School of Science Computing and Engineering Technologies Swinburne HawthornVIC3122 Australia

ISBN: (纸本)9798350383485

Shade significantly reduces the efficiency of a photovoltaic (PV) system, resulting in power loss and lower output. Energy from solar panels can be maximized by using MPPT algorithms to maximize power points, minimize shading's negative effects, and enhance power collection. This article compares two MPPT techniques, Incremental Conductivity (INC) and Grey Wolf Optimization (GWO), under shaded conditions with uniform and uneven irradiance and temperature variation. To demonstrate the practical application of MPPT techniques, a configuration of photovoltaic panels connected in a series under shade is described. In addition, the study examines the effectiveness of these algorithms in maintaining accurate maximum power point tracking as well as their adaptability to changing environmental conditions. Based on the results of our study, it can be concluded that the GWO algorithm outperforms the INC algorithm in precisely tracking the maximum power point over a diverse range of environmental variables, such as shadow, irradiance, and temperature, among others. © 2023 IEEE.

关键词： Maximum power point trackers

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