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arXiv 2024年

作者： Dhinakaran, D. Selvaraj, D. Dharini, N. Raja, S. Edwin Priya, C. Sakthi Lakshmi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India R.M.K College of Engineering and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India

The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS-Kyber, and Zero-Knowledge Proofs (ZKPs) for securing data in cloud-based blockchain systems. The primary objective is to fortify data against quantum threats through the implementation of QKD, a quantum-safe cryptographic protocol. We leverage the lattice-based cryptographic mechanism, CRYSTALS-Kyber, known for its resilience against quantum attacks. Additionally, ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment. A significant focus of this research is the performance evaluation of the proposed framework. Rigorous analyses encompass encryption and decryption processes, quantum key generation rates, and overall system efficiency. Practical implications are scrutinized, considering factors such as file size, response time, and computational overhead. The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats. The findings contribute a robust quantum-safe and ZKP-integrated security framework tailored for cloud-based blockchain storage. By addressing critical gaps in theoretical advancements, this research offers practical insights for organizations seeking to secure their data against quantum threats. The framework's efficiency and scalability underscore its practical feasibility, serving as a guide for implementing enhanced data security in the evolving landscape of quantum computing and blockchain integration within cloud environments. © 2024, CC BY.

关键词： Quantum cryptography

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Enhancement of Channel Capacity in 5G Ultra Dense Network-UDN

Enhancement of Channel Capacity in 5G Ultra Dense Network-UD...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Ritesh Rastogi T. Shanthi Pramod Kumar Naik m. Sahaya Sheela A.K mishra Nandhini T J Department of IT and CS M.Tech(Int) Noida Institute of Engineering and Technology Delhi India Department of ECE Paavai Engineering College Chennai India Departmeny of Computer Science Engineering Dayananda Sagar University Bangalore India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Math Unit Foundation Program Dhofar University Salalah Oman Saveetha School of Engineering Saveetha Institute of Medical and Technical Science - (SIMATS) Chennai India

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the “channel capacity” in a 5G ultradense network, when the phrase “channel capacity” is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes; both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process.

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Deep Learning Assisted Blind modulation Classifier for Blockage-Infused Sparse Visible Light Communications

Deep Learning Assisted Blind Modulation Classifier for Block...

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Communications and Networking (mECOm), IEEE middle East Conference on

作者： Parvez Shaik Cihat Keçeci Kamal K. Garg muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electrical and Computer Engineering Texas A&M University College Station TX USA School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India Department of Computer Science Tennessee Tech University Cookeville TN USA

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

This study investigates the performance of a deep learning-assisted convolutional neural network (CNN) based modulation classifier for blockage-infused sparse visible light communications (VLCs). A single VLC access point-based office setup is considered with human blockages obstructing the receiver node (user). System performance is assessed by a CNN-based neural network to distinguish the received noisy modulated symbol at the receiver without having prior knowledge of the transmitted symbols. modulation schemes such as binary phase shift keying, quadrature amplitude modulation, and on-off shift keying are considered. Performance metrics illustrate that the blockages cause a significant impact on signal classification. computer simulations corroborate the correctness of the obtained analytical expressions.

关键词： Quadrature amplitude modulation Phase modulation System performance Symbols Pattern classification Receivers Binary phase shift keying Convolutional neural networks Noise measurement Visible light communication

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Revolutionizing Agriculture: Detection of Plant Disease using machine Learning and Deep Learning Algorithms

Revolutionizing Agriculture: Detection of Plant Disease usin...

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Sustainable Expert Systems (ICSES), International Conference on

作者： L Sherin Beevi P. m. Joe Prathap Kolagotla Vishnu Vardhan Reddy Kommini Balaji mallavaram Nishith W Vinil Dani Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of EEE St. Peter's College of Engg.and Tech. Chennai INDIA

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The threat of plant diseases to global food security highlights the importance of prompt and precise detection. for effective management. Traditional methods are labor-intensive and subjective, prompting the exploration of automated systems. By integrating the advancements in Deep learning and machine learning, this research study investigates the efficacy of algorithms like SVm, Random Forest, CNN, RNN, and ResNet in classifying plant diseases from leaf images. We aim to contribute to enhanced agricultural productivity and food security through automated disease detection.

关键词： Support vector machines Deep learning Training Plant diseases machine learning algorithms Plants (biology) Transfer learning Food security Classification algorithms Random forests

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A Conceptualized Study of mL Classifiers in Solid Waste management

A Conceptualized Study of ML Classifiers in Solid Waste Mana...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Prabha Shreeraj Nair Akanksha Srivastav mir Aadil Department of Information Technology & M. Tech Integrated Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The phenomenon of population development and the subsequent acceleration of urbanization has resulted in a significant upsurge in the generation of municipal solid garbage. Consequently, scholars and experts have endeavoured to employ sophisticated technology in order to address this pressing issue. In recent years, there has been a progressive integration of machine learning (mL) algorithms into the field of municipal solid waste management (mSWm) with the aim of facilitating sustainable environmental growth. mL algorithms have demonstrated their effectiveness in modelling intricate nonlinear systems. This study involved the assessment and analysis of over 200 papers that were published throughout the timeframe of two decades, namely from 2016 to 2023. This study provides a comprehensive overview of the utilization of machine learning algorithms throughout the entirety of mSWm, encompassing trash creation, collection and transportation, and ultimate disposal stages. This thorough study aims to address the gaps and identify future directions in the application of mL in mSWm. The findings of this review offer both theoretical and practical guidelines for future research in this field.

关键词： Waste management Waste materials machine learning algorithms Reviews Urban areas Transportation Pressing machine learning Solids Nonlinear systems

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Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and mobileNetV2 for Accurate mRI Diagnostics

Deep Learning Advances in Brain Tumor Classification: Levera...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Alex David S Almas Begum S Ismail Kalilulah Ruth Naveena N Rajathi K Hemalatha D Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Dr. M. G. R. Educational and Research Institute Chennai India Department of Mathematics Hindustan Institute of Technology & Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

These modern deep learning models, such as VGG16 and mobileNetV2 will be utilized in this study to classify Brain tumors from a unique specialized mRI dataset The dataset consists of brain tumor original and augmented images as a two-class (yes/no) classification problem domains, where yes indicates the presence of a tumor while learns no corresponds to its absence. Two models VGG16 and mobileNetV2 support deep convolutional layer operations, well known for their very deep architecture; the other one is optimized over efficiency with depth-wise separable convolutions and is used to evaluate their performance in accurately classifying mRI images. The models use similar data preprocessing and regularization techniques; however, their performance metrics are different. Accuracy, precision, recall, and F1-score are the evaluation metrics for each of the five models to understand how well these differentiate between tumor images versus non-tumor images. This study is designed to improve the ability of brain tumor detection, thereby assisting in more reliable diagnosis and clinical decision-making.

关键词： measurement Deep learning Accuracy magnetic resonance imaging Decision making Data preprocessing Brain modeling Reliability engineering Control systems Data models

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Performance Analysis of Novel Saw-Tooth Shaped Fractal Boundary Square micro Strip Patch Antenna 7

Performance Analysis of Novel Saw-Tooth Shaped Fractal Bound...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Sathesh, m. Govindaraju, P. Kalyanasundaram, P. Gurusamy, Ravikumar Sengottaiyan, N. Rajasekar, m. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Avadi Department of ECE Tamil Nadu Chennai600062 India KSR Institute for Engineering and Technology Department of ECE Tiruchengode637215 India KSR Institute for Engineering and Technology Department of IT Tiruchengode637215 India M.P.Nachimuthu M.Jaganathan Engineering College Department of ECE Erode India Alliance College of Engineering and Design Computer Science Faculty of Science and Technology Alliance University Karnataka Anekal562106 India Institute of CSE SIMATS School of Engineering Saveetha Institute for Medical and Technical Sciences Chennai602105 India

ISBN: (纸本)9798350340600

Aim: This study contributes in the modelling of a high gain Saw-Tooth shaped fractal boundary square antenna (SFBS Antenna) in comparison with the square shaped microstrip patch antenna (mPA) in the 1.0 GHz to 10.0 GHz band of frequency. materials and methods: Group 1 is assumed to be SFBS Antenna with sample size (n = 26). Group 2 is a Square shaped mPA with sample size (n = 26). The substrate is FR4 Epoxy type. The confidence interval is 95%, the threshold is 0.05%, and the G power value is 80%. Results: The Saw-Tooth shaped fractal boundary square antenna has higher gain between 4.34 dB to 43.56 dB than the Square shaped micro strip patch antenna gain of 3.56 dB to 38.78 dB. The Saw-Tooth shaped fractal boundary square antenna has maximum gain at the frequency range of 10.0 GHz with significance 0.005. The square shaped microstrip patch antennas have maximum gain at 10.0 GHz. Conclusion: This study concluded that the Saw-Tooth shaped fractal boundary square antenna gives significantly better gain at frequency range of 10.0 GHz in comparison with the square shaped mPA. © 2023 IEEE.

关键词： Fractals

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Deep Learning Based Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach

Deep Learning Based Driver Drowsiness Detection in Real Time...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Ashin Ajay Naveen Kumar Ashok Gokul Balajiram Abinesh Tamizhselvan Senthil Kumar Thangavel Somasundaram K M. Tech in Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India B. Tech in Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics School of Physical Sciences Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Effective driver behavior monitoring is necessary due to the increase in traffic accidents caused by distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-based methodology. The model uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a publicly accessible dataset. The strategy seeks to improve road safety by offering a reliable way to identify distractions.

关键词： Deep learning Sentiment analysis Transfer learning Road safety Real-time systems Reliability Convolutional neural networks Driver behavior monitoring Vehicles

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An IoT Based Framework for monitoring Power Consumption in Smart Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： R Raghavendra Nidhi Chauhan Gaurav mittal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Information Technology & M. Tech Integrated Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The electrical power system grid in India, commonly referred to as the power grid, has been in operation for an extensive duration. During this time period, there were no significant advancements or alterations documented in the electricity grid infrastructure. The need for electrical power consumption is seeing a significant increase, but the current grid infrastructure is unable to meet these growing demands. In order to meet the demands of future power load, it is imperative to develop an enhanced system that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power management system and explains its automation and functionality by utilising smart grid and IoT technologies. The research suggests a method for keeping an eye on consumer power use and controlling the use of electrical devices by putting in place a system that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problems with power usage. Future system improvements that are region-specific and intended to reduce loads and maximise system performance are made possible by the data collected.

关键词： Power demand System performance Power system management Voltage System improvement Smart grids Power system reliability

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Decision for vertical handover using k-means clustering algorithm

Decision for vertical handover using k-means clustering algo...

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2020 IEEE Bombay Section Signature Conference, IBSSC 2020

作者： Goutam, Siddharth Unnikrishnan, Srija Kudu, Neel Mumbai Maharashtra400050 India M.Tech Computer Engineering VJTI Matunga Mumbai Maharashtra400019 India

ISBN: (纸本)9781728189932

In this research paper, we have implemented the decision for Vertical Handover using k-means Clustering. We have analyzed the accuracy of the algorithm. The input parameters considered are Received Signal Strength (RSS), Quality of Service (QoS), Bandwidth and Network Coverage. We have derived the Quality of Service for a network using network parameters like packet loss, latency and jitter. © 2020 IEEE.

关键词： Quality of service

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