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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Thomas, Aby K Laxmikanth Reddy, C. Kumar Suman, Sanjay Tamilselvi, S. Devarasu, Neelamegam Arulananth, T.S. Alliance University Alliance College of Engineering and Design Dept. of Ece Karnataka Bengaluru India Malla Reddy Engineering College Dept. of Ece Telangana Secunderabad India Dean R&d St. Martin's Engineering College Dept. of Ece Secunderabad India Kongu Engineering College Dept. of Computer Technology TN Erode India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Ece TN Chennai India Mlr Institute of Technology Dept. of Ece Telangana Hyderabad India

ISBN: (纸本)9798350364729

Web 3.0's dynamic environment calls for an intelligent transition toward decentralized, privacy-aware, and ethical AI. We have provided a detailed analysis of how the suggested technique, which brings together federated learning and reinforcement learning, stacks up against more conventional approaches to machine learning. We examined these solutions across critical aspects, including data protection, customization, flexibility, ethical compliance, and scalability. The findings have been successfully communicated via the use of visualizations. Our research shows that the suggested approach, which utilizes distributed learning, is superior in many important respects. By putting sensitive data where it belongs - on users' devices - it ensures the highest levels of privacy possible while yet allowing for customization. Reinforcement learning's incorporation guarantees Web 3.0 intelligent systems can adapt to user behavior. In addition, the process is ethically compliant, meeting the requirements of the modern internet age. The capacity to scale up as the number of users increases is also fully supported. While conventional approaches do provide some useful resources, they fall short when compared to the new way. They are less suited to the requirements of Web 3.0 because they typically fail to achieve the optimal balance between data protection, customization, flexibility, and ethical compliance. © 2024 IEEE.

关键词： Federated learning

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Performance Analysis of RFID and IoT Based Smart Library Management System 2

Performance Analysis of RFID and IoT Based Smart Library Man...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Achary, Rathnakar Alliance College of Engineering and Design Alliance University Department of Information Technology Bangalore India

ISBN: (纸本)9781665433686

The proliferation of the Internet of Things (IoT) has impacted individuals' lives and the operations of organizations. This transformation is due to the smart feature of these IoT technologies along with radio frequency identification (RFID) devices and wireless sensor networks. Applications of IoT with RFID have extended sizes and dimensions in improving the performance of library management systems. In this paper, the characteristics of RFID and IoT based library management system is explored and proposed an architecture for seamless integration of library local network to the Internet and the cloud servers. The parameters of the RFID system's degrading its performance is also analysed, and the techniques for maximizing them is proposed. A prototype is designed for the simulation and results are provided, together with demonstration of a RFID tag attached to a book. © 2021 IEEE.

关键词： Natural frequencies

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Machine Learning-Based Optimization for Identifying Effective Drugs in Breast Cancer on an In Vitro Platform

Machine Learning-Based Optimization for Identifying Effectiv...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Mahima Saxena P v v s Eswara Rao Amar Choudhary P. Srinivas Reddy Vipin Kumar Sharma Vinish Kumar Department of Computer Science Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science Sasi Institute of Technology and Engineering Tadepalligudem India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Mechanical Engineering Department CVR College of Engineering Rangareddy India Department of MCA IMS Engineering College City Country Department of Computer Science (AIML) Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this innovative quest for more efficient irrigation, we investigate the combined use of sensor technology and machine intelligence to optimize agricultural water usage. We collect precise data on essential variables, including water flow, temperature, humidity, soil moisture, and water level, every 15 days by promptly deploying sensors in the field to monitor them in real-time. We use the vast amount of data available to us to train machine learning models, including Recurrent Neural Networks (RNN), K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), specifically for streams that are anticipated to have a certain distribution. CNN has exceptional precision with a decoding accuracy of 94.5% in visual signal interpretation, surpassing ANN, KNN, and RNN which achieve lower accuracies of 91.1%, 88.7%, and 83.6% respectively. Our investigation uncovers a fluid interaction between sensor data and model training, leading to each model exhibiting unique characteristics. These findings not only showcase the accuracy with which machine learning can distribute water, but they also signify a noteworthy progress in resource-efficient and sustainable agriculture. In addition to generating mathematical discoveries, our research establishes the foundation for advanced irrigation systems that smoothly integrate mechanical and natural processes in agricultural fields.

关键词： Temperature sensors Radio frequency Irrigation Accuracy Recurrent neural networks Machine learning Nearest neighbor methods

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The Integration of Connectivity and System Integrity Approaches using Machine Learning for Enhancing Network Security 10

The Integration of Connectivity and System Integrity Approac...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar, K Sudheer Jain, Amit Kumar Shelke, Chetan Janagi, R. Rawat, Amar Jeet Kapila, Neha School of Cs & Ai Sr University Telangana Warangal India Delhi University Computer Science Delhi India Alliance College of Engineering and Design Alliance University Bangalore India Bannari Amman Institute of Technology Cse Sathyamangalam India Uttaranchal University India Lovely Professional University Department of Electronics and Electrical Engineering Punjab Phagwara India

ISBN: (纸本)9798350382471

The practise of environment monitoring has substantially advanced because to recent developments in technology for communication, 'like the Internet of Things (IoT)'. Technological solutions have the capacity to track, analyse, and understand our surroundings, enabling modernizations that improve the standard of living. Billion intelligent gadgets are connected via the 'Internet of Things (IoT)', so they may easily communicate with each other. It is the computer industry with the quickest rate of growth. On the one hand, Technological solutions are essential for improving a number of practical smart applications that can raise the quality of life. 'Machine learning, the Internet of Things', and other cutting-edge digital technologies enable management to acquire, analyse, interpret, and store huge volumes of data quickly and effectively. Content that may be retrieved later can also be saved using these techniques. This also puts the organization's ability to protect the information and data needed to sufficiently improve network security in grave danger. The purpose of this study is to illustrate how machine learning may be used to integrate connectivity and system integrity techniques for boosting network security. The researchers have critically explained the research topic comprehensively by adopting appropriate research methodology. Furthermore, This study has taken into account secondary and empirical research to gather relevant information related to research topic. © 2023 IEEE.

关键词： 'Internet of Things or IoT' 'Machine Learning or ML' Cyber networks security

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EHLM: Empirical design of Novel Road Curve and Lane Identification Scheme using Effective Hybrid Learning Methodology

EHLM: Empirical Design of Novel Road Curve and Lane Identifi...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Mercy, S. Sudha Ramya, G. Sankar, R. Saravanan, L. Krishnamoorthi, K. R.M.K. Engineering College Department of Computer Science and Design Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai India S.A. Engineering College Department of MCA Chennai India PERI Institute of Technology Department of ECE Chennai India Sona College of Technology Department of EEE Salem India

ISBN: (纸本)9798350319200

In this study, we present a novel Road Curve and Lane Identification Scheme that harnesses the power of an Effective Hybrid Learning Methodology (EHLM). This advanced approach combines Convolutional Neural Networks (CNN), Mask R-CNN, and ResNet, creating a formidable framework for road curve detection and lane identification in complex driving scenarios. The EHLM framework offers a versatile solution that excels in detecting road curves and accurately identifying lanes, crucial components of autonomous driving systems and advanced driver assistance. It leverages the strengths of each architecture, from CNN's feature extraction capabilities to Mask R-CNN's precise instance segmentation and ResNet's deep learning prowess. This study provides a comprehensive overview of the EHLM approach, showcasing its efficacy in real-world road scenarios. Through extensive experimentation and evaluation, we demonstrate the superiority of our methodology, achieving precise road curve detection and lane identification. Our research contributes to the development of safer and more efficient autonomous vehicles, ultimately enhancing road safety and transportation *** our evaluation, we have considered several models, including CNN, DCNN, MRCNN, CNN-LSTM, ANN, and our Proposed Model. Among these contenders, the Proposed Model stands out prominently in terms of accuracy, achieving an impressive 97.23%. This indicates its remarkable ability to correctly classify and recognize the target elements within the dataset. © 2023 IEEE.

关键词： Artificial Neural Network (ANN) Convolutional Neural Network Effective Hybrid Learning Methodology (EHLM) Lane Identification Scheme

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Deep Learning for Automated Classification of Kidney Lesions in Chronic Kidney Disease Patients 2

Deep Learning for Automated Classification of Kidney Lesions...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Lakshmi, B. Seetha Anand, Vijay Omotunde, Habeeb Birundadevi, M. Mahalakshmi, S. Brahmam, Madala Guru Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Namakkal India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Department of Information Systems Riyadh Saudi Arabia Sri Sairam Siddha Medical College and Research Centre Department of Maruthuvam Chennai India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data - specifically quantitative and qualitative data - to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care. © 2024 IEEE.

关键词： Automated Classification Chronic Kidney Disease Deep Learning Diagnostic Accuracy Kidney Lesions Medical Image Analysis

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Numerical investigation of smart material-based structures for vibration energy-harvesting applications

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Energy Harvesting and Systems 2024年第1期11卷

作者： Atul Deepak, G. Divya Department of Mechanical Engineering Alliance College of Engineering and Design Alliance University Bangalore562106 India Department of Mechanical and Industrial Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

The present work deals with energy-harvesting devices, which are useful in scavenging power using piezoelectric materials. Utilizing classical beam theory and classical plate theory, finite element modelling has been carried out to optimize the performance of output power of a cantilever beam and a flexible rectangular plate. Harmonic oscillations and base excitation will be the two different forcing functions used to drive the system. Based on this, numerical investigations of the performance of output power for the piezoelectric cantilever beam and flexible rectangular plate with different cases are considered. The present work is also useful for designing the piezoelectric cantilever beams and plates to extract maximum output power within the frequency ranges from 0 to 200 Hz. Numerical investigations on the piezoelectric cantilever beam and flexible rectangular plate with different cases reveal that the performance of output power is influenced by factors like load resistance, applications of with and without host structures, and different design parameters like unimorph, bimorph, embedded, line- and cross-type piezoelectric patch arrangements. © 2024 the author(s), published by De Gruyter.

关键词： Energy harvesting

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Healthcare 4.0: A Review of Phishing Attacks in Cyber Security

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Procedia computer Science 2023年 230卷 874-878页

作者： K S N Sushma Viji C Rajkumar N Jayavadivel Ravi Stalin M Najmusher H Research Scholar School of Computer Science and Engineering REVA University Bangalore Karnataka India Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance UniversityBangalore Karnataka India Department of Computer Science & Engineering HKBK College of EngineeringBangalore Karnataka India

The rapidly developing scene of Healthcare 4.0, has turned into a fundamental concern. This paper dives into the multi-layered domain of medical care network safety, with specific emphasis on the persistent phishing assaults. We examine the motivation for cyberattacks and the challenges involved. In this paper we review the phishing attacks in medical care, talking about preventive measures to handle this danger. By embracing this through methodology, Healthcare organizations can sustain their protections, shield patient information, and maintain the trustworthiness of medical care frameworks, all while encouraging a culture of network safety status in the computerized age.

关键词： Cybersecurity healthcare systems 4.0 threat vulnerability social and Phishing attacks

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Protecting SDIIOT Network from Key Breach Attack by using Secret Hash Chain Techniques

Protecting SDIIOT Network from Key Breach Attack by using Se...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Jatin Arora Kamal Saluja G S Pradeep Ghantasala Rajesh Sharma R Akey Sungheetha Susheel Kumar Singh Pellakuri Vidyullatha Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Heera Lal Yadav Balika Degree College Lucknow UP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Attacks in a software-defined network (SDN) are hard to find. In light of this, this article offers a versatile modular architecture that enables DDoS attack prevention and mitigation in SDN environments. The interlinked computational devices present in the small items over the communication channel, which allows those devices to transmit industrial data, is known as the Industrial Internet of Things (IIoT). Since the conversation is conducted online, security and privacy breaches are possible. As a result, the authenticated key agreement (AKA) of communicating devices is crucial. However, every IOT device is limited in computational resources, so it might be difficult to add robust privacy and security features. AKA research is now focused on forward secrecy (FS) feasibility that provides control over future keys when a secret key is compromised. IoT devices with limited resources cannot process public key cryptosystems because of intensive calculations. The primary goal of AKA with FS for IIoT is to provide a lightweight security framework. Instead of using a public key security framework, it is using multiple hash values. The security strength of the suggested framework is built on challenge-response mechanisms, one-way hash functions, and symmetric key encryption. The inclusion of this suggested architecture demonstrates that it provides a protection against a variety of attacks, secret key negotiation, anonymity, and unlinkability. The proposed approach is analyzed to assess the performance of the security framework and demonstrates that it is a lightweight mechanism for IIoT.

关键词： Hash functions Protocols Prevention and mitigation Authentication computer architecture Public key cryptography Security Software defined networking Protection Industrial Internet of Things

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Plant Disease Detection and Classification Using Transfer Learning

Plant Disease Detection and Classification Using Transfer Le...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karthigaiveni, S. Madhubala, C. Avinash, G. Saranya, E. Rohini, P. Yogapriya, G. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Avadi Tamilnadu Chennai India School of Environment and Architecture Design Srm Institute of Science and Technology Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science Engineering Chennai India Saranathan College of Engineering Department of Computer Science and Engineering Tamilnadu Trichy India Chettinad School of Architecture Chettinad Academy of Research and Education Tamilnadu Chennai India

ISBN: (纸本)9798331543617

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to forecast and identify plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavoring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Farmers using automated image processing techniques in their fields may mitigate losses and enhance productivity. This study proposes using a deep learning framework utilizing the latest convolutional neural network model EfficientNet to identify tomato illnesses based on 18,161 plain and segmented photos of tomato leaves. The efficacy of two segmentation theories, namely U-net and Modified U-net, for leaf segmentation is shown. The proposed methodology for plant disease identification is straightforward and computationally efficient, necessitating less time for predictions compared to previous deep learning methodologies. This document presents the computed accuracies for several plant and leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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