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Hidden Markov Model with Machine learning-Based Black hole Attack Identification in Wireless Sensor Networks

Hidden Markov Model with Machine Learning-Based Black hole A...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Balasubadra, K. Shiny, X.S.Asha Pramila, P.V. Solainayagi, P. Maniraj, S.P. R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Telangana Hyderabad India Department of Computer Science Engineering Tamil Nadu Chennai India Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu Paiyanoor India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Wireless Sensor Network (WSN) is lying to several attacks because the Black hole is problematic to distinguish and protect. Impostor internments and re-programs block the packets rather than transmitting and obtaining them at the base station (BS) in a black hole attack. Consequently, any data that arrives in the black hole area is taken and cannot reach the recipient, triggering high packet losses and decreasing network performance. To overcome this problem, a hidden Markov model with machine learning (HMMl)-based black hole attack identification in WSN is introduced. This approach uses the Hidden Markov Model (HMM) to detect the blackhole node in the WSN. This model examines the shortest routes between the sender and the recipient to discover the black hole route. This model contains states as well as the observation sequence. Forward and received count and reply of non-adjacent nodes are used for verifying the shortest routes. Here, the supervised and unsupervised training machine learning methods decide for detecting black hole nodes. © 2023 IEEE.

关键词： Hidden Markov models

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A Novel Framework for Efficient Handover Mechanism in d2d Communication Using Networking Principles 4

A Novel Framework for Efficient Handover Mechanism in D2D Co...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jayanthi, S. Raja, Mandapati Jackson, Beulah Jayasree, S. Nithya, A. Jeneetha Jebanazer, J. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India CMR Engineering College Department of Electronics and Communication Engineering Telangana India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Chemistry Tamil Nadu India R. M. K College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Software defined Networking (SdN) technology utilizes software to manage and alter the current networks to improve the networking and interfacing capabilities. It does this by providing a split between the control plane and the data plane. On the SdN controller, each function is run as an application. This paper suggests a structure made up of a unified RAN and an SdN-based core network. For mobile terminals to access the services of the network operator, as well as the Internet and other cloud-based apps, all radio access points in the RAN are connected with the aid of OpenFlow interfaces. decentralized Content delivery Networks (CdN) manage mobility without requiring data to pass via the core network, which minimizes signaling. To gauge the signaling burden, traffic steering between the framework's different entities is examined. The suggested work establishes criteria based on a combination of fundamental factors, such as Signal Strength, distance, and Speed, to assure service continuity of mobile nodes in heterogeneous networks. The parametric analysis of the handover is contrasted with that of the current designs. The suggested framework also provides ProSe capability for a select number of desirable mobile devices in the network and is configured for d2d connection. © 2023 IEEE.

关键词： Heterogeneous networks

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Exploration of Pretrained Models used in Automatic Speech Recognition System: A Review and Analysis 6

Exploration of Pretrained Models used in Automatic Speech Re...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suhasini, S. Pandiaraj, A. Ramshankar, N. R. M. D. Engineering College Department of Computer Science and Engineering India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350394702

Automatic Speech Recognition (ASR) systems have made significant strides in recent years, we have advent of deep learning and the availability of large-scale pretraining methods. This research paper presents a comprehensive review and analysis of leveraging pretrained models for ASR systems. It explores the benefits of using pretrained models, examines various architectures and techniques, discusses challenges and limitations, and suggests future directions for research and development in this field. © 2023 IEEE.

关键词： deep learning

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Smart Tracking and Surveillance Systems for Automated delivery Robots in Food and E-Commerce Systems 2

Smart Tracking and Surveillance Systems for Automated Delive...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimala Ithayan, J. Ranganathan, Chitra Sabapathy Vinola, C. Venkatesh, G. Meenakshi, R. Muthulekshmi, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Mphasis Corporation Tamil Nadu Chennai India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

This research study provides an innovative approach to creating smart monitoring and surveillance systems for autonomous delivery robots in the food and e-commerce industries. The system aims to increase the effectiveness and security of delivery operations by integrating ultrasonic sensors, radio frequency (RF) modules, cameras, remote controls, and speaker technologies. The robot can recognize obstacles and avoid them due to the use of an ultrasonic sensor. The system includes an RF module to provide seamless connection and remote control. This allows the robot and the central control unit to exchange data in real-time, simplifying remote monitoring, navigational corrections, and operational management. The camera module monitors the robot's surroundings while recording real-time video visually. Operators or managers are given a remote control so users can monitor and manage the robot's motions, observe the live video feed, and make smart decisions during the delivery process. The system utilizes a speaker to provide voice-based alerts and notifications. The work advances delivery services in the food and e-commerce industries by enhancing operational efficiency, security, and safety. The performance of the system is analysed in terms of response times, reliability, and accuracy rates and a comparative analysis is also made to show the effectiveness of the system. © 2024 IEEE.

关键词： Ultrasonic sensors

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Machine learning Classifiers to decrease diabetic Patients Probability of Hospital Readmission 4

Machine Learning Classifiers to Decrease Diabetic Patients P...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Thenappan, S. Ramshankar, N. Hemavathy, N. Subashree, V. Manjul, Ravi Ranjan Rajeswari, J. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Velammal Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Ramchandra Chandarvansi Institute of Technology Department of Mechanical Engineering JharkhandBishrampur Palamu India Agni College of Technology Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350300093

diabetes is a chronic condition that affects millions of individuals around the globe. Unfortunately, patients with diabetes have a significant risk of hospital readmission following discharge. Using machine learning classifiers, proposed a strategy to reduce the risk of readmission for 768 diabetes patients. This utilized a dataset containing patient demographic information, medical history, and laboratory results which are collected from Pima India dataset. To predict the risk of readmission, and train several Machine learning models, including logistic Regression, Random Forest, and Support Vector Machines. The research indicates that the Random Forest model has the maximum degree of precision, at 97%. Age, glucose level, Hba1c levels, and comorbidities have been identified as the most significant factors contributing to the risk of readmission. On the basis of these findings, this recommends a personalized approach to diabetes management that takes individual patient characteristics and risk factors into account. This strategy could reduce the likelihood of readmission for diabetic patients and enhance their overall health. © 2023 IEEE.

关键词： Hospitals

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A Comparative Analysis of denoising PCG Signals using Adaptive Wiener-Kalman Filtering

A Comparative Analysis of Denoising PCG Signals using Adapti...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： A. Pandiaraj N. Ramshankar A. Gnanasekar Arunchandran R Azariah John B S Febin C Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering R.M D. Engineering College Department of Computer Science and Engineering R.M D. Engineering College kavaraipettai Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Cardiovascular disease (CVd) is the group of disorder occurs on heart and blood vessel, and they are coronary heart disease, cerebrovascular disease, rheumatic heart disease, etc. Particularly people under the age of 70 are mostly affected by this disease. Earlier cardiologist analyse phonocardiography (PCG) signals to know the condition of heart. Because of the undesirable noise present in PCG signal makes complication in diagnosis by experts. This study analyse and denoising heart sound using five different techniques namely Short-Time Fourier Transform (STFT), Empirical Mode decomposition (EMd), Recurrent Neural Network (RNN), weiner filter (WF), adaptive Wiener-Kalman filtering (AWKF). There are lot of noise removal techniques available as discussed in the literature, failed to achieve higher performance in denoising audio signal. The developed denoising techniques were applied on various pathological heart sounds and on heart sound recorded in a noisy environment. The AWKF filtering has great impact on removing distortion and interference present on the signal. This method reserves the major characteristics of heart beat sound. The experimental results showed that AWKF acquired higher SNR, PSNR, lower RMSE and great denoising effect.

关键词： Heart Adaptation models Recurrent neural networks Filtering Heart beat Computational modeling Noise reduction Predictive models Phonocardiography Signal to noise ratio

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Enhancing Network Security in distributed Environments using Block Chain-based Solutions 2

Enhancing Network Security in Distributed Environments using...

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2nd International Conference on data Science and Information System, ICdSIS 2024

作者： Ram Sundar, G. Rajput, Kapil dhanasekaran, S. Aeri, Manisha Shukla, Rishi Prakash Singh, Sandeep Kumar Sri Ramakrishna Engineering College Department Of Information Technology Tamil Nadu Coimbatore India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Sri Eshwar College Of Engineering Department Of Electronics And Communication Engineering Coimbatore India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Ait Management Mohali India Dr. D. Y. Patil Vidyapeeth-Centre For Online Learning Pune India

ISBN: (纸本)9798350381665

Block chain generation has been gaining improved attention in recent years because of its ability to enhance the security and performance of community systems. In allotted surroundings, wherein more than one nodes talk with every different, traditional security measures may not be sufficient in preventing malicious assaults or statistics breaches. This is because a centralized authority is often responsible for the safety of the whole community, making it an unmarried point of failure. The block chain era makes use of a decentralized machine, wherein information is stored throughout more than one node and is tested through a consensus mechanism. This eliminates the need for a central authority, offering an extra at-ease and obvious network environment. Additionally, block chain generation permits the implementation of smart contracts, which can automate and put in force security protocols, lowering the capacity for human error. © 2024 IEEE.

关键词： Network security

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SMOTE-Enhanced Classification of land Cover and land use Patterns: A Comparative Evaluation

SMOTE-Enhanced Classification of Land Cover and Land use Pat...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Priya Surana Bhagwan Phulpagar Pramod Patil Sakshi Thorat Sharvari Ratnaparkhi Mansi Bhadke Computer Engineering Dr.D.Y.Patil Institute of Technology Pune India Pimpri Chinchwad College of Engineering Pune India Computer Engineering PES Modern College of Engineering Pune India Computer Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

This study uses the Machine learning Algorithms and satellite images to provide a thorough analysis of land use and land cover changes (lUlC) in Ethiopia. Using landsat imagery for classification, the study looks at the years 2015, 2017, 2019, 2021, and 2023. data preparation, image processing, classification, and accuracy evaluation are all part of the methodology. The outcomes show how well the method works to find lUlC, offering insightful information for environmental monitoring and land management. This research adds to the growing corpus of information on land cover dynamics in Ethiopia region by providing insightful information on the causes, patterns, and effects of land cover changes. By combining cutting-edge analytical methods with landsat imagery , we can better comprehend the complex interactions between human activity and the environment, enabling stakeholders to make decisions that will support Ethiopia's natural resource conservation and sustainable development.

关键词： Natural resources Roads Urban areas land surface Process control landsat Soil Satellite images Stakeholders Sustainable development

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient digital Identity Management with Token-based Selective disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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Advancements in Cybersecurity Using deep learning Techniques Attack detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Hosanna Princye, P. Ahila, A. lakshmi, A. Anna Ragavendran, N. Purushothaman, K.E. Uma Maheswari, G. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India R.M.K. Engineering College Department of Information Technology Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Software Analyst Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study. © 2024 IEEE.

关键词： Cyber attacks

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