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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Dhawan, Udit Tyagi, Preksha pandimurugan, v. Department of Networking and Communications School of Computing Chennai India

ISBN: (纸本)9798350361155

The research illuminates the intriguing potential of applying Natural Language Processing (NLP) methodologies to scrutinize Twitter API, construct predictive performance models, pinpoint nascent corporations, and acquire an understanding of the employment market panorama. By capitalizing on data analytics and machine learning, the objective is to formulate proficient models capable of distilling meaningful insights from a wide array of tweets, thereby revealing potential business opportunities. The article intends to illustrate how the amalgamation of NLP, data analytics, and machine learning can be employed to exploit the capabilities of Twitter data in forecasting job opportunities and offering valuable intelligence for businesses navigating in a dynamic and swiftly evolving marketplace. © 2024 IEEE.

关键词： Sentiment analysis

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Enriched energy optimized LEACH protocol for efficient data transmission in wireless sensor network

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WIRELESS NETWORKS 2025年第1期31卷 825-840页

作者： Rajaram, v. pandimurugan, v. Rajasoundaran, S. Rodrigues, Paul Kumar, S. v. N. Santhosh Selvi, M. Loganathan, v. SRMIST Sch Comp Dept Networking & Commun Kattankulathur Campus Chennai India King Khalid Univ Coll Comp Sci Dept Comp Engn Al Faraa Saudi Arabia Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore India Vellore Inst Technol Sch Comp Sci & Engn Vellore India Saveetha Engn Coll Dept Comp Sci & Engn Chennai India

A wireless sensor network (WSN) is made up of many sensor nodes with insufficient energy, storage, and processing capabilities. Data gathering and transmission to the base station are two of the main responsibilities of the sensor nodes (BS). As a result, the network lifespan becomes the key factor in the successful design of data collection strategies in WSN. In this study, we provide the Enriched energy optimized LEACH (EE-OLEACH) protocol for data transfer. Through a combination of efficient optimum clustering and an optimal route selection mechanism, it provides a means for energy-efficient routing in WSN. The Homogeneous Hunter-Wolf optimization (HHWO) is used for clustering, and a cluster head is chosen for each cluster to reduce energy loss among sensor nodes and maximize efficiency in their use of available resources. Nodes with the highest residual energy may receive the most energy-efficient routing. In order to send the data to BS, the nodes with the greatest residual energy are chosen. The pheromone-profound Ant optimization (PPAO) method was then used to reduce energy consumption throughout the path-selection process. It contributes to a higher packet delivery ratio while reducing power consumption. According to the experimental findings, the proposed EE-OLEACH performs better than the current Protocol in terms of packet delivery ratio, end-to-end latency, and energy usage. In this paper, we compare the performance of the existing hierarchical routing protocols under varying conditions (packet size, starting energy level, etc.) and demonstrate how the optimal CH selection based on a suggested algorithm improves both network lifetime and energy consumption. The Simulation results shows that the EE-OLEACH enhances energy efficiency by 30%, delay by 35%, node survived by 45%, network lifetime by 56%, packet delivery ratio by 47% and throughput by 38% when compared with other existing protocols. The results clearly show that the suggested EE-OLEACH extends

关键词： Wireless sensor network Enriched energy optimized LEACH Pheromone profound ant optimization algorithm Homogeneous hunter wolf optimization

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Secure Smart Home Data by Using Blockchain Technology with Hash Algorithm 13

Secure Smart Home Data by Using Blockchain Technology with H...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Lukesh, S. Jagadesh, J. pandimurugan, v. School of Computing Department of Networking and Communications Chennai India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305463

The project 'Secure Smart Home Data by Using Blockchain Technology with Hash Algorithm' focuses on incorporating two timeless technological concepts that is, blockchain and secure hash algorithms. Smart home is a concept that is been trending these days with IoT devices taking control of the houses making people's lives much easier. But the data collected from them is not stored properly. With the traditional methodology where the data is stored on a centralized cloud network, it is prone to cyberattacks, single point of failure and also involves tampering. Because of this, the sensitive data of the users are compromised from time to time. This arises mainly due to the centralized network system of the cloud and to tackle that, we approached a decentralized network system which Blockchain. It enables us to store data across the world that too in a decentralized format resulting in a more secure and safe environment for those sensitive data. This is our proposed solution as well. To secure the smart home data with blockchain technology and to further enhance reliability, our proposed approach incorporates a modified version of SHA-512. In summary, our focus is on securing Smart Home IoT data in the cloud by integrating blockchain technology, advanced encryption methods, and robust hash algorithms like SHA-512 to ensure the utmost protection and authorized access for large-scale datasets from diverse Smart Home sources. © 2024 IEEE.

关键词： Blockchain

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Facial Emotion Recognition for Students Using Machine Learning

Facial Emotion Recognition for Students Using Machine Learni...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： pandimurugan, v. Singh, Angad Tiwari, Akash SRMIST Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350348217

It is important to note that while machine learning and artificial intelligence can have a positive impact on society, they also come with ethical concerns. One major concern in this case is privacy. It is important to ensure that the students' privacy is respected and their facial data is not misused. It is also important to ensure that the system is not used for discriminatory purposes, such as identifying students based on race or ethnicity. Another concern is the accuracy of the emotion recognition system. While machine learning algorithms can be trained to recognize emotions, there is still a margin of error. It is important to regularly test and improve the accuracy of the system, and to ensure that the system is not making incorrect assumptions about a student's emotional state. Overall, the use of machine learning and artificial intelligence in identifying student emotions can have a positive impact on their mental wellbeing. However, it is important to approach this technology with caution and to ensure that it is being used ethically and responsibly. © 2023 IEEE.

关键词： Emotion Recognition

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Detecting the symptoms of COvID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. pandimurugan, v. Mohandas, R. Sambath, M. Baiju, B.v. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COvID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COvID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial vehicle-based Covid Data Analysis (Thermal Images) (I-UvAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COvID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COvID-19

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Hybrid compression technique for image hiding using Huffman, RLE and DWT

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Materials Today: Proceedings 2022年 57卷 2228-2233页

作者： v, pandimurugan L, Sathish Kumar J, Amudhavel M, Sambath SCSE VIT Bhopal University Madhya Pradesh Sehore India Hindustan Institute of Technology and Science Chennai India

All kinds of information should be compressed before being sent over the web to save time & expense. The primary goal of this article is to create and construct a systematic and safe technique for encryption data that can be utilized to carry out a crisp compacting steganography technique. The approach may be used to reduce the volume of data sent with each transfer, allowing for faster delivery when utilizing sluggish web or taking up less space on a drive. The study offers a densification with steno approach that may be utilized to hide information with substantial privacy and complete invisible by combining RSA encryption technique with steganography expertise such as RLE (Run-length encoding), Huffman coding, DWT (Discrete Wavelet Transform) with LSB cryptographic technology. The very first process is based on RSA's ability to decode and encode private texts. The next process is compressing, which will include Huffman encoding based approaches to compress confidential stuff using efficient steganography technologies, RLE, which is a traditional approach to encrypt information, or DWT based on hatchbacks covering imagery by loss encryption in order to decrease the covering picture's size. The encrypted information will be implanted in the compressed cover picture using LSB (Least Significant Bit). When compared to various developed countries (Chen et al., 2021;Lakshmanan, 2020;Song et al., 2011;Yang et al., 2021;Ali et al., 2020) of the architecture that uses simulation results, this technique has a good ranking efficiency and a methodical system. © 2021

关键词： Steganography

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Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network

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WIRELESS NETWORKS 2024年第3期30卷 1493-1511页

作者： Bangali, Harun Rodrigues, Paul pandimurugan, v. Rajasoundaran, S. Kumar, S. v. N. Santhosh Selvi, M. Kannan, A. King Khalid Univ Coll Comp Sci Dept Comp Engn Al Faraa Saudi Arabia SRM Inst Sci & Technol Sch Comp Networking & Commun Chennai India Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore India Vellore Inst Technol Sch Comp Sci & Engn Vellore India

Middle-box based attacks create serious functional defects in the devices such as firewalls, address translators, load balancers, server units, and other data inspecting devices. Middle-box based attacks are more severe than terminal node-based attacks. These attacks are mainly initiated to malfunction the internal events of middle-level devices. Notably, Internet of Healthcare Things (IoHT) is a completely distributed and heterogeneous environment that leads into more vulnerable events. In the distributed medical system field, the middle-box devices manage secret medical data transactions, internal network communication, patient data protection principles and packet inspection procedures. The medical data collected from each patient needs secrecy and stability in various aspects in the IoHT environment. The middle-box based attacks injected into the IoHT nodes create inefficiency in maintaining patient data, Denial of Service (DoS) at middle-box nodes, excessive diagnosis time, and lack of data protection. For handling these issues, a new security architecture and a new attack detection model are proposed in this paper which has been developed using ranking subsets and Convolutional Neural Network (CNN) principles. The proposed CNN with ranking principles model is designed with binary class fuzzy fisher face optimization technique and flower pollination optimization method to initiate feature extraction. The extracted features of each data flow are analyzed using CNN and ranking subset methodologies. In this proposed model, the continuous involvement of middle-box events in respective devices are classified under various anomaly cases and legitimate cases. The proposed IoHT model attains maximum success rate than the existing models as indicated in implementation section.

关键词： Medical data security Middle box-based attacks Internet of Healthcare Things Convolutional neural network Classification Intrusion detection

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Developing Advanced Software Keylogger using Python and Creating Awareness of their Functionalities 2

Developing Advanced Software Keylogger using Python and Crea...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Joy, Jerin Rajaram, v. Aditya, A.R. pandimurugan, v. Department of Networking and Communications School of computing SRM Institute of Science and Technology Kattankulathur603 203 India

ISBN: (纸本)9798350340235

Computers are being utilizedeverywhere in daily life as technology advances, and they play an important part in carrying out our normal duties. Keyboards and mice are input devices that are useful in feeding information to computers. Keyloggers (spyware or software) are a serious concern since they secretly record private data, they represent a pervasive and concerning issue in the realm of digital security. Keystroke logging, sometimes called keylogging or keyboard capture, is the process of keeping track of the keystrokes that are made on a keyboard. keyloggers come in hardware and software forms with different types. The goal of this research is to raise awareness of the capabilities of keyloggers and to build a software-based keylogger with advanced features that include capturing keystrokes, clipboard information- an image or text, system information, and, screenshots at regular intervals. The captured data is then sent through email to a third party or stored in a specific folder without the user's knowledge. The use of Keyloggers can be occasionally justifiable for purposes like parental control and corporate surveillance but also carries the ominous potential for illicit data capture and misuse. The objective of the research is to develop an efficient software-based keylogger capable of collecting clipboard data and acquiring crucial system information. This highlights the rising complexity of keylogger technology and its potential threat to user confidentiality. © 2023 IEEE.

关键词： Clipboard Computers Confidentiality Information Keylogger Keystrokes S oftware S ystem

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Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第4期26卷 2325-2337页

作者： Kumar, L. Sathish Routray, Sidheswar Prabu, A., v Rajasoundaran, S. pandimurugan, v Mukherjee, Amrit Al-Numay, Mohammed S. VIT Univ Sch Comp Sci & Engn Bhopal India Indrashil Univ Sch Engn Dept Comp Sci & Engn Mehsana Gujarat India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur Andhra Pradesh India SRMIST Dept Networking & Commun Sch Comp Kattankulathur Campus Chennai 603203 Tamil Nadu India Univ South Bohemia Fac Sci Dept Comp Sci Ceske Budejovice Czech Republic King Saud Univ Dept Elect Engn Riyadh Saudi Arabia

Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease clas

关键词： Artificial Intelligence Medical Hypothesis Deep Learning Convolutional Neural Networks Health Indicators

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Investigation of COvID-19 symptoms using deep learning based image enhancement scheme for x-ray medical images

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INTERNATIONAL JOURNAL OF BIOMETRICS 2023年第3-4期15卷 327-343页

作者： pandimurugan, v. Prabu, A. v. Rajasoundaran, S. Routray, Sidheswar Bahadure, Nilesh Bhaskarrao Kishore, D. Ratna VIT Univ Sch Dept Comp Sci & Engn Bhopal Madhya Pradesh India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur India Indrashil Univ Sch Engn Dept Comp Sci & Engn Mehsana Gujarat India Sanjay Ghodawat Univ Sch Elect Engn Kolhapur Maharashtra India Andhra Loyola Inst Engn & Technol Dept IT Vijayawada Andhra Pradesh India

Image enhancement is the inevitable technique for investigating various biological features. The biological image data can be obtained from computer tomography (CT), magnetic resonance imaging (MRI), and X-ray imaging. X-ray imaging is useful for getting the information from lungs and respiratory system. COvID-19 is a life-threatening contiguous disease for the past two years in the world. Patient's chest images playing an important role in the diagnosis of early detection of disease intensity. We propose a generative adversarial network (GAN) method that identifies COvID-19 from medical images and improves diagnostic sensitivity. Here we used virtual colouring methods and a platform for training the images by using a deep parental training method. Similarly, it gives optimal classification results with the help of well-defined image enhancement techniques and image extraction approaches. In our method, the accuracy level lies between 87.8% and 89.6% correspondingly for the dataset and synthetic dataset.

关键词： COvID-19 image classification medical image enhancement generative adversarial network deep learning

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