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

作者： Rangini M Sumit Pundir Manzoore Elahi M. Soudagar Daxa Vekariya Harshal Patil S. Rajkumar Assistant Professor Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

Acute Lymphocytic Leukaemia (ALL) is a dangerous malignancy that not only attacks adults but also contributes to roughly 25% of children. A precise and on-time diagnosis of cancer is a critical prerequisite for effective therapy to enhance survival rates. It is challenging to identify cancer cells from normal cells because leukemic cells seem extremely identical to normal cells beneath the microscope. Normally, morphological analysis for the detection of ALL is conducted physically on blood cells by qualified medical individuals, which has various drawbacks, like a lack of medical experts, slow analysis, and prognosis which depends on the knowledge of medical professionals. For this reason, we provide a robust method for improving the reliability of ALL identification using feature extraction and a Machine Learning (ML) model to discriminate between cancerous and healthy cells in images. To extract texture features for analysis, we employ the First Order Histogram (FOH) and the Gray-Level Co-occurrence Matrix (GLCM) in our technique. Logistic Regression (LR), Naive Bayes (NB), and Artificial Neural Network (ANN) are used in the categorizing method. The simulation results demonstrate that combining GLCM feature extraction with the ANN model yields the highest accuracy, correctly predicting 595 out of 600 data samples. The accuracy was 99.17%, which is quite remarkable.

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IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

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Developing a New MapReduce Framework with SVM-based Software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Developing a New MapReduce Framework with SVM-based Software...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Saravanan. M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.

关键词： Support vector machines Accuracy Machine learning algorithms Software algorithms Machine learning Predictive models Feature extraction Software Classification algorithms Alzheimer's disease

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Shielding NLP Systems: An In-depth Survey on Advanced AI Techniques for Adversarial Attack Detection in Cyber Security

Shielding NLP Systems: An In-depth Survey on Advanced AI Tec...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： T. Sai Lalith Prasad K. B. Manikandan J. Vinoj Research Scholar Department of Computer Science and Engineering Vignan's Foundation for Science and Technology & Research (Deemed to be University) Guntur Andhra Pradesh India Assistant Professor Department of Artificial Intelligence and Data Science Vignan Institute of Technology and Science Telangana India Assistant Professor Department of CSE Vignan's Foundation for Science Technology and Research (Deemed to be University) Guntur Andhra Pradesh India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research investigates the challenges posed by adversarial perturbations to the robustness and reliability of Natural Language Processing (NLP) models. The study focuses on three key paradigms for enhancing the resilience of NLP systems: meta-learning, reinforcement learning, and self-supervised learning. A comprehensive evaluation of these strategies is conducted, analyzing their strengths and weaknesses in mitigating adversarial attacks. The study provides insights into the most promising approaches for enhancing the robustness of NLP models and offers directions for future research in this critical area. By advancing the resilience of NLP systems, this research contributes to the development of more robust and trustworthy AI applications across various domains, including healthcare, finance, and security.

关键词： Training Surveys Renewable energy sources Scalability Self-supervised learning Natural language processing Robustness Security Artificial intelligence Resilience

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Early Localized Lyme Disease prediction using Image Processing and Machine Learning Techniques

Early Localized Lyme Disease prediction using Image Processi...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Saravanan M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The purpose of this study is to predict the early Lyme disease with the set of images and applying few machine learning algorithms. For this dataset taken from the public medical image repositories such as DermNet NZ, HAPiD, etc., using this two Convolutional Neural Network (CNN) machine learning algorithms such as VGGNet and GoogLeNet. The dataset size predicted using gpower tool with eighty percent of sample setting. With the sample size prediction the CNN type of VGGNet and GoogLeNet both are iterated and the results are recorded using training and testing data. The results were such as accuracy, precision, recall, F1-score, and ROC-AUC are determined to predict early localized Lyme disease. It was found that the novel CNN VGGNet model achieved an accuracy of 96.84% and other performance metrics compared to the CNN GoogLeNet algorithm’s 91.34% for the Lyme disease dataset. Hence this research study can help the healthcare practitioners or experts to identify the early growth of Lyme also this study result accepted the statistical significant of p value of 0.001, which is less than 0.05 in the independent t-test on both the machine learning algorithms. Therefore, the results indicate that the CNN VGGNet machine learning algorithm proves useful for early Lyme disease prediction and is effective at recognizing skin lesions.

关键词： Measurement Training Machine learning algorithms Accuracy Image processing Machine learning Skin Convolutional neural networks Lesions Diseases

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A Review on Forensic Investigation Analysis in Cloud Computing Environments

A Review on Forensic Investigation Analysis in Cloud Computi...

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International Conference on Computational science and Technology (ICCST)

作者： R.Sharmikha Sree K. Raja Department of Computer Science and Engineering Sri Sairam Engineering College SRM University Assistant Professor Department of CSE SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (纸本)9781665476560

In cloud forensics, scientific concepts, methods, and approaches are used to find, collect, retain, evaluate, and report digital data in order to reorder events. For everyone involved, including cloud service providers (CSPs), cloud consumers, researchers, and forensic practitioners, cloud forensics is getting increasingly complex. If a criminal want to conceal their dirty laundry, they can use cloud storage to conceal sexual photographs of children, documents related to terrorist groups, etc. To investigate crimes involving the cloud, forensic investigations must be undertaken in the cloud. While attempting to collect evidence, forensic investigators face significant obstacles posed by the cloud's dynamic and dispersed nature. In this paper, Cloud-based forensics professionals are only able to conduct investigations if they have access to the proper tools and techniques for retrieving relevant digital evidence from a device is discussed. It further presents the challenges associated with cloud based forensics and applications to move forward the research in current area.

关键词： Cloud computing Scientific computing Forensics Terrorism Taxonomy Faces Logistics

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

Heart Rate Anomaly Detection in Healthcare Using Elliptic En...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Information Technology Velammal Engineering College Tamilnadu Chennai600062 India Assistant Professor Panimalar Engineering College Tamilnadu Chennai600123 India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision - 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement. © 2025 The Author(s).

关键词： Cardiology

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Message Communication Challenges in Heterogeneous Architecture of Internet of Vehicles: A Review

Message Communication Challenges in Heterogeneous Architectu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Jitendra Kumar Y.D.S. Arya Akash Sanghi Inverters University Bareilly & Assistant Professor Teerthanker Mahaveer University Moradabad UP India Invertis University Bareilly UP India Department of Computer Science & Engineering Invertis University Bareilly UP India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Inclusion of Internet of vehicles (IoV) an emerging technology in smart city for vehicular communication is covering the Internet of things (IoT) technology into vehicular networks for benefiting from omnipresent Internet connectivity. Smart city as a demand of new way of living of current and coming generation by gathering the information through the various electronic devices. The one of the key point in smart city is making traffic planning more secure and accurate by using the better message communication between vehicles. A huge number of researchers have been worked on vehicular networks for message communication but researchers are still having to do work on to achieve the higher data rate, faultless network connectivity, message scalability, message security and improved quality of services for message communication for making IoV more effective. IoV is more capable for comprehensively information processing that is collected from the vehicles, roads and surroundings and helps the drivers for driving the vehicles in safer zone. The Internet of Vehicles (IoV) model integrates vehicular intelligence in addition to leveraging ubiquitous vehicle connection for a multitude of applications. This research technology used in network communication including the smart city requirements, traffic control for increasing the life security.

关键词： Wireless communication Accuracy Smart cities Scalability Roads Traffic control Security Standards Internet of Vehicles Systematic literature review

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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computers, Materials & Continua 2021年第6期67卷 4005-4018页

作者： V.Manikandan M.Sivaram Amin Salih Mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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Evaluative Analysis of Secure approaches in Simulated Real-World Contexts

Evaluative Analysis of Secure approaches in Simulated Real-W...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A Kannagi Pavan Chaudhary Muthupandi G Associate Professor Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.

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