K. G. Maheswari

Institute of Transport Science and Technology

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G. Nalinipriya

Saveetha University

C. Siva

Goverment Siddha Medical College

Balamurugan Balusamy

Shiv Nadar University

R. Anita

Karunya University

Jay Kiruthika

KPR Institute of Engineering and Technology

J Dhivya

G. Nalini Priya

Jain University

C. Siva

Vinayaka Missions University

G. Nagappan

Saveetha University

K. Karthikeyan

SRM Institute of Science and Technology

Cooperative Institutions

Anna University, Chennai

Saveetha University

Vellore Institute of Technology University

SRM Institute of Science and Technology

Maulana Azad Medical College

Indian Institute of Technology Madras

Institute of Medical Sciences

Amity University

Institute of Engineering

KPR Institute of Engineering and Technology

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A hybrid soft computing technique for intrusion detection in web and cloud environment

Concurrency and Computation Practice and Experience (2022)

K. G. Maheswari C. Siva G. Nalinipriya

Abstract Cloud computing environment contains important, essential, or confidential information; therefore, a security solution is needed to prevent this environment from potential attacks. In short, cloud computing has become one of the most sought after technologies in the field of information technology, and among the most dangerous threats. In this article, we propose a hybrid soft computing technique for intrusion detection in web and cloud environment (ST‐IDS). In ST‐IDS, we illustrate whale integrated slap swarm optimization algorithm for pre‐processing which remove the unwanted/repeated data's in dataset. We introduce new clustering technique based on modified tug‐of‐war optimization algorithm which groups the data in different segments. Then, we develop hybrid machine learning technique that is, capsule learning based neural network which categorize the attack in cloud environment. Finally, the proposed ST‐IDS technique can evaluate through standard open source datasets are KDD cup'99 and NSL‐KDD. The performance comparison of the proposed ST‐IDS technique using existing innovative technologies in terms of accuracy, precession, recall, specificity, F measure, false positive rate, and false negative rate.

Soft Computing

10.1002/cpe.7046

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Association of Vitamin D Binding protein gene polymorphism with serum 25-hydroxy Vitamin D levels in COPD

K. G. Maheswari Trinath Kumar Mishra Saroj Choudhary Mradul Kumar Daga Jamsheed Javid

Background: Vitamin D deficiency is associated with several diseases including Chronic Obstructive Pulmonary Disease (COPD).Vitamin D binding protein (DBP) is the major carrier for vitamin D in plasma. Objectives To dete rmine whether vitamin D binding protein gene (GC) polymorphism is associated with COPD and its severity and to correlate the GC genotypes with Serum 25-hydroxy vitamin D (25-OHD) levels. Study Design Fifty COPD patients and fifty healthy contro ls sampled from north Indian population were genotyped for GC polymorphism(rs7041 & rs4588) by Restriction fragment length polymorphism method and genotypes were correlated with disease severity and serum 25-OHD levels. Results GC 1F allele frequency was increased significantly in COPD patients compared to healthy controls (30% versus 15%,chi square=6.76,p value=0.03).GC2 allele frequency was lower in COPD patients than controls (29% versus 39%).GC genotype 1F- 1F was found to contribute to the risk of developing COPD (OR=5.6, 95% CI=0.91-34.57) and GC 2 was found to be a protective allele in COPD patients (OR=0.4, 95% CI=0.10-2.16). Median serum 25-OHD levels (ng/ml) in COPD patients was significantly low compared to healthy controls (13.65 versus 23.45, p value-0.045). III & IV GOLD class s taging (Global Initiative for Obstructive Lung Disease) COPD patients exhibited deficient median Serum 25-OHD levels (<20ng/ml).The geometric means of 25-OHD were compared among genotypes, being lowest in GC 1F-1F individuals. Conclusion Vitamin D deficiency is more common among COPD patients. GC polymorphism is significantly associated with susceptibility to COPD and serum 25-OHD levels. This data suggests adequate Vitamin D supplementation in COPD patients at risk.

Vitamin D-binding protein

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Sentiment Analysis and Exploratory Data Analysis on the Impact of COVID-19 Using Tweets

K. G. Maheswari

Sentimental analysis is an important concept for any occasion, administration or item delivered. To comprehend the feelings of individuals dependent on the effect of COVID-19 occurring across the globe utilizing tweets, this paper predominantly centres around sentiment analysis and text mining of twitter information utilizing R programming, which assists with extracting tweets where conclusions are profoundly unstructured, heterogeneous and impartial at times

Sentiment Analysis

Globe

Exploratory analysis

Exploratory data analysis

10.1109/icaeca56562.2023.10200589

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Liberation from patriarchy in the select novels of anita nair and manju kapur

Shodhganga (2014)

K. G. Maheswari

Patriarchy

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Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

International Journal of Communication Systems (2023)

G. Nagappan K. G. Maheswari C. Siva M. Shobana

Summary Industrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.

10.1002/dac.5682

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Early Detection of COVID-19 using CT-scan Lungs Images

2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (2021)

A. Thilakesh Raj G. Nalinipriya K. G. Maheswari Rahul Kumar C. Siva

Corona virus (COVID19) has become a deadly disease, which targets the people who were senior citizens to that country. The researches for vaccine are being done by the biotechnology specialist. But unfortunately, we lost many of them in the pandemic, to treat them doctors use CT-SCAN, covid test kit etc., Doctors are also got too stressed. To overcome this situation we have developed a Project for faster identifying the patients to reduce death rates using machine learning algorithms using the CT-SCAN images.Images of CT-scans are trained to that machine,so the similar type or partially suspect persons liable to covid can be found easily compared to before methods used, the doctors are given a portal to check their prediction about covid. The patient login is also there to collect general information like patient age, WHO proposed symptoms for covid also been collected to perform algorithms using the help of technology. Hence the death rate of people can be controlled to a certain range also, vaccination to the people can be given to the targeted person in advance.

Pandemic

10.1109/icaeca52838.2021.9675663

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A Comprehensive Review of SDP Using Machine and Deep Learning Techniques in Software Testing

Suguna Angamuthu K. G. Maheswari

Software testing

10.1109/iccda64887.2024.10867332

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Awareness of Students’ wellness in COVID’19 lockdown

2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (2021)

K. G. Maheswari D. Chandrakala Jay Kiruthika

The objective of this paper is to identify and analyse the students' wellness with regard to health, fitness and psychology in the advent of COVID'19 lock down period. The analysis of the survey data identifies the key points about the respondents' attitude towards awareness in health with respect to proper diet, fitness, sleeping rate, depression and usage of devices. Based on all these attributes, correlation has been drawn in the respondents' health and mental stability as well as respondent's sleeping rate and usage of mobile devices. A predictive data analysis underlies the values concerning the individual responsibilities towards health and studies and the same has been showcased in the post COVID'19 in order to foster the respondents' mind set. The comparison charts are rendered for the effectiveness of the proposed method that henceforth helps the administrative heads and concerned people to take decisions regarding counselling, conducting examination, safety of the students and their family concerns'.

Lock (firearm)

10.1109/icaeca52838.2021.9675738

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COVID – 19 Future forecasting using Time Series Model

2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (2021)

J Dhivya K. G. Maheswari K Swathy P Tamil Selvan

The increase of COVID-19 has affected the entire nation and is therefore declared to be pandemic. Due to its vast disruption among the humanity, measures have been taken to forecast the future spread of COVID-19 trending disease in India using time series model. In this experiment, we have used prophet forecasting model that takes comparison between performance and accuracy of the active cases in India, that has been observed from Kaggle database. This model compares the second wave of COVID-19 data which is present in the last four months so as to measure the future forecasting. The result thus obtained predicts the future spread of active COVID-19 cases in our nation using the time series model.

Pandemic

2019-20 coronavirus outbreak

10.1109/icaeca52838.2021.9675649

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Bibliometric Study on Ecology (1999 – 2010)

Indian Journal of Information Sources and Services (2012)

K. G. Maheswari Sujith Ravi

The study analyses 58611 research papers published in Ecology as ISI Web of Knowledge database for the period 1999- 2010. The study reports the country growth in this field is computed during the study period. The study also maps ecology research on other dimensions such as institutional productivity, nature of collaboration in research, and institutional specialization. It examines highly cited papers, and lists prominent and productive scientists in this field. This study highlights the research priorities of few important countries in collaborative research. The focus of research is slowly shifting from internal collaboration to domestic and international collaboration, supported by increasing funding from government agencies in ecology research.

10.51983/ijiss.2012.2.1.350

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