Arthi A

@kce.ac.in

Assistant Professor/Information Technology
Karpagam College of Engineering

Arthi A


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https://researchid.co/arthiarignan

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Information Systems, Software
7

Scopus Publications

45

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A Fuzzy Logic Based (FLB) Hybrid Level of Approach in the Evaluation of Security Impact in Healthcare Type of Web Applications for Secure Informations
    A. Arthi, Dillip Narayan Sahu
    Proceedings 2024 1st International Conference on Innovative Sustainable Technologies for Energy Mechatronics and Smart Systems Istems 2024, 2024
    The persistent breaches of private medical information in the healthcare sector provide a significant obstacle for companies operating in the sector. In addition to protecting sensitive data, building a strong and secure data safety structure for healthcare web apps is essential for improving the standing and earnings of healthcare organizations. Adopting a multi-criteria decision process becomes a crucial step towards achieving this goal. This study provides developers with a methodical road map by contributing both theoretical frameworks and real-world implementations. The research intends to assist practitioners in developing safe and efficient information security measures inside web applications by determining and putting first critical aspects, guaranteeing a noticeable influence on the entire cybersecurity environment.
  • Leukemia Detection Using Invariant Structural Cascade Segmentation Based on Deep Vectorized Scaling Neural Network
    A. Arthi, V. Vennila, U. Arun Kumar
    Cybernetics and Systems, 2024
    AbstractLeukemia is one of the deadliest diseases that occur in white blood cells which is identified in microscopic images from blood samples. Researchers have developed various techniques to diagnose leukemia using a machine learning approach by analyzing micro imaging blood samples. During image screening, the Damaged cells be identified based on manually counting on similar structure lymphocytes and monocytes structures, but the variant difference of both cells aren’t identified accurately. So the identification failures that occur lead to failed accurate cancer detection because feature variants project equal cell structure. To resolve this problem, we propose an Invariant Structural Cascade Segmentation (ISCS) based on Deep Vectorized Scaling Neural Network (DVSNN) is implemented to detect the Leukemia Cancer automatically from bio-blood samples micro image for early diagnosis. First, the Leukemia Cancer micro-image dataset (LCMID) was collected and preprocessed into a noise-free dataset based on adaptive median filters. Then the segmentation was carried to partition the microcells using Invariant structural cascade segmentation (ISCS) optimized with watershed to identify the features such as texture, pixel color, pixel intensity. For identifying the structural components of cells variation difference using Angular Vector Projection (AVP) was applied to find the structural variance. Then the histogram color equalizer (HCE) was applied to select the damaged cells using K-counts and their feature weight. Then the selected feature weights are trained into Deep vectorized scaling the neural network to classify the risk of the Leukemia cancer cells. This identifies the cancer cells effectively from micro image cells for detecting the risk of the patients. This improves the sensitivity, specificity rate as well in classification accuracy compared to the other methods.Keywords: Adaptive median filtersAngular Vector Projection (AVP)Deep Vectorized Scaling Neural Network (DVSNN)histogram color equalizer (HCE)invariant structural cascade segmentation (ISCS)leukemiamicroscopic images Author ContributionsArthi A: Conceptualization, Methodology, Visualization, Investigation, Data Validation and Writing - Original draft preparation; Vennila V: Supervision and Data Curation; Arun Kumar U: Reviewing and Editing.Availability of Data and MaterialsData sharing not applicable to this article as no datasets were generated or analyzed during the current study.Competing InterestsThe authors declare that they do not have any competing interest. The authors of this research acknowledge that they are not involved in any financial interest.Consent for PublicationNot Applicable.DeclarationsThe authors certify that this material or similar material has not been and will not be submitted to or published in any other publication before. Furthermore, the authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript.Ethics Approval and Consent to ParticipateNot Applicable.
  • Fuzzy-Based Hybrid Approach for Security Impact Evaluation in Healthcare Web Applications
    Jitendra Kumar Chaudhary, A. Arthi, S. Shalini, C. Gunasundari, Abhishek Sharma, Dillip Narayan Sahu
    Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024
    In order to connect patients, technology, and healthcare facilities, as well as to efficiently and carefully address the changing needs of medical ecosystems, the smart medical system is evolving into a medical policy service that makes use of wearables, online services, and mobile devices. This article examines some of the challenges that users must overcome in order to adopt intelligent medical technology more quickly and gain access to constant healthcare. The essay examines the integration of a fuzzy-based hybrid strategy to produce intelligently designed health solutions. The patient can access medical services, including monitoring, medication management, and emergency readiness, from anywhere at any time with the help of the intelligent healthcare management system. The adaptive neuro-fuzzy inference system (ANFIS) is a tool that this study suggests using to identify security concerns and evaluate them while developing online applications. First, the security risk concerns associated with developing web applications for healthcare have been identified in this paper. The ANFIS technique has since been used to assess these security issues. Additionally, a fuzzy regression model is suggested by this study.
  • Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer
    Kumar Laxmikant, Arthi A, V Vinodhini, B Natarajan, R Bhuvaneswari, Prabu Selvam
    2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024
    Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains.
  • Cross-Platform Multimodal Frogeye Leaf Spot Recognition and Classification: Advancing with Cutting-Edge CNN Technologies and Transfer Learning in Diverse Plant Species
    G. Mohan, R Usharani, Vatsala Tomar, Arthi A, Suniti Kumar Kuriyal, K.P. Yuvaraj
    Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024
    Frogeye Leaf Spot, a common agricultural disease, poses a significant threat to crop yields. Using multimodal data from different platforms successfully has been a hurdle for existing research in leaf spot detection. Using cutting-edge convolutional neural network (CNN) and transfer learning techniques, this research presents a Cross-Platform Multimodal Recognition system to solve this issue. With a recall of 0.88, F1-score of 0.90, accuracy of 0.91, and precision of 0.92, the suggested method demonstrates exceptional performance. Our accuracy is 6% higher, F1-score is 8% higher, and recall is 9% higher than the preceding state-of-the-art, according to comparisons with previous studies. Improved Frogeye Leaf Spot detection on many platforms is a direct result of our method's success in combining picture and sensor data. This study improves farmers' ability to identify diseases and provides a solid answer for precision agriculture. Our Cross-Platform Multimodal Recognition system has shown to be effective in enhancing agricultural disease recognition, and the shown advances highlight its potential influence on crop management tactics.
  • Soil Health Intelligence System using Multispectral Imaging and Advanced Deep Learning Techniques (SHIDS-ADLT)
    B. Dhanalakshmi
    Communications on Applied Nonlinear Analysis, 2024
    The Soil Health Intelligence System using Multispectral Imaging and Advanced Deep Learning Techniques (SHIDS-ADLT) is a cutting-edge solution designed to revolutionize the assessment and management of soil health. By leveraging the power of multispectral imaging, this system captures high-resolution data across various wavelengths, providing a comprehensive view of soil properties. Advanced deep learning algorithms are then applied to analyze this data, identifying patterns and insights that are not discernible through traditional methods. This integration of multispectral imaging with deep learning enhances the accuracy and efficiency of soil health monitoring, enabling precise identification of nutrient deficiencies, soil contamination, and other critical parameters that affect agricultural productivity.SHIDS-ADLT offers a scalable and user-friendly platform for farmers, agronomists, and researchers, facilitating informed decision-making and sustainable agricultural practices. The system’s ability to provide real-time analysis and actionable recommendations ensures that soil health is maintained at optimal levels, promoting higher crop yields and reducing the reliance on chemical fertilizers. Moreover, the continuous monitoring capabilities of SHIDS-ADLT help in early detection of soil degradation, allowing for timely interventions. This innovative approach to soil health management represents a significant advancement in agricultural technology, supporting the goal of achieving food security and environmental sustainability.
  • A Novel Q-Learning Optimization Approach for Flight Path Prediction in Asian Cities
    Keshavagari Smithin Reddy, B Natarajan, Arthi A, M Tamilselvi, Sridevi R
    2023 3rd Asian Conference on Innovation in Technology Asiancon 2023, 2023
    The domains of logistics and transportation have long been interested in the optimization of flight paths between cities. This research aims to use Skyscanner data to estimate the optimal flight path between 42 Asian destination cities using Reinforcement Learning (RL) techniques, notably Q-learning. RL is a great strategy for addressing the Travelling Salesman Problem (TSP) connected to aircraft route optimization because of the distinctive reward structure it provides. The main objective of the proposed research is to create a model that learns to suggest aircraft routes based on factors such as cost, time, and number of intermediate points that maximize benefits. The proposed research work incorporates a novel Q-learning approach for training an RL agent to predict optimal flight paths. The proposed research work showcases the power-fullness of Q-Learning based RL agents in suggesting optimal flight routes and lays the groundwork for future developments in this research domain. The proposed algorithm provides useful information for tourists and business people looking for accurate and affordable flight path forecasts in the Asian region. The various performance metrics such as Reward Accumulation(RA), Episode Length(EL), and Exploration and Exploitation evaluates the proposed model performance and yields an optimal solution.

RECENT SCHOLAR PUBLICATIONS

  • Soil Health Intelligence System using Multispectral Imaging and Advanced Deep Learning Techniques (SHIDS-ADLT)
    VS B. Dhanalakshmi, K. Rejini, V Viswanath Shenoi, Arthi A, R. Rajkumar, S ...
    Communications on Applied Nonlinear Analysis 31 (6), 417 , 2024
    2024
  • Cross-Platform Multimodal Frogeye Leaf Spot Recognition and Classification: Advancing with Cutting-Edge CNN Technologies and Transfer Learning in Diverse Plant Species
    G Mohan, R Usharani, V Tomar, SK Kuriyal, KP Yuvaraj
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
    Citations: 1
  • Leukemia detection using invariant structural cascade segmentation based on deep vectorized scaling neural network
    A Arthi, V Vennila, U Arun Kumar
    Cybernetics and Systems 55 (4), 804-822 , 2024
    2024
    Citations: 10
  • Fuzzy-Based Hybrid Approach for Security Impact Evaluation in Healthcare Web Applications
    JK Chaudhary, A Arthi, S Shalini, C Gunasundari, A Sharma, DN Sahu
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024
    Citations: 1
  • A Fuzzy Logic Based (FLB) Hybrid Level of Approach in the Evaluation of Security Impact in Healthcare Type of Web Applications for Secure Informations
    A Arthi, DN Sahu
    2024 1st International Conference on Innovative Sustainable Technologies for … , 2024
    2024
  • Deep convolutional neural networks for early-stage detection and prognostication of lung and colon cancer
    K Laxmikant, A Arthi, V Vinodhini, B Natarajan, R Bhuvaneswari, ...
    2024 International Conference on Integrated Circuits and Communication … , 2024
    2024
    Citations: 6
  • A novel q-learning optimization approach for flight path prediction in asian cities
    KS Reddy, B Natarajan, M Tamilselvi
    2023 3rd Asian Conference on Innovation in Technology (ASIANCON), 1-9 , 2023
    2023
    Citations: 9
  • An Android-Based Water Quality Monitoring System and Alerting Through SMS
    SSH S.Prabu, A. Arthi, B.Natarajan, V. Deepak
    Turkish Online Journal of Qualitative Inquiry 12 (3), 150-170 , 2021
    2021
  • Anti Theft Control of Automatic Teller Machine Using Wireless Sensors
    R Jaiganesh, L Nagarajan, A Arthi, V Venkatesh
    Biosc. Biotech. Res. Comm. Special Issue 13 (3), 18-22 , 2020
    2020
    Citations: 3
  • Intelligent Transportation System Based On Fingerprint Biometric In Cloud Systems
    LXNI A Arthi
    International Journal of Research in Engineering, Science and Management. 1 … , 2018
    2018
  • IOT based low cost smart locker security system
    L Nagarajan, A Arthi
    International Journal of Advance Research, Ideas and Innovations in … , 2017
    2017
    Citations: 15

MOST CITED SCHOLAR PUBLICATIONS

  • IOT based low cost smart locker security system
    L Nagarajan, A Arthi
    International Journal of Advance Research, Ideas and Innovations in … , 2017
    2017
    Citations: 15
  • Leukemia detection using invariant structural cascade segmentation based on deep vectorized scaling neural network
    A Arthi, V Vennila, U Arun Kumar
    Cybernetics and Systems 55 (4), 804-822 , 2024
    2024
    Citations: 10
  • A novel q-learning optimization approach for flight path prediction in asian cities
    KS Reddy, B Natarajan, M Tamilselvi
    2023 3rd Asian Conference on Innovation in Technology (ASIANCON), 1-9 , 2023
    2023
    Citations: 9
  • Deep convolutional neural networks for early-stage detection and prognostication of lung and colon cancer
    K Laxmikant, A Arthi, V Vinodhini, B Natarajan, R Bhuvaneswari, ...
    2024 International Conference on Integrated Circuits and Communication … , 2024
    2024
    Citations: 6
  • Anti Theft Control of Automatic Teller Machine Using Wireless Sensors
    R Jaiganesh, L Nagarajan, A Arthi, V Venkatesh
    Biosc. Biotech. Res. Comm. Special Issue 13 (3), 18-22 , 2020
    2020
    Citations: 3
  • Cross-Platform Multimodal Frogeye Leaf Spot Recognition and Classification: Advancing with Cutting-Edge CNN Technologies and Transfer Learning in Diverse Plant Species
    G Mohan, R Usharani, V Tomar, SK Kuriyal, KP Yuvaraj
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
    Citations: 1
  • Fuzzy-Based Hybrid Approach for Security Impact Evaluation in Healthcare Web Applications
    JK Chaudhary, A Arthi, S Shalini, C Gunasundari, A Sharma, DN Sahu
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024
    Citations: 1
  • Soil Health Intelligence System using Multispectral Imaging and Advanced Deep Learning Techniques (SHIDS-ADLT)
    VS B. Dhanalakshmi, K. Rejini, V Viswanath Shenoi, Arthi A, R. Rajkumar, S ...
    Communications on Applied Nonlinear Analysis 31 (6), 417 , 2024
    2024
  • A Fuzzy Logic Based (FLB) Hybrid Level of Approach in the Evaluation of Security Impact in Healthcare Type of Web Applications for Secure Informations
    A Arthi, DN Sahu
    2024 1st International Conference on Innovative Sustainable Technologies for … , 2024
    2024
  • An Android-Based Water Quality Monitoring System and Alerting Through SMS
    SSH S.Prabu, A. Arthi, B.Natarajan, V. Deepak
    Turkish Online Journal of Qualitative Inquiry 12 (3), 150-170 , 2021
    2021
  • Intelligent Transportation System Based On Fingerprint Biometric In Cloud Systems
    LXNI A Arthi
    International Journal of Research in Engineering, Science and Management. 1 … , 2018
    2018