Preethi C

@psnacet.edu.in

Assistant Professor and Department of Computer Science and Business systems
PSNA College of Engineering and Technology

Preethi C


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https://researchid.co/preethic20

EDUCATION

B.Tech Information Technology
M.E Computer Science and Engineering
18

Scopus Publications

Scopus Publications

  • Multi-level Cloud Security with Encryption Using Bio-metric Authentication in Decentralized Network
    S. Shahul Hammed, S. Pavalarajan, C. Preethi, K. Haripriya
    Iranian Journal of Science and Technology Transactions of Electrical Engineering, 2026
  • A Secure Meta-Heuristic Framework for MANET based IoT Privacy Protection Using AMU-HHO Algorithm
    P. Satyanarayana, Nerusu Siva Vennela, Chilla Preethi, Sakhamuri Suryanarayana, Kanaka Raju Penmetsa, et al.
    2026 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2026, 2026
  • Protected Framework Employing Flexible and Optimum Arrangement in Cloud Computing
    S. Shahul Hammed, S. Pavalarajan, C. Preethi, K. Haripriya
    Transactions on Emerging Telecommunications Technologies, 2025
    The idea of cloud computing (CC) originates from making resources available for task execution. Cloud computing is an advancement of supercomputing. The main challenges in CC are the varying resources and workloads, leading to the need for efficient tasking and scheduling. Distributed task scheduling can help us better understand workflow scheduling; autonomous task scheduling that accounts for security and execution time, mutual trust among system participants, better energy efficacy, and system utilization, among other aspects. The MBABE technique expanded as multi‐level blockchain attribute‐based encryption, which is used to ensure data security. ABE is a combined encryption method that can be effectively utilized for security and access control. Additionally, a new algorithm is presented, which is optimized with a convolutional neural network and snoop slingshot spider optimization (CNN‐S3SO). The cost functions are minimized using this S3SO for multipleusers in multiple cloud tasks computing progress. The technique relies on the actions of the arachnid in capturing targets and is utilized to arrange tasks for optimal throughput and minimal makespan. It is additionally recommended as a means of achieving convergence in a brief time frame. In addition, a protocol reliant on blockchain technology is utilized to encode data, ensuring secure transmission. Ultimately, the method is tested through a cloudlet simulator, and its efficiency is assessed through the analysis of the results. The outcome of the resource utilization rate is around 98%. It demonstrates that this methodology outperforms other task scheduling methods.
  • IoT-Enabled Water Conservation and Leakage Control System
    C. Preethi, S. Shahul Hammed, K. Haripriya, S. Pavalarajan, Harini Shree P, et al.
    Proceedings of 7th International Conference on Inventive Material Science and Applications Icima 2025, 2025
  • Traffic and Pollution Control Using IoT-Enabled Smart Routing Algorithm
    S Shahul Hammed, C. Preethi, K Haripriya, S. Pavalarajan, M.R. Gowtham, et al.
    2nd International Conference on Machine Learning and Autonomous Systems Icmlas 2025 Proceedings, 2025
    Urbanization has led to increased traffic congestion and air pollution, primarily from vehicle emissions, posing risks to public health and the environment. Existing traffic management systems are inefficient in integrating real-time pollution data, leading to reactive control measures. This project proposes an IoT-based smart traffic management system that integrates real-time air quality monitoring with dynamic traffic control. IoT sensors collect pollution and vehicle data, which is processed using cloud computing and machine learning to optimize traffic flow and reduce emissions. AI techniques provide transparency, helping city authorities understand and trust the system's decisions. The system also offers predictive analytics for proactive pollution management and a user-friendly dashboard for real-time visualization. The solution aims to reduce urban emissions, and enhance traffic efficiency, offering a sustainable approach for modern cities.
  • Automated Drainage Management System: AI and IoT-based Blockage Detection for Smarter Cities
    K. Haripriya, S. Shahul Hammed, C. Preethi, S. Pavalarajan, R. Padmakumar, et al.
    Proceedings of 7th International Conference on Inventive Material Science and Applications Icima 2025, 2025
  • Development of a Comprehensive Deep Learning Framework for Enhanced Detection and Accurate Classification of Renal Cancer
    J Bino, C. Preethi, M. Renukadevi, T.K.S Rathish babu, S. Kanageswari, et al.
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
    Accurate and early diagnosis of renal cancer is important for the improvement of outcomes in patients; hence, timely intervention has much to do with the effectiveness of treatments and survival rates. While there has been an increase in medical imaging modalities, early detection of renal cancer has become quite plausible, though the complexity introduced in the characteristics of tumors calls for advanced methods for their reliable classification. In this work, a new LSTM+CNN-based model is developed for renal cancer disease detection by integrating sequential learning capability from LSTM networks together with the powerful feature extraction abilities of CNN. The system is designed to improve both the accuracy and efficiency in renal cancer diagnosis based on the medical imaging data using spatial and temporal features. Among these, the proposed LSTM+CNN-based model has turned in better accuracy with quicker processing time and better overall classification performance compared to the state-of-the-art models. The proposed model also allows for the non-invasive, high-precision differentiation of renal tumors into low- and high-grade ones, with a view to early diagnosis and prediction. These results provide a proof of the enormous potentials of deep learning models, especially the LSTM+CNN architecture, toward making renal cancer detection an efficient and practical clinical solution.
  • Smart Sanitation Management: Integrating IoT, AI-Driven Analytics, and Automated Sensing for Efficient Public Toilet Maintenance
    K. Haripriya, C. Preethi, S.Shahul Hammed, S. Pavalarajan, S. Akhil Sharon, et al.
    2nd International Conference on Machine Learning and Autonomous Systems Icmlas 2025 Proceedings, 2025
    An IoT-based self-sustained public toilet maintenance system aims to improve hygiene, efficiency, and resource management. It integrates smart sensors, microcontrollers, and cloud connectivity to monitor parameters such as water levels, air quality, and toilet usage in real-time. AI-driven predictive maintenance optimizes cleaning schedules, minimizes downtime, and ensures efficient resource use. Automated water control prevents wastage, while air quality sensors detect harmful gases and activate ventilation. A smart notification system alerts municipal authorities about supply shortages, enabling timely replenishment of water, soap, and tissues. By reducing manual intervention, the system lowers operational costs and improves service quality. Cloud connectivity allows seamless data processing, offering insights for better sanitation management. The integration of IoT and AI ensures proactive maintenance responses. A comparative analysis highlights the novelty of this system in automating and optimizing maintenance.
  • Enhancing URL Security and Access Control Using Hash-Based Shortening Algorithm
    K. Haripriya, C. Preethi, S. Shahul Hameed, S. Pavalarajan, T. Shushmmitha, et al.
    2nd International Conference on Emerging Research in Computational Science Icercs 2024, 2024
    Shortening URLs has become an integral part of modern digital communication, as it simplifies long and complex internet addresses, making them more user-friendly. However, conventional URL shortening methods often lack adequate security provisions, leaving shortened links vulnerable to various risks. This project aims to develop a secure URL-shortening service by incorporating a hash-based access control mechanism. By using a hash-based approach, we can significantly enhance the security of shortened URLs. In this method, a unique hash value is generated for each URL, which serves as a link to the shortened URL. The generated hash is highly resistant to decryption, making it difficult for unauthorized users to retrieve the original URL, thereby ensuring that only the intended recipient can access the link. The goal of this project is to develop a protected and secure URL-shortening service that utilizes a hash-based access control mechanism. By generating a unique hash for each shortened URL, the system will enhance both security and user privacy. Furthermore, the implementation of granular access control will be a key factor in maintaining the system's overall security. SHA-256 can be utilized to generate unique, unpredictable shortened URLs by creating secure hash values. Integrating OAuth2 and JWT provides robust access control, enabling seamless user authentication and authorization across multiple systems. Additionally, AES encryption ensures the protection of sensitive data by offering strong encryption, safeguarding both the original URL and user information.
  • Heart Attack and Alcohol Detection for Drivers Using Ubiquitous Smart Transportation Incorporate with Sensors
    S. Pavalarajan, S.Shahul Hammed, C. Preethi, K. HariPriya, S. Balasubramani, et al.
    2nd International Conference on Sustainable Computing and Smart Systems Icscss 2024 Proceedings, 2024
    In this increasingly interconnected world, the reliance on devices for daily activities is ubiquitous. This paper presents a novel system aimed at enhancing driver safety through the integration of two critical sensors: an alcohol sensor and a heartbeat sensor. The system is meticulously designed to monitor the seriousness and physical well-being of drivers, thereby mitigating the risks associated with impaired or unhealthy individuals behind the wheel. The alcohol sensor detects the presence of alcohol in a driver's breadth, ensuring they are not under the influence—a primary contributor to road accidents. These techniques leverage sophisticated sensor technologies and real-time data analysis to enhance road safety. Despite its potential, several challenges obstruct the system's effectiveness and widespread adoption. Current systems often face high false-positive rates, privacy concerns, and issues with integrating heterogeneous sensor data. Additionally, real-time processing requirements and maintaining accuracy under varying environmental conditions pose significant hurdles. The proposed system addresses these issues by using the ThingSpeak web platform to present sensor data and take immediate action as necessary. This system offers a promising solution to modern road safety concerns, providing a robust approach to monitoring and ensuring driver fitness.
  • IBS: Secured Healthcare Data Management over Cloud Environment using Integrated BlockChain Strategy
    K. Haripriya, C. Preethi, S. Shahul Hammed, S. Pavalarajan, A. Meenambika
    1st International Conference on Emerging Research in Computational Science Icercs 2023 Proceedings, 2023
  • Scrap Management using E-com Online Machine Learning Iscrap Algorithm
    S.Shahul Hammed, M.P. Karthikeyan, R.Pranesh Raj, C. Preethi, K. Haripriya, et al.
    International Conference on Sustainable Computing and Smart Systems Icscss 2023 Proceedings, 2023
  • Sell and Buy Homegrown Vegetables and Fruits Online Using E-Commerce UML Algorithm
    C. Preethi, V.S. Shree Saran, M. Meikannan, S.Shahul Hammed, K. Haripriya, et al.
    Proceedings of the 2023 International Conference on Intelligent Systems for Communication Iot and Security Iciscois 2023, 2023
  • Reading Technology for Blind People Using OCR and Neura Machine Learning Method
    C. Preethi, K Haripriya, S Shahul Hammed, S. Pavalarajan, S. Priyadharshini, et al.
    Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing Icrtac 2023, 2023
  • Radioactive Metal Detection in Dustbin using Metaheuristic with Augmented Reality for Enhanced Safety and Security
    C. Preethi, S Shahul Hammed, K Haripriya, S. Pavalarajan, V. S.Shree Saran, et al.
    Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing Icrtac 2023, 2023
  • Detection of Alzheimer's disease at Early Stage using Machine Learning
    S. Pavalarajan, B.Arun Kumar, S.Shahul Hammed, K. Haripriya, C. Preethi, et al.
    2022 International Conference on Advanced Computing Technologies and Applications Icacta 2022, 2022
  • Exploring malicious webpages using machine learning concept
    N.C. Brintha, C. Preethi, J.T. Winowlin Jappes
    2021 2nd International Conference for Emerging Technology Incet 2021, 2021
  • An comprehensive survey on applications of precision agriculture in the context of weed classification, leave disease detection, yield prediction and UAV Image analysis
    Preethi C, Brintha NC, Yogesh CK
    Advances in Parallel Computing, 2021