Dr. Chetana Tukkoji

@gitam.edu

Assistant Professor, Department of CSE
GITAM School of Technology, GITAM University Bangalore

Dr. Chetana Tukkoji


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https://researchid.co/chetana

RESEARCH INTERESTS

Bigdata Analytics, Machine Learning and Artificial Intelligence
16

Scopus Publications

Scopus Publications

  • Empirical Survey on Identification of Risk Factors and Statistical Data Analysis in Software Engineering Using SDLC Models
    Varadaraj Rajnana, Seetharam Keshavrao, Chetana Tukkoji, Satish Kumar Thyagaraju
    Aip Conference Proceedings, 2025
  • A Reliable Environment with Extensive Advanced Encryption Standard Algorithm in Cloud Computing
    Geetha Rani E, Chetana Tukkoji
    Ssrg International Journal of Electronics and Communication Engineering, 2025
    Web-based Cloud Computing is widely used to store data. Most firms who are shifting to the cloud find it cost-effective. Despite its popularity, it has presented several security concerns to its users. Cloud Computing data security has become a critical issue that requires prompt response. This paper proposes an appropriate usage of AES and an effective cloud-based data storage solution. The proposed data security solution is implemented by improving the standard AES algorithm. Furthermore, the proposed techniques use de-duplication to save storage space for user data. The Extensive Advanced Encryption Standard (EAES) methodology was applied, and the results were superior to the conventional Advanced Encryption Standard method. The research tries to tackle the issues stated above in stages. Initially, a broad library of strategies addressing the data security problem is explored, and their limitations are noted. The Extensive Advanced Encryption normal technology is used, yielding considerable results compared to the normal Advanced Encryption Standard approach.
  • Contrasting Various Cryptographic Algorithms for Storage and Cloud Computing Services
    E. Geetha Rani, Chetana Tukkoji
    Lecture Notes in Networks and Systems, 2025
  • Secure Data Storage in Cloud Computing Using Code Based McEliece and NTRU Cryptosystems
    Geetha Rani E, Chetana Tukkoji
    SN Computer Science, 2024
  • Secure Framework Optimizes QAES Technique Used for Computing in the Cloud
    E. Geetha Rani, Dr. Chetana D. Tukkoji
    Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2024
    Several consumers are concerned about the security of their data kept in the cloud, and many see local servers as a safer choice due to perceived control. However, cloud service companies frequently promise greater security procedures and employ dedicated security specialists, making data stored in the cloud potentially more secure. When the data holder expires, new files are saved in the cloud and encrypted with the QAES technology. The data owner then receives the encrypted data request. The sender and user both analyze data before sending encoded documents to the cloud. The key will be used to remove encryption from subsequent data. If users are unable to access the desired data, they should submit a fresh request. The most recent innovation focuses on combining an enhanced form of Quantum Key Distribution (QKD) with AES. This integration resulted in Quantum-AES (QAES), a novel quantum symmetric encryption scheme. QAES is based on the development of a quantum encryption key using dynamic quantum S-Boxes (DQS-Boxes), as opposed to the frequently utilized static S-Boxes. This strategy enhances security. Comparably, time is required to build files faster than they do now. This approach prevents brute force attacks since it uses the QAES algorithm, which provides additional security.
  • Water Management for IoT-Based Smart Agriculture Using Machine Learning Algorithms
    E. Geetha Rani, Chetana Tukkoji, D. Anusha, M. Dhanalakshmi, B. Bharath
    Lecture Notes in Electrical Engineering, 2024
  • OpenCv Based Enhanced Criminal Identification Mechanism
    E Geetha Rani, Vaishnavi, Taniya Maiti, D Anusha, Sandyarani Vadlamudi, Chetana Tukkoji
    IEEE International Conference on Signal Processing and Advance Research in Computing Sparc 2024, 2024
    This study examines how a facial recognition system might be used in real-world situations to demonstrate its efficacy. The core operation of this system is ensured by meticulously selected datasets that comprise categorized photos. An important part is a carefully constructed file called “simple faces,” which is optimized for necessary libraries such as glob, OS, NumPy, OpenCV, and OS. Important tasks like image processing and matching faces to their corresponding photos are made easier by these frameworks. The study also emphasizes the significance of having a specific file for face detection and uses OpenCV to locate facial features precisely. In addition, a sophisticated face recognition library with strong features detection, encoding, and comparison capabilities is used. This library uses CNNs and other cutting-edge pretrained deep learning models to deliver exceptional facial recognition accuracy and speed.
  • A Survey of Recent Cloud Computing Data Security and Privacy Disputes and Defending Strategies
    E. Geetha Rani, D. T. Chetana
    Smart Innovation Systems and Technologies, 2023
  • Stock Price Analysis Using LSTM
    Shyamala Boosi, Chetana Tukkoji, Archana S. Nadhan, A. Usha Ruby
    Lecture Notes in Networks and Systems, 2023
  • An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal
    Sasi Kumar Gurumoorthy, D T Chethana, M Ramesh, B.P Upendra Roy, C Sapna Kumari
    2023 IEEE International Conference on Integrated Circuits and Communication Systems Icicacs 2023, 2023
    It is well-established that biomedical signals convey crucial data regarding the functioning of living systems. The physiological and clinical information included in these signals can be improved with adequate processing. Modern qualitative and quantitative analyses of physiological systems and events rely on digital signal processing and pattern recognition methods. Analysis and interpretation of a medical practitioner's signal carry the weight of the analyst's knowledge and expertise, yet such analysis is inherently subjective. If done logically, computer analysis of biomedical information might provide credibility to the expert's interpretation by providing an objective second opinion. Furthermore, it allows for enhanced diagnosis and online monitoring of critically ill patients. The current research intends to develop effective methods for utilizing sleep-monitoring health gadgets. When it comes to handling complex classification or pattern recognition issues, the Support Vector Machine (SVM) is the instrument of choice. In this article, we focus on using support vector machines (SVMs) to identify and categorize apnea. When compared to other methods of categorization, such as sophisticated statistical approaches, SVM performed better. Both an adaptive classification model and a novel approach to merging the decisions of ensemble-based classification models are proposed in the work. The current method relies on an ensemble classifier system and a huge number of features, making it both effective and trustworthy.
  • To Increase Security and Privacy, the QAES Encryption Algorithm is used for Storage of Data for Cloud Computing
    E Geetha Rani, D T Chetana
    Indicon 2022 2022 IEEE 19th India Council International Conference, 2022
  • Smart Attendance Monitoring Technology for Industry 4.0
    Archana S. Nadhan, Chetana Tukkoji, Boosi Shyamala, N. Dayanand Lal, A. N. Sanjeev Kumar, V. Mohan Gowda, Zameer Ahmed Adhoni, Melaku Endaweke
    Journal of Nanomaterials, 2022
  • Impact of coronavirus (Covid-19) on the public
    International Journal of Advanced Science and Technology, 2020
  • Memory constraint parallelised resource allocation and optimal scheduling using oppositional GWO for handling big data in cloud environment
    Chetana Tukkoji, K. Seetharam
    International Journal of Cloud Computing, 2020
  • Csii-tsbcc: Comparative study of identifying issues of task scheduling of big data in cloud computing
    Chetana Tukkoji, K. Seetharam, T. Srinivas Rao, G. Sandhya
    Advances in Intelligent Systems and Computing, 2020
  • ITM-CLD: Intelligent traffic management to handling cloudlets of the large data
    Chetana Tukkoji, K. Seetharam
    Advances in Intelligent Systems and Computing, 2019