A S N CHAKRAVARTHY

@jntucek.ac.in

PROFESSOR OIF CSE
University College of Engineering Kakinada, JNTUK



                 

https://researchid.co/drasnchakravarthy

EDUCATION

PhD in Computer Science and Engineering
(Graduated in November 2011)
(Dissertation: A Probabilistic Approach for Authenticating Text or Graphical Passwords -using Associative Memories)
Acharya Nagarjuna University, Guntur, India

M.Tech in Computer Science
(Graduated in December 2006)
(Thesis: A New Approach for Scheduling Task Graphs on Heterogeneous Computing)
Jawaharlal Nehru Technological University, Hyderabad, India

B.E in Computer Science and Engineering
(Graduated in March 2002)
(Project: Multi-Function Display for a Real-Time System in Client-Server
Architecture)
Bangalore University, Bangalore, India

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Networks and Communications, Artificial Intelligence

44

Scopus Publications

398

Scholar Citations

10

Scholar h-index

10

Scholar i10-index

Scopus Publications


  • A novel user centric privacy mechanism in cyber physical system
    Manas Kumar Yogi and A.S.N. Chakravarthy
    Elsevier BV

  • Application of variants of nature-inspired optimization for privacy preservation in cyber-physical systems
    Manas Kumar Yogi and A. S. N. Chakravarthy
    IGI Global
    The integration of Cyber-Physical Systems (CPS) into critical infrastructure demands optimization techniques that ensure both high performance and privacy preservation. This paper presents the Privacy-Preserving Hybrid Bee-Evolutionary Optimization Algorithm (PP-BEOA), a novel variant of nature-inspired optimization tailored for CPS applications. PP-BEOA synergizes the exploratory capabilities of Artificial Bee Colony (ABC) algorithms with the exploitative strength of Genetic Algorithms (GA), enhanced by advanced differential privacy mechanisms and secure multi-party computation to safeguard sensitive data. Machine learning-driven parameter adjustments further improve adaptability and robustness in dynamic environments. Comprehensive evaluations demonstrate the effectiveness of PP-BEOA, showcasing superior results in scalability, real-time optimization, and resilience compared to traditional approaches. The results affirm PP-BEOA's potential as a transformative approach to addressing complex CPS optimization challenges.


  • Secure data communication in WSHN using EXP-MD5 and DHSK-ECC
    Tamarapalli Anjikumar and A.S.N. Chakravarthy
    SAGE Publications
    BACKGROUND: In the Healthcare (HC) sector, the usage of Wireless Sensor Healthcare Networks (WSHN) is attaining specific importance. The sensor device is implanted into the patient’s body, and the sensed health information of patients is transformed via data aggregating devices like mobile devices, cameras, and so on, to the doctors. Thus, the early signs of diseases are identified, and remote monitoring of the patient’s health is carried out by the physician on time. This aids in improving the health condition of the people and reduces the severity of disorders. But, the security gap in HC remains unresolved, despite various advantages. OBJECTIVE: This work proposes secured data communication in WSHN using Exponential Message Digest5 (EXP-MD5) and Diffie Hellman Secret Key-based Elliptic Curve Cryptography (DHSK-ECC) techniques. METHODS: Primarily, the patient registers their details in the Hospital Cloud Server (HCS). With hospital ID and patient ID, public and private keys are generated during registration. Afterward, by utilizing the Navie Shuffling (NS) technique, nCr combinations are created and shuffled. After shuffling, any of the randomly selected combinations are encoded utilizing the American Standard Code for Information Interchange (ASCII) code. For patient authentication, the ASCII code is further converted into a Quick Response(QR) code. Upon successful registration, the patient logs in to HCS. The patient can book for doctor’s appointment if the login details are verified with those of the registered details. On consulting the doctor at the pre-informed time, the digital signature is created utilizing the Universal Unique Salt-based Digital Signature Algorithm (UUS-DSA) for authenticating the patient details. Further, for providing accessibility to all the authorized patients, the registered patients on HCS are considered as nodes. Then, an authorized path is created using the EXP-MD5 technique to protect each individual patient’s details. The patient’s IoT data is sensed, followed by authorized path creation. The data is encrypted via the DHSK-ECC algorithm for secure data transmission. Lastly, all the information is stored in HCS, so that the patient’s health condition is regularly monitored by the doctor and the needy advice is suggested to the patients in the future. Also, hash matching is carried out when the doctor needs to access data. RESULTS: The proposed technique’s efficacy is validated by the performance analysis in comparison with other conventional techniques. CONCLUSION: In this proposed research, the authentication is performed in multiple scenarios to enhance data security and user privacy. The patient details are authenticated during registration and verification to access the online consultation only by the authorized person. Further, the patient health information is encrypted in the proposed work after consultation so that the intrusion of medical records by malicious users and data tampering is prevented. Also, the sensed data gathered from patients are transferred to the HCS by creating the authorized path, which further enhances the security of patient data. Thus, the data communication of the WSHN is well-secured in this work through multi-level authentication and improved cryptography techniques.

  • Single channel speech enhancement using time-frequency attention mechanism based nested U-net model
    Anil Kumar Prathipati and A S N Chakravarthy
    IOP Publishing
    Abstract Deep-learning models have used attention mechanisms to improve quality and intelligibility of noisy speech, demonstrating the effectiveness of attention mechanisms. We rely on either spatial or temporal-based attention mechanisms, resulting in severe information loss. In this paper, a time-frequency attention mechanism with a nested U-network (TFANUNet) is proposed for single-channel speech enhancement. By using TFA, learns which channel, frequency and time information is more significant for speech enhancement. Basically, the proposed model is an encoder-decoder model, where each layer in the encoder and decoder is followed by a nested dense residual dilated DensNet (NDRD) based multi-scale context aggression block. NDRD involves multiple dilated convolutions with different dilatation factors to explore the large receptive area at different scales simultaneously. NDRD avoids the aliasing problem in DenseNet. We integrated the TFA and NDRD blocks into the proposed model to enable refined feature set extraction without information loss and utterance-level context aggregation, respectively. Under seen and unseen noise conditions, the proposed TFAD3MNet model produces an average of 87.02% and 85.04% of STOI values, and 3.19 and 3.01 averaged PESQ values. The trainable parameters of proposed model are 2.09 million,which is very less compared to baselines. TFANUNet model results outperform baselines in terms of STOI and PESQ.

  • Optimal Routing with Machine Learning Classification in Delay Tolerant Networks
    Ashapu Bhavani, A. Venkata Ramana, and A.S.N. Chakravarthy
    IEEE
    Communication networks built to function well in the face of erratic connections, frequent interruptions, or appreciable delays in data transmission are known as delay-tolerant networks or DTNs. Conventional routing protocols have difficulty operating at their best, guaranteeing effective message delivery. Machine learning (ML) techniques have shown great promise in recent years as instruments to enhance DTN performance. To forecast network traffic and identify which source nodes have the best chance of successfully delivering a message to its intended recipient, this makes use of machine learning classifiers. Based on past delivery data, it also locates the nearby nodes engaged in the message delivery process. This research investigates the processing efficiency of Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting (GB), and XGBoost algorithms in combination with ensemble techniques like voting and stacking classifiers. Through a systematic training and assessment process, select features and adjust hyperparameters to improve prediction accuracy. Through this, the suggested machine learning models' adaptability to various network conditions is showcased, along with their robustness in predicting the best routes for message delivery, optimizing routing choices, and cutting down on network overhead, all of which contribute to the DTN environment's overall efficiency.


  • Novel Security Mechanism for AI Enabled Wastewater Treatment Systems
    Manas Kumar Yogi and A. S. N. Chakravarthy
    Springer Nature Switzerland

  • Privacy Preservation in Cyber Physical Systems Using Entropy-Based Techniques
    Manas Kumar Yogi and A. S. N. Chakravarthy
    Auerbach Publications


  • A review on machine learning based routing protocols for delay tolerant networks
    Ashapu Bhavani, A. Venkata Ramana, and A.S.N. Chakravarthy
    IOS Press
    Nowadays, Delay Tolerant Network plays an important role in improving the communication between the network nodes. Applications of Delay Tolerant Network are disaster recovery, vehicular communication, sensor networks, interplanetary networks, and communication in remote and rural areas. Routing is one of the important tasks for enhancing the energy effectiveness of data transmission among the mobile nodes under network congestion and dynamic topology. Machine Learning-based routing algorithms are used for improving network communication in Delay Tolerant Networks. Its objective is to reduce the delay, minimize the overhead, reduce energy consumption, improve throughput, minimize packet loss, and efficient data transmission. This paper presents a comprehensive review of routing algorithms using machine learning for Delay Tolerant Networks.

  • Novel and Heuristic MolDoc Scoring Procedure for Identification of Staphylococcus Aureus


  • Application of novel AI mechanism for minimizing private data release in cyber-physical systems



  • Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model
    Sridevi Gadde, A. S. N. Charkravarthy, S. Satyanarayana, and M. Murali
    Springer Science and Business Media LLC


  • Content-Based Collaborative Filtering with Predictive error Reduction-Based CNN Using IPU Model
    Chakka S. V. V. S. N. Murty, G. P. Saradhi Varma, and Chakravarthy A. S. N.
    IGI Global
    Recommender systems (RS) are strong tools for addressing the internet networking overload problems by considering past user ratings on multiple items with auxiliary data and suggests the better item to the end user. Traditional collaborative filtering (CF) and content-based methods were identified the interaction or correlation between users and the items. But they have failed to identify the join user-item interactions and suffering from incomplete cold start (ICS) and complete cold start (CCS) issues. To address the deficiencies of CF-based approaches, this article offers a novel deep learning based error predictions method along with CF based user-item interactions. Initially, incentivized/penalized user-based content-based collaborative filtering (IPU-CBCF) method is introduced for learning low-dimensional vectors of users and items, separately. The simulation results shows that IPU-CBCF using PER-CNN resulted in better performance as compared to the conventional approaches for all performance metrics like F1-score, recall, and precision, respectively.

  • Comparative Analysis between LSTM and GRU in Stock Price Prediction
    A. Bhavani, A. Venkata Ramana, and A. S. N. Chakravarthy
    IEEE
    Having the idea about stock price is not a big deal but investing the assets and maintaining profitably needs in-depth knowledge of shares, trading, stock exchanges, financial marketing, and activities. Without having prior knowledge of stocks, the person cannot invest the assets. The advantages of predicting in advance are if the stock price is high, it is acceptable to invest in the stocks, if the price is low, then it is not recommended to invest. Currently, there are many methods to achieve high accuracy of stock market trend prediction. To get benefit out of it, deep learning techniques can be used. There will be a lot of fluctuations when dealing with stock market data. When compared to machine learning, deep learning algorithms can solve better for non-linear problems. To forecast the stock price, the grated Recurrent Unit (GRU) approach is applied. Long Short-Term Memory (LSTM) and GRU are comparable, although GRU has fewer characteristics. GRU outperforms LSTM in terms of performance.

  • Human Identification with their VOC distribution through CMS – SEN Model
    Prathyusha Kanakam and A. S. N. Chakravarthy
    Springer Science and Business Media LLC

  • Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN
    Siva Rama Krishna Tummalapalli and A. S. N. Chakravarthy
    Springer Science and Business Media LLC

  • A secure and light weight privacy preserving data aggregation algorithm for wireless sensor networks


  • Chemical sensing through cogno-monitoring system for air quality evaluation
    Kanakam Prathyusha and ASN Chakravarthy
    Springer International Publishing

  • Emotional Intelligence through Body Temperature using Hybrid Progressive Approach
    Prathyusha Kanakam and A. S. N. Chakravarthy
    IEEE
    Novel methodologies are the combination of budding technologies. A machine needs to be trained in such a way to work along with sensors for understanding human perception. Thermal-ID is one such metric that combines sensor technologies with machine learning to identify the mental state of an individual based on their body temperature. The emotional intelligence is indulged in the machine to predict a particular state of emotion of the individual based on the body temperature. Hidden Markov model is the basis for making the decision by obtaining each emotion when experimented on sample data of an individual's body temperature. This work mainly focuses on the state-of-art metric Thermal-ID or body temperature-ID, its characteristics and factors. Hybrid Progressive Approach (HPA) is the methodology that applies double normalized values to a machine for predicting the body temperature change (ΔTb) with respect to an emotional state of an individual.

  • Cogno-Detective System for Body Odor Sensing
    Prathyusha Kanakam, S Mahaboob Hussain, and ASN Chakravarthy
    IEEE
    The Modern decade of future Internet stepping into all the technologies includes sensors, artificial intelligence, pattern recognition and machine learning. As sensor technology increases its liabilities by extending to novel recognition methods for identifying an individual. Smell print is a state-of-art technique to perform authentication, verification and validation of a person to apply in fields of human tracking, criminal investigation and canine training etc. This paper describes the compounds combination of human odor, the factors that influence the human odor and sources of smell print in a human body. It gives a prototype for Cogno-detective system for the human odor in order to imitate the human olfaction and procedure to implement the artificial olfaction mechanism. $\\square$

RECENT SCHOLAR PUBLICATIONS

  • A Customised Privacy Preservation Mechanism for Cyber‐Physical Systems
    MK Yogi, ASN Chakravarthy
    Securing the Digital Frontier: Threats and Advanced Techniques in Security 2025

  • A bio-inspired optimal feature with convolutional GhostNet based squeeze excited deep-scale capsule network for intrusion detection
    T Ammannamma, ASN Chakravarthy
    Computers & Security 150, 104221 2025

  • A novel user centric privacy mechanism in cyber physical system
    MK Yogi, ASN Chakravarthy
    Computers & Security 149, 104163 2025

  • Application of Variants of Nature-Inspired Optimization for Privacy Preservation in Cyber-Physical Systems
    MK Yogi, ASN Chakravarthy
    Nature-Inspired Optimization Algorithms for Cyber-Physical Systems, 283-312 2025

  • Retraction Note: Human identification with their VOC distribution through CMS–SEN Model
    P Kanakam, ASN Chakravarthy
    Soft Computing 28 (2), 967-967 2024

  • Secure data communication in WSHN using EXP-MD5 and DHSK-ECC
    T Anjikumar, ASN Chakravarthy
    Technology and Health Care 32 (6), 5081-5103 2024

  • Novel Security Mechanism for AI Enabled Wastewater Treatment Systems
    MK Yogi, ASN Chakravarthy
    The AI Cleanse: Transforming Wastewater Treatment Through Artificial 2024

  • Cyber Security Education: Enhancing Cyber Security Capabilities, Navigating Trends and Challenges in a Dynamic Landscape
    SM Hussain, SRK Tummalapalli, ASN Chakravarthy
    Advances in Cyber Security and Digital Forensics, 9-33 2024

  • FUSION OF INFORMATION THEORETICAL MODELS WITH PERSONALIZED DIFFERENTIAL PRIVACY TO MINIMIZE PRIVACY
    MK YOGI, ASN CHAKRAVARTHY
    Security Implementation in Internet of Medical Things, 177 2023

  • Privacy Preserving Mechanism using No-regret learning in Cyber-Physical Systems
    MK Yogi, ASN Chakravarthy
     2023

  • Algorithms
    W Rao
     2023

  • Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber‐Physical Systems
    MK Yogi, ASN Chakravarthy
    A Roadmap for Enabling Industry 4.0 by Artificial Intelligence, 127-139 2022

  • Application of temporal logic for construction of threat models for intelligent Cyber-Physical Systems
    MK Yogi, ASN Chakravarthy
    Intelligent Cyber-Physical Systems Security for Industry 4.0, 159-176 2022

  • UNMASK-II STUDY (UTILITY OF NON CONTRAST MRI IN ACUTE STROKE) IMAGING RECLASSIFICATION OF ACUTE ISCHEMIC STROKE (UPDATED I-ASCOD PHENOTYPING)
    S Varadharajan, M Nedunchelian, K Athiyappan, N Reddy, A Monga, ...
    INTERNATIONAL JOURNAL OF STROKE 17 (3_ SUPPL), 18-18 2022

  • Application of Distributed Constraint Optimization Technique for Privacy Preservation in Cyber-Physical Systems
    MK Yogi, ASN Chakravarthy
    International Conference on Intelligent Cyber Physical Systems and Internet 2022

  • Investigation of Holistic Approaches for Privacy Aware Design of Cyber‐Physical Systems
    MK Yogi, ASN Chakravarthy, JM Chatterjee
    Cyber‐Physical Systems: Foundations and Techniques, 257-271 2022

  • Application of Exact Barrier-Penalty Function for Developing Privacy Mechanisms in Cyber-Physical Systems
    MK Yogi, ASN Chakravarthy
    Security Analytics, 209-220 2022

  • Privacy Preserving Mechanism by Application of Constrained Nonlinear Optimization Methods in Cyber‐Physical System
    MK Yogi, ASN Chakravarthy
    Cyber Security and Network Security, 157-168 2022

  • RETRACTED ARTICLE: Human Identification with their VOC distribution through CMS–SEN Model
    P Kanakam, ASN Chakravarthy
    Soft Computing 25 (20), 13015-13025 2021

  • Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN
    SRK Tummalapalli, ASN Chakravarthy
    Evolutionary Intelligence 14 (2), 699-709 2021

MOST CITED SCHOLAR PUBLICATIONS

  • OPTICAL CHARACTER RECOGNITION TECHNIQUE ALGORITHMS.
    NV Rao, A Sastry, ASN Chakravarthy, P Kalyanchakravarthi
    Journal of Theoretical & Applied Information Technology 83 (2) 2016
    Citations: 94

  • Survey on android forensic tools and methodologies
    V Rao, ASN Chakravarthy
    International Journal of Computer Applications 154 (8), 17-21 2016
    Citations: 26

  • Forensic analysis of android mobile devices
    VV Rao, ASN Chakravarthy
    2016 International Conference on Recent Advances and Innovations in 2016
    Citations: 23

  • Palm vein biometric technology: An approach to upgrade security in ATM transactions
    BV Prasanthi, SM Hussain, P Kanakam, AS Chakravarthy
    International Journal of Computer Applications 112 (9), 1-5 2015
    Citations: 21

  • Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN
    SRK Tummalapalli, ASN Chakravarthy
    Evolutionary Intelligence 14 (2), 699-709 2021
    Citations: 19

  • Electronic noses: Forestalling fire disasters: A technique to prevent false fire alarms and fatal casualties
    P Kanakam, SM Hussain, ASN Chakravarthy
    2015 IEEE International Conference on Computational Intelligence and 2015
    Citations: 19

  • Analysis and bypassing of pattern lock in android smartphone
    VV Rao, ASN Chakravarthy
    2016 IEEE International Conference on Computational Intelligence and 2016
    Citations: 14

  • Accelerating SQL queries by unravelling performance bottlenecks in DBMS engine
    VK Myalapalli, ASN Chakravarthy, KP Reddy
    2015 International Conference on Energy Systems and Applications, 7-12 2015
    Citations: 14

  • A hybrid cryptographic system for secured device to device communication
    AR Krishna, ASN Chakravarthy, A Sastry
    International Journal of Electrical and Computer Engineering (IJECE) 6 (6 2016
    Citations: 12

  • Handwritten text image authentication using back propagation
    ASN Chakravarthy, PVK Raja, PS Avadhani
    arXiv preprint arXiv:1110.1488 2011
    Citations: 10

  • Robust and indiscernible multimedia watermarking using light weight mutational methodology
    R Sailaja, R Ch, ASN Chakravarthy
    Traitement du signal 34 (1-2), 45 2017
    Citations: 9

  • Variable modulation schemes for AWGN channel based device to device communication
    AR Krishna, ASN Chakravarthy, A Sastry
    Indian Journal of Science and Technology 9, 20 2016
    Citations: 8

  • BSC: A Novel Scheme for Providing Security using Biometric Smart Card
    SM Hussain, ASN Chakravarthy, GS Sarma
    International Journal of Computer Applications 80 (1) 2013
    Citations: 8

  • Cryptanalysis of Design and Analysis of a Provably Secure Multi-server Authentication Scheme.
    NBM Mohan, ASN Chakravarthy, C Ravindranath
    Int. J. Netw. Secur. 20 (2), 217-224 2018
    Citations: 7

  • Revamping SQL queries for cost based optimization
    VK Myalapalli, ASN Chakravarthy
    2016 International Conference on Circuits, Controls, Communications and 2016
    Citations: 7

  • Algorithms
    W Rao
     2023
    Citations: 6

  • A unified model for cherishing privacy in database system
    VK Myalapalli, ASN Chakravarthy
    1st IEEE International Conference on Soft Computing 2014
    Citations: 6

  • A novel soft computing authentication scheme for textual and graphical passwords
    PSV Vachaspati, ASN Chakravarthy, PS Avadhani
    International Journal of Computer Applications 71 (10) 2013
    Citations: 6

  • Design of a rescue robot assist at fire disaster
    AR Krishna, GS Bala, ASN Chakravarthy, BBP Sarma, GS Alla
    International Journal of Computer Applications 975, 888 2012
    Citations: 6

  • Cogno-detective system for body odor sensing
    P Kanakam, SM Hussain, ASN Chakravarthy
    2017 IEEE International Conference on Computational Intelligence and 2017
    Citations: 5