MOORAMREDDY SREEDEVI

@svuniversity.edu.in

Associate Professor, Dept.of Computer science
SRI VENKATESWRA UNIVERSITY, TIRUPATI A.P

MOORAMREDDY SREEDEVI


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https://researchid.co/himakar
Research papers published: 119
International conferences : 73
National conferences : 61
FDP's :25
Awards:6
ACEDEMIC ACTIVITIES :

Acting as “BOS Member for Computer Science S.V. University, Tirupati, since 2007.
Acted as “EC Member” for S.V.University Teachers Association from Feb 2014 to 2016
Acted as “Lady Representative” for S.V.University Teachers Association from Feb 2016 to Till-to-date
Acted as “Deputy warden” for Women Hostel from November 1st 2014 to 2017.
Acting as UG “BOS Member for Bangalore University, Bangalore since 2018.
Acting as UG “BOS Member for University, Bangalore since 2018.
Acting as as UG Examination Co-Ordinator for S.V.University, Tirupati Since 2021.
Acting as DDE M.Sc Co-Ordinator for DDE, S.V.University, Tirupati Since March 2021.
Acting as DDE On-Line Co-Ordinator for DDE, S.V.University, Tirupati Since 2023.
Acting as “BOS Member for SGS Arts College (Autonomous) TTD , Tirupati.
Act as Course Coordinator for Refresher

EDUCATION

MCA, M.Phil and Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications, Information Systems and Management, Computer Science

FUTURE PROJECTS

DST "Design and Development of Lattice-Based Cryptography Software for Post-Quantum Security "

The advancement of quantum computing threatens classical cryptographic standards like RSA and ECC, necessitating the development of quantum-resistant algorithms. Lattice-based cryptography (LBC) is the leading candidate for post-quantum security standards, having been selected by NIST for key encapsulation and signature algorithms. This proposal outlines a six-year project to develop an open-source lattice cryptography suite incorporating NIST-approved schemes (Kyber, Dilithium, Falcon, NTRU), quantum-classical cryptanalytic tools harnessing cloud-access quantum processors, and automated tooling for seamless migration from legacy cryptosystems. The project is designed for the Indian context with a 7-member team based in Bengaluru, a total budget of ₹2.22 Crore, and a focus on painstaking validation, security benchmarking, and practical deployment.


Applications Invited
2.5 Crores
10

Scopus Publications

41

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • GATBPSO based Framework for Unknown Malicious Activities Classification and Prediction in Networks
    Yerininti Venkata Narayana, Mooramreddy Sreedevi
    Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications Icimia 2025, 2025
    Detecting and classifying malicious network activities has become increasingly difficult due to the advanced techniques used by attackers to avoid detection. Many of these attacks do not follow known patterns, and the vast amount of network data, combined with limited examples of new threats, makes accurate detection challenging. As a result, existing systems often fail to identify rare or emerging attacks, highlighting the need for more intelligent and adaptable detection methods. To counter these problems this, study suggested GATBPSO, a hybrid framework for Unknown Malicious Activities Classification and Prediction in Networks by integrating Graph Attention Networks (GAT) with feature refinement through Binary Particle Swarm Optimization (BPSO) algorithm. The BPSO component effectively reduces the feature space, improving both accuracy and model efficiency. The entire model is implemented using Python and evaluated on CICIDS2018 and UNSW-NB15 datasets. The model demonstrated outstanding results on the CICIDS2018 dataset, attaining 99.88% accuracy, 100.00% precision, 99.88% recall, and an F1-score of 99.94%. While performance was relatively lower on UNSW-NB15 (accuracy: 69.41%, F1-score: 76.86%), the model retained a high recall of 92.29%, demonstrating its effectiveness in detecting malicious attacks even under challenging condition.
  • EvoPhysNet: A Physics-Informed Evolutionary Framework for Sustainable and Robust Prosthetic Design Optimization
    Anapalli Vinaya Kumar, M.Sreedevi
    Conference Proceedings 2025 IEEE Silchar Subsection Conference IEEE Silcon 2025, 2025
    In this research, a comprehensive system called EvoPhysNet was proposed. It combines data-driven material modeling, physics-based surrogate analysis, gait pattern generation, and evolutionary optimization to design high-performance prostheses. In this approach, the regression model evaluates the mechanical properties of different material combinations. At the same time, a differentiable physics surrogate model trained with basic Finite Element Simulations can rapidly predict structural responses without the need for a full-scale simulation .In order for the prosthetic design to work consistently even in real-life walking or motion changes, various gait variations are created and tested using a deep generative network. The design optimization is designed to meet the objectives of strength, load reduction, carbon impact reduction at the same time. A key role in this is played by the Gradient-Guided Mutation Operators and the Quality-Diversity Archive that explores the design space extensively. In addition, Active Learning Loop identifies key designs to improve the accuracy of the surrogate model, verifies them through Finite Element analysis, and updates the training data frequently. The test results showed that the prosthetic designs created by EvoPhysNet not only achieve a more robust and diverse Pareto front, but also show stable structural behavior despite gait changes. They also produce designs that are significantly more environmentally friendly than traditional methods. These results make it clear that EvoPhysNet can be used as a prosthetic design system that can be used in patient-specific, environmentally friendly, clinical and manufacturing processes in the future.
  • Machine Learning and MANET-Driven 5G Technology for Smart Healthcare: Enhanced Patient Diagnosis and Treatment
    S BhaskaraNaik, M Sreedevi, Rafick S, G Ravivarman, Rajendiran M, Ramya Maranan
    Proceedings 2024 International Conference on Expert Clouds and Applications Icoeca 2024, 2024
    This article presents the design, development, implementation, and evaluation of a system for patient health monitoring by synergistically combining machine learning models, mobile ad hoc networks (MANET), and Internet of Things (IoT). In this patient health monitoring system, data are gathered from numerous sensors installed on IoT devices in real-time to support dynamic and continuous health monitoring of patients within predefined constraints. The data are sent over MANET, which has nodes that send data to cloud storage for a centralized management. The data collected from the patients are used to train various Machine Learning (ML) models to learn when the patient is likely to experience a health problem. The analysis carried out in this study reveal that the Support Vector Machine (SVM) is found to be the most accurate model for predicting patient health problems, with an accuracy of 91.23%. The Artificial Neural Network (ANN), Random Forest (RF), and Naive Bayes (NB) rank next, respectively, with accuracy of 88.7%, 86.5%, and 84.4% respectively, following SVM. Recall, accuracy, and F1 score are given to provide comparative information on the relative merits of these models. This integrated healthcare monitoring system, with its timely alert system, real-time monitoring, and powerful predictive analytics, provide patients with personalized and proactive care. It enhances healthcare practices by showing that the data-driven strategic approach and its associated technologies are transforming patient outcomes in the emerging field of healthcare.
  • DEEP NEURAL SYSTEM FOR IDENTIFYING CYBERCRIME ACTIVITIES IN NETWORKS
    Journal of Theoretical and Applied Information Technology, 2023
  • DDCATF: Deep Learning Approach for Detection of Cybercrime Activities Based on Temporal Features
    Yerininti Venkata Narayana, Mooramreddy Sreedevi
    International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2023 Proceedings, 2023
    The detection of cybercrime activities holds paramount importance in today's interconnected digital landscape. As technology becomes more integrated into every aspect of one's lives, the potential for cybercrime and its impacts have increased exponentially. In this context, the presented research employs a Deep Learning approach for Detection of Cybercrime Activities based on Temporal Features (DDCATF), specifically leveraging Convolutional Neural Networks (CNNs), to effectively detect and combat cybercrime activities. The significance of this approach lies in its potential to empower numerous organizations in identifying intricate and ever-evolving threats. By integrating temporal features into the detection process, these organizations can substantially strengthen their cybersecurity capabilities. To accomplish this objective, the research makes use of publicly accessible datasetnamed SIMARGL2022 and compared with other benchmark datasets: KddCup99 and CICIDS2017. These datasets serve as valuable resources for evaluating the CNN efficacy in identifying a diverse range of cybercrime activities. To determine the performance, a comprehensive set of evaluation metrics including accuracy, precision, recall, and f-measure are employed.
  • CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques †
    Vinaya Babu Maddala, Mooramreddy Sreedevi
    Engineering Proceedings, 2023
    Mining technologies depend upon their outcomes, focusing only on certain data features within the database. They select only certain features related to the process from diverse integrated data resources and transform them into a form suitable for mining tasks. Different implementations of mining techniques run on data sources, which may be of considerable volume, to extract different knowledge outcomes suitable for various analyses and decision-making processes. The proposed study provides the design and development of the Clustering of Non-Associated Items set (CNAIS) within a transactional database. The development of the algorithm and its application to the data set are described and the results are noted. Comparisons with state-of-the-art methods show that CNAIS exhibits better performance.
  • Performance Analysis on Advances in Frequent Pattern Growth Algorithm
    M Vinaya Babu, M Sreedevi
    Proceedings IEEE International Conference on Advances in Computing Communication and Applied Informatics Accai 2022, 2022
    In data mining, association-rules mining are a crucial technology. The Frequent-Growth (FP) algorithm is a well-known algorithm for association mining. However, FP-Growth method in mining requires two scans of the database which affects the process's efficiency. We have compared two different FP-Growth algorithms namely Painting-Growth algorithm as well as not Painting-Growth algorithm via the study of association mining and FP-Growth algorithm. With the FP-Growth algorithm, we have compared two different types of enhanced algorithms. Performance of these enhanced algorithms is absolutely better than FP-Growth algorithm, according to the experimental results. Painting-Growth algorithm has a data volume of greater than 1050 and also, N Painting-Growth algorithm has a data volume of less than 10000.
  • A Comprehensive Study on Enhanced Clustering Technique of Association Rules over Transactional Datasets
    M. Vinaya Babu, M. Sreedevi
    Proceedings of the 5th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2021, 2021
    The most well-recognized fields in data mining is association rule mining. It’s been used within various applications including industry baskets, computer networks, recommendation systems and healthcare. Exploratory data analysis and data mining (DM) applications rely heavily on clustering. Cluster analysis seeks to categorize a group of patterns into groups based on their similarity. This paper aims to enhance the clustering technique of association rules over transactional datasets. At the outset the concepts behind association rules are explained followed by an overview of some of the recent research in this field. The benefits and drawbacks are addressed and a conclusion is drawn.
  • A framework to improve virtual private network architecture for secure data transmission using CMT-PA and multi-homing
    International Review on Computers and Software, 2012
  • Real time movement detection for human recognition
    Lecture Notes in Engineering and Computer Science, 2012

RECENT SCHOLAR PUBLICATIONS

  • GATBPSO based Framework for Unknown Malicious Activities Classification and Prediction in Networks
    YV Narayana, M Sreedevi
    2025 4th International Conference on Innovative Mechanisms for Industry … , 2025
    2025
  • CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques
    VB Maddala, M Sreedevi
    Engineering Proceedings 59 (1), 14 , 2023
    2023
    Citations: 1
  • DDCATF: Deep Learning Approach for Detection of Cyber Crime Activities based on Temporal features.
    DMS Y. Venkat narayana
    IEEE, 458-465 , 2023
    2023
  • DDCATF: Deep learning approach for detection of cybercrime activities based on temporal features
    YV Narayana, M Sreedevi
    2023 International conference on self sustainable artificial intelligence … , 2023
    2023
    Citations: 13
  • Clustering of Non-Associated Item Sets for Analyzing show rooms Sales Dataset
    DMS M.Vinay Babu
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
  • Deep Neural System for Identifying Cyber Crime Activities in Networks
    DMS Y. Venkat narayana
    Journal of Theoretical and Applied Information Technology 101 (16), 6414-6424 , 2023
    2023
  • ASSOCIATION RULES METHOD FOR THE ANALYSIS OF AFFINITY BETWEEN TEXT TYPE OBJECTS
    DMSM Vinaybabu
    2023
  • An Enhanced Novel Clustering Technique for Non-Associative Item Set(CNAIS) of Transaction Data Sets
    DMS M.Vinay Babu
    TIJER- International Research Journal 10 (7), 874-885 , 2023
    2023
  • A Literature Study on Various Techniques of Association Rule Mining.
    DMS M.Vinay Babu
    TIJER- International Research Journal 10 (6), 648-654 , 2023
    2023
  • Performance Analysis on Advances in frequent Pattern Growth Algorithm
    DMS M.Vinay Babu
    IEEE 10 (6), 648-654 , 2023
    2023
  • Performance Evaluation and Analysis of different Association Rule Mining ARM Algorithms
    DM Sreedevi
    Advancements in AI and IOT Convergence Technologies , 2023
    2023
  • Introduction to Internet Technologies
    DM Sreedevi
    978-620-3-47236-3 , 2023
    2023
  • Performance Evaluation and Analysis of Different Association Rule Mining (ARM) Algorithms
    V Babu, S Mooramreddy
    Handbook of Research on Advancements in AI and IoT Convergence Technologies … , 2023
    2023
  • A Proficient Coronary illness Discovery Framework using Naïve Bayes Characterization
    DMS Pamba Arun
    International Multispecialty Journal of Health (IJM Heath) 8 (11), 17-19 , 2022
    2022
  • Coronary Ailment Recognition Proof Using Information Mining Procedures: A Test Review
    DMS P. Sailakshmi
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 13-16 , 2022
    2022
  • A Comparitive Study and Investigation of Bosom Malignant Growth Identification Utilizing AI Methods
    DMS P. Sivasai
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 24-26 , 2022
    2022
  • A Concern on Post-Usable Future of Cellular Breakdown in the Lungs Patients Anticipated by Ada boost Model
    DMS P. Sudheer
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 27-30 , 2022
    2022
  • A Test and Relative Review for Fetal Health Arrangement utilizing Cardiotocogram Information.
    DMS P. Mahesh
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 35-37 , 2022
    2022
  • An Exact Examination of Multi-Class Gathering Model Involving SVM for Recognition Essential Tumer
    DMS P. Sravani
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 38-41 , 2022
    2022
  • Expecting the Seriousness of Mammographic Mass using Information Mining Strategy
    DMS M. Rekha Sri
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 5-8 , 2022
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • Real time movement detection for human recognition
    M Sreedevi, YK Avulapati, G AnjanBabu, R Sendhil Kumar
    Proceedings of the world congress on Engineering and Computer Science 1, 24-26 , 2012
    2012
    Citations: 14
  • DDCATF: Deep learning approach for detection of cybercrime activities based on temporal features
    YV Narayana, M Sreedevi
    2023 International conference on self sustainable artificial intelligence … , 2023
    2023
    Citations: 13
  • Performance analysis on advances in frequent pattern growth algorithm
    MV Babu, M Sreedevi
    2022 International Conference on Advances in Computing, Communication and … , 2022
    2022
    Citations: 5
  • A comprehensive study on enhanced clustering technique of association rules over transactional datasets
    MV Babu, M Sreedevi
    2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile … , 2021
    2021
    Citations: 3
  • Threshold Sr2n Public key Cryptosystem
    DM Sreedevi
    International Journal of Engineering Trends and Technology 31 (1), 15-17 , 2016
    2016
    Citations: 3
  • A Reputation Based Scheme to Prevent Routing Misbehavior in MANETs
    M Sreedevi
    International Journal of Computer Science and Information Technologies 3 (2 … , 2012
    2012
    Citations: 2
  • CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques
    VB Maddala, M Sreedevi
    Engineering Proceedings 59 (1), 14 , 2023
    2023
    Citations: 1
  • GATBPSO based Framework for Unknown Malicious Activities Classification and Prediction in Networks
    YV Narayana, M Sreedevi
    2025 4th International Conference on Innovative Mechanisms for Industry … , 2025
    2025
  • DDCATF: Deep Learning Approach for Detection of Cyber Crime Activities based on Temporal features.
    DMS Y. Venkat narayana
    IEEE, 458-465 , 2023
    2023
  • Clustering of Non-Associated Item Sets for Analyzing show rooms Sales Dataset
    DMS M.Vinay Babu
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
  • Deep Neural System for Identifying Cyber Crime Activities in Networks
    DMS Y. Venkat narayana
    Journal of Theoretical and Applied Information Technology 101 (16), 6414-6424 , 2023
    2023
  • ASSOCIATION RULES METHOD FOR THE ANALYSIS OF AFFINITY BETWEEN TEXT TYPE OBJECTS
    DMSM Vinaybabu
    2023
  • An Enhanced Novel Clustering Technique for Non-Associative Item Set(CNAIS) of Transaction Data Sets
    DMS M.Vinay Babu
    TIJER- International Research Journal 10 (7), 874-885 , 2023
    2023
  • A Literature Study on Various Techniques of Association Rule Mining.
    DMS M.Vinay Babu
    TIJER- International Research Journal 10 (6), 648-654 , 2023
    2023
  • Performance Analysis on Advances in frequent Pattern Growth Algorithm
    DMS M.Vinay Babu
    IEEE 10 (6), 648-654 , 2023
    2023
  • Performance Evaluation and Analysis of different Association Rule Mining ARM Algorithms
    DM Sreedevi
    Advancements in AI and IOT Convergence Technologies , 2023
    2023
  • Introduction to Internet Technologies
    DM Sreedevi
    978-620-3-47236-3 , 2023
    2023
  • Performance Evaluation and Analysis of Different Association Rule Mining (ARM) Algorithms
    V Babu, S Mooramreddy
    Handbook of Research on Advancements in AI and IoT Convergence Technologies … , 2023
    2023
  • A Proficient Coronary illness Discovery Framework using Naïve Bayes Characterization
    DMS Pamba Arun
    International Multispecialty Journal of Health (IJM Heath) 8 (11), 17-19 , 2022
    2022
  • Coronary Ailment Recognition Proof Using Information Mining Procedures: A Test Review
    DMS P. Sailakshmi
    International Multispecialty Journal of Health (IJM Heath) 8 (9), 13-16 , 2022
    2022

Publications

Sl.
No
Topic
Name of the Journal
Month &Year of Publications

1 A New Variant Blind Multi signature Scheme
M.Sreedevi, International Journal of Computer Science and Network Security IJCSNS
Impact Factor : 2.56 Vol.9,
November 2009.

2 A Reputation Based Scheme to Prevent Routing Misbehavior in Manets
M.Sreedevi (IJCSIT) International Journal of Computer Science and Information Technologies 2012,ISSN: 0975-9646 Pages: 3526-3529
Impact Factor : 3.32

3 Data Centric Knowledge Management system using Post Clustering Technique
Asadi Srinivasulu, . Subba Rao
M. Sreedevi VSRD International Journal of Computer Science and Information Technology Impact Factor : 3.711 Vol. 2 (4), 2012 1-5
April 2012 ISSN No. 2231-2471 Pages :285-295

4 Optimization of Cost through Effective
Use of Resources in Cloud Computing
M.Sreedevi VSRD International Journal of Computer Science and Information Technology Impact Factor : 3.711 Vol.2 (4), 2012 1-5 April 2012 ISSN No. 2231-2471 Pages: 296-304

5 Information Technology in Knowledge Management M.Sreedevi International Journal of Research in Commerce, IT & Management
Impact Factor : 2.37 Vol. No. 2 (2012), Issue , ISSN 2231-5756 Pages : 132-135

6 ICT for Women Empowerment – A Case study of Sri Venkateswara University
M.Sreedevi, R.Nagarjuna Reddy VSRD International Journal of Computer Science and Information Technology Impact Factor : 3.711 Vol.2 (5), 2012 1-5 May

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patent Title :-ASSOCIATION RULES METHOD FOR THE ANALYSIS OF AFFINITY BETWEEN TEXT TYPE OBJECTS

Application No: 202341048126

Ref No : TEMP/E -1/55557/2023

Application E filling Date : 17.07.2023

IPR website - Application Status