Dr.S.Venkatramulu

@kitsw.ac.in

Associate Professor, Dept. of Computer Science and Engg.
Kits Warangal

Dr.S.Venkatramulu


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https://researchid.co/venkatramulu0674
34

Scopus Publications

239

Scholar Citations

8

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Large Transformer Model With Haar Wavelet Transform for Channel Estimation in Massive MIMO Systems
    S. Venkatramulu, N. Sathyanarayana, M. G. Kavitha, Smitha Kurian, P. Shreedevi
    Internet Technology Letters, 2026
    In recent scenario, the accurate Channel State Information (CSI) is essential for reliable performance of massive Multiple Input Multiple Output (MIMO) systems, particularly in spatially correlated channels of wireless communications. Traditional channel estimation techniques heavily relied on the pilot symbol transmission from all antennas which resulted in pilot overhead. Therefore, this research proposes Large Transformer Model with Haar Wavelet Transform (LT‐HWT) for channel estimation in massive MIMO systems. The proposed LT‐HWT model captures both spatial and temporal dependencies by processing the sequences of partial CSI with a dual‐stage encoder. Initially, the temporal patterns are extracted and then integrated with spatial embeddings to reduce the missing inference of CSI. Consequently, the pretrained transformer which is a large model decoder is utilized for robust sequence modeling and improved generalization. The proposed LT‐HWT model significantly reduced the pilot overhead while maintain high estimation accuracy with high results when compared to existing Deep Neural Network (DNN) in terms of Bit Error Rate (BER) of 0.955 and throughput of 0.891, respectively.
  • CyberShieldDL: A Hybrid Deep Learning Architecture for Robust Intrusion Detection and Cyber Threat Classification
    S. Venkatramulu, John Babu Guttikonda, Desidi Narsimha Reddy, Y. Madhavi Reddy, M. Sirisha
    Indonesian Journal of Electrical Engineering and Informatics, 2025
    In modern network environments, securing systems from newly emerging attacks is essential, and a constructive approach is the use of an IDS (Intrusion Detection System). When faced with attacks that are not in the list of predefined patterns, traditional IDS methods such as signature-based detection or standalone machine learning models may not function properly to detect such attacks because they are not adaptable and not designed to deal with this type of attack. The current IDS systems that employ deep-learning architectures have enhanced detection capabilities; however, most prior art systems are limited by partial feature learning, which only learns features of either spatial or temporal traffic structures. Meanwhile, the lack of contextaware mechanisms, such as attention, limits their ability to attend more to the most informative network components, leading to suboptimal detection performance and generalization. To counter this issue, in this work, we introduce CyberShieldDL, which is the first deep learning-based IDS framework with a novel hybrid architecture: IntruNet-Hybrid, combining Convolutional Neural Networks (CNN) for spatial pattern extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential feature extraction, and an attention mechanism to learn the salient features for intrusion detection dynamically. To create the framework, an optimized preprocessing and feature selection pipeline is presented to effectively and costeffectively prepare the model input. Extensive experiments on the CICIDS2017 dataset demonstrate that CyberShieldDL consistently outperforms the state-of-the-art, achieving an overall accuracy of 98.35% and high precision, recall, and F1-score in various attack scenarios. Cross-dataset validations on NSL-KDD and UNSW-NB15 also verify the system's generalization. The design provides a scalable and flexible solution for realworld network security, offering the flexibility and adaptability necessary to enhance classification accuracy and robustness against evolving attack patterns. Its modular construction enables us to extend it for real-time deployment and future adversarial robustness easily.
  • Development of self sustaining system by integration of AI and IoT
    Convergence of Self Sustaining Systems with AI and Iot, 2024
  • Cluster Head Selection for Single and Multiple data Sinks in Heterogeneous WSN using Wild Horse Optimization
    Mohammed I. Habelalmateen, Gotte Ranjith Kumar, Nayana B P, S. Venkatramulu, N. Naga Saranya
    2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024
    Enhancing a network lifetime by balancing energy as well as minimizing delay is a significant aim of Wireless Sensor Network (WSN). Besides, number of clustering approaches are developed to this process through selecting the optimum Cluster Heads (CHs) in heterogeneous WSN, but it has a significant challenge. In this research, the Wild Horse Optimization (WHO)-based Optimized Clustering approach for single and multiple data sinks is proposed for optimizing CH selection through combining the parameters of sink distance, residual energy in its formulated fitness function. The proposed method achieves better results and it is evaluated by various performance metrices like residual energy, network lifetime, throughput, alive nodes, FND as well as LND and it achieves the values of about each as 61.37, 98.36, 7.8, 96.39, 6129 and 8347 respectively when compared to the existing methods such as Energy-Efficient CH selection utilizing Improved version of Grey Wolf Optimization-based (EECHIGWO) and Enhanced Pelican Optimization Algorithm for the CH Selection (EPOA-CHS).
  • Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on Biomedical Images
    M. Suresh Anand, S. Venkatramulu, Sesha Vidhya S, W T Chembian, Yashaswini B M, Magesh Kumar B
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    The emergence of Cervical Cancer (CC) Recognition and Classification Model for Biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. By providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on Biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. Besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models.
  • Crypto Ballot: Safeguarding democracy with Blockchain Voting
    S. Venkatramulu, Rishitha Reddy Gopu, Naresh Badavath, Shreya Karimilla, Sowmith Reddy Arram, Krishna Adithya Pannala
    2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024
    The conventional voting system is widely distrusted, which makes democratic voting essential in any nation. People have seen their basic rights violated. Other digital voting techniques are facing issues due to a lack of accountability. Most voting methods are not transparent enough, which makes it very difficult for the administration to gain public support. Due to their ease of manipulation, both traditional and modern computerized voting systems have shown to be ineffective. The primary objective is to resolve any problems with the conventional and digitized voting systems, covering any inaccuracy or injustice that might have happened during the voting procedure. Blockchain based technologies has the capacity to improve election fairness and decrease injustice in the voting process. This research work presents a platform powered by blockchain that optimizes system reliability and openness to promote a reliable voter-official connection. The proposed technology provides a framework for digital voting based on blockchain that eliminates the requirement for physical polling booths. Our proposed architecture enables a flexible blockchain by utilizing flexible consensus methods.
  • SMART HEALTHCARE DATA PROTECTION AND ANALYSIS THROUGH FUZZY-BASED CYBER SECURITY
    Journal of Environmental Protection and Ecology, 2024
  • A NEW SMART COMMUNICATION PROTOCOL AND INTERNET OF THINGS (IOT) FOR WASTE MANAGEMENT SYSTEM
    Journal of Theoretical and Applied Information Technology, 2023
  • NABGAMES: NASH BARGAINING GAME FOR IMPROVING COVERAGE IN UNMANNED AERIAL VEHICLES (UAV)
    Journal of Theoretical and Applied Information Technology, 2023
  • A Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer for cardiovascular disease detection and classification
    Siripuri Kiran, Ganta Raghotham Reddy, Girija S.P., Venkatramulu S., Kumar Dorthi, Chandra Shekhar Rao V.
    Healthcare Analytics, 2023
    Cardiovascular disease (CVD) is a common disorder frequently resulting in death. An increase in the death rate among adults is attributed to several factors, including smoking, high blood pressure, obesity, and cholesterol. Early diagnosis of CVDs can lower mortality rates. Algorithms that use machine learning and data mining offer the potential for finding risk variables and predicting CVD. Developing countries often need more CVD experts, and a high percentage of misdiagnosis. These concerns could be alleviated using an accurate and effective early-stage heart disease prediction system. This study explores the effectiveness of machine learning classifiers for diagnosing and detecting CVD. Several supervised machine-learning algorithms are investigated, and their performance and accuracy are compared. The Gradient Boosted Decision Tree (GBDT) with Binary Spotted Hyena Optimizer (BSHO) suggested in this work was used to rank and classify all attributes. Discrete optimization problems can be resolved using the binary form of SHO. The recommended method compresses the continuous location using a hyperbolic tangent function. The updated spotted hyena positions on the relevance score are utilized to find those with high heart disease predictions. The efficiency of the suggested model is then confirmed using the UCI dataset. The proposed GBDT-BSHO approach, with an accuracy of 97.89%, was significantly more effective than the comparative methods. • Machine learning algorithms offer the potential for finding risk variables and predicting cardiovascular disease (CVD). • Several supervised machine-learning algorithms are investigated, and their performance and accuracy are compared. • The proposed Gradient Boosted Decision Tree with Binary Spotted Hyena Optimizer best predicts CVD. • The efficiency of the suggested model is then confirmed using the UCI dataset. • The proposed method compresses the continuous location using a hyperbolic tangent function.
  • An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms
    V. Chandra Shekhar Rao, Kallepelly Spandhana, C. Srinivas, M. Sujatha, Bojja Vani, S. Venkatramulu
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET
    K. Vinay Kumar, S. Venkatramulu, Vidya Rao, Chintakindi Srinivas, Sreenivas Pratapagiri, et al.
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications
    S. Venkatramulu, Vidya Rao, K. Vinay Kumar, Chintakindi Srinivas, B. Raghuram, et al.
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol
    V. Chandra Shekhar Rao, K. Vinay Kumar, Sreenivas Pratapagiri, C. Srinivas, B. Raghuram, et al.
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)
    Chetana Srinivas, S. Venkatramulu, Vidya Rao, B. Raghuram, K. Vinay Kumar, et al.
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • Violation of Traffic Rules and Detection of Sign Boards
    S. Venkatramulu, Bairy Yugasri, Triveni Mohan Sadala, Garidepalli Revathi, V. Chandra Shekhar Rao
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • A Novel Blockchain Approach for Improving the Security and Reliability of Wireless Sensor Networks Using Jellyfish Search Optimizer
    Viyyapu Lokeshwari Vinya, Yarlagadda Anuradha, Hamid Reza Karimi, Parameshachari Bidare Divakarachari, Venkatramulu Sunkari
    Electronics Switzerland, 2022
  • Optimization of load balancing of containers workload in cloud to minimize make span by using swarm intelligence (SI) algorithm
    C. Srinivas, Kummari Jyothi, Voore Subba Rao, V. Chandra Shekhar Rao, S. Venkatramulu
    Aip Conference Proceedings, 2022
  • IP spoofing controlling with design science research methodology
    S. Venkatramulu, Anusha Kandukuri, V. Chandra Shekhar Rao, C. Srinivas, Sreenivas Pratapagiri, E. Sudharshan
    Aip Conference Proceedings, 2022
  • Internet of things (IoT) based shortest route between departments within a university by applying nature inspired computing (NIC) algorithm
    M. S. B. Phridviraj, C. Srinivas, S. Venkatramulu, V. Chandra Shekhar Rao, Sreenivas Pratapagiri, K. Mahender
    Aip Conference Proceedings, 2022
  • Prediction of Covid-19 using Kalman filter algorithm
    V. Chandra Shekhar Rao, Bhairy Gnaneshwari Devi, Sreenivas Pratapagiri, C. Srinivas, S. Venkatramulu, D. Raghavakumari
    Aip Conference Proceedings, 2022
  • IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles
    V. Chandra Shekhar Rao, Chintakindi Srinivas, M.S.B. Phridviraj, Niranjan Polala, S. Venkatramulu, Siripuri Kiran
    International Journal of Heavy Vehicle Systems, 2022
  • Usage Patterns and Implementation of Machine Learning for Malware Detection and Predictive Evaluation
    S. Venkatramulu, M. PhridviRaj, Sreenivas Pratapagiri, Sujatha Madugula, Siripuri Kiran, V. C. S. Rao
    Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy Icais 2022, 2022
  • Machine Learning Based Predictive Analytics on Social Media Data for Assorted Applications
    M S B Phridviraj, Sreenivas Pratapagiri, Sujatha Madugula, Siripuri Kiran, V. Chandra Shekhar Rao, Venkatramulu Venkatramulu
    Proceedings of the International Conference on Electronics and Renewable Systems Icears 2022, 2022
  • Text Classification Using Deep Neural Networks
    Johnson Kolluri, V. Chandra Shekhar Rao, Gouthami Velakanti, Siripuri Kiran, Sumukham Sravanthi, S. Venkatramulu
    Lecture Notes in Networks and Systems, 2022
  • Database Patterns for the Cloud and Docker Integrated Environment using Open Source Machine Learning
    Siripuri Kiran, V. Chandra Shekhar Rao, S. Venkatramulu, M S B Phridviraj, Sreenivas Pratapagiri, Sujatha Madugula
    Proceedings of the International Conference on Electronics and Renewable Systems Icears 2022, 2022
  • Advanced Machine Learning Scenarios for Real World Applications using Weka Platform
    Sujatha Madugula, Siripuri Kiran, V. Chandra Shekhar Rao, S Venkatramulu, M S B Phridviraj, Sreenivas Pratapagiri
    Proceedings of the International Conference on Electronics and Renewable Systems Icears 2022, 2022
  • ML based Implementation for Documents Forensic and Prediction of Forgery using Computer Vision Framework
    Sreenivas Pratapagiri, Sujatha Madugula, Siripuri Kiran, V. C. S. Rao, S. Venkatramulu, M. PhridviRaj
    Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy Icais 2022, 2022
  • COVID-19 Patterns Identification using Advanced Machine Learning and Deep Neural Network Implementation
    V. C. S. Rao, S. Venkatramulu, M. PhridviRaj, Sreenivas Pratapagiri, Sujatha Madugula, Siripuri Kiran
    Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy Icais 2022, 2022
  • Object Recognition using Novel Geometrical Feature Extraction Techniques
    Narasimha Reddy Soora, Snehith Reddy Puli, Venkatramulu Sunkari
    Proceedings of the 2021 IEEE International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2021, 2021
  • 5G Enabled Industrial Internet of Things (IIoT) Architecture for Smart Manufacturing
    V. Chandra Shekhar Rao, P. Kumarswamy, M. S. B. Phridviraj, S. Venkatramulu, V. Subba Rao
    Lecture Notes on Data Engineering and Communications Technologies, 2021
  • CSES: Cuckoo search based exploratory scale to defend input-type validation vulnerabilities of HTTP requests
    S. Venkatramulu, C. V. Guru Rao
    Advances in Intelligent Systems and Computing, 2018
  • RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests
    International Journal of Applied Engineering Research, 2017
  • Preventing input type validation vulnerabilities using network based intrusion detection systems
    Venkatramulu Sunkari, C.V.Guru Rao
    Proceedings of 2014 International Conference on Contemporary Computing and Informatics Ic3i 2014, 2014

RECENT SCHOLAR PUBLICATIONS

  • Deep learning-based early detection of crop diseases using leaf image analysis in smart agricultural systems
    S Venkatramulu, V Srinivas, TM Sadala, R Rajoju, R Kamalakar
    Int J Environ Sci 11 (5s), 294-303 , 2025
    2025
    Citations: 4
  • A secure blockchain based student certificate generation and sharing system
    S Venkatramulu, KV Kumar, MS Waseem, S Mahveen, V Vaidya, ...
    Journal of Sensors, IoT & Health Sciences 2 (01), 17-27 , 2024
    2024
    Citations: 20
  • Research on SQL injection attacks using word embedding techniques and machine learning
    S Venkatramulu, MS Waseem, A Taneem, SY Thoutam, S Apuri
    Journal of Sensors, IoT & Health Sciences 2 (01), 55-66 , 2024
    2024
    Citations: 30
  • Decentralized machine learning based energy efficient routing and intrusion detection in unmanned aerial network UAV
    C Srinivas, S Venkatramulu, VCS Rao, B Raghuram, K VinayKumar, ...
    Int. J. Recent Innov. Trends Comput. Commun 11, 517-527 , 2024
    2024
    Citations: 4
  • A new smart communication protocol and Internet of Things (IoT) for waste Management System
    B Raghuram, C Srinivas, S Venkatramulu, V Chandrashekar, DK RAO, ...
    Journal of Theoretical and Applied Information Technology 101 (24), 8156-8165 , 2023
    2023
    Citations: 3
  • Nabgames: Nash bargaining game for improving coverage in Unmanned Aerial Vehicles (UAV)
    U Dulhare, B Raghuram, C Srinivas, S Venkatramulu, VC Rao, ...
    Journal of Theoretical and Applied Information Technology 101 (24), 8167-8176 , 2023
    2023
    Citations: 1
  • A LIGHT WEIGHT OF PARALLEL ENCRYPTION WITH DIGIT ARITHMETIC OF COVERTEXT ENCRYPTION MODEL
    TRIH WIDIYANTO, EKA ARDHIANTO, H MURTI, RS REDJEKI, DJ TATAJI, ...
    Journal of Theoretical and Applied Information Technology 101 (24) , 2023
    2023
    Citations: 1
  • A gradient boosted decision tree with binary spotted hyena optimizer for cardiovascular disease detection and classification
    S Kiran, GR Reddy, K Dorthi
    Healthcare Analytics 3, 100173 , 2023
    2023
    Citations: 30
  • A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications
    S Venkatramulu, VCS Rao, KV Kumar, C Srinivas, B Raghuram, S Rasool
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
    Citations: 1
  • Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET
    KV Kumar, S Venkatramulu, VCS Rao, C Srinivas, S Pratapagiri, ...
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
    Citations: 1
  • Text Classification Using Deep Neural Networks
    J Kolluri, V Chandra Shekhar Rao, G Velakanti, S Kiran, S Sravanthi, ...
    Data Engineering and Intelligent Computing: Proceedings of 5th ICICC 2021 … , 2022
    2022
  • Optimization of load balancing of containers workload in cloud to minimize make span by using swarm intelligence (SI) algorithm
    C Srinivas, K Jyothi, VS Rao, VCS Rao, S Venkatramulu
    AIP Conference Proceedings 2418 (1), 030062 , 2022
    2022
  • Prediction of Covid-19 using Kalman filter algorithm
    VCS Rao, BG Devi, S Pratapagiri, C Srinivas, S Venkatramulu, ...
    AIP Conference Proceedings 2418 (1), 030067 , 2022
    2022
    Citations: 6
  • Internet of things (IoT) based shortest route between departments within a university by applying nature inspired computing (NIC) algorithm
    MSB Phridviraj, C Srinivas, S Venkatramulu, VCS Rao, S Pratapagiri, ...
    AIP Conference Proceedings 2418 (1), 030068 , 2022
    2022
    Citations: 4
  • IP spoofing controlling with design science research methodology
    S Venkatramulu, A Kandukuri, VCS Rao, C Srinivas, S Pratapagiri, ...
    AIP Conference Proceedings 2418 (1), 030075 , 2022
    2022
    Citations: 9
  • Database Patterns for the Cloud and Docker Integrated Environment using Open Source Machine Learning
    S Kiran, VCS Rao, S Venkatramulu, MSB Phridviraj, S Pratapagiri, ...
    2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022
    2022
    Citations: 1
  • Advanced Machine Learning Scenarios for real world applications using Weka platform
    S Madugula, S Kiran, VCS Rao, S Venkatramulu, MSB Phridviraj, ...
    2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022
    2022
    Citations: 5
  • ML based Implementation for Documents Forensic and Prediction of Forgery using Computer Vision Framework
    S Pratapagiri, S Madugula, S Kiran, VCS Rao, S Venkatramulu, ...
    2022 Second International Conference on Artificial Intelligence and Smart … , 2022
    2022
  • Usage patterns and implementation of machine learning for malware detection and predictive evaluation
    S Venkatramulu, MSB Phridviraj, S Pratapagiri, S Madugula, S Kiran, ...
    2022 Second International Conference on Artificial Intelligence and Smart … , 2022
    2022
    Citations: 4
  • COVID-19 Patterns Identification using Advanced Machine Learning and Deep Neural Network Implementation
    VCS Rao, S Venkatramulu, MSB Phridviraj, S Pratapagiri, S Madugula, ...
    2022 Second International Conference on Artificial Intelligence and Smart … , 2022
    2022
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • 5G enabled industrial internet of things (IIoT) architecture for smart manufacturing
    V Chandra Shekhar Rao, P Kumarswamy, MSB Phridviraj, ...
    Data Engineering and Communication Technology: Proceedings of ICDECT 2020 … , 2021
    2021
    Citations: 36
  • Research on SQL injection attacks using word embedding techniques and machine learning
    S Venkatramulu, MS Waseem, A Taneem, SY Thoutam, S Apuri
    Journal of Sensors, IoT & Health Sciences 2 (01), 55-66 , 2024
    2024
    Citations: 30
  • A gradient boosted decision tree with binary spotted hyena optimizer for cardiovascular disease detection and classification
    S Kiran, GR Reddy, K Dorthi
    Healthcare Analytics 3, 100173 , 2023
    2023
    Citations: 30
  • Various solutions for address resolution protocol spoofing attacks
    S Venkatramulu, CG Rao
    International Journal of Scientific and Research Publications 3 (7), 1 , 2013
    2013
    Citations: 25
  • WITHDRAWN: Implementation of Grafana as open source visualization and query processing platform for data scientists and researchers
    S Venkatramulu, MSB Phridviraj, C Srinivas, VCS Rao
    Materials Today: Proceedings , 2021
    2021
    Citations: 22
  • A secure blockchain based student certificate generation and sharing system
    S Venkatramulu, KV Kumar, MS Waseem, S Mahveen, V Vaidya, ...
    Journal of Sensors, IoT & Health Sciences 2 (01), 17-27 , 2024
    2024
    Citations: 20
  • IoT and artificial intelligence enabled state of charge estimation for battery management system in hybrid electric vehicles
    S Kiran, N Polala, MSB Phridviraj, S Venkatramulu, C Srinivas, VCS Rao
    International Journal of Heavy Vehicle Systems 29 (5), 463-479 , 2022
    2022
    Citations: 12
  • IP spoofing controlling with design science research methodology
    S Venkatramulu, A Kandukuri, VCS Rao, C Srinivas, S Pratapagiri, ...
    AIP Conference Proceedings 2418 (1), 030075 , 2022
    2022
    Citations: 9
  • Secure communication using two party authenticated quantum key distribution protocols
    S Venkatramulu, S Veena
    Int. J. Comput. Sci. Netw. Secur 10 (8), 233-238 , 2010
    2010
    Citations: 7
  • Prediction of Covid-19 using Kalman filter algorithm
    VCS Rao, BG Devi, S Pratapagiri, C Srinivas, S Venkatramulu, ...
    AIP Conference Proceedings 2418 (1), 030067 , 2022
    2022
    Citations: 6
  • Advanced Machine Learning Scenarios for real world applications using Weka platform
    S Madugula, S Kiran, VCS Rao, S Venkatramulu, MSB Phridviraj, ...
    2022 International Conference on Electronics and Renewable Systems (ICEARS … , 2022
    2022
    Citations: 5
  • CSES: Cuckoo search based exploratory scale to defend input-type validation vulnerabilities of HTTP requests
    S Venkatramulu, CV Guru Rao
    Proceedings of the Second International Conference on Computational … , 2018
    2018
    Citations: 5
  • Deep learning-based early detection of crop diseases using leaf image analysis in smart agricultural systems
    S Venkatramulu, V Srinivas, TM Sadala, R Rajoju, R Kamalakar
    Int J Environ Sci 11 (5s), 294-303 , 2025
    2025
    Citations: 4
  • Decentralized machine learning based energy efficient routing and intrusion detection in unmanned aerial network UAV
    C Srinivas, S Venkatramulu, VCS Rao, B Raghuram, K VinayKumar, ...
    Int. J. Recent Innov. Trends Comput. Commun 11, 517-527 , 2024
    2024
    Citations: 4
  • Internet of things (IoT) based shortest route between departments within a university by applying nature inspired computing (NIC) algorithm
    MSB Phridviraj, C Srinivas, S Venkatramulu, VCS Rao, S Pratapagiri, ...
    AIP Conference Proceedings 2418 (1), 030068 , 2022
    2022
    Citations: 4
  • Usage patterns and implementation of machine learning for malware detection and predictive evaluation
    S Venkatramulu, MSB Phridviraj, S Pratapagiri, S Madugula, S Kiran, ...
    2022 Second International Conference on Artificial Intelligence and Smart … , 2022
    2022
    Citations: 4
  • A new smart communication protocol and Internet of Things (IoT) for waste Management System
    B Raghuram, C Srinivas, S Venkatramulu, V Chandrashekar, DK RAO, ...
    Journal of Theoretical and Applied Information Technology 101 (24), 8156-8165 , 2023
    2023
    Citations: 3
  • RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests
    S Venkatramulu, R Guru
    International Journal of Applied Engineering Research 12 (24), 14033-14039 , 2017
    2017
    Citations: 3
  • COVID-19 Patterns Identification using Advanced Machine Learning and Deep Neural Network Implementation
    VCS Rao, S Venkatramulu, MSB Phridviraj, S Pratapagiri, S Madugula, ...
    2022 Second International Conference on Artificial Intelligence and Smart … , 2022
    2022
    Citations: 2
  • Digital Image Processing and Applications
    VCS Rao, S Venkatramulu, P Sammulal
    Horizon Books (A Division of Ignited Minds Edutech P Ltd) , 2021
    2021
    Citations: 2