Manjunath H R

Verified @gmail.com

Associate Professor, Artificial Intelligence and Machine Learning Department
Jyothy Institute of Technology

Manjunath H R


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https://researchid.co/manjuhr

EDUCATION

PhD in Computer Science and Engineering
M.Tech in Computer science and Engineering
B.E in Computer Science and Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Artificial Intelligence, Computer Engineering
10

Scopus Publications

Scopus Publications

  • Zoneout regularization-gated recurrent unit algorithm on NIDS with class imbalance handling
    Mala Kariyappa, Manjunath Hanumanthappa Rangappa, Venugopal Dasappa, Gururaja Hebbur Satyanarayana, Girish Keshava Rao, et al.
    Iaes International Journal of Artificial Intelligence, 2026
    Network intrusion detection system (NIDS) is primarily utilized tool to identify malicious threats on the network. It plays an essential role in safeguarding against an increasing variety of attacks and ensures enhanced security for the network. The existing model struggled to handle the imbalance of class issues during the process of classification due to their biased nature, which reduced the performance of the algorithm. In this paper, the zoneout regularization–gated recurrent unit (ZR-GRU) algorithm is developed to detect and classify intrusions in the network. Incorporating the ZR into GRU reduces overfitting by preventing the model from becoming overly dependent on specific features. It provides good generalization by maintaining diversity in learned representation. Synthetic minority oversampling technique (SMOTE) and Near Miss methods are utilized to balance the samples in the dataset, which helps to increase the performance of a classifier in NIDS. The ZR-GRU technique attained 99.91% accuracy on UNSW-NB15, 99.92% accuracy on CIC-IDS2018, and 99.14% accuracy on CIC-DDoS2019 when comparing with a convolutional neural network bidirectional long short-term memory (CNN-BiLSTM).
  • Deep Learning for Traffic Safety:A YOLOv5 Approach for Helmet and Number Plate Recognition
    Shruthi Shetty J, Bhavatarini N, Janardhana D R, Shivanna K, Manjunath H R, et al.
    2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025
  • Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset
    Manjunath. H., Saravana Kumar
    Fusion Practice and Applications, 2023
    Increase in network activity of transferring information online allows network breeches where intruders easily avail the most important information or data. The growth of online functioning and many other governmental data over the internet without security has caused data vulnerability; attackers can easily detect the data and misuse them. Network Intrusion Detection System (NIDS) has allowed this whole process of online data transfer to occur safely and secured transactions. Due to the cloud usage in network the huge amount of traffic is created as well as number of attacks are increased day by day. To prevent the vulnerability and its types are social, environmental, cognitive, military attacks in the network are classified using CRNN model. We used ensemble learning methods in machine learning algorithms are used to detect and prevent the malicious packets in the network. Our model detects the unauthorized users intruding into any network and alerts the organization regarding the same. When a typical firewall is unable to effectively stop certain sorts of attacks on computer system usage and network communications, a network intrusion detection system may be used. First, we are classifying the unauthorized packets using machine learning algorithm. Using our concept, we have used neural networks in this paper to detect any such attack. For the Network Security Laboratory - Knowledge Discovery in Databases data set using CNN and RNN algorithms, we also applied a few well-known techniques as boosting and pasting methods. In this CRNN approach, we demonstrate that neural networks are more effective than other methods at detecting attacks.
  • A System for Network Based Intrusion Avoidance Using Dedicated Machine Learning and Artificial Intelligence-Based Model for Application and Data Safety
    H. Manjunath, S. Saravana Kumar
    Communications in Computer and Information Science, 2022
  • Energy-Efficient Routing Protocol for Hybrid Wireless Sensor Networks Using Falcon Optimization Algorithm
    International Journal of Intelligent Engineering and Systems, 2022
  • Energy-Efficient Cluster based Routing for Hybrid Wireless Sensor Networks using Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization Algorithm
    Manjunath Rangappa, Guruprakash Dyamanna, and
    International Journal of Intelligent Engineering and Systems, 2021
  • Energy Efficient Heterogeneous Wireless Sensor Networks - Recent Trends & Research Challenges
    H. R. Manjunath, C. D. Guruprakash
    Lecture Notes on Data Engineering and Communications Technologies, 2020
  • The New Approach for Creating the Knowledge Base Using WikiPedia
    Prasad E. Ganesh, H. R. Manjunath, V. Deepashree, M. G. Kavana, Raviraja
    Lecture Notes on Data Engineering and Communications Technologies, 2020
  • New Automated Vehicle Crash Avoidance System Based on Dipping and RF Techniques
    Pooja T. Shetty, R. Roopalakshmi, H. R. Manjunath, S. Pooja, M. Akshatha, et al.
    Lecture Notes on Data Engineering and Communications Technologies, 2019
  • Steganography using indexed texture synthesis with indexed color synthesis
    H. R. Manjunath, S. H. Brahmananda, S. Lokesh
    Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing Iccsp 2017, 2017
    A novel approach for digital steganography using a indexed texture synthesis with indexed color synthesis is proposed in this paper. A texture is a small image, and is arranged arbitrarily or synthesized to obtain a large image with same appearance. A small piece of texture weaved one another with secret message. Compared to other steganographic algorithm which make use of cover image to embed the secret data, our proposed algorithm make use of indexed texture sysnthesis and indexed color synthesis to embed secret data. This method has advantages over other steganography methods. The size of the embedded data is proportional to the size of the image, it is very highly difficult to reveal the secret data by hackers since we are using double indexed approach, it is easy to get back the original texture image. Experimental results have showed that our proposed approach can provide various number of embedding capacities which produce visually plausible texture images.

Publications

ENERGY AWARE MOBILE DISPATCHMENT AND SCHEDULING IN HYBRID WIRELESS SENSOR NETWORK European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 07, Issue 11, 2020

Energy-Efficient Cluster based Routing for Hybrid Wireless Sensor Networks using Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization Algorithm International Journal of Intelligent Engineering and Systems, , No.5, 2021 DOI: 10.22266/
Energy-Efficient Routing Protocol for Hybrid Wireless Sensor Networks Using Falcon Optimization Algorithm International Journal of Intelligent Engineering and Systems, , No.4, 2022 DOI: 10.22266/

SKIN CANCER DETECTION USING MACHINE LEARNING International Journal of Information Technology (IJIT) Volume 4, Issue 02, July-December 2023, pp. 10-19 Article ID: IJIT_4_02_002 Available online at olume=4&Issue=2 Journal ID: 4573-3410, DOI: © IAEME Publication