Dr. Madhu B R

@jyothyit.ac.in

Professor and Head, Department of Artificial Intelligence & Machine Learning



              

https://researchid.co/brmadhu
10

Scopus Publications

68

Scholar Citations

5

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Cognitive Object Detection: A Deep Learning Approach with Auditory Feedback
    Pooja S, Prasanna Kumar K, A S Sushmitha Urs, Vaibhavi B Raj, Madhu B R, and Vinod Kumar S

    IEEE
    Amidst the backdrop of technological convergence, this study delves into the realm of augmented visual intelligence. It does so by orchestrating a harmonious fusion of the YOLOv3 framework, deep learning paradigms, and the seamless integration of Text-to-Speech (TTS) capabilities. The research paper meticulously dissects a methodological blueprint designed for the creation of an object recognition system, impeccably tailored to the precise identification of objects within the comprehensive COCO dataset. With the YOLOv3 architecture as our cornerstone, meticulous parameter fine-tuning transpires within the Darknet framework, ensuring an unswerving alignment with the diverse object categories that define the COCO dataset. Our system demonstrates proficiency by deploying TTS technology to deliver real-time auditory interpretations of recognized objects, enhancing both user accessibility and engagement. The ethical compass steadfastly guides our approach, encompassing privacy safeguards that underscore our commitment to the conscientious and responsible utilization of data. System performance is rigorously assessed through the lens of pivotal metrics, including precision, recall, and the F1 score, validating the system’s precision and reliability. This research elucidates the transformative potential innate to the amalgamation of deep learning and TTS integration within the sphere of object recognition, thus carving a path for pioneering applications and the evolution of technology.

  • 2D Mapping and Exploration Using Autonomous Robot
    N. Shravan, M. Manoj Kumar, Sriraag Jayanth, R. S. Bindu, B. R. Madhu, and K. S. Sreekeshava

    Springer Nature Singapore

  • Action Detection for Sign Language Using Machine Learning
    A S Sushmitha Urs, Vaibhavi B Raj, Pooja S, Prasanna Kumar K, Madhu B R, and Vinod Kumar S

    IEEE
    This research intends to build an effective and quick algorithm for identifying the alphabets in American Sign Language (ASL) using natural hand movements, increasing communication accessibility for people with hearing impaired limitations. The system's ultimate goal is to act as a translator between spoken language and sign language, enabling more effective and efficient communication between those with hearing loss and others who don't have any hearing loss. The research uses image processing, machine learning, and CNN-based artificial intelligence to recognize ASL movements and generate outputs that are simple to interpret. The potential impact of this work on communication accessibility for people who have hearing loss is significant.

  • Predicting unlabeled traffic for intrusion detection using semi-supervised machine learning
    Anku Jaiswal, A. S. Manjunatha, B.R. Madhu, and Murthy P. Chidananda

    IEEE
    Intrusion is one of the most serious problems with network Security, as new types of intrusions are getting much more challenging to detect. Large amount of network traffic has been generated due to the use of internet; most of the generated traffic is in the format which cannot be used directly to arrive at meaningful information. The cleansing and labeling of data each time needs a considerable amount of human effort, and is time consuming. In this paper we show how, Semi supervised machine learning technique can be used in intrusion detection, for both labeled and unlabeled data. In the proposed technique we take a small amount of labeled data to create model and using this model we show how to predict the unlabeled traffic. Machine Learning tool is used for this purpose which uses semi-supervised classifier to build the model. The created model is then integrated in Pentaho which with the help of Weka Scoring provides the expected output. The proposed technique helps the network administrator to take quick decision by classifying the incoming traffic as either malicious or normal and hence efficient detection of intrusion.

  • Minimizing execution time of cloudlets through optimal allocation of virtual machines using genetic algorithm
    Prakash Chandra, A. S. Manjunatha, P. Chidananda Murthy, and B. R. Madhu

    IEEE
    The features like on-demand self service, rapid elasticity, measured service, broad network access has increased the popularity of cloud computing and has motivated the business organization to adopt the cloud as part of their IT services and solutions. Various statistics and survey has proved that the user-base of the cloud is increasing day by day, which has enforced the cloud service providers to ensure the high availability and reliability of their services to maintain good credibility in the market. The cloud is powered by the virtualization technology, which makes it possible to deliver the computing services in the form of Virtual Machines (VMs). The efficiency of any cloud system greatly depends on its VM allocation policy, which is the policy adopted by cloud service provider to allocate the virtual machine to the available physical server. This paper presents the proposed technique of using genetic algorithm to find the optimal solution to allocate and map the virtual machines to the hosts and hence maximizing the utilization of resources. The experimental analysis carried out on CloudSim tool has proved that the proposed technique is more efficient and can provide good scope for future research.

  • A comparative study of algorithms for efficient dynamic consolidation of virtual machines in cloud


  • Building efficient classifiers for intrusion detection with reduction of features


  • Minimizing energy consumption in cloud datacenters using task consolidation
    Madhu B.R., Manjunatha A.S., Prakash Chandra, and Chidananda Murthy P

    ENGG Journals Publications
    Cloud service providers are experiencing the huge demand for the computational power as most of the companies are outsourcing their IT services to the cloud. This increased popularity of cloud computing has resulted in the establishment of large-scale datacenters with the huge number of computational intensive servers. These datacenters are very much expensive to maintain as they consume enormous amount of electrical energy, which results in the emission of green house gases like carbon dioxide. One of the main reasons for the huge power consumption is the inefficiency of the existing resource scheduling algorithm, which utilizes the existing resources in the non-power aware manner. This paper is based on the idea that the power consumption is directly proportional to the CPU utilization of the server, and hence proposes the task consolidation technique which maps the users requests i.e. task to the virtual machine in such a way that it maximizes the CPU utilization and minimizes the energy consumption at the same time. CloudSim is used as the simulation tool kit to show how the proposed task scheduling technique is more power efficient compared to the existing task scheduling technique. KeywordCloud computing, Task consolidation, CloudSim, Energy consumption, Green computing

  • Detecting Malicious Cloud Bandwidth consumption using machine learning
    Chidananda Murthy P., Manjunatha A.S., Anku Jaiswal, and Madhu B.R.

    ENGG Journals Publications
    One of the most difficult and unsolved issues in network is the security issue, because of continuous evolving nature of both threats and the measures used to detect and avoid threats. Among different types of attacks, one of the most vulnerable attacks in network security are bots that consume the resources maliciously and exhaust them. Malicious Cloud Bandwidth Consumption (MCBC) attack is a new type of attack, where the aim of the attacker is to consume the bandwidth maliciously, in turn causing the financial burden to the cloud service host. MCBC is generally vulnerable to the internet based web services in public cloud. MCBC mainly aims at frequently consuming the bandwidth in a slow manner, hence affecting the pay-as-you-go utility model, causing the consumer in the form of monetary loss. Unlike DDOS attack which is short lived and makes the resource unavailable to the user, MCBC attack is a long term attack which slowly attacks the target for an extended period and remains undetectable. As this attack does not affect the availability issue immediately, it is not discussed much as DDOS attack. This paper discuss about how machine learning technique can be used to detect the MCBC attack in the form of request per second, any traffic violating this range are classified as MCBC attack. The proposed system consists of using semi supervised machine learning which uses labeled network traffic for building model and unlabeled traffic to classify using the built model. KeywordNetwork Security, Machine learning, MCBC attack, Supervised learning

  • Data mining based CIDS: Cloud intrusion detection system for masquerade attacks [DCIDSM]
    P. Jain Pratik and B. R. Madhu

    IEEE
    Data mining has been gaining popularity in knowledge discovery field. In recent years, data mining based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in design and implementation of production quality IDSs. Masquerade attacks pose a serious threat for cloud system due to the massive amount of resource of these systems. This paper presents a Cloud Intrusion Detection System (CIDS) for CIDD dataset, which contains the complete audit parameters that help in detecting more than hundred instances of attacks and masquerades that exist in CIDD. It also offers numerous advantages in terms of alert infrastructure, security, scalability, reliability and also has data analysis tools.

RECENT SCHOLAR PUBLICATIONS

  • Cognitive Object Detection: A Deep Learning Approach with Auditory Feedback
    S Pooja, ASS Urs, VB Raj, BR Madhu, V Kumar
    2024 IEEE International Conference for Women in Innovation, Technology 2024

  • Action Detection for Sign Language Using Machine Learning
    ASS Urs, VB Raj, S Pooja, BR Madhu, V Kumar
    2023 International Conference on Network, Multimedia and Information 2023

  • Blind Assistance System using Digital Image Processing
    PKK Prasanna Kumar K
    2023

  • 2D Mapping and Exploration Using Autonomous Robot
    N Shravan, M Manoj Kumar, S Jayanth, RS Bindu, BR Madhu, ...
    International Conference on Emerging Research in Computing, Information 2023

  • A Survey On Detection of Falsified and Substandard Drugs
    J Arpitha, DV Bhat, N Shashikanth, BS Sharanya, BR Madhu
    Perspectives in Communication, Embedded-systems and Signal-processing-PiCES 2022

  • A Survey on Silk supply chain management using blockchain
    AS Prasad, DB Shetty, HS Divyashree, R Neha, BR Madhu
    Perspectives in Communication, Embedded-systems and Signal-processing-PiCES 2021

  • A Contrast on Blockchain Consensus
    MBR Ayush Kamal Anand, Manisha R Rao
    Journal of Computer Science Engineering and Software Testing 6 (1), 1-5 2020

  • STOCK MARKET PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES
    MBR Prerana C, Pratheeksha Mahishi J, N Tahmin Taj, Anusha B Shetty
    International Research Journal of Engineering and Technology (IRJET) 7 (4 2020

  • A Survey of Issues in Health Insurance System and Solution through Blockchain
    SB Manisha R Rao, Adithya H N, Pavan M S, Madhusudhan K, Madhu B R
    International Research Journal of Engineering and Technology (IRJET) 7 (4 2020

  • Algorithmic Trading using Mean Reversion Indicators
    M B R, H P, M M, YA Reddy, N Chowdary K
    International Journal of Computer Science and Mobile Computing 8 (6), 7-13 2019

  • SECURE DATA SHARING ON CLOUD USING TRANSPARENCY SERVICE MODEL
    BR Madhu, M Sindhu, HA Aravinda, S Aishwarya, AM Nesara
    2019

  • IoT based home automation system over cloud
    B Madhu, KR Vaishnavi, NG Dushyanth, SC Tushar Jain
    Int. J. Trend Sci. Res. Dev. 3 (4), 966-968 2019

  • Algorithm for Task Consolidation in Cloud Computing: A Comparative Survey
    R Pugaliya, BR Madhu
    International Journal of Research Granthaalayah 6 (5) 2018

  • Two-layered honeypot system implemented on a cloud server
    MBR Abhinandan Shetty, K Sriram, Nandish R, Ruthwik Soudry
    International Journal of Advance Research, Ideas and Innovations in 2018

  • Secure Auditing for the Data Stored on Cloud by Third Party Auditor
    BM Devaiah, G Yadav, HS Karthik, BR Madhu
    International Journal for Research in Applied Science & Engineering 2018

  • Big Data Science and Its Applications in Biomedical Research and Healthcare: A Review
    N Raj, A Karki, BR Madhu, CR Manjunath
    International Journal of Engineering Research and Application 8 (5), 45-52 2018

  • A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS
    DR Paneru, BR Madhu, S Naik
    Naik,'A Survey For Energy Efficiency In Cloud Data Centers 2017

  • A Comparative Study of Load Balancing Algorithms in Cloud Computing
    MBR Aayushi Sharma, Anshiya Tabassum, G.L. Vasavi, Shreya Hegde
    International Journal of Innovative Research in Computer and Communication 2017

  • SURVEY ON POWER AWARE LOAD BALANCING IN CLOUD COMPUTING
    BR Madhu, Manjula
    International Research of Research 5 (4) 2017

  • A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS
    SN Dinesh Raj Paneru, BR Madhu
    International Journal of Research 5 (4) 2017

MOST CITED SCHOLAR PUBLICATIONS

  • Predicting unlabeled traffic for intrusion detection using semi-supervised machine learning
    A Jaiswal, AS Manjunatha, BR Madhu, MP Chidananda
    2016 International Conference on Electrical, Electronics, Communication 2016
    Citations: 13

  • An Efficient Approach to Find Best Cloud Provider Using Broker
    BR Madhu, KK Amrutha
    International Journal of Advanced Research in Computer Science and Software 2014
    Citations: 9

  • IoT based home automation system over cloud
    B Madhu, KR Vaishnavi, NG Dushyanth, SC Tushar Jain
    Int. J. Trend Sci. Res. Dev. 3 (4), 966-968 2019
    Citations: 7

  • Building efficient classifiers for intrusion detection with reduction of features
    PC Murthy, AS Manjunatha, A Jaiswal, BR Madhu
    International Journal of Applied Engineering Research 11 (6), 4590-4596 2016
    Citations: 6

  • Data mining based CIDS: Cloud intrusion detection system for masquerade attacks [DCIDSM]
    PJ Pratik, BR Madhu
    2013 Fourth International Conference on Computing, Communications and 2013
    Citations: 6

  • A Comparative Study of Algorithms For Efficient Dynamic Consolidation of Virtual Machines In Cloud
    BR Madhu, AS Manjunatha, P Chandra, C Murthy
    J Applied Engineering Research 11 (6), 4597-4600 2016
    Citations: 5

  • STOCK MARKET PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES
    MBR Prerana C, Pratheeksha Mahishi J, N Tahmin Taj, Anusha B Shetty
    International Research Journal of Engineering and Technology (IRJET) 7 (4 2020
    Citations: 3

  • Algorithm for Task Consolidation in Cloud Computing: A Comparative Survey
    R Pugaliya, BR Madhu
    International Journal of Research Granthaalayah 6 (5) 2018
    Citations: 3

  • Minimizing execution time of cloudlets through optimal allocation of virtual machines using genetic algorithm
    P Chandra, AS Manjunatha, PC Murthy, BR Madhu
    2016 International Conference on Electrical, Electronics, Communication 2016
    Citations: 3

  • Minimizing Energy Consumption in Cloud Datacenters using Task Consolidation,
    BR Madhu, DAS Manjunatha, P Chandra, PC Murthy
    International Journal of Engineering and Technology (IJET) 8 (5) 2016
    Citations: 3

  • Prevent DDOS Attack in Cloud Using Machine Learning
    BR Madhu, A Jaiswal, PC Murthy
    International Journal of Advanced Research in Computer Science and Software 2016
    Citations: 3

  • Algorithmic Trading using Mean Reversion Indicators
    M B R, H P, M M, YA Reddy, N Chowdary K
    International Journal of Computer Science and Mobile Computing 8 (6), 7-13 2019
    Citations: 2

  • Data Mining based CIDS: Cloud Intrusion Detection System for Masquerade attacks [DCIDSM]
    P Jain Patik, BR Madhu
    IEEE 4th ICCCNT 2013
    Citations: 2

  • Action Detection for Sign Language Using Machine Learning
    ASS Urs, VB Raj, S Pooja, BR Madhu, V Kumar
    2023 International Conference on Network, Multimedia and Information 2023
    Citations: 1

  • Big Data Science and Its Applications in Biomedical Research and Healthcare: A Review
    N Raj, A Karki, BR Madhu, CR Manjunath
    International Journal of Engineering Research and Application 8 (5), 45-52 2018
    Citations: 1

  • A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS
    DR Paneru, BR Madhu, S Naik
    Naik,'A Survey For Energy Efficiency In Cloud Data Centers 2017
    Citations: 1