Dr. KONDA HARI KRISHNA

@mbu.asia

ASSOICATE PROFESSOR, Dept. of CSE, School of Computing, MBU, Tirupati.
Mohan Babu University

Dr. KONDA HARI KRISHNA


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https://researchid.co/dr.kondaharikrishna
Dr. KONDA HARI KRISHNA, Associate professor has more than 11 years of teaching experience in the Department of Computer Science & Engineering. He is an active member in Academics & Administrative activities & Publication Member in SKRGC Journal & Life member in Technical bodies like IE, ISTE, CSI, IAENG, IRED, INSTICC. Presently he is working as an Associate professor in Department of CSE, School of Computing, Mohan Babu University, Tirupati, A.P. He received his Doctorate Degree Ph.D. in Computer Science & Engineering from LINGAYA's VIDYAPEETH Deemed to be University, Faridabad, HARYANA. He published Various Patents & Research Papers in Various International Journals of Reputed & Presented Research papers in Conferences & Seminars and Editorial Board member & Reviewer for the various Journals & Conferences. He was awarded recently with Best Young Faculty, Researcher & Academic & Research Excellence in 2022, 2023 & 2024.

EDUCATION

from Lingaya’s Vidyapeeth University(Deemed), 2016-2022.
 M.Tech - COMPUTER SCIENCE from Nimra Institute of Scie & Tech College, JNTUK
University (2011-2013) with 75.44%.
 MCA from NOVA PG College, Nagarjuna University (2008-2011) with 71.03%.
 . (M.E.C’s) from S.R.R & C.V.R GOVT. Degree College, Nagarjuna
University (2005-2008) with 61.50%.
 Intermediate (M.P.C) from SARADHA Junior College, Board of Intermediate Education
(2003-2005) with 74.70%.
 SSC from GOWTHAM Public School, Board of Secondary Education (2002-2003) with 81.33%.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science, Computer Science Applications
45

Scopus Publications

Scopus Publications

  • Hybrid LLM-Vision Pipeline for Detection of Malicious QR Codes in Social Engineering Attacks
    Konda Hari Krishna, Patnam Ravi Kumar, V Herah, Ponnolu Vishnu Vardhan Reddy, Kopparthi Jwalapati Suhas Reddy
    Proceedings of the 2026 6th International Conference on Image Processing and Capsule Networks Icipcn 2026, 2026
    Quick Response (QR) codes work well as a convenient information sharing option but are being more commonly used in social engineering attacks to provide phishing links, malware, and other malicious payloads. Hybrid LLM Vision Pipeline This paper suggests a hybrid model that combines computer vision to detect structural anomalies and LLMs to detect semantic threats. The visualization module utilizes convolutional neural networks to detect the violation, absence and abnormalities in the QR code picture and the LLM checks the deciphered message about suspicious URL structure, domain names, and phrases denoting intent to action. A fusion layer network is a multimodal network that jointly infers the two modalities and improves accuracy and reliability. According to experiments, its accuracy in detecting objects with 98.4% wins over single-modality baselines and is also robust to adversarial distortion. The proposed framework offers a scalable and flexible defense against the attacks exploiting the QR codes, it is part of the expansive defense against the AI-motivated attack on societal engineering.
  • Vision-Driven Dynamic Traffic Light System for Smart Cities using Edge AI
    Konda Hari Krishna, Avatala Sai Chetana Reddy, Chinthakani Mohana Priya, Chennuru Tejesh, Amanchi Rishi Srinivas, G.Rajesh
    Proceedings of the 2026 6th International Conference on Image Processing and Capsule Networks Icipcn 2026, 2026
    Urban traffic congestion remains a persistent and complex challenge across metropolitan areas, leading to increased travel delays, excessive fuel consumption, and heightened safety risks. To mitigate these issues, this work proposes a vision-driven dynamic traffic signal control system that leverages real-time visual data to optimize signal timing and improve overall traffic flow within smart city environments. The proposed system employs advanced artificial intelligence techniques to enable adaptive traffic signal control without requiring additional hardware installations at each intersection. The system utilizes the YOLO (You Only Look Once) object detection framework to analyze real-time video streams from existing traffic surveillance cameras and estimate vehicle density. A Convolutional Neural Network (CNN) is employed to classify detected vehicles into categories such as cars, buses, trucks, motorcycles, and emergency vehicles, enabling priority-based traffic management. Data security and user privacy are preserved through encryption and anonymization mechanisms applied during data transmission and storage. The proposed framework is scalable, cost-effective, and adaptable for deployment across diverse smart city environments, using vision and artificial intelligence to enable intelligent traffic management without extensive additional hardware.
  • Driver Vigilance and Behaviour Monitoring with Automated Alerts using YOLO
    K. Hari Krishna, Vakati Jayasree, Velpula Venkata Naimesha, Upendra Vungarala, Lingeshwarudu Telugu, Paidipogu Sowjanya
    Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026
    The use of cellphones, drowsiness and distraction are forms of unsafe driver behaviors that are major causes of road accidents, making it a necessity to have automated system of monitoring. This paper is a report on Driver Behavior Analysis and Alerting System which is aimed to identify and alert on important driver behaviors in real time, and will increase road safety and help with Advanced Driver Assistance Systems (ADAS). The system can use YOLOv10 and YOLOv8 object detection models on live in-vehicle video to detect distracted driving, drowsiness, eating, phone usage, seatbelt use, and driving behaviors. YOLOv10 attained a precision rate of 90.5, a recall rate of 86.7 and mAP of 0.5 with a rate of 91.6, and YOLOv8 attained a precision rate of 91.4, a recall rate of 88.1 and a mAP of 0.5 with a rate of 92.9 that indicates that both are highly accurate in detection. The critical events generate automatic email notifications, and the system has an easy to use web interface developed using Flask, HTML, CSS, and JavaScript. This framework offers a scalable and efficient driver safety management and automated ADAS application by combining the advanced models of deep learning with real-time web-based monitoring.
  • Integrating Explicit Risk Representation into the Core of Automated Driving Systems
    K. Hari Krishna, Ponde Geethika, Rangam Reddy Satish Reddy, SeelamLakshmiTulasi, Panjagalla Balaji, M. Narmadha
    Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026
    Safety assurance for Automated Driving Systems (ADS) today is still an important concern because of the lack of explicit and quantitative risk acceptance criteria in the existing rules of ISO 26262 and ISO 21448 regarding the safety of the systems and the associated risks and hazards of ADS deployments. Despite the existence of formal rules regarding the analysis and mitigation of hazards, the notion of residual risks and those rules do not convey clear guidelines about the measurement, assessment, and acceptance of risks and hazards in ADS deployments, casting uncertainty regarding the process of traceable and quantifiable safety assurance of ADS deployments to the stakeholders and the public, in general. To bridge the gap and ensure traceable and quantifiable risk assessment and management in ADS deployments, the following paper proposes the use of an Explicit Risk Management Core (RMC) as an operational solution designed and implemented as a cloud-native solution in the Salesforce platform for the assessment and management of risks and hazards associated with ADS deployments.
  • Computer intelligence in health care field: Transforming the practice of medical applications
    Sukhdeep Kaur, Kawerinder Singh Sidhu, Konda Hari Krishna, Kapil Joshi, Ajay Singh, Nadia Mahmood Hussien
    Technology Developments in Computer Intelligence and their Applications in the Era of Industry 5 0, 2025
    Computational intelligence is a field of study that tries to comprehend, explain, and predict intelligent behavior. It uses the fundamentals of statistics, engineering, mathematics, and computer science to build artificial systems that are capable of handling challenging issues. The primary objectives of computational intelligence are to develop solutions that can self-adapt their behavior to changing settings and models that can accurately reflect and forecast real-world phenomena. To accomplish this, membership functions and artificial neural networks must be developed in order to distinguish patterns in data and produce precise judgments or predictions. Computational intelligence finds its main uses in medical diagnostics, image processing, natural language processing, autonomous navigation, fault detection, and robotics. We can create solutions that are not just faster than before, but also far more precise, by merging these disciplines. Furthermore, due to its adaptableness, it can be used in a variety of sectors, including healthcare and banking. Smart computational intelligence in biomedical and health informatics offers cutting-edge discoveries as well as methodological and algorithmic approaches to data processing issues, including an examination of developing trends in computer-aided diagnosis and health informatics.
  • Correction to: Classification of intrusion using Multi-Stream Generative Adversarial Networks in Wireless Sensor Networks (Peer-to-Peer Networking and Applications, (2025), 18, 2, (67), 10.1007/s12083-024-01846-0)
    D. Prabakar, Konda Hari Krishna, D. Prabhu, Femila L
    Peer to Peer Networking and Applications, 2025
  • Enhancing organizational systems with brain-computer interface technologies
    Konda Hari Krishna, Navruzbek Shavkatov, Anantha Murthy, Muntadher Abed Hussein, Amal Mansour Hassan
    Concepts and Applications of Brain Computer Interfaces, 2025
    This research is being conducted to determine whether or not the application of brain-computer interface technology has the potential to improve organizational systems. As a result of the fact that they enable a direct connection to be made between the brain and the outside world, brain-computer interfaces (BCIs) have the potential to transform how business tasks are carried out, thereby boosting both efficiency and production. The purpose of this research is to examine the operation of brain-computer interfaces (BCIs) to improve hand engagement, speed workflow procedures, and optimize decision-making. By evaluating recent developments in brain-computer interfaces (BCI) and the practical counterarguments that have been made against them, this study reveals critical areas within organizational fabrics where these technologies can be effectively implemented. It appears that BCIs have the potential to make a significant contribution to the development of organizational systems that are more responsive and adaptive, which would ultimately lead to enhanced innovation and competitiveness.
  • Classification of intrusion using Multi-Stream Generative Adversarial Networks in Wireless Sensor Networks
    D. Prabakar, Konda Hari Krishna, D. Prabhu, Femila L
    Peer to Peer Networking and Applications, 2025
  • TrustRIDR-Net: A Hybrid Trust-Aware Routing Framework Using RFO and DRL for Scalable IoT Networks
    Shaik Shafiuddin, Konda Hari Krishna
    Engineering Technology and Applied Science Research, 2025
    This paper presents TrustRIDR-Net, a novel hybrid trust-based routing framework for Internet of Things (IoT) networks that intelligently integrates biologically-inspired optimization and adaptive machine learning for secure, energy-efficient, and scalable communication. TrustRIDR-Net achieves a throughput of 17,560 kbps, a Packet Delivery Ratio (PDR) of 98.9%, and reduces energy consumption to 68%, significantly outperforming state-of-the-art models. The proposed model combines Rider Foraging Optimization (RFO) for trust-aware clustering and Deep Reinforcement Learning (DRL) for dynamic routing decisions, while incorporating a comprehensive trust metric based on seven behavioral and energy-related factors: Direct Trust, Indirect Trust, Forwarding Rate, Integrity, Availability, Consistency, and Energy Trust. The model dynamically computes trust scores and routing policies, while ensuring energy-aware transmission using a scalable power control formula. Extensive simulations conducted in a 200×200 m virtual IoT environment with 100 nodes and a centrally placed base station showed that TrustRIDR-Net outperformed existing models such as LSTM, GRU, LEACH, BFA, LOA, and AODV-based techniques. These results validate TrustRIDR-Net's capacity to support resilient, intelligent communication in large-scale, trust-sensitive IoT networks, setting the stage for its deployment in critical applications such as smart cities, healthcare monitoring, and industrial automation.
  • Federated Learning for Enhanced and Private Autism Spectrum Disorder Detection in Children Across Diverse Populations
    Idimadakala Madhavilatha, Konda Hari Krishna
    Proceedings of the 3rd International Conference on Intelligent and Innovative Technologies in Computing Electrical and Electronics Iitcee 2025, 2025
    The contribution of this work focuses on the results of applying Federated Learning(Fl) to the improvement of performance and generalizability for Autism Spectrum Disorder (ASD) detection models, which include children, assuming high standards for data privacy. In FL, institutions can collaborate on model training by means of decentralized datasets that share knowledge without exposing sensitive information explicitly. This would be fully in line with all the regulations for privacy and would protect patient information. Our federated model achieved an accuracy of 91% and outperformed the traditional centralized way with a gain in generalization for several diverse demographic groups by 5% on the F1-score. FL combines five sources of input-eye-tracking data, speech prosody, EEG, and physiological signals-used by the clients into a staunch, adaptable model suitable for a wide range of populations. Our results suggest that FL might represent a game-changer in ASD Diagnostics, given the pursuit of a scalable and privacy-centric solution for sensitive health care contexts.
  • Optimizing Moving Object Tracking in Sensor Networks: A Novel Approach with SLAM and GES Sensor-Based Filter
    R. Kabilan, R. Ravi
    Technological Applications for Smart Sensors Intelligent Applications for Real Time Strategies, 2025
  • Empowering Communication: A Web Application for Deaf, Mute and Sign Language Interpretation
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Virtual Herbal Garden: An Interactive Learning Platform for Ayush Medical Plants
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Different Requirements in Quality of Service Using an Adaptive Network Algorithm
    Advances in AI for Cloud Edge and Mobile Computing Applications, 2025
  • Applications, Enabling Technologies, Vision of Edge Artificial Intelligence for 6G
    Advances in AI for Cloud Edge and Mobile Computing Applications, 2025
  • Application of Artificial Intelligence for Enhancement of Privacy and Security in Smart Environment
    Advances in AI for Cloud Edge and Mobile Computing Applications, 2025
  • Design and Implementation of Innovative Statistically Secured Hash Algorithm using Decentralized Hash Function
    Bhagvant Ram Ambedkar
    Panamerican Mathematical Journal, 2025
  • Exploring the use of the internet of things in cybersecurity and data science
    Vinay Kumar Nassa, Konda Hari Krishna, Ranichandra Subbiah, J. Somasekar, G. Poshamallu, Mohit Tiwari
    Interdisciplinary Approaches to AI Internet of Everything and Machine Learning, 2024
  • An Adaptive Secure Timestamp-Based Replay Attack Detection System For Wsns
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Enhancing Energy Efficiency and Security in Wireless Sensor Networks: A Survey and Proposed Methodology Integrating Prediction Algorithms and Deep Neural Networks
    Shaik Shafiuddin, Konda Hari Krishna
    Icccmla 2024 6th International Conference on Cybernetics Cognition and Machine Learning Applications, 2024
  • KOA Management in CDS using AI: A Review
    Suman Rani, Richa Pandey, Minkashi Memoria, Kapil Joshi, Konada Hari Krishna
    2024 International Conference on Smart Devices Icsd 2024, 2024
  • Power Trading Framework of Cloud-Edge Computing in the Artificial Intelligence Market
    Gopal Krishna, Vineeta Singh, Deepak Pandey, Konda Hari Krishna, Kapil Joshi, Tushar Gupta
    2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2024, 2024
  • Design of Software Reliability Growth Model for Improving Accuracy in the Software Development Life Cycle (SDLC)
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Long range and server inspired internet of smart street lights
    Rajesh Singh, Konda Hari Krishna, Rajesh Kumar, Anita Gehlot, Shaik Vaseem Akram, Sushabhan Chodhury, Yashwant Singh Bisht, Kailash Bisht, Kapil Joshi
    Bulletin of Electrical Engineering and Informatics, 2023
  • Unsupervised Machine Learning for Unusual Crowd Activity Detection
    Pooja Bagane, Konda Hari Krishna, Shehab Mohamed Beram, Priyambada Purohit, B. Gayathri
    Lecture Notes in Networks and Systems, 2023
  • A review approach on deep learning algorithms in computer vision
    Kapil Joshi, Vivek Kumar, Harishchander Anandaram, Rajesh Kumar, Ashulekha Gupta, Konda Hari Krishna
    Intelligent Systems and Applications in Computer Vision, 2023
  • Efficiency of Wireless Communication in Cybersecurity for Data Security
    R. Shashank, Konda Hari Krishna, Tamirat Tagesse Takore, Atul Singla, Ch Veena, Mohit Tiwari
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2023, 2023
  • Integration of PLCC modem and Wi-Fi for Campus Street Light Monitoring
    Shagun Tyagi, Konda Hari Krishna, Kapil Joshi, Tanvi Ajit Ghodke, Anshu Kumar, Ashulekha Gupta
    Proceedings 4th IEEE 2023 International Conference on Computing Communication and Intelligent Systems Icccis 2023, 2023
  • Secured Traceability System Using Block Chain Technology in Supply Chain Management
    A Rajeshwar Rao, Monika Saxena, Konda Hari Krishna, Anandhi R J, B Pravallika, Amit Dutt
    2023 10th IEEE Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2023, 2023
  • A Sensitivity Study of Machine Learning Techniques Based on Multiprocessing for the Load Forecasting in an Electric Power Distribution System
    Ajay Singh, Kapil Joshi, Konda Hari Krishna, Rajesh Kumar, Neha Rastogi, Harishchander Anandaram
    Lecture Notes in Networks and Systems, 2023
  • Identical and Tenable Course-Plotting Approach for MANET with Acquitted Assessment
    Konda Hari Krishna
    2022 International Conference on Machine Learning Big Data Cloud and Parallel Computing Com IT Con 2022, 2022
  • Vitality Efficiency Info Gathering using Shortest Route in WSN's
    13th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2022, 2022
  • A Hybrid Optimization Approach with Deep Learning Technique for the Classification of Dental Caries
    Riddhi Chawla, Konda Hari Krishna, Araddhana Arvind Deshmukh, K. V. Daya Sagar, Mohammed Saleh Al Ansari, Ahmed I. Taloba
    International Journal of Advanced Computer Science and Applications, 2022
  • Piezo Electricity and Object Presence Based Street Light
    Ssrg International Journal of Electrical and Electronics Engineering, 2019
  • Indiscriminate assortment navigation methods for secure statistics transmission in wireless sensor networks
    Konda Hari Krishna, Tapas Kumar, Y. Suresh Babu
    Proceedings of the International Conference on Iot in Social Mobile Analytics and Cloud I Smac 2017, 2017
  • Energy effectiveness practices in WSN over simulation and analysis of S-MAC and leach using the network simulator NS2
    Konda Hari Krishna, Tapas Kumar, Y. Suresh Babu
    Proceedings of the International Conference on Iot in Social Mobile Analytics and Cloud I Smac 2017, 2017
  • Retraction Notice: Energy effectiveness practices in WSN over simulation and analysis of S-MAC and leach using the network simulator NS2 (2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) DOI: 10.1109/I-SMAC.2017.8058314)
    Konda Hari Krishna, Tapas Kumar, Y. Suresh Babu
    Proceedings of the International Conference on Iot in Social Mobile Analytics and Cloud I Smac 2017, 2017
  • Retraction Notice: Indiscriminate assortment navigation methods for secure statistics transmission in wireless sensor networks (2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) DOI: 10.1109/I-SMAC.2017.8058276)
    Konda Hari Krishna, Tapas Kumar, Y. Suresh Babu
    Proceedings of the International Conference on Iot in Social Mobile Analytics and Cloud I Smac 2017, 2017
  • Clustering protocols and a few concerns with Clustering algorithms intended for Wireless Sensor systems
    Konda. Hari Krishna, Tapas Kumar, Y.Suresh Babu
    Proceedings of 2016 International Conference on ICT in Business Industry and Government Ictbig 2016, 2017
  • Review upon distributed facts hard drive schemes throughout wireless sensor communities
    Konda.Hari Krishna, Kumar Tapus, Y.Suresh Babu, N. Sainath, R. Madana Mohana
    Communication and Power Engineering, 2017
  • Blockage With in Wi-Fi Sensor Networks in Addition to Systems Regarding Controlling Congestion
    Konda Hari Krishna, Tapas Kumar, Y. Suresh Babu, R. Madan Mohan, N. Sainath, V. Satyanarayana
    Lecture Notes in Networks and Systems, 2017
  • Diverse execution dimensions for information conveyance in distant sensor systems
    Konda Hari Krishna, Tapas Kumar, Y. Sureshbabu, N. Sainath, R. Madanmohan, J. Rethna Virgil Jeny
    IFIP International Conference on Wireless and Optical Communications Networks Wocn, 2016
  • Wireless Sensor Network Topology Control Sing Clustering
    K. Hari Krishna, Y. Suresh Babu, Tapas Kumar
    Procedia Computer Science, 2016
  • Wireless Network Topological Routing in Wireless Sensor Networks
    K. Hari Krishna, Y. Suresh Babu, Tapas Kumar
    Procedia Computer Science, 2016
  • A Generic Algorithm to Improve the Performance of Wireless Sensor Network Protocol
    Konda. Hari Krishna, Y. Suresh Babu, Tapas Kumar
    Procedia Computer Science, 2016

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patent Publications: 16
 Text Books Published in Different Subjects: 14
 Publications in SCI / ESCI / Web of Science / Scopus / IEE Xplore / Springer Lecture Series /
Elsevier Procedia Computer Science : 25
 Publications in Indexed / Referred / Impact Factor Journals: 13
 Papers Published / Presented & Participation in Conferences : 22 (International:15,
National:7)
 Participation in Workshops / Seminars / FDPs : 75
Projects guided: B.Tech : 25+
o M.Tech : 12+
o MCA : 11+