Mukesh Chinta

@vrsiddhartha.ac.in

Assistant Professor, Computer Science and Engineering
V R Siddhartha Engineering College

Mukesh Chinta


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https://researchid.co/mukeshchinta

RESEARCH INTERESTS

Computer Networks
31

Scopus Publications

Scopus Publications

  • Revolutionizing sentiment analysis of employee reviews: A novel deep learning approach
    Yalanati Ayyappa, A. Peter Soosai Anandaraj, Naresh Cherukuri, Mukesh Chinta, Jai Prakash Konakalla, R. Sam Globen
    Aip Conference Proceedings, 2026
  • Systematic Literature Review on IoT-based Botnet Attacks: Techniques, Trends, and Challenges
    Mukesh Chinta, R Srinivasan
    Proceedings of 5th International Conference on Soft Computing for Security Applications Icscsa 2025, 2025
    The exponential rise of the Internet of Things (IoT) has enhanced digital services but also opened pathways for security threats such as botnet attacks. These attacks exploit IoT’s resource limitations and lack of security frameworks. This paper presents a systematic literature review (SLR) of IoT-based botnet attacks. 5465 publications were analyzed from top academic databases and selected 34 relevant studies based on strict inclusion and quality criteria. The paper highlights contributions in detection and prevention models, forensic techniques, datasets used, and evaluation metrics. The findings indicate a strong reliance on network flow analysis and emphasize the need for more avoidance-based research and real-world dataset development.
  • Enhancing Industrial Monitoring with Xgboost and Ai: A Predictive Maintenance Approach
    Mukesh Chinta, Rahamatunnisa Shaik, Lakshmi Sirisha Veturi
    2025 International Conference on Computing and Communications Computingcon 2025, 2025
    In the era of Industry 4.0, predictive maintenance is pivotal for enhancing equipment reliability, reducing unplanned downtimes, and improving operational efficiency. This paper proposes a next-generation industrial monitoring system that synergizes Machine Learning (ML) and Artificial Intelligence (AI) for real-time fault prediction and operator support. The core of the system is an XGBoost-based classifier trained on sensor telemetry (e.g., temperature, torque, rotational speed) to accurately detect impending machinery failures. To improve interpretability and user interaction, we integrate a fine-tuned GPT-2 model that delivers contextual maintenance insights through natural language responses. The solution is deployed via a Streamlit web interface, enabling real-time visualization and decision support. Experimental evaluation shows that our model outperforms traditional approaches, including Random Forest, in predictive accuracy. This work bridges the gap between intelligent diagnostics and human-machine collaboration, offering a practical pathway toward smarter industrial maintenance.
  • Blockchain-Enhanced AI Framework for Secure Industrial Predictive Maintenance
    Lakshmi Sirisha Veturi, Rahamatunnisa Shaik, Mukesh Chinta
    13th International Conference on Intelligent Embedded Microelectronics Communication and Optical Networks Iemecon 2025, 2025
    The rapid growth of Industry 4.0 has transformed traditional manufacturing systems into connected cyber-physical environments. These systems continuously produce large amounts of operational and sensor data, which are vital for predictive maintenance and informed decision-making. However, the reliability, integrity, and security of this data remain significant challenges. This paper introduces a blockchain-based industrial monitoring framework that integrates Artificial Intelligence (AI) for predictive analytics with blockchain for decentralized and tamper-proof data management. The framework enhances transparency, traceability, and secure collaboration among stake-holders by maintaining verified records of equipment health and maintenance activities. Smart contracts also automate fault alerts, compliance checks, and maintenance scheduling, removing the need for centralized authorities. Experimental results on industrial datasets demonstrate better data integrity, lower risk of cyber threats, and improved predictive accuracy. The proposed hybrid architecture offers a scalable, auditable, and secure foundation for next-generation industrial systems.
  • Vehicle Door Safety Monitoring and Alert System
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Opinion-Mining Based Fake Review Detection for Genuine Online Products
    Mukesh Chinta, Jaswanthi Machcha, Chandrika Chagamreddy, Moseen Shaik
    2025 International Conference on Computing Technologies and Data Communication Icctdc 2025, 2025
    The widespread growth of e-commerce platforms has significantly increased the reliance of consumers on online product reviews. Unfortunately, this growth is accompanied by a parallel rise in deceptive practices, particularly the proliferation of fake reviews, misleading consumers and harming genuine businesses. This study presents a system for identifying and classifying fake reviews utilizing techniques from Opinion Mining and Sentiment Analysis. The methodology involves three major stages: Initially, review texts are preprocessed to enhance textual quality through methods such as tokenization, stop-word removal, and stemming. Following preprocessing, sentiment analysis is performed using the VADER (Valence Aware Dictionary and sEntiment Reasoner) model to capture the emotional tone and polarity of reviews. In the final stage, machine learning and deep learning techniques-including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and Long Short-Term Memory (LSTM)-are employed to distinguish fake reviews from genuine ones. Comprehensive experimentation conducted on an Amazon review dataset reveals superior performance by the LSTM model, achieving an impressive accuracy of 94.71%, compared to 83.97% and 88.03% by RFC and SVC, respectively. These results underscore the effectiveness of combining sentiment-driven features with advanced neural network architectures. By accurately identifying deceptive content, this approach not only protects consumers from fraudulent reviews but also helps businesses maintain credibility and integrity in a highly competitive digital marketplace.
  • A Hybrid Deep Learning Approach for the Automated Classification of Lung Diseases Utilizing Chest X-ray Images
    Sravani Nalluri, Mukesh Chinta, Sai Bhargav Kasetty, Issac Neha Margret
    Proceedings of the 2025 11th International Conference on Communication and Signal Processing Iccsp 2025, 2025
    The increase in respiratory diseases such as COVID-19, pneumonia, and tuberculosis continues to pose a treatment challenge. This paper introduces a deep learning framework that combines CNN, MLP, & RNN architectures to improve classification accuracy. This study used a dataset comprising 538 COVID-19 images, 1341 normal images, 3875 pneumonia images, and 650 tuberculosis images with a training-to-test ratio of 80:20. The performance metric values of individual models of CNN, MLP, and RNN are compared with the hybrid model. Hybrid outperforms all other methods with 90% accuracy, outperforming CNN (83%), MLP (81%), and RNN (84%). The confusion matrix shows how the hybrid model reduces the classification error, resulting in better understanding and specificity. The training/testing and real schemes also ensure the strength and extensibility of the model. The results showed that integrating multiple deep architectures can improve the detection of lung diseases; future work will focus on optimal selection and multivariate integration to improve classification.
  • Military Aircraft Classification using Resnet50 based Faster R-CNN for Advanced Military Aircraft Surveillance
    Mukesh Chinta, Jaswanthi Machcha, Chandrika Chagamreddy, Moseen Shaik
    2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025
    Satellite-based aircraft surveillance has become a very important aspect in the monitoring of air vehicle activities in military operations. The automatic classification of military aircraft from satellite imagery is very challenging. Military operations require an accurate classification of military aircraft from real-time satellite images. Traditional approaches tend to be less accurate because of the complexity of aircraft shapes and time-varied illuminations and occlusions. Hence, this study proposes a new approach to leveraging satellite-based faster R-CNN (Region-Based Convolutional Neural Network) for military aircraft classification. The proposed scheme includes two main phases. In the first phase, preprocessing techniques help improve the quality of satellite images and remove noise, with the result that the aircraft becomes more clearly detected. Using principal component analysis (PCA) for feature enrichment and dimensionality reduction in our methodology, we are paying attention to the improvement of the accuracy of Faster R-CNN to better classify military aircraft in satellite imagery. In the second phase, an optimized Faster R-CNN model with ResNet50 as its backbone for satellite imagery classifies military aircraft accurately. From the study, impressive results were obtained: accuracy 97.07%, precision 97.34%, recall 98.74%, and an F1 score of 98.04%.
  • DDoS Classification Through Web Application: Leveraging Algorithms for Cybersecurity
    Bezawada Sasaank, Mukesh Chinta, Basireddy Duggireddy, Redipalli Lalitha Karthik, Paturi Sai Sri Vatsa
    Proceedings of the International Conference on Intelligent Computing and Control Systems Iciccs 2025, 2025
    This study and project proposes a method for classifying Distributed Denial of Service (DDoS) attacks using the Random Forest Algorithm, with a focus on determining measurement metrics for distinguishing between DDoS attacks and benign network activity. DDoS attacks represent a significant threat to network availability and security, necessitating robust classification mechanisms. The Random Forest Algorithm is employed due to its effectiveness in handling large pattern datasets and its ability to detect complex attack patterns. By analyzing various measurement metrics such as packet rate, packet size distribution, and traffic volume, features indicative of DDoS attacks are identified. These features are then used to train the Random Forest model to classify incoming network traffic as either DDoS or Benign. Evaluation of the classification performance is conducted using metrics such as accuracy, precision, recall, and F1 score, providing insights into the effectiveness of the proposed approach. Experimental results demonstrate the ability of the Random Forest Algorithm to accurately classify DDoS attacks while minimizing false positives, thereby enhancing network security and mitigating potential disruptions to web services.
  • Performance Analysis of InceptionV3, VGG16, and Resnet50 Models for Crevices Recognition on Surfaces
    Ch. Mukesh, A. Likhita, A. Yamini
    Lecture Notes in Networks and Systems, 2024
  • Bug Classification Using Stacking Classifier
    V. Kusumaniswar, Mukesh Chinta, Sk. Nawaz Shareff
    2024 2nd International Conference on Disruptive Technologies Icdt 2024, 2024
  • IM System - A Mobile Application for Dormitory Management in Academic Institutes
    Ashraf Bano Jigar, Mukesh Chinta, Shaik Kaif
    Proceedings IEEE 2024 1st International Conference on Advances in Computing Communication and Networking Icac2n 2024, 2024
  • RAKTAMITRA- An Alert Centric Blood Donation Initiative
    Inti Alekhya, Mukesh Chinta, Likitha Maddela
    Proceedings of the 2024 International Conference on Emerging Techniques in Computational Intelligence Icetci 2024, 2024
  • Hash Based Secure and Efficient Scaling Approach for Detecting the Diabetic Retinopathy Disorder
    Naga Venkata Rishika. G, Mukesh. Ch, Rupa. Ch, Akhil Babu. N
    2024 International Conference on Emerging Smart Computing and Informatics Esci 2024, 2024
  • A Phishing URL Detection Model based on Horse Herd Optimization and Random Forest Algorithms
    P. Hemannth, Mukesh Chinta, S. Sarat Satya, P. Sri Aneelaja Devasena
    Proceedings 2024 4th International Conference on Pervasive Computing and Social Networking Icpcsn 2024, 2024
  • A Phishing Email Detection Model Based on Horse Herd Optimization and Random Forest Algorithms
    P. Hemannth, Mukesh Chinta, S. Sarat Satya, P. Sri Aneelaja Devasena
    2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
  • Crevices Recognition on Asphalt Surfaces using Convolutional Neural Network
    Mukesh Chinta, Anagani Likhita, Yamini Aravapalli
    Idciot 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things Proceedings, 2023
  • Classification of Software Bugs using Support Vector Machine
    V. Kusumaniswari, Mukesh Chinta, Sk. Nawaz Shareff
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • STUMART - Resource Management Application
    Venkata Reddy Venna, Mukesh Chinta, Hari Sai Babu Avvaru, Kushal Kumar Chintakayala
    2023 4th International Conference for Emerging Technology Incet 2023, 2023
  • Location Tracking via Bluetooth
    Jasthi Siva Sai, Mukkamala Namitha, Routhu Ramya Dedeepya, Mulugu Suma Anusha, Angadi Lakshmi, Mukesh Chinta
    Smart Innovation Systems and Technologies, 2023
  • Essential Computational Intelligence in Real-Time Applications
    Computational Intelligence of Blockchain Systems, 2022
  • Classification of Pneumonia using InceptionNet, ResNet and CNN
    Vijaya N, Mukesh Chinta, Kavya E, Sumanth Varma L
    6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2022, 2022
  • Prediction of Severity after Lung Cancer Surgery
    Mukkamala Namitha, Mulugu Suma Anusha, Gampa Bhavana, Mukesh Chinta
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
  • Detection of Lung Cancer and Treatment Suggestion based on the Severity of the cancer
    Suma Anusha Mulugu, Namitha Mukkamala, Bhavana Gampa, Mukesh Chinta
    Proceedings 2022 International Conference on Recent Trends in Microelectronics Automation Computing and Communications Systems Icmacc 2022, 2022
  • Multimedia concealed data detection using quantitative steganalysis
    Rupa Ch., Sumaiya Shaikh, Mukesh Chinta
    International Journal of Digital Crime and Forensics, 2021
  • Real-Time Soil Nutrient detection and Analysis
    Hema Pallevada, Siva parvathi Potu, Teja Venkata Kumar Munnangi, Bharath Chandhra Rayapudi, Sai Raghava Gadde, Mukesh Chinta
    2021 International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2021, 2021
  • Deep Residual Convolutional Neural Network Based Detection of Covid-19 from Chest-X-Ray Images
    Valaparla Rohini, M. Sobhana, Ch. Smitha Chowdary, Mukesh Chinta, Deepa Venna
    Lecture Notes in Networks and Systems, 2021
  • Blockchain based Decentralized Vehicle Booking Service
    Hema Pallevada, Gayathri Phani Kumar Kanuri, Sravani Posina, Satyendra Paruchuri, Mukesh Chinta
    Proceedings 2nd International Conference on Smart Electronics and Communication Icosec 2021, 2021
  • Stock market prediction for time-series forecasting using prophet upon ARIMA
    Ch.Raga Madhuri, Mukesh Chinta, V V N V Phani Kumar
    2020 7th International Conference on Smart Structures and Systems Icsss 2020, 2020
  • Smart meter analytics for optimizing the utilization of electricity using Arima, Navie and holt winter
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Water quality monitoring system using IOT
    Nageswara Rao Moparthi, Ch. Mukesh, P. Vidya Sagar
    Proceedings of the 4th IEEE International Conference on Advances in Electrical and Electronics Information Communication and Bio Informatics Aeeicb 2018, 2018