Manikanta Sirigineedi

@vishnu.edu.in

Assistant Professor and Department of Information Technology
Vishnu Institute of Technology

Manikanta Sirigineedi


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https://researchid.co/manikanta19071992

EDUCATION

Both my Bachelor of Technology and Master of Technology degrees in Computer Science and Engineering were earned at JNTUK. Now I am pursuing Ph.D at GIET University.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence
7

Scopus Publications

112

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Improving fish image detection speed with hybrid VGG16 and darknet
    Manikanta Sirigineedi, R. N. V. Jagan Mohan, Bandita Sahu
    Multimedia Tools and Applications, 2025
  • Design of next-generation field-effect transistors using machine learning
    K. Girija Sravani, M. Srikanth, Manikanta Sirigineedi, Padma Bellapukonda
    Field Effect Transistors, 2025
    Conventional design methodologies are encountering growing complexity with field-effect transistors (FETs), which are essential to contemporary electronic products. A new approach to improving FET design is presented in this research, utilizing machine learning (ML). Collecting and assembling large datasets is the first step in the process. Then comes feature engineering and the use of different ML models, such as neural networks, decision trees, and regression. This model training and validation process rapidly explores the FET design space. ML-driven FET designs can be applied in real-world applications due to advancements in the manufacturing process. Developing a design framework that can adapt to new technological breakthroughs and growing requirements is made possible through continuous data collection and model changes. This study shows how ML improves electronic technology by tackling design issues in semiconductor devices. The suggested method enables the creation of electronic devices that are more powerful, energy efficient, and dependable while also speeding up the design process.
  • Enhanced Security in Smart City GAN-Based Intrusion Detection Systems in WSNs
    Enhancing Security in Public Spaces Through Generative Adversarial Networks Gans, 2024
  • Advanced Dual Module Weapon Detection for Public Safety and Surveillance System
    Manikanta Sirigineedi, M. Sowmiya, HemavathI. U, V.C. Bharathi, Sayali S Karmode, R. Sathya
    4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
    This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.
  • Improving Fisheries Management through Deep learning based Automated fish counting
    Manikanta Sirigineedi, R N V Jagan Mohan, Bandita Sahu
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
    The process of quantifying fish populations holds significant value in the realm of fisheries management, as it enables a precise evaluation of population sizes and facilitates comprehension of the current state of the fish stock. Nonetheless, the process of manually counting fish is demanding in terms of labor, time, and susceptible to inaccuracies. In order to tackle this issue, there have been advancements in automated fish counting techniques utilising computer vision and deep learning algorithms. The present study introduces an automated fish counting system based on deep learning, which employs a Convolutional neural network (CNN) to identify and enumerate the fish present in an image. The system under consideration has been assessed on a dataset comprising of underwater images that encompass diverse fish species. The findings of the evaluation indicate that the system attains a mean absolute error of 0.5 fish per image. The system under consideration exhibits a high degree of precision in quantifying fish populations across diverse settings, thereby presenting a viable avenue for enhancing fisheries governance. The system exhibits the ability to identify distinct fish species, rendering it appropriate for employment in fisheries management contexts, including stock evaluation and species categorization. In summary, the present study presents empirical support for the efficacy of the deep learning-based automated fish counting system, which has the capability to accurately quantify the quantity of fish in an image. This technology holds significant potential for enhancing fisheries management practices.
  • Deep Learning Approaches for Autonomous Driving to Detect Traffic Signs
    Manikanta Sirigineedi, T. Kumaravel, P. Natesan, V. Kavya Shruthi, M. Kowsalya, M S. Malarkodi
    International Conference on Sustainable Communication Networks and Application Icscna 2023 Proceedings, 2023
    Traffic signs serve a vital function in regulating traffic flow, ensuring driver compliance with rules, and ultimately, enhancing road safety by reducing accidents and fatalities. The effective management of traffic signs, especially through automated Identification and acknowledgment are crucial elements within any Intelligent Transportation System (ITS). Given the advancements in self-driving vehicles, the demand for automated both automated detection and recognition of traffic signs has progressed increasingly imperative. This research study introduces an autonomous system that utilizes deep learning for the identification of traffic signs in India. The method for autonomously identifying and acknowledging traffic signs relies on the YOLOv5 framework, offering a comprehensive learning solution from start to finish. The proposed concept was evaluated using a novel benchmark known as the German Traffic Sign Recognition Benchmark. This benchmark comprises an extensive dataset containing greater than 50,001 visuals regarding roadway signage, categorized into 43 distinct classes or categories. Two-cascaded feature extraction method is employed which is a two-stage process, allowing the network to learn progressively more abstract and discriminative features, enhancing its Capability to apprehend intricate patterns. This method enhances the model's resilience to fluctuations in the dataset and is adaptable to specific tasks, reducing overfitting and potentially improving efficiency while maintaining interpretability. Furthermore, we carried out a performance assessment in contradiction to traditional Faster R-CNN and Mask R-CNN are two deep learning network configurations. Our suggested design demonstrated a remarkable training accuracy of 97.80% compared to other CNN methods.
  • Dynamic Load Balancing Framework for Context Sensitive Offloading Scheme in Mobile Cloud Computing
    Pothuraju Raju, Phaneendra Varma Chintalapati, Gurujukota Ramesh Babu, Parimi LVD Ravi Kumar, Manikanta Sirigineedi, Kode Satish Kumar
    Proceedings 2023 International Conference on Advanced Computing and Communication Technologies Icacctech 2023, 2023
    A new technology called Mobile Cloud Computing (MCC) support in raising the quality of mobile services. The rise of computational offloading in MCC is a result of its rapid developments. There is a wide range of application types, including mobile games with online access, internet browsing, audio and video calls, and healthcare applications. Utilizing a mobile cloud system to distribute and store these applications could save batteries and memory by avoiding large computations. This paper presents Dynamic Load Balancing Framework for Context Sensitive Offloading Scheme in Mobile Cloud Computing, In this paper, they propose a new framework that makes dynamic load balancer to efficiently in allocating resources. U sing context-aware offloading decision algorithms, code can be offloaded at runtime depending on the device context, wireless medium, and potential cloud resources to offload code. The described resource allocation approach effectively distributes resources to increase application processing speed for data and cloud resources stored depending on various mobile device contexts, and produce significant performance improvement.

RECENT SCHOLAR PUBLICATIONS

  • Improving fish image detection speed with hybrid VGG16 and darknet
    M Sirigineedi, RNV Jagan Mohan, B Sahu
    Multimedia Tools and Applications 84 (12), 10551-10566 , 2025
    2025.0
    Citations: 3
  • Design of Next‐Generation Field‐Effect Transistors Using Machine Learning
    KG Sravani, M Srikanth, M Sirigineedi, P Bellapukonda
    Field Effect Transistors, 269-286 , 2025
    2025.0
    Citations: 17
  • Advanced Dual Module Weapon Detection for Public Safety and Surveillance System
    M Sirigineedi, M Sowmiya, VC Bharathi, SS Karmode, R Sathya
    2024 4th International Conference on Sustainable Expert Systems (ICSES … , 2024
    2024.0
    Citations: 1
  • Symptom-Based Disease Prediction: A Machine Learning Approach
    PT Manikanta Sirigineedi, Matta Eswar Surya Manikanta Kumar, Rali Surya ...
    Journal of Artificial Intelligence Machine Learning and Neural Network (e … , 2024
    2024.0
    Citations: 5
  • Enhanced Security in Smart City GAN-Based Intrusion Detection Systems in WSNs
    M Sirigineedi, A Manke, S Verma, K Baskar
    Enhancing Security in Public Spaces Through Generative Adversarial Networks … , 2024
    2024.0
    Citations: 3
  • Dynamic load balancing framework for context sensitive offloading scheme in mobile cloud computing
    P Raju, PV Chintalapati, GR Babu, PLVDR Kumar, M Sirigineedi, ...
    2023 International Conference on Advanced Computing & Communication … , 2023
    2023.0
    Citations: 3
  • Deep Learning Approaches for Autonomous Driving to Detect Traffic Signs
    M Sirigineedi, T Kumaravel, P Natesan, VK Shruthi, M Kowsalya, ...
    2023 International Conference on Sustainable Communication Networks and … , 2023
    2023.0
    Citations: 9
  • Improving Fisheries Management through Deep learning based Automated fish counting
    M Sirigineedi, RNVJ Mohan, B Sahu
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023.0
    Citations: 5
  • Integrated Technologies For Proactive Bridge-Related Suicide Prevention.
    M Srikanth, M Sirigineedi, P Bellapukonda
    Journal of Namibian Studies 33 , 2023
    2023.0
    Citations: 19
  • Protecting tribal peoples nearby patient care centres use a hybrid techniques based on a distribution network
    M Sirigineedi
    IJHS , 2022
    2022.0
    Citations: 20
  • Recent Trends and Challenges with Applications of Cyber Physical Systems and Internet of Things
    NP Challa, T Suma Bharathi, B Padma, M Sirigineedi, JSS Mohan, ...
    Challenges and Applications of Cyber Physical Systems and Internet of Things … , 2022
    2022.0
    Citations: 3
  • Using machine learning and neural networks technologies, a bottom-up water process is being used to reduce all water pollution diseases
    P Bellapukonda, M Sirigineedi, M Srikanth
    Journal of Artificial Intelligence, Machine Learning and Neural Networks … , 2022
    2022.0
    Citations: 19
  • Predictive Disease Data Analysis of Air Pollution Using Supervised Learning
    manikanta. sirigineedi
    International Journal of Scientific Research in Computer Science … , 2022
    2022.0
    Citations: 3
  • A survey on Semantic-Enhanced Marginalized Denoising Auto-Encoder
    DDDS S MANIKANTA, ADBUL AHAD
    Journal of Emerging Technologies and Innovative Research (JETIR) 5 (6), 589-591 , 2018
    2018.0
    Citations: 2
  • Protecting tribal peoples nearby patient care centres use a hybrid techniques based on
    M Srikanth, P Bellapukonda, M Sirigineedi

MOST CITED SCHOLAR PUBLICATIONS

  • Protecting tribal peoples nearby patient care centres use a hybrid techniques based on a distribution network
    M Sirigineedi
    IJHS , 2022
    2022.0
    Citations: 20
  • Integrated Technologies For Proactive Bridge-Related Suicide Prevention.
    M Srikanth, M Sirigineedi, P Bellapukonda
    Journal of Namibian Studies 33 , 2023
    2023.0
    Citations: 19
  • Using machine learning and neural networks technologies, a bottom-up water process is being used to reduce all water pollution diseases
    P Bellapukonda, M Sirigineedi, M Srikanth
    Journal of Artificial Intelligence, Machine Learning and Neural Networks … , 2022
    2022.0
    Citations: 19
  • Design of Next‐Generation Field‐Effect Transistors Using Machine Learning
    KG Sravani, M Srikanth, M Sirigineedi, P Bellapukonda
    Field Effect Transistors, 269-286 , 2025
    2025.0
    Citations: 17
  • Deep Learning Approaches for Autonomous Driving to Detect Traffic Signs
    M Sirigineedi, T Kumaravel, P Natesan, VK Shruthi, M Kowsalya, ...
    2023 International Conference on Sustainable Communication Networks and … , 2023
    2023.0
    Citations: 9
  • Symptom-Based Disease Prediction: A Machine Learning Approach
    PT Manikanta Sirigineedi, Matta Eswar Surya Manikanta Kumar, Rali Surya ...
    Journal of Artificial Intelligence Machine Learning and Neural Network (e … , 2024
    2024.0
    Citations: 5
  • Improving Fisheries Management through Deep learning based Automated fish counting
    M Sirigineedi, RNVJ Mohan, B Sahu
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023.0
    Citations: 5
  • Improving fish image detection speed with hybrid VGG16 and darknet
    M Sirigineedi, RNV Jagan Mohan, B Sahu
    Multimedia Tools and Applications 84 (12), 10551-10566 , 2025
    2025.0
    Citations: 3
  • Enhanced Security in Smart City GAN-Based Intrusion Detection Systems in WSNs
    M Sirigineedi, A Manke, S Verma, K Baskar
    Enhancing Security in Public Spaces Through Generative Adversarial Networks … , 2024
    2024.0
    Citations: 3
  • Dynamic load balancing framework for context sensitive offloading scheme in mobile cloud computing
    P Raju, PV Chintalapati, GR Babu, PLVDR Kumar, M Sirigineedi, ...
    2023 International Conference on Advanced Computing & Communication … , 2023
    2023.0
    Citations: 3
  • Recent Trends and Challenges with Applications of Cyber Physical Systems and Internet of Things
    NP Challa, T Suma Bharathi, B Padma, M Sirigineedi, JSS Mohan, ...
    Challenges and Applications of Cyber Physical Systems and Internet of Things … , 2022
    2022.0
    Citations: 3
  • Predictive Disease Data Analysis of Air Pollution Using Supervised Learning
    manikanta. sirigineedi
    International Journal of Scientific Research in Computer Science … , 2022
    2022.0
    Citations: 3
  • A survey on Semantic-Enhanced Marginalized Denoising Auto-Encoder
    DDDS S MANIKANTA, ADBUL AHAD
    Journal of Emerging Technologies and Innovative Research (JETIR) 5 (6), 589-591 , 2018
    2018.0
    Citations: 2
  • Advanced Dual Module Weapon Detection for Public Safety and Surveillance System
    M Sirigineedi, M Sowmiya, VC Bharathi, SS Karmode, R Sathya
    2024 4th International Conference on Sustainable Expert Systems (ICSES … , 2024
    2024.0
    Citations: 1
  • Protecting tribal peoples nearby patient care centres use a hybrid techniques based on
    M Srikanth, P Bellapukonda, M Sirigineedi