Avuthu Avinash Reddy

@chennai.vit.ac.in

Assistant Professor and CSE
Vellore Institute of Technology (VIT) Chennai

Avuthu Avinash Reddy


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https://researchid.co/avuthuavinash

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Networks and Communications
14

Scopus Publications

49

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • S2HConv-Caps-BiGRU: Deep Learning-Based Heterogeneous Face Recognition Model with Divergent Stages
    Narasimhula Balayesu, Avuthu Avinash Reddy
    Journal of Circuits Systems and Computers, 2024
    Real-time images of faces captured in different spectrum bands are considered heterogeneous images. Heterogeneous Face Recognition (HFR) matches faces across domains and is crucial to public safety. This paper proposes an HFR approach based on Deep Neural Networks (DNN). Feature maps are extracted from two images, such as gallery and sketch images, using Squirrel Search Heterogeneous Convolutional-Capsule- Bidirectional Gated Recurrent Unit (S2HConv-Caps-BiGRU). As a method of efficiently recognizing faces, coupled representation similarity metric (CRSM) will use the measure for the similarity of two feature maps. The experimental results will be evaluated to state-of-the-art (SOTA) statistical measures in terms of accuracy, recall, Jaccard score, dice score, mean square error (MSE), image similarity, performance and root mean square error (RMSE). Compared to other SOTA, the model produces the best results. The accuracy value of a CUFS dataset is 98.7%.
  • Performance analysis of ML models on 5G sub-6 GHz bands: An experimental study
    Avuthu Avinash Reddy, Ramesh babu Battula, Dinesh Gopalani
    Cluster Computing, 2024
  • Deep pelican based synthesis model for photo-sketch face synthesis and recognition
    Narasimhula Balayesu, Avuthu Avinash Reddy
    Multimedia Tools and Applications, 2024
  • Secure spectrum sensing in CRNs: A study on SSDF attacks and countermeasure using USRP
    Avuthu Avinash Reddy, Venkat Sai Akshay Gandlapalli, Katam Vital Sai, Ramesh Babu Battula
    2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024
    In Cognitive Radio Networks (CRNs), the Medium Access Control (MAC) protocol is essential for managing dynamic spectrum access among secondary users (SUs) without disrupting primary users (PUs) transmissions. The MAC protocol’s decisions are based on the availability of spectrum bands determined through spectrum sensing (SS). To improve SS efficiency, SUs collaborate to detect unused spectrum bands more accurately, reducing false alarm rates and enhancing SS reliability. However, cooperative SS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attacks, compromising SS integrity and affecting MAC protocol decisions. An ElGamal-based digital signature (EGDS) algorithm is used to authenticate SS information and protect against unauthorized access. Using digital signatures, SUs can securely authenticate spectrum access requests and messages, mitigating unauthorized access and tampering risks. The EGDS algorithm ensures the authenticity and integrity of communication between SUs and the spectrum access system. This approach detects modifications post-signature generation, maintaining SS reliability and enabling MAC protocols to make better decisions on token distribution and spectrum access in CRNs. Universal Software Radio Peripheral (USRP) devices are utilized to demonstrate SSDF attacks and showcase vulnerabilities and countermeasures.
  • Stacking Ensemble Model for Celestial Object Classification: Galaxies, Stars and Quasars
    S Sudharson, R Annamalai, Avuthu Avinash Reddy, P Varsha
    Proceedings of the IEEE International Conference Image Information Processing, 2023
    In the field of astronomy, it is essential to classify celestial objects like stars, galaxies, and quasars based on their spectral characteristics. This spectral data provides valuable information about various properties, such as the elements present, temperature, density, and magnetic field. To tackle this classification task, we investigate the application of different classification and ensemble algorithms. The proposed approach uses a variety of machine learning classifiers, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and XGBoost. These classifiers are combined to create a stacking classifier, which is then evaluated on its accuracy, precision, recall, F1 score, and support. The Stacking classifier demonstrates the highest accuracy, reaching an impressive 99.99% on the training data. Train Logloss is 0.011. The precision, recall, and f1 score values (all 1.00) indicate a robust classification capability a cross all classes of celestial objects. This outstanding accuracy means that it effectively identifies almost all celestial objects in the training data-set. Consequently, the Stacking model serves as a highly dependable and precise tool for recognizing galaxies, stars, and quasars based on their spectral characteristics.
  • Smart Farming for Agriculture Management Using IOT
    G Balu Narasimha Rao, K Venkateswara Rao, Raviteja Kamarajugadda, Avuthu Avinash Reddy, P Padmini Rani
    2023 9th International Conference on Advanced Computing and Communication Systems Icaccs 2023, 2023
    Smart farming is an raising idea because IOT sensors are giving information about agricultural fields and take action based on the given input. The scarcity of food and huge growth of population are the most provocation facing the growth of global. Latest technologies like Internet of Things (IoT), Artificial Intelligence(AI) and the high speed internet are providing the practical solutions to the new challenges facing by the global. smart farming creates a feasible to record and identify many assorted and scientific arguments through latest tools and devices to trace the operations performed by the end users. The article of this paper contain evolution Smart farming is a raising idea because internet of things (IOT) sensors are efficient of a technique which can measure temperature, pH level and condition of the soil. It sends an SMS alert to the farmer mobile through GSM module so the farmers can keep track of the fields condition from any place. It also helps in maintaining water by automatically providing water to the fields depending on the water requirements. It is mostly used in fields, lawns and parks. In addition to this, effect of environment is reduced and enlarges the quality & quantity and more importantly overall viability will increase.
  • A Comparative Analysis of Transformers for Multilingual Neural Machine Translation
    R Prasanna Kumar, S Sudharson, Avuthu Avinash Reddy, B Siva Jyothi Natha Reddy, Vemireddy Anvitha
    2023 IEEE 7th Conference on Information and Communication Technology Cict 2023, 2023
    Multilingual neural machine translation (NMT) has emerged as a promising solution to break down language barriers and promote cross-lingual communication. Transformer-based NMT models have gained significant attention due to their ability to learn contextual information and their effectiveness in handling long-range dependencies. The transformer model, with its self-attention and cross-attention mechanisms, fundamentally alters machine translation when compared to other neural machine translation models and traditional machine translation models. Token alignments between source and target sentences are successfully modelled by these mechanisms. This paper deals with the performance of a transformer-based multilingual NMT model that uses a shared encoder and decoder. We conducted experiments on the publicly available Multi30k dataset for translation jobs involving German, French, and Czech into English. In comparison of models, the Transformer model has performed better against the Convolutional SeqtoSeq and Attention-Based Seq2Seq models. We have evaluated our model on the metric Bilingual Evaluation Understudy (BLEU), where the transformer achieved 36.67, 48.47, and 33.66 for DE, FR, and CZ to EN translations.
  • Enhanced COVID-19 Detection and Privacy Preserving Using Federated Learning
    Paleru Pravallika, Vemireddy Gnanasri, Makineni Gireesh, Avuthu Avinash Reddy, Vadlamudi Kalpana, Jasthi Sumitha Chowdary
    Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023
    COVID-19 is rapidly spreading internationally, and early detection and diagnosis are mandatory to prevent the spread of the virus. Machine learning and deep learning models are considered for the detection of viruses accurately. However, these need to be improved to maintain the privacy of the patient's information. To keep the patient's private information and to detect COVID-19 with maximum accuracy, Federated learning allows multiple users to train models locally without sharing local data. This research study uses a distributed learning system to build a global model. The objective is to develop and evaluate a federated learning method for COVID-19 identification using chest X-ray pictures. Developed a decentralised and collaborative system that allows clinics to transmit sensitive information while remaining anonymous. The experiments and evaluations were conducted to emphasise the applicability of the federated learning technique, which is beneficial in COVID-19 circumstances.
  • Implemented global model for brain tumor detection using Federated Learning
    Vadlamudi Kalpana, Jasthi Sumitha Chowdary, Thota N V Laxmi Sravya, Avuthu Avinash Reddy, Paleru Pravallika, Vemireddy Gnanasri
    2023 7th International Conference on Electronics Materials Engineering and Nano Technology Iementech 2023, 2023
    Brain tumor (BT) is a severe medical issue that is challenging to detect and treat. For a few decades, there has been interest in applying machine learning and deep learning models to automate BT diagnosis. However, patients’ medical data is generally sensitive, and employing these models for BT diagnosis creates privacy problems. To overcome the issue mentioned above, federated learning (FL) architecture considers automating BT diagnosis. The proposed architecture develops and evaluates a global model using FL for BT identification. Using Backpropagation and DenseNet architecture, a model is designed for the MRI images and efficiently detects tumors for the patient. This approach uses a convolutional neural network to train a model on each device locally. The local models are combined to develop a global model to identify BT that allows clinics to transmit sensitive information while remaining anonymous. The results demonstrated that the framework accurately classified BT with good clinical accuracy and reliability.
  • An efficient spectrum sensing over η- μ fading on sub 6 GHz bands: A real-time implementation on USRP RIO
    Avinash Reddy Avuthu, Ramesh Babu Battula, Dinesh Gopalani
    Wireless Networks, 2022
  • Location based detection mechanism for PUEA on CR enabled 5G-IoT network
    Avuthu Avinash Reddy, Ramesh Babu Battula, Dinesh Gopalani, Arunima Sharma
    International Symposium on Advanced Networks and Telecommunication Systems Ants, 2021
  • DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi-Fi bands
    Avuthu Avinash Reddy, Ramesh Babu Battula, Dinesh Gopalani, Kurra Chaithanya
    International Journal of Communication Systems, 2020
  • An adaptive threshold energy detection mechanism using GNU Radio on USRP
    Avuthu Avinash Reddy, Ramesh Babu Battula, Dinesh Gopalani, Chaithanya Kurra
    2019 11th International Conference on Communication Systems and Networks Comsnets 2019, 2019
  • SAMAR - spectrum aware token MAc for cognitive radio networks
    Ramesh Babu Battula, Avinash Reddy Avuthu, Venkata Narayana S.
    Proceedings International Conference on Advanced Information Networking and Applications AINA, 2017

RECENT SCHOLAR PUBLICATIONS

  • Transformer-Guided Cross-View Correlation Network for Mammogram Classification
    K Ramakrishnan, K Mohideen, AA Reddy
    2025 IEEE 9th International Conference on Information and Communication … , 2025
    2025.0
  • S2HConv-Caps-BiGRU: Deep Learning-Based Heterogeneous Face Recognition Model with Divergent Stages
    N Balayesu, AA Reddy
    Journal of Circuits, Systems and Computers 33 (18), 2550009 , 2024
    2024.0
    Citations: 2
  • Performance analysis of ML models on 5G sub-6 GHz bands: An experimental study
    AA Reddy, R Battula, D Gopalani
    Cluster Computing 27 (10), 14283-14294 , 2024
    2024.0
    Citations: 1
  • Deep pelican based synthesis model for photo-sketch face synthesis and recognition
    N Balayesu, AA Reddy
    Multimedia Tools and Applications 83 (28), 71285-71310 , 2024
    2024.0
    Citations: 2
  • Secure spectrum sensing in CRNs: A study on SSDF attacks and countermeasure using USRP
    AA Reddy, VSA Gandlapalli, KV Sai, RB Battula
    2024 15th International Conference on Computing Communication and Networking … , 2024
    2024.0
    Citations: 1
  • Implemented global model for brain tumor detection using federated learning
    V Kalpana, JS Chowdary, TNVL Sravya, AA Reddy, P Pravallika, ...
    2023 7th International Conference on Electronics, Materials Engineering … , 2023
    2023.0
    Citations: 3
  • A Comparative Analysis of Transformers for Multilingual Neural Machine Translation
    RP Kumar, S Sudharson, AA Reddy, BSJN Reddy, V Anvitha
    2023 IEEE 7th Conference on Information and Communication Technology (CICT), 1-6 , 2023
    2023.0
    Citations: 3
  • Stacking Ensemble Model for Celestial Object Classification: Galaxies, Stars and Quasars
    S Sudharson, R Annamalai, AA Reddy, P Varsha
    2023 Seventh International Conference on Image Information Processing (ICIIP … , 2023
    2023.0
    Citations: 3
  • Enhanced COVID-19 detection and privacy preserving using federated learning
    P Pravallika, V Gnanasri, M Gireesh, AA Reddy, V Kalpana, JS Chowdary
    2023 5th international conference on inventive research in computing … , 2023
    2023.0
    Citations: 4
  • Smart farming for agriculture management using IOT
    GBN Rao, KV Rao, R Kamarajugadda, AA Reddy, PP Rani
    2023 9th International Conference on Advanced Computing and Communication … , 2023
    2023.0
    Citations: 11
  • An efficient spectrum sensing over fading on sub 6 GHz bands: A real-time implementation on USRP RIO
    AR Avuthu, RB Battula, D Gopalani
    Wireless Networks 28 (6), 2567-2577 , 2022
    2022.0
    Citations: 4
  • Location based detection mechanism for PUEA on CR enabled 5G-IoT network
    AA Reddy, RB Battula, D Gopalani, A Sharma
    2021 IEEE International Conference on Advanced Networks and … , 2021
    2021.0
    Citations: 3
  • DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi‐Fi bands
    A Avinash Reddy, R Babu Battula, D Gopalani, K Chaithanya
    International Journal of Communication Systems 33 (15), e4550 , 2020
    2020.0
    Citations: 4
  • An adaptive threshold energy detection mechanism using GNU Radio on USRP
    AA Reddy, RB Battula, D Gopalani, C Kurra
    11th IEEE International Conference on Communication Systems & Networks … , 2019
    2019.0
    Citations: 5
  • An adaptive threshold energy detection mechanism using GNU Radio on USRP
    AA Reddy, RB Battula, D Gopalani, C Kurra
    11th IEEE International Conference on Communication Systems & Networks … , 2019
    2019.0
    Citations: 5
  • SAMAR—Spectrum Aware token MAc for Cognitive Radio Networks
    RB Battula, AR Avuthu
    31st IEEE International Conference on Advanced Information Networking and … , 2017
    2017.0
    Citations: 3
  • Secure spectrum aware medium access control mechanism for Cognitive Radio based 5G IoT
    AA Reddy
    Jaipur , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Smart farming for agriculture management using IOT
    GBN Rao, KV Rao, R Kamarajugadda, AA Reddy, PP Rani
    2023 9th International Conference on Advanced Computing and Communication … , 2023
    2023.0
    Citations: 11
  • An adaptive threshold energy detection mechanism using GNU Radio on USRP
    AA Reddy, RB Battula, D Gopalani, C Kurra
    11th IEEE International Conference on Communication Systems & Networks … , 2019
    2019.0
    Citations: 5
  • An adaptive threshold energy detection mechanism using GNU Radio on USRP
    AA Reddy, RB Battula, D Gopalani, C Kurra
    11th IEEE International Conference on Communication Systems & Networks … , 2019
    2019.0
    Citations: 5
  • Enhanced COVID-19 detection and privacy preserving using federated learning
    P Pravallika, V Gnanasri, M Gireesh, AA Reddy, V Kalpana, JS Chowdary
    2023 5th international conference on inventive research in computing … , 2023
    2023.0
    Citations: 4
  • An efficient spectrum sensing over fading on sub 6 GHz bands: A real-time implementation on USRP RIO
    AR Avuthu, RB Battula, D Gopalani
    Wireless Networks 28 (6), 2567-2577 , 2022
    2022.0
    Citations: 4
  • DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi‐Fi bands
    A Avinash Reddy, R Babu Battula, D Gopalani, K Chaithanya
    International Journal of Communication Systems 33 (15), e4550 , 2020
    2020.0
    Citations: 4
  • Implemented global model for brain tumor detection using federated learning
    V Kalpana, JS Chowdary, TNVL Sravya, AA Reddy, P Pravallika, ...
    2023 7th International Conference on Electronics, Materials Engineering … , 2023
    2023.0
    Citations: 3
  • A Comparative Analysis of Transformers for Multilingual Neural Machine Translation
    RP Kumar, S Sudharson, AA Reddy, BSJN Reddy, V Anvitha
    2023 IEEE 7th Conference on Information and Communication Technology (CICT), 1-6 , 2023
    2023.0
    Citations: 3
  • Stacking Ensemble Model for Celestial Object Classification: Galaxies, Stars and Quasars
    S Sudharson, R Annamalai, AA Reddy, P Varsha
    2023 Seventh International Conference on Image Information Processing (ICIIP … , 2023
    2023.0
    Citations: 3
  • Location based detection mechanism for PUEA on CR enabled 5G-IoT network
    AA Reddy, RB Battula, D Gopalani, A Sharma
    2021 IEEE International Conference on Advanced Networks and … , 2021
    2021.0
    Citations: 3
  • SAMAR—Spectrum Aware token MAc for Cognitive Radio Networks
    RB Battula, AR Avuthu
    31st IEEE International Conference on Advanced Information Networking and … , 2017
    2017.0
    Citations: 3
  • S2HConv-Caps-BiGRU: Deep Learning-Based Heterogeneous Face Recognition Model with Divergent Stages
    N Balayesu, AA Reddy
    Journal of Circuits, Systems and Computers 33 (18), 2550009 , 2024
    2024.0
    Citations: 2
  • Deep pelican based synthesis model for photo-sketch face synthesis and recognition
    N Balayesu, AA Reddy
    Multimedia Tools and Applications 83 (28), 71285-71310 , 2024
    2024.0
    Citations: 2
  • Performance analysis of ML models on 5G sub-6 GHz bands: An experimental study
    AA Reddy, R Battula, D Gopalani
    Cluster Computing 27 (10), 14283-14294 , 2024
    2024.0
    Citations: 1
  • Secure spectrum sensing in CRNs: A study on SSDF attacks and countermeasure using USRP
    AA Reddy, VSA Gandlapalli, KV Sai, RB Battula
    2024 15th International Conference on Computing Communication and Networking … , 2024
    2024.0
    Citations: 1
  • Transformer-Guided Cross-View Correlation Network for Mammogram Classification
    K Ramakrishnan, K Mohideen, AA Reddy
    2025 IEEE 9th International Conference on Information and Communication … , 2025
    2025.0
  • Secure spectrum aware medium access control mechanism for Cognitive Radio based 5G IoT
    AA Reddy
    Jaipur , 0