DEVARAYASAMUDRAM VENKATESH

@gitam.edu

Associate Professor Department of CSE
GITAM Deemed to be University

DEVARAYASAMUDRAM VENKATESH


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https://researchid.co/deancseit

EDUCATION

Doctor of Philosophy in Computer Science and Engineering

RESEARCH INTERESTS

MANETS, AI, NEURAL NETWORKS, MACHINE LEARNING
23

Scopus Publications

90

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • HYBRID DEEP LEARNING AND OPTIMIZATION-DRIVEN FRAMEWORK FOR ENHANCED SKIN CANCER DETECTION AND CLASSIFICATION
    D VENKATESH A CHANDRA SEKHAR
    Journal of Theoretical and Applied Information Technology, 2026
    Globally, skin cancer is one of the most common diseases caused by excessive exposure to ultraviolet (UV) radiation. The outcomes and prognosis for treating skin malignancies significantly improve with early detection and accurate diagnosis. However, many diagnostic methods have limitations, entail high computational costs, rely heavily on manual feature extraction, lack good generalization across datasets, and are susceptible to adversarial attacks. This paper addresses these limitations in skin cancer diagnosis by proposing an improved Wavelet-AHE Diffusion Enhanced Hybrid Network (WADE-HNet) for enhancing detection as well as classification. The proposed ensemble technique integrates ResEff-FuseNet for feature extraction, Firefly-Bitterling Adaptive Selection Optimization (FBASO) for optimal feature selection, Multi-Stage Attention Capsule Network (MSA-CapsNet) for enhanced classification performance, and Modified U-Net++ for lesion segmentation. Such improvements increase the interpretability, generalizability, and computational efficiency of model performance. Finally, empirical results for both the HAM10000 and ISIC 2019 datasets validate that WADE-HNet outperforms benchmark models with a high accuracy of 99.39%. In this way, the proposed strategy ensures consistency in clinical usage while reducing false positives and false negatives.
  • Automated Detection and Severity Grading of Diabetic Retinopathy from Retinal Fundus Images using Deep Learning
    Shashank, D. Venkatesh, M. Mallikarjuna Rao
    2026 IEEE 3rd International Conference on Emerging Trends in Engineering and Medical Sciences Icetems 2025, 2026
    Diabetic Retinopathy (DR) is an evolving microvascular impediments of diabetes and major reason of blindness globally. Early identification through automated screening can help to limit visual loss, particularly in countries where diabetes is becoming more common. This paper presents a hybrid DL model as per EfficientNet-B3 and ResNet-50 for automated detection of DR with five-stage disease severity classification from the retinal fundus images. Preprocessing methods, like histogram equalization, CLAHE and vessel enhancement filters are used in order to increase contrast and emphasize pathology structures. The model is trained and tested on APTOS 2019, EyePACS and Messidor datasets. Performance measures comprise of accuracy, recall, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score, and quadratic weighted kappa. Results show that the hybrid model can compete with current state-of-the-art approaches and thus offers a practical solution in DR screening.
  • Design and Implementation of Predictive Location Analytics using Google Maps Data and Machine Learning
    Chillangi Manasa, Devanga Dharani Lakshmi, D. Venkatesh, M. Mallikarjuna Rao
    Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026
    Predictive location analytics is vital as a component of intelligent mobility, transportation planning, and city decisions. The given paper represents a proposal of a single machine-learning system, which will use Google Maps APIs, traffic data, and the POI distributions to predict the travel time, the 3D congestion, and the hotspots of demand. In that methodology, it combines spatial preprocessing based on geohashing, temporal window modeling, POI density modeling, and a hybrid prediction architecture based on Gradient Boosted Trees (GBT) and Long Short-Term Memory (LSTM) networks. The combination of these two approaches (LSTM and GBT) is shown to be effective in predicting the travel-time in Google Maps with high accuracy by incorporating nonlinear temporal dependencies and estimating the intensity of spatial hotspots, respectively. The reliability of the system is established through graphs and statistical findings which show that the system reduces the errors by a large margin compared to the baseline models. The suggested architecture offers a well-grounded scalable architecture of predictive mobility analytics within smart-city settings.
  • An Adaptive Predictive Threshold (APT) Algorithm for Load Balancing in Cloud Environments
    P V Anusha, Ms Y. Avanija, D. Venkatesh, M. Mallikarjuna Rao
    7th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2026, 2026
    Cloud computing environments necessitate effective load balancing strategies to manage the growing volume of high-demand applications. This paper introduces an Adaptive Predictive Threshold (APT) Load Balancing Algorithm aimed at improving performance and energy efficiency through smart decision-making. The APT algorithm utilizes two thresholds that are updated dynamically-an adaptive overload threshold and a dynamic underload threshold-to precisely identify overloaded and underloaded Virtual Machines (VMs). Additionally, a predictive assessment enhances the mechanism by anticipating future VM loads, thereby averting sudden overloading and ensuring consistent task assignments. In contrast to traditional static algorithms, APT implements a consolidation strategy where an overloaded VM must finish a designated number of tasks before shifting to an underloaded state. Experimental findings indicate that the APT algorithm significantly surpasses commonly used methods such as Round Robin, First Come First Serve, and Equally Spread Load Balancing, especially under substantial task loads. Its adaptive, prediction-based methodology results in decreased instability, enhanced resource utilization, and improved overall system performance in practical cloud environments.
  • Longitudinal Risk Prediction of Hospital Readmission in Diabetes Management Using a Temporal Attention-Gated BiLSTM with Focal Loss
    M.Mallikarjuna Rao, C. Manjunath, D. Venkatesh, S. Vasundhara
    Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning Icsadl 2026, 2026
    Thirty-day hospital readmission continues to be a critical quality-of-care indicator in chronic diseases, particularly Diabetes Mellitus. Readmissions significantly increase healthcare expenditure and patient morbidity. Although Electronic Health Records (EHRs) contain rich longitudinal patient information, many existing prediction models rely on snapshot-based features extracted from a single hospital encounter, thereby failing to capture disease progression over time. To address this limitation, this paper proposes a Temporal Attention-Gated Bidirectional Long Short-Term Memory (TA-BiLSTM) model that leverages sequential patient visit histories and incorporates a focal loss objective to handle severe class imbalance inherent in medical datasets. Experiments conducted on the UCI Diabetes 130-US Hospitals dataset (over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0 0, 0 0 0}$</tex> encounters) demonstrate that the proposed model achieves an AUROC of 0.696, outperforming Logistic Regression (0.582) and standard LSTM (0.621) models with statistical significance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p}&lt;0.05)$</tex>. Furthermore, the integrated temporal attention mechanism enhances clinical interpretability by identifying influential visits contributing to readmission risk. The findings indicate that longitudinal, explainable deep learning models can support accurate and clinically meaningful readmission risk stratification.
  • CKD-Net: A Hybrid Deep Learning Model for Kidney Disease Prediction
    Srihitha Chinthaginjala, M. Mallikarjuna Rao, D. Venkatesh, M. Bhagya Lakshmi
    2026 IEEE 3rd International Conference on Emerging Trends in Engineering and Medical Sciences Icetems 2025, 2026
    Chronic Kidney Disease (CKD) is a severe global health issue, which has crippled millions of people across the world where the timely detection is the most important element in preventing a transition to the End-Stage Renal Disease (ESRD). Although the conventional machine learning methods have been promising, they are not usually able to deal with the high-dimensional non-linear characteristics of clinical information. This study provides an in-depth study of the state-of-the-art Deep Learning (DL) methods to predict CKD, in particular, it analyses Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Deep Belief Networks (DBN). We also suggest a new hybrid CNN-LSTM network that combines both time dependency modeling and space feature extraction. We use a dataset of 400 patient records consisting of 24 attributes, apply a strict preprocessing procedure consisting of SMOTE to balance classes and MICE to handle missing data. Experimental data shows that the hybrid model proposed is able to perform best with 99.2 % accuracy, which is better than standalone architectures. The present study confirms the effectiveness of deep learning in nephrology and adds to the increasing list of studies that prove AI-based personalized medicine.
  • Reassessing model complexity in reservoir outflow forecasting: A multi-site, physics-informed benchmark of deep learning and ensemble method
    M. Mallikarjuna Rao, C. Manjunath, D. Venkatesh, P. Rajendran, D. Dharani Lakshmi, S. Venkatasivanagaraju, S. Vasundra, D.V. Bhaskar
    Ecological Engineering and Environmental Technology, 2026
    The recent progress in deep learning also makes one reconsider the reality that architecturally more complicated models tend to perform better at hydrological forecasting.Despite the common belief that long short-term memory (LSTM) models are useful in rainfall-runoff models, the transferability to the reservoirs that are managed by deterministic operation rules is understudied.In this article, we do a comparative study of physics-informed bi-directional LSTM with Temporal Attention and a Random Forest (RF) algorithm to daily predict a reservoir outflow in Shasta Dam and Oroville Dam located in California.To reduce the difficulty associated with low density measurements, we combine NASA POWER satellite data with ground-based measurements and thus they enhance the dataset.The physics-informed properties, one of which is the mass-balance proxies and another one is seasonal encodings, are used to secure the models into a system of physical consistency.Empirical findings also show that the Random Forest model performs better in terms of Nash-Sutcliffe efficiency scores of 0.909 in Shasta Dam and 0.683 in Oroville Dam compared to the Bi-LSTM scores of 0.827 and 0.681, respectively.Ensemble approaches, including the Random Forest, seem to be more accurate in modelling the rule-of-thumb operational regimes of the reservoirs, but the deep-learning approach tends to regulate the changes dynamics of transition issues related to outflow releases.Formal statistical significance testing using the Diebold-Mariano test and bootstrap confidence intervals confirmed that the Random Forest advantage is statistically significant at Shasta Dam (p < 0.001) but not at Oroville Dam (p = 0.378), revealing site-dependent performance patterns linked to operational complexity.The totality of these results implies that in the case of rule-dominated reservoir systems, the simpler ensemble learning approaches may even perform better than the sophisticated deep-learning systems.In this regard, the selection of the model must be driven by the correspondence with the characteristics of the system, but not the bias with the architectural elaboration.
  • Transferable multi-site digital twin for wastewater treatment: Real-time prediction, economic assessment, and climate resilience
    Pamidi Archana, G. Swathi, N.V. Divya, M. Mallikarjuna Rao, D. Venkatesh, Devanga Dharani Lakshmi, Venkata Sivanagaraju S., C. Manjunath
    Ecological Engineering and Environmental Technology, 2026
    Wastewater treatment plants worldwide face increasing operational challenges due to population growth, aging infrastructure, variable climate conditions, and stricter regulatory standards.Addressing these challenges requires predictive frameworks that can monitor current performance, forecast future burdens, quantify associated costs, and assess system resilience.In this study, we develop and validate a transferable multi-site digital twin framework for wastewater treatment, integrating advanced machine learning models, probabilistic forecasting, and comprehensive economic and environmental assessments.The framework was applied to three plants with different technologies: a conventional activated sludge plant (50 MGD), a membrane bioreactor with ultraviolet disinfection (75 MGD), and a hybrid system combining advanced treatment with constructed wetlands (35 MGD).Our results show that Transformer networks outperform random forest, achieving an R of 0.98 in biochemical oxygen demand prediction versus 0.92 for the baseline.Life-cycle analysis indicates that the hybrid system reduces operating expenses by 32% and lowers carbon footprint by 45% while remaining compliant with regulatory standards.Monte Carlo simulations quantify probabilistic compliance under variable conditions, and climate projections suggest that high-emission scenarios could increase effluent violations up to 50% by the end of the century.The framework operates in real time, generating predictions in 50 milliseconds, with monthly cloud costs between $120 and $850 depending on update frequency.These findings demonstrate that transferable digital twins can provide accurate real-time predictions, guide cost-effective and environmentally sustainable treatment strategies, and enhance resilience to climate variability, representing a significant advance over previous offline single-site models.
  • Levy Flight Strategy-based Tasmanian Devil's Optimization and Autoencoder Approach for Intrusion Detection in Cloud Computing
    International Journal of Intelligent Engineering and Systems, 2025
    Cloud Computing (CC) is an Internet-based technology that offers shared resources such as storage, software and platforms to users on demand.However, Intrusion Detection System (IDS) often encounter challenges when processing high-dimensional data, resulting in low accuracy and increased false alarm rate, which contribute to unreliable model predictions.To enhance the accuracy and reliability of IDS in processing high-dimensional information, it is essential to address errors in the training data.This research proposes a Levy Flight strategy-based Tasmanian Devil's Optimization (LFS-TDO) approach for feature selection and an Autoencoder (AE) for classifying network attacks.The LFS-TDO strategy demonstrates a significant improvement in optimization performance, where the small step size in levy flight enhances the exploitation ability.The AE mechanism effectively captures complex and nonlinear relationships in data, making it highly effective for detecting subtle anomalies and variations in network traffic.The proposed LFS-TDO-based AE technique is evaluated using UNSW-NB15, NSL-KDD and CICIDS2017 datasets, achieving accuracies of 93.81%, 99.82%, 99.70%, respectively, with precision scores of 99.75%, 99.80%, 99.78%, recall rates of 92.28%, 99.76%, 97.35%, and F1-scores of 98.95%, 99.78%, 99.80, respectively.These results demonstrate a significant improvement over existing techniques like Convolutional Neural Network (CNN) and Genetic Algorithm and Particle Swarm Optimization (GA-PSO) approach.
  • Early Detection of Alzheimer's Disease Using Deep Learning Models on MRI Scans
    T. Saravanan, Devaraya Harshitha, D. Venkatesh
    2025 International Conference on Engineering Innovations and Technologies Icoeit 2025, 2025
    Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly affects memory, behavior, and cognitive abilities. Early detection is crucial for timely medical intervention and improved patient outcomes. This study presents an automated classification framework using brain MRI scans from the ADNI dataset, leveraging deep learning models such as CNN, VGG19, DenseNet, and EfficientNetB7. Preprocessing steps including normalization, segmentation, and data augmentation were applied to enhance model performance. Among the evaluated models, DenseNet and EfficientNetB7 demonstrated superior accuracy, achieving 96.94% and 96.88%, respectively. These models excel in capturing subtle anatomical variations in early AD stages. Automated feature extraction minimizes human error, enhances diagnostic precision, and reduces processing time. Performance was assessed using accuracy, precision, recall, and F1-score metrics. EfficientNetB7, with its compound scaling technique, proved highly efficient for clinical applicability. While results are promising, future work will focus on improving interpretability through Explainable AI and validating models across diverse datasets for real-world deployment.
  • Improving Data Delivery and Energy Efficiency in MANETs: A Stacking Based SVM Approach for Multipath
    D. Venkatesh, V. Narahari, K. Ranga Swamy, M. Vijaya Bhaskar, T. Saravanan, S. Vasundra
    Lecture Notes in Networks and Systems, 2025
  • An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs
    Venkatesh Devarayasamudram, Rakesh Chandrashekar, Chandra Mohan Chetla, Kumar Raja Depa Ramachandraiah, Purushotham Nimmala, Singaravelan Arumugam
    Mathematical Modelling of Engineering Problems, 2024
  • Integrating Fuzzy Logic and Deep Learning for Effective Network Attack Detection with Fuzzified Deep Convolutional Neural Network
    D. Venkatesh, T. Saravanan, S. Vasundra
    Eai Springer Innovations in Communication and Computing, 2024
  • Quality of Service Protection in 5G Mobile Ad Hoc Networks with Behavior Learning-Enhanced CNN-AODV Routing
    T. Saravanan, D. Venkatesh, S. Deepa
    Eai Springer Innovations in Communication and Computing, 2024
  • Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
    Ramesh Vatambeti, D. Venkatesh, Gowtham Mamidisetti, Vijay Kumar Damera, M. Manohar, N. Sudhakar Yadav
    Scientific Reports, 2023
  • Various wavelet transformation of an image using Convolutional Neural Networks
    D. Venkatesh, A. Venkata Subramanian, Vasundra S
    2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2023, 2023
  • Prediction of heart disease using machine learning and hybrid methods
    D Venkatesh, T Saravanan, D Raghavaraju, M Vijaya Bhaskar, S Vasundra
    2023 1st International Conference on Optimization Techniques for Learning Icotl 2023 Proceedings, 2023
  • Performance Analysis of Bollinger Bands and Long Short-Term Memory(LSTM) models based Strategies on NIFTY50 Companies
    Varun Seshu, Hritik Shanbhag, Samrudhi R Rao, Disha Venkatesh, Pooja Agarwal, Arti Arya
    Proceedings of the Confluence 2022 12th International Conference on Cloud Computing Data Science and Engineering, 2022
  • Enhanced detection of retinopathy affected blood vessels using deep convolutional neural networks
    International Journal of Advanced Science and Technology, 2020
  • Advanced feature selection methodology for cancer datasets to improve accuracy of classification
    International Journal of Advanced Science and Technology, 2020
  • Lifetime enhancement of a node using I—leach protocol in WSN
    D. Venkatesh, A. Subramanyam
    Advances in Intelligent Systems and Computing, 2018
  • Product recommendation system using priority ranking
    S. Vasundra, D. Raghava Raju, D. Venkatesh
    Advances in Intelligent Systems and Computing, 2018
  • Blind spectrum sensing techniques in cognitive radio-survey
    S. Vasundara, D. Raghavaraju, D. Venkatesh
    Lecture Notes in Electrical Engineering, 2017

RECENT SCHOLAR PUBLICATIONS

  • Enhanced Fuzzy-BERT Synergy: A Multi-Scale Adaptive Framework for Emotion Intensity and Narrative Strength in Multilingual Text Classification
    C Manjunath, MM Rao, D Venkatesh, DV BHASKAR
    Journal of BioDigital Intelligence and Technology 1 (1), 14-20 , 2026
    2026
  • Smart Review Analyser: A Multiplatform AI System for Aspect-based Sentiment Analysis on E-Commerce Products
    DL Prasanna, L Malleswari, P Preeti, MM Rao, D Venkatesh, C Manjunath
    2026 International Conference on Smart Electronic Devices and Intelligent … , 2026
    2026
  • Automated Detection and Severity Grading of Diabetic Retinopathy from Retinal Fundus Images using Deep Learning
    D Venkatesh, MM Rao
    2026 3rd International Conference on Emerging Trends in Engineering and … , 2026
    2026
  • CKD-Net: A Hybrid Deep Learning Model for Kidney Disease Prediction
    S Chinthaginjala, MM Rao, D Venkatesh, MB Lakshmi
    2026 3rd International Conference on Emerging Trends in Engineering and … , 2026
    2026
  • Transferable multi-site digital twin for wastewater treatment: Real-time prediction, economic assessment, and climate resilience
    P Archana, G Swathi, NV Divya, MM Rao, D Venkatesh, DD Lakshmi, ...
    Ecological Engineering & Environmental Technology 27 (3) , 2026
    2026
  • Reassessing model complexity in reservoir outflow forecasting: A multi-site, physics-informed benchmark of deep learning and ensemble methods
    MM Rao, C Manjunath, D Venkatesh, P Rajendran, DD Lakshmi, ...
    Ecological Engineering & Environmental Technology 27 (3), 239-252 , 2026
    2026
  • Longitudinal Risk Prediction of Hospital Readmission in Diabetes Management Using a Temporal Attention-Gated BiLSTM with Focal Loss
    MM Rao, C Manjunath, D Venkatesh, S Vasundhara
    2026 5th International Conference on Sentiment Analysis and Deep Learning … , 2026
    2026
  • A robust offline digital twin for regenerative wastewater treatment: Multi-pollutant stress testing and resilience analysis
    MM Rao, C Manjunath, D Venkatesh, S Venkatasivanagaraju, ...
    Journal of Ecological Engineering 27 (6) , 2026
    2026
    Citations: 1
  • Integrated Disease Management of Gummy Stem Blight in Bottle Gourd
    V Suresh, BH Hudge, BSK Nikhil, RP Goud, DA Kumari, D Venkatesh
    Journal of Scientific Research and Reports 32 (1), 481-488 , 2026
    2026
  • Design and Implementation of Predictive Location Analytics using Google Maps Data and Machine Learning
    C Manasa, DD Lakshmi, D Venkatesh, MM Rao
    2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026
    2026
  • An Adaptive Predictive Threshold (APT) Algorithm for Load Balancing in Cloud Environments
    PV Anusha, MY Avanija, D Venkatesh, MM Rao
    2026 7th International Conference on Mobile Computing and Sustainable … , 2026
    2026
  • QUESTIONS AND ANSWERS IN HEMATOLOGY IN MAMMALS VOLUME–I
    E Muralinath, R Pulikanti, SMDU Reddy, M Pallavi, D Venkatesh, MK Rao, ...
    Ashok Yakkaldevi , 2025
    2025
  • Artificial Intelligence in Horticulture: Current Trends and Future Prospects
    B Santhosha, BSK Nikhil, V Suresh, DA Kumari, RP Goud, BP Kumar, ...
    Journal of Scientific Research and Reports 31 (8), 698-711 , 2025
    2025
  • Early Detection of Alzheimer's Disease Using Deep Learning Models on MRI Scans
    T Saravanan, D Harshitha, D Venkatesh
    2025 International Conference on Engineering Innovations and Technologies … , 2025
    2025
    Citations: 1
  • Levy Flight Strategy-based Tasmanian Devil's Optimization and Autoencoder Approach for Intrusion Detection in Cloud Computing.
    S Vasundra, D Venkatesh, HK Bella
    International Journal of Intelligent Engineering & Systems 18 (2) , 2025
    2025
    Citations: 1
  • A Prospective Study of Functional Outcome of Arthroscopic Labral Repair in Adults for Traumatic Shoulder Instability
    DL Venkatesh, KLP Babu, KMM Rao, P Uppala
    International Journal of Pharmacy Research & Technology (IJPRT) 14 (2), 126-130 , 2024
    2024
  • Recognition of energy harvesting techniques for enhanced operation in WSN using IoT with deep learning
    S Rajalakshmi
    J. Electrical Systems 20 (3s), 142-149 , 2024
    2024
    Citations: 7
  • Improving Data Delivery and Energy Efficiency in MANETs: A Stacking Based SVM Approach for Multipath
    D Venkatesh, V Narahari, KR Swamy, MV Bhaskar, T Saravanan, ...
    International Conference on Soft Computing and Pattern Recognition, 35-45 , 2023
    2023
  • Prediction of heart disease using machine learning and hybrid methods
    D Venkatesh, T Saravanan, D Raghavaraju, MV Bhaskar, S Vasundra
    2023 1st International Conference on Optimization Techniques for Learning … , 2023
    2023
    Citations: 11
  • Integrating Fuzzy Logic and Deep Learning for Effective Network Attack Detection with Fuzzified Deep Convolutional Neural Network
    D Venkatesh, T Saravanan, S Vasundra
    International Conference on Big Data Innovation for Sustainable Cognitive … , 2023
    2023
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Lifetime enhancement of a node using I—Leach protocol in WSN
    D Venkatesh, A Subramanyam
    Artificial Intelligence and Evolutionary Computations in Engineering Systems … , 2018
    2018.0
    Citations: 33
  • Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
    R Vatambeti, D Venkatesh, G Mamidisetti, VK Damera, M Manohar, ...
    Scientific reports 13 (1), 15371 , 2023
    2023.0
    Citations: 18
  • Prediction of heart disease using machine learning and hybrid methods
    D Venkatesh, T Saravanan, D Raghavaraju, MV Bhaskar, S Vasundra
    2023 1st International Conference on Optimization Techniques for Learning … , 2023
    2023.0
    Citations: 11
  • Recognition of energy harvesting techniques for enhanced operation in WSN using IoT with deep learning
    S Rajalakshmi
    J. Electrical Systems 20 (3s), 142-149 , 2024
    2024.0
    Citations: 7
  • Performance evaluation of routing protocols for voice and video traffics
    S Vasundra, D Venkatesh
    Asian Journal of Computer Science and Technology 7 (3), 86-90 , 2018
    2018.0
    Citations: 6
  • Integrating Fuzzy Logic and Deep Learning for Effective Network Attack Detection with Fuzzified Deep Convolutional Neural Network
    D Venkatesh, T Saravanan, S Vasundra
    International Conference on Big Data Innovation for Sustainable Cognitive … , 2023
    2023.0
    Citations: 3
  • A robust offline digital twin for regenerative wastewater treatment: Multi-pollutant stress testing and resilience analysis
    MM Rao, C Manjunath, D Venkatesh, S Venkatasivanagaraju, ...
    Journal of Ecological Engineering 27 (6) , 2026
    2026.0
    Citations: 1
  • Early Detection of Alzheimer's Disease Using Deep Learning Models on MRI Scans
    T Saravanan, D Harshitha, D Venkatesh
    2025 International Conference on Engineering Innovations and Technologies … , 2025
    2025.0
    Citations: 1
  • Levy Flight Strategy-based Tasmanian Devil's Optimization and Autoencoder Approach for Intrusion Detection in Cloud Computing.
    S Vasundra, D Venkatesh, HK Bella
    International Journal of Intelligent Engineering & Systems 18 (2) , 2025
    2025.0
    Citations: 1
  • Product Recommendation System Using Priority Ranking
    S Vasundra, D Raghava Raju, D Venkatesh
    Artificial Intelligence and Evolutionary Computations in Engineering Systems … , 2018
    2018.0
    Citations: 1
  • The power of cloud computing: Integration and intelligence
    D Venkatesh, S Pabboju, VK Damera
    International Journal of Advanced Research in Computer Science 9 (2), 656-662 , 2018
    2018.0
    Citations: 1
  • Multipath Optimized Link State Routing Protocol with Multi-Variant Deficit Round Robin
    D Venkatesh, S Vasundra
    2018 IADS International Conference on Computing, Communications & Data … , 2018
    2018.0
    Citations: 1
  • Blind Spectrum Sensing Techniques in Cognitive Radio-Survey
    S Vasundara, D Raghavaraju, D Venkatesh
    Emerging Trends in Electrical, Communications and Information Technologies … , 2016
    2016.0
    Citations: 1
  • An Adaptive Framework for Bandwidth Allocation in Cloud Networks
    D Venkatesh, PS Reddy
    International Journal of Computer (IJC) 23 (1), 1-9 , 2016
    2016.0
    Citations: 1
  • Determining the Appropriate Thresholds for P2P Content File Sharing in Disconnected MANETs
    V Suresh, D Venkatesh, J Raghunath
    IJRSCSE 2, 6-15 , 2015
    2015.0
    Citations: 1
  • HDRI (High Dynamic Range Image) Acquisition by Multiple ExposureFusion
    SK Banu, DK Jawad, D Venkatesh
    International Journal of Research Communication Technology 2 (8) , 2013
    2013.0
    Citations: 1
  • Performance Enhancement to WCDMA Multimedia Network using MAC Protocol
    S Vasundara, D Venkatesh, A Sathyanarayana
    i-Manager's Journal on Software Engineering 1 (2), 71 , 2006
    2006.0
    Citations: 1
  • Evaluation of Hybrids for Growth, Yield and Yield Attributes in Okra (Abelmoschus esculentus L. Moench)
    D Venkatesh, J Srinivas, D Lakshminarayana, K Nagaraju, BSK Nikhil, ...
    Citations: 1
  • Enhanced Fuzzy-BERT Synergy: A Multi-Scale Adaptive Framework for Emotion Intensity and Narrative Strength in Multilingual Text Classification
    C Manjunath, MM Rao, D Venkatesh, DV BHASKAR
    Journal of BioDigital Intelligence and Technology 1 (1), 14-20 , 2026
    2026.0
  • Smart Review Analyser: A Multiplatform AI System for Aspect-based Sentiment Analysis on E-Commerce Products
    DL Prasanna, L Malleswari, P Preeti, MM Rao, D Venkatesh, C Manjunath
    2026 International Conference on Smart Electronic Devices and Intelligent … , 2026
    2026.0