Dr. Sanjeev J Wagh

@gcekarad.ac.in

Professor and Head Information Technology
Government College of Engineering Karad

Dr. Sanjeev J Wagh


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https://researchid.co/sanjeev.wagh

RESEARCH INTERESTS

Wireless Sensor Networks
Data Science and Analytics
Internet of Things
54

Scopus Publications

786

Scholar Citations

14

Scholar h-index

27

Scholar i10-index

Scopus Publications

  • ILENET-LINKNET ARCHITECTURE TRAINED ON PATTERN AND COLOR FEATURES FOR SKIN LESION CLASSIFICATION: SEGMENTATION WITH IMPROVED ATTENTION-BASED RCNN MODEL
    Sadanand S. Howal, S. J. Wagh
    Biomedical Engineering Applications Basis and Communications, 2025
    Skin abnormalities are increasingly prevalent, attributed to various factors such as pandemics and lifestyle changes, with sun exposure emerging as a primary culprit, often leading to the development of melanomas, a form of skin cancer. These abnormalities manifest as skin lesions, which are broadly categorized into melanocytic and non-melanocytic types, each exhibiting a spectrum of diverse characteristics. Skin lesions can range from benign to malignant, highlighting the critical need for accurate classification to guide appropriate treatment decisions. Dermoscopy, an advanced imaging technique, plays a pivotal role in dermatology by facilitating the visualization of deeper skin lesions, thus enhancing diagnostic accuracy beyond what traditional examination methods can offer. Accurate classification of skin lesions is paramount for ensuring timely disease detection and ultimately improving patient outcomes. The advent of deep learning methods has significantly revolutionized the field of dermatology, particularly in skin lesion classification. This study introduces LinkNet-Improved LeNet (LILNet), a novel deep learning architecture that integrates various stages including preprocessing, segmentation, feature extraction, and classification. The Convolutional Neural Autoregressive Density Estimation (ConvNADE) model is employed for preprocessing tasks, while the IA-MRCNN model is utilized for lesion segmentation and precise feature extraction. Classification is accomplished through a hybrid model combining elements of LinkNet and Improved LeNet (ILeNet) models, with score-level fusion techniques further enhancing accuracy. The integration of these stages within the LILNet architecture represents a significant advancement in skin lesion classification, aiming to improve diagnostic accuracy and efficiency in dermatological practice.
  • Comparative Evaluation of Deep Learning Models for Exhaled Breath-Based Liver Disease Prediction
    Archana D. Dantakale, Sanjeev J. Wagh
    2025 6th International Conference for Emerging Technology Incet 2025, 2025
    Liver diseases are on the rise due to factors such as excessive alcohol intake, poor dietary habits, and sedentary lifestyles, along with metabolic disorders like obesity and diabetes. For instance, non-alcoholic fatty liver disease (NAFLD) affects nearly a quarter of the global population, and liver cirrhosis causes over 1.32 million deaths annually, as reported by the WHO. Traditional diagnostic methods, including liver biopsies and imaging, are invasive, expensive, and impractical for large-scale or routine screening. This creates a need for non-invasive, cost-effective, and reliable diagnostic alternatives. One such approach is exhaled breath analysis, which utilizes volatile organic compounds (VOCs) to detect liver dysfunction. However, analyzing this data effectively requires advanced computational techniques capable of handling complex, high-dimensional sequences. This study explores the performance of various deep learning models—LSTM, BiLSTM, GRU, and 1D CNN—in predicting liver disease. Each architecture has distinct advantages: LSTM and BiLSTM efficiently capture long-term dependencies in sequential data, GRU provides computational efficiency while maintaining accuracy, and 1D CNNs are effective in feature extraction from raw input. By evaluating these models based on accuracy, sensitivity, and computational efficiency, this research aims to identify their respective strengths and limitations in liver disease classification. The findings will aid in selecting the most suitable model for real-time applications, ensuring both high predictive accuracy and optimal computational performance. Ultimately, this work contributes to the advancement of non-invasive diagnostic tools, facilitating early and precise liver disease detection while minimizing the need for invasive procedures and enhancing patient care.
  • INVESTIGATION FOR DIABETIC RETINOPATHY DETECTION USING PSEUDO-LABELING CLASSIFIER
    Umesh Anandrao Patil
    International Journal of Applied Mathematics, 2025
    As a progressive retinal disease linked to diabetes, Diabetic Retinopathy (DR) continues to be a major contributor to visual impairment and blindness in adults in diabetic persons. Identifying diabetic retinopathy at an early stage is necessary to stop the onset of lasting retinal impairment. Conventional screening techniques often depend on manual assessment by experts. It may be subjective, labor-intensive and limited in scalability. This research presents an automated approach for diabetic retinopathy identification that use a hybrid deep learning architecture. In the methodology advanced image enhancement techniques are applied to improve fundus image quality. Specifically CLAHE [33] is used to correct low contrast regions, while morphological transformations are used to highlight retinal structures. The enhanced retinal images are fed into an EfficientNet-L2 CNN, which is responsible for extracting rich deep features and performing segmentation. To improve generalization and reduce dependence on extensive labeled datasets a pseudo-labeling [34] is incorporated. This enables the framework to leverage both annotated and unannotated samples effectively, thereby strengthening model robustness and training efficiency. Experimental findings on an extensive dataset of 100,000 fundus pictures demonstrate that the proposed method attains a 96.5% classification accuracy and 96.3% F1-score across various phases of diabetic retinopathy. The suggested approach mitigates deficiencies in current diagnostic systems by providing superior resilience, scalability and advanced early-stage illness detection capabilities.
  • Advancements in Hybrid Deep Learning for Stroke Classification: A Comprehensive Review of Genetic Algorithms and Bilstm Networks
    Balasaheb S. Waidande, Sanjeev J. Wagh
    2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025
    Timely diagnosis and treatment planning are crucial in improving patient outcomes, making stroke classification a vital area of research. Recent advancements in hybrid deep learning frameworks have shown promise in enhancing classification accuracy by integrating powerful methodologies. This paper comprehensively reviews the integration of Genetic Algorithms (GA) for feature optimization and Bidirectional Long Short-Term Memory (BiLSTM) networks for learning complex temporal dependencies in medical data. GA enables the selection of relevant features from high-dimensional datasets, while BiLSTM improves predictive performance by modeling sequence information in both forward and backward directions. By analyzing existing hybrid models, we identify strengths, limitations, and areas of improvement. This review not only consolidates current progress but also critically examines overlooked challenges in clinical deployment, including high computational costs, lack of interpretability, ethical considerations, and generalizability across diverse datasets. Furthermore, it outlines future directions such as integrating explainable AI, lightweight model architectures, and robust validation protocols. The findings demonstrate that GA-BiLSTM hybrid models hold significant potential for creating reliable, accurate, and clinically viable stroke diagnostic tools.
  • Exploring Machine Learning Strategies for Classifying Online Toxicity
    Piyusha Urunkar, Bhushan Yelure, Sanjeev Wagh, Priyanka Shinde
    2025 International Conference on Future Technologies Icft 2025, 2025
  • A Hybrid CNN-SVM Approach for Skin Lesion Classification Using The ISIC 2019 Dataset
    Sadanand S. Howal, Sanjeev J. Wagh, Siddheshwash V. Patil, Bhushan S. Yelure
    2025 IEEE 14th International Conference on Communication Systems and Network Technologies Csnt 2025, 2025
    Identifying skin lesions accurately is necessary for immediate diagnosis of skin cancer, particularly melanoma, which can significantly reduce mortality rates. However, automating the categorization of skin lesions poses challenges due to subtle variations in their appearance on the skin. This article presents a hybrid approach that combines Support Vector Machines (SVM) for categorization with Convolution Neural Networks (CNN) for feature extraction. Random Forest (RF) and Decision Tree (DT) methods are also included for comparative analysis. Using ISIC 2019 dataset encompassing dermoscopic images, the evaluation results show that the CNN-SVM hybrid paradigm surpasses other classifiers for accuracy, precision, recall, and F1 score. Experimental findings indicate performance improvements of 3.6% and 6.9% for CNN-SVM model compared to the CNN-RF and CNN-DT models, respectively. These results emphasize the ability of hybrid methods to enhance classification efficiency for medical image analysis tasks.
  • Potentials of AI in Disease Detection and Medical Image Processing
    Asif I. Tamboli, Kollur A. B. Revanth, S J Wagh, Jayant Pawar, K Aishwarya
    Proceedings 2024 International Conference on Healthcare Innovations Software and Engineering Technologies Hiset 2024, 2024
    Artificial intelligence (AI) may help clinicians with a wide range of patient care including intelligent systems of healthcare. In healthcare, AI approaches that encompass machine learning (ML) to deep learning (DL) are used for illness diagnosis, medication discovery, especially patient risk detection. Furthermore, AI improved the hospital experience and accelerated the process of preparing sufferers to continue their recovery at home. AI has been used to many picture modalities that are employed at various phases of therapy and specifically, tumor identification and therapy evaluation. AI is the critical boosting capacity for processing vast numbers of medical photos and thereby uncovering illness features that are not visible to the human eye. The goals of this study are to discuss the development of AI in healthcare imaging research, its current function, the problems that must be overcome before AI can be broadly utilized in the hospitals and its possible future.
  • Realm of Medical Image Processing Under the Light of Artificial Intelligence
    Asif I. Tamboli, Samarth Shah, Gaurav Saxena, S J Wagh, Addagatla Prashanth, Mamadou Yero Diallo
    Proceedings 2024 International Conference on Healthcare Innovations Software and Engineering Technologies Hiset 2024, 2024
    Over the previous century, a fundamental breakthrough in medical imaging processing as well as storage occurred, with the switch from traditional detectors in radiography to digital detectors. As a result, medical imaging digitalization constitutes a big achievement and promises considerable breakthroughs in cancer. Thus, goal of study is to analyze the advanced imaging process in medical field using artificial intelligence (AI). AI, on the other hand, has the potential to go beyond these applications by producing picture-based biomarkers that are able to accurately predicting survival or response to therapy, thereby extracting far more information from photographic images than used to be conceivable. Such biomarkers might be incorporated into existing prognostic as well as predictive models used in clinical treatment, functioning as integrative biomarkers. So, the study helps in understanding importance of medical imaging processes and further helps in diagnosing minor diseases and eliminates it without human intervention with help of AI.
  • Analysis on the Integration of Healthcare and Management with Software and Technology
    Sarita Chaudhary, V C. Patil, Vibha Vyas, S J Wagh, Aparna Patange, A Naveen Krishna
    Proceedings 2024 International Conference on Healthcare Innovations Software and Engineering Technologies Hiset 2024, 2024
    Health information technology has accelerated and gained popularity since the first "Institute of Medicine (IOM)" research was released; yet, data on how health IT affects patient safety remains contradictory. The aim of this article is to present a summary of the most recent studies on the ways in which various health information technologies might enhance patient safety. Technological advancements have a significant impact on the healthcare industry; they have an impact on radiation, medicines, anesthetics, and the use of MRI scanners. Future technical advancements will continue to transform healthcare, but human considerations will always place limitations on the creation of new instruments, medications, and social media platforms. The three primary application areas are diagnosis and treatment suggestions, administrative chores, and patient involvement in conjunction with adherence. While AI can often execute healthcare duties just as well as humans, if not better, there are still implementation challenges that will delay the full automation about medical professional professions. Concerns of ethics surrounding the application of AI in healthcare are also discussed.
  • Efficient land cover classification for urban planning
    Vandana Tulshidas Chavan, Sanjeev J. Wagh
    Object Detection by Stereo Vision Images, 2022
    Understanding spatiotemporal urban dynamics is incredibly vital within the context of the speedy urban boom with severe social and environmental challenges, like urban impoverishment, numerous sorts of pollution, vulnerabilities to seasoning activities, climate alternate effects, modifications in native weather, and their probable impacts on water level and so on. Findings of the strategies is expected so that it will facilitate in making plans belonging urban improvement rules and complete framework for its designing and control. Knowledge of land cover, land use, and land change is very essential for understanding human activity and creating plans, policies, and solutions for urban planning. This paper proposes the development of a land cover classification system that can classify images efficiently based on the land cover in an efficient manner without any human intervention.
  • Data-driven approches for fake news detection on social media platforms: Review
    Pradnya Patil, Sanjeev J. Wagh
    Object Detection by Stereo Vision Images, 2022
  • Preface
    Object Detection by Stereo Vision Images, 2022
  • Object detection by stereo vision images
    Object Detection by Stereo Vision Images, 2022
  • ENERGY OPTIMIZATION PROTOCOL DESIGN FOR SENSOR NETWORKS IN IOT DOMAINS
    Sanjeev J. Wagh, Manisha Sunil Bhende, Anuradha D. Thakare
    Energy Optimization Protocol Design for Sensor Networks in Iot Domains, 2022
  • Blockchain Driven Secure and Efficient Logging for Cloud Forensics
    Sagar S. Rane, Sanjeev J. Wagh, Arati M. Dixit
    International Journal of Computing and Digital Systems, 2022
  • Preface
    Ceur Workshop Proceedings, 2022
  • Machine Learning Based Predictive Mechanism for Internet Bandwidth
    Swapnil R. Pokharkar, Sanjeev J. Wagh, Sachin N. Deshmukh
    2021 6th International Conference for Convergence in Technology I2ct 2021, 2021
  • Securing trustworthy evidences for robust forensic cloud in spite of multi-stakeholder collusion problem
    Sagar Rane, Sanjeev Wagh, Arati Dixit
    Advances in Intelligent Systems and Computing, 2021
  • Preface
    Icmlca 2021 2nd International Conference on Machine Learning and Computer Application, 2021
  • Design of a Forensic Enabled Secure Cloud Logging
    Sagar Rane, Sanjeev Wagh, Arati Dixit
    ACM International Conference Proceeding Series, 2020
  • Food monitoring using adaptive naïve bayes prediction in IoT
    Pramod D. Ganjewar, Selvaraj Barani, Sanjeev J. Wagh, Santosh S. Sonavane
    Advances in Intelligent Systems and Computing, 2020
  • A hierarchical fractional LMS prediction method for data reduction in a wireless sensor network
    Pramod Ganjewar, Barani S., Sanjeev J. Wagh
    Ad Hoc Networks, 2019
  • Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust
    Deepak C. Mehetre, S. Emalda Roslin, Sanjeev J. Wagh
    Cluster Computing, 2019
  • HFBLMS: Hierarchical Fractional Bidirectional Least-Mean-Square prediction method for data reduction in wireless sensor network
    Pramod D. Ganjewar, S. Barani, Sanjeev J. Wagh
    International Journal of Modeling Simulation and Scientific Computing, 2018
  • An efficient congestion control methodology to enhance performance of TCP in wired network
    Sanjesh S. Pawale, Sanjeev J. Wagh, Ranjana S. Jadhav
    2017 4th International Conference on Image Information Processing Iciip 2017, 2017
  • Efficient Routing in Wireless Sensor Network using Data Collection Node
    Varsha sid, Deepak C Mehetre, S. Emalada Roslin, Sanjeev J Wagh
    2017 International Conference on Computing Communication Control and Automation Iccubea 2017, 2017
  • Extending lifetime of wireless sensor networks using multi-sensor data fusion
    SOUMITRA DAS, S BARANI, SANJEEV WAGH, S S SONAVANE
    Sadhana Academy Proceedings in Engineering Sciences, 2017
  • Tree based aggregation for preventing compromised node's intention in WSN
    Priyanka Kamthe, Sanjeev J. Wagh
    International Conference on Automatic Control and Dynamic Optimization Techniques Icacdot 2016, 2017
  • Energy efficient and trustable routing protocol for Wireless Sensor Networks based on Genetic Algorithm (E2TRP)
    Soumitra Das, Barani S, Sanjeev Wagh, S.S. Sonavane
    International Conference on Automatic Control and Dynamic Optimization Techniques Icacdot 2016, 2017
  • Threshold based data reduction technique (TBDRT) for minimization of energy consumption in WSN
    Pramod D. Ganjewar, Sanjeev. J. Wagh, Barani S.
    International Conference on Energy Systems and Applications Icesa 2015, 2016
  • Data reduction using incremental Naive Bayes Prediction (INBP) in WSN
    Pramod D. Ganjewar, S. Barani, Sanjeev J. Wagh
    Proceedings IEEE International Conference on Information Processing Icip 2015, 2016
  • Energy efficient deflate (EEDeflate) compression for energy conservation in wireless sensor network
    Pramod Ganjewar, S. Barani, Sanjeev J. Wagh
    Advances in Intelligent Systems and Computing, 2016
  • Energy aware routing based on multi-sensor data fusion for wireless sensor networks
    Soumitra Das, S. Barani, Sanjeev Wagh, S. S. Sonavane
    Communications in Computer and Information Science, 2016
  • I-learning IoT: An intelligent self learning system for home automation using IoT
    Vishwajeet Hari Bhide, Sanjeev Wagh
    2015 International Conference on Communication and Signal Processing Iccsp 2015, 2015
  • An architectural approach of internet of things in E-Learning
    Madhavi Vharkute, Sanjeev Wagh
    2015 International Conference on Communication and Signal Processing Iccsp 2015, 2015
  • A proposed architectural model for vital sign monitoring system
    Vishakha Deshmukh, Sanjeev Wagh
    2015 International Conference on Communication and Signal Processing Iccsp 2015, 2015
  • Extending lifetime of wireless sensor network using connected dominating set and energy harvester
    Manisha Bhende, Sanjeev Wagh
    Proceedings 1st International Conference on Computing Communication Control and Automation Iccubea 2015, 2015
  • Energy efficient disjoint path routing using genetic algorithm for wireless sensor network
    Deepak Mehetre, Sanjeev Wagh
    Proceedings 1st International Conference on Computing Communication Control and Automation Iccubea 2015, 2015
  • Extending lifetime of wireless sensor network using cellular automata
    Manisha Sunil Bhende, Sanjeev Wagh
    Advances in Intelligent Systems and Computing, 2015
  • Comparative analysis of topology control algorithms to enhance network lifetime
    Arpn Journal of Engineering and Applied Sciences, 2015
  • Chameleon based clustering for wireless sensor network
    International Journal of Applied Engineering Research, 2015
  • Lifetime maximization in heterogeneous wireless sensor network based on metaheuristic approach
    Manisha Bhende, Suvarna Patil, Sanjeev Wagh
    Advances in Intelligent Systems and Computing, 2015
  • Power backup density based clustering algorithm for maximizing lifetime of wireless sensor networks
    Sanjeev Wagh, Ramjee Prasad
    2014 4th International Conference on Wireless Communications Vehicular Technology Information Theory and Aerospace and Electronic Systems Vitae 2014 Co Located with Global Wireless Summit, 2014
  • A cross-layer topology control with clustering and routing for energy efficient wireless sensor networks
    Suhas Patil, Deepali B. Gothawal, Sanjeev J. Wagh
    Proceeding of the IEEE International Conference on Green Computing Communication and Electrical Engineering Icgccee 2014, 2014
  • Reliable and energy efficient topology control algorithm based on connected dominating set for wireless sensor network
    Manisha Bhende, Sanjeev Wagh, , and
    Journal of Green Engineering, 2014
  • Maximizing lifetime of wireless sensor networks using genetic approach
    Sanjeev Wagh, Ramjee Prasad
    Souvenir of the 2014 IEEE International Advance Computing Conference Iacc 2014, 2014
  • Prolonging the lifetime of the wireless sensor network based on blending of genetic algorithm and ant colony optimization
    Soumitra Das, , Sanjeev Wagh, and
    Journal of Green Engineering, 2014
  • Improving efficiency of topology control algorithm using RSSI as link metric for wireless sensor network
    International Journal of Applied Engineering Research, 2014
  • A quick survey on wireless sensor networks
    Manisha Bhende, Sanjeev J. Wagh, Amruta Utpat
    Proceedings 2014 4th International Conference on Communication Systems and Network Technologies Csnt 2014, 2014
  • Energy optimization in wireless sensor network through natural science computing: A survey
    Sanjeev Wagh, CTiF - Centre for TeleInFrastruktur, Aalborg University, Aalborg, Ramjee Prasad, CTiF - Centre for TeleInFrastruktur, Aalborg University, Aalborg
    Journal of Green Engineering, 2013
  • Secure data transmission using steganography based data hiding in TCP/IP
    R. M. Goudar, S. J. Wagh, M. D. Goudar
    International Conference and Workshop on Emerging Trends in Technology 2011 Icwet 2011 Conference Proceedings, 2011
  • Genetic algorithm based filter and ANN based classifier for face recognition
    D. P. Gaikwad, S. J. Wagh
    Icwet 2010 International Conference and Workshop on Emerging Trends in Technology 2010 Conference Proceedings, 2010
  • TEAPC: Time Efficient Algorithm for multidimensional Packet Classifilcation
    Sanjeev Wagh, T. R Sontakke
    2009 IEEE International Advance Computing Conference Iacc 2009, 2009
  • S3: Packet classification using simple split header structure
    Sanjeev Wagh, T. R. Sontakke, Anup Vasudeva
    Proceedings 2008 International Conference on Advanced Computer Theory and Engineering Icacte 2008, 2008

RECENT SCHOLAR PUBLICATIONS

  • AI-Driven Mock Interview Systems: A Comprehensive Survey of Emotion and Performance Evaluation Platforms
    SJW Piyusha N. Patil
    Indian Journal of Technical Education (IJTE) 48 (02), 76-82 , 2025
    2025
  • Ml-Based Fertilizer Recommendation: Predicting Crop Response and Optimizing Fertilizer Type & Dosage with Machine Learning
    MDS Arun M. Patokar, Sanjeev J. Wagh, Rehan I. Mokashi
    Indian Journal of Technical Education (IJTE) 48 (02), 01-08 , 2025
    2025
  • Comprehensive Review on De-raining of Image & Video Using Deep Learning and Hybrid Approaches
    AP Tejas P. Shinde, Nikita Shetty, Sanjeev Wagh
    Journal Indian Journal of Technical Education (IJTE) 48 (02), 261-267 , 2025
    2025
  • Food Waste to Fertilizer: A Systematic Review of Technology Enabled Rapid Composting
    SJW Rehan I. Mokashi, Manish D. Sandanshiv
    Journal Indian Journal of Technical Education (IJTE) 48 (02), 299-307 , 2025
    2025
  • Bridging the Bench-to-Bedside Gap: External Validation and Refinement of a YOLO-Based System for Brain Tumor Diagnosis in Clinical Practice
    SJW Manish D. Sandanshiv, Rehan I. Mokashi
    Indian Journal of Technical Education (IJTE) 48 (02), 291-298 , 2025
    2025
  • Generating 2D Game Assets using Generative Adversarial Networks
    AP Mithilesh M. Pawar, Raj Kulkarni, Sanjeev J. Wagh
    Indian Journal of Technical Education (IJTE) 48 (02), 268-274 , 2025
    2025
  • Emotion-Aware Conversational Agents for Mental Health: A Comprehensive Survey
    SJW Piyusha V. Urunkar
    Indian Journal of Technical Education (IJTE) 48 (Special Issue No. 2), 63-71 , 2025
    2025
  • Exploring Machine Learning Strategies for Classifying Online Toxicity
    P Urunkar, B Yelure, S Wagh, P Shinde
    2025 International Conference on Future Technologies (ICFT), 1-6 , 2025
    2025
  • ILENET-LINKNET ARCHITECTURE TRAINED ON PATTERN AND COLOR FEATURES FOR SKIN LESION CLASSIFICATION: SEGMENTATION WITH IMPROVED ATTENTION-BASED RCNN MODEL
    SS Howal, SJ Wagh
    Biomedical Engineering: Applications, Basis and Communications 37 (05), 2550007 , 2025
    2025
    Citations: 1
  • Advancements in Hybrid Deep Learning for Stroke Classification: A Comprehensive Review of Genetic Algorithms and Bilstm Networks
    BS Waidande, SJ Wagh
    2025 2nd International Conference on Integration of Computational … , 2025
    2025
  • Comparative Evaluation of Deep Learning Models for Exhaled Breath-Based Liver Disease Prediction
    AD Dantakale, SJ Wagh
    2025 6th International Conference for Emerging Technology (INCET), 1-10 , 2025
    2025
  • A Hybrid CNN-SVM Approach for Skin Lesion Classification Using The ISIC 2019 Dataset
    SS Howal, SJ Wagh, SV Patil, BS Yelure
    2025 IEEE 14th International Conference on Communication Systems and Network … , 2025
    2025
    Citations: 2
  • Energy Optimization Protocol Design for Sensor Networks in IoT Domains
    SJ Wagh, MS Bhende, AD Thakare
    CRC Press , 2022
    2022
    Citations: 3
  • Virtual Moratorium System
    M Bhende, M Badger, P Kumbhar, V Bhatkar, P Chavan
    Object Detection by Stereo Vision Images, 171-183 , 2022
    2022
  • Efficient Land Cover Classification for Urban Planning
    VT Chavan, SJ Wagh
    Object Detection by Stereo Vision Images, 185-194 , 2022
    2022
    Citations: 1
  • Data‐Driven Approches for Fake News Detection on Social Media Platforms
    P Patil, SJ Wagh
    Object Detection by Stereo Vision Images, 195-206 , 2022
    2022
    Citations: 1
  • Object Detection by Stereo Vision Images
    RA Priya, AV Patil, M Bhende, AD Thakare, S Wagh
    John Wiley & Sons , 2022
    2022
    Citations: 4
  • Cyber Threats: Fears for Industry
    S Rane, G Devi, S Wagh
    Cyber Security Threats and Challenges Facing Human Life, 43-54 , 2022
    2022
    Citations: 6
  • Privacy threat reduction using modified multi-line code generation algorithm (MMLCGA) for cancelable biometric technique (CBT)
    PD Ganjewar, SJ Wagh, AL Gilbile
    International Conference on Intelligent Cyber Physical Systems and Internet … , 2022
    2022
    Citations: 1
  • Detection of diabetic retinopathy (DR) using convolutional neural network (CNN) and multiple classifier techniques in machine learning
    UA Patil, SJ Wagh
    Handbook of Research on Applied Intelligence for Health and Clinical … , 2022
    2022
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust
    DC Mehetre, SE Roslin, SJ Wagh
    Cluster Computing 22 (Suppl 1), 1313-1328 , 2019
    2019
    Citations: 96
  • i-learning IoT: An intelligent self learning system for home automation using IoT
    VH Bhide, S Wagh
    2015 international conference on communications and signal processing (iccsp … , 2015
    2015
    Citations: 95
  • Monitoring and detection of agricultural disease using wireless sensor network
    S Datir, S Wagh
    International Journal of Computer Applications 87 (4) , 2014
    2014
    Citations: 45
  • Wireless sensor network based pollution monitoring system in metropolitan cities
    S Raipure, D Mehetre
    2015 international conference on communications and signal processing (ICCSP … , 2015
    2015
    Citations: 44
  • Applied machine learning for smart data analysis
    N Dey, S Wagh, PN Mahalle, MS Pathan
    CRC Press , 2019
    2019
    Citations: 41
  • A quick survey on wireless sensor networks
    M Bhende, SJ Wagh, A Utpat
    2014 fourth international conference on communication systems and network … , 2014
    2014
    Citations: 39
  • A hierarchical fractional LMS prediction method for data reduction in a wireless sensor network
    P Ganjewar, S Barani, SJ Wagh
    Ad Hoc Networks 87, 113-127 , 2019
    2019
    Citations: 29
  • Extending lifetime of wireless sensor networks using multi-sensor data fusion
    S Das, S Barani, S Wagh, SS Sonavane
    Sādhanā 42 (7), 1083-1090 , 2017
    2017
    Citations: 22
  • Epidemic peak for COVID-19 in India, 2020
    S Wagh
    2020
    Citations: 17
  • An architectural approach of internet of things in E-Learning
    M Vharkute, S Wagh
    2015 International Conference on Communications and Signal Processing (ICCSP … , 2015
    2015
    Citations: 17
  • Trust based energy efficient clustering using genetic algorithm in wireless sensor networks (teecga)
    NB Nimbalkar, SS Das, SJ Wagh
    International Journal of Computer Applications 112 (9), 30-33 , 2015
    2015
    Citations: 17
  • Food monitoring using adaptive naïve bayes prediction in IoT
    PD Ganjewar, S Barani, SJ Wagh, SS Sonavane
    International Conference on Intelligent Systems Design and Applications, 424-434 , 2018
    2018
    Citations: 16
  • Secure data transmission using steganography based data hiding in TCP/IP
    RM Goudar, SJ Wagh, MD Goudar
    Proceedings of the International Conference & Workshop on Emerging Trends in … , 2011
    2011
    Citations: 16
  • Color Image Restoration For An Effective Steganography
    DP Gaikwad, SJ Wagh
    I-manager's Journal on Software Engineering 4 (3), 65 , 2010
    2010
    Citations: 16
  • Maximizing lifetime of wireless sensor networks using genetic approach
    S Wagh, R Prasad
    2014 IEEE International Advance Computing Conference (IACC), 215-219 , 2014
    2014
    Citations: 14
  • Fundamentals of data science
    SJ Wagh, MS Bhende, AD Thakare
    Chapman and Hall/CRC , 2021
    2021
    Citations: 13
  • Rising issues in vanet communication and security: A state of art survey
    SP Godse, PN Mahalle, SJ Wagh
    International Journal of Advanced Computer Science and Applications (IJACSA … , 2017
    2017
    Citations: 13
  • Data reduction using incremental Naive Bayes Prediction (INBP) in WSN
    PD Ganjewar, S Barani, SJ Wagh
    2015 international conference on information processing (ICIP), 398-403 , 2015
    2015
    Citations: 13
  • Intelligent car park management system using wireless sensor network
    M Bhende, S Wagh
    International Journal of Computer Applications 122 (10), 1-6 , 2015
    2015
    Citations: 13
  • HFBLMS: Hierarchical fractional bidirectional least-mean-square prediction method for data reduction in wireless sensor network
    PD Ganjewar, S Barani, SJ Wagh
    International Journal of Modeling, Simulation, and Scientific Computing 9 … , 2018
    2018
    Citations: 12