Passionate educator and researcher with over 14 years of academic experience in Computer Science & Engineering, specializing in Artificial Intelligence, Machine Learning, Data Science, and Web Mining.
My doctoral research focused on “Optimization of Multimodal Webpage Genre Classification Using Efficient Machine Learning Techniques”, developing intelligent models integrating audio, video, and textual modalities for enhanced web information retrieval.
Throughout my career, I have:
Guided 80+ undergraduate and postgraduate projects in AI and software systems.
Served as Chairman, Moderator, and Examiner for BCA and B.Sc (CS) programs at Karnataka University, Dharwad.
Published 4 Scopus-indexed research papers across Q1–Q3 journals.
Coordinated institutional activities, including AICTE affiliation, UUCMS, and IGNOU LSC-1303.
I aim to bridge academic excellence and applied research by mentoring students in next-generation computing technologies.
EDUCATION
BE(CSE), MTech(CSE), Ph.D, UGC-NET and KSET
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Science
5
Scopus Publications
15
Scholar Citations
2
Scholar h-index
1
Scholar i10-index
Scopus Publications
Enhanced Multimodal Webpage Classification Using Deep Learning for Efficient Information Retrieval Manjunath Pujar, Monica Mundada, Sowmya B. J., Supreeth Shivashankar, Ganesh Dalappagari Ramanjinappa, Shambulingana Gouda Journal of Computational and Cognitive Engineering, 2026 Web data mining has become a crucial tool for efficiently retrieving valuable information, as users increasingly rely on the World Wide Web for data exchange. Traditional web classification methods often struggle with handling multimodal data, leading to challenges in accurately classifying diverse web contents. Online classification plays a key role in facilitating efficient retrieval of information from multimedia content. This study presents a novel multimodal approach for webpage classification by integrating deep learning techniques for audio-visual analysis. The personalized Long Short-Term Memory (LS) TM model, which is a specific version of Long Short-Term Memory (LSTM), has improved classification accuracy by combining deep audio and video features. Artificial Convolutional Neural Networks (A-CNNs) extract complex audio features, while transformer networks capture long-range dependencies from video data. The present study proposes a log-sigmoid activation function that provides a more flexible thresholding method in logistic regression, thus greatly improving the classification performance. The focus of this study, which is on single-modality classification, presents an innovative method of integrating deep learning-based multimodal fusion, thus setting a new standard for web classification. Experimental results show that the Logistic Sigmoid Long Short-Term Memory ((LS)²TM) model has an accuracy of 88.09%, sensitivity of 89.14%, and specificity of 89.01%, outperforming state-of-the-art techniques such as LSTM, Deep Belief Network DBN, and A-CNN. The model also enhanced its precision (93.15%), recall (92.84%), and F-measure (93.25%), which are generally 5% higher than classical control methods. These findings highlight the potential of (LS)²TM for improving web content mining through multimodal analysis. Future research should focus on the real-world validation and scalability of dynamic web environments. Received: 19 January 2025 | Revised: 18 March 2025 | Accepted: 7 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/shaurov/website classificationusing-url. The data that support the findings of this study are openly available at https://doi.org/10.1109/78.650093, reference number [12]. Author Contribution Statement Manjunath Pujar: Methodology, Software, Validation, Resources, Writing – original draft. Monica Mundada: Conceptualization, Formal analysis, Writing – review & editing, Project administration. Sowmya B. J.: Methodology, Software, Validation, Investigation, Data curation. Supreeth Shivashankar: Writing – review & editing, Supervision, Project administration. Ganesh Dalappagari Ramanjinappa: Data curation, Visualization. Shambulingana Gouda: Visualization, Supervision.
A Systematic Review Web Content Mining Tools And Its Applications Manjunath Pujar, Monica R Mundada International Journal of Advanced Computer Science and Applications, 2021 In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page. Keywords—Web content mining; web structure mining; web usage mining; data mining; information retrieval; information extraction
RECENT SCHOLAR PUBLICATIONS
Enhanced Multimodal Webpage Classification Using Deep Learning for Efficient Information Retrieval M Pujar, M Mundada, S Shivashankar, GD Ramanjinappa, S Gouda Journal of Computational and Cognitive Engineering , 2025 2025
An Efficient Optimization of Multimodal Web Page Genre Classification Based on Objects Using LR-YoloV4 and (BM)2-CWRNN Deep Learning Techniques M Pujar, M R Mundada, S B J, S S, S G, R S International Journal of Computing and Digital Systems 18 (1), 1-13 , 2025 2025
An efficient framework for web content mining systems using improved cd-pam clustering and the a-cnn technique M Pujar, MR Mundada, BJ Sowmya, S Supreeth, G Shruthi SN Computer Science 4 (5), 692 , 2023 2023 Citations: 5
Privacy-Preserving Techniques in Distributed Evolving Data Stream Clustering for IoT Environments S Balutagi, A Ranjan, M Pujar, MR Mundada, RHS Karthica, M Zaki, ... 2023
A Systematic Review Web Content Mining Tools and its Applications M Pujar, MR Mundada International Journal of Advanced Computer Science and Applications 12 (8 … , 2021 2021 Citations: 10
MOST CITED SCHOLAR PUBLICATIONS
A Systematic Review Web Content Mining Tools and its Applications M Pujar, MR Mundada International Journal of Advanced Computer Science and Applications 12 (8 … , 2021 2021 Citations: 10
An efficient framework for web content mining systems using improved cd-pam clustering and the a-cnn technique M Pujar, MR Mundada, BJ Sowmya, S Supreeth, G Shruthi SN Computer Science 4 (5), 692 , 2023 2023 Citations: 5
Enhanced Multimodal Webpage Classification Using Deep Learning for Efficient Information Retrieval M Pujar, M Mundada, S Shivashankar, GD Ramanjinappa, S Gouda Journal of Computational and Cognitive Engineering , 2025 2025
An Efficient Optimization of Multimodal Web Page Genre Classification Based on Objects Using LR-YoloV4 and (BM)2-CWRNN Deep Learning Techniques M Pujar, M R Mundada, S B J, S S, S G, R S International Journal of Computing and Digital Systems 18 (1), 1-13 , 2025 2025
Privacy-Preserving Techniques in Distributed Evolving Data Stream Clustering for IoT Environments S Balutagi, A Ranjan, M Pujar, MR Mundada, RHS Karthica, M Zaki, ... 2023
