A multimodal AI-enabled system using advanced visual transformer and data science approaches for early oral cancer detection Sahil Sharma, Geeta Kasana, Seema Wazarkar, Singara Singh Kasana Proceedings of SPIE the International Society for Optical Engineering, 2026 Traditional research work focuses on solving the problem of detecting oral cancer and often relies on only visual data. Single-modality approaches are unable to fully capture the complex nature of the disease, which limits the performance of the diagnostic system in accuracy and reliability. To address this problem, a multimodal Artificial Intelligence (AI) system is proposed utilizing advanced visual transformers such as Data-efficient Image Transformer (DeiT) and Shifted Window Transformer (Swin) and data science approaches for visualization and data modeling that combine different types of data, such as images and clinical information to provide a more complete and optimal diagnostic system. This approach aims to improve the overall detection performance and make the system more useful in real-world healthcare settings.
Exploring Data Modalities and Advances in Related AI Technologies for Oral Cancer Detection Sahil Sharma, Seema Wazarkar, Geeta Kasana Iet Image Processing, 2025 Oral cancer diagnosis represents a significant public health burden; late‐stage detection of oral cancer is a major issue for ineffective treatment. Multimodal approaches from artificial intelligence have emerged as a pretty promising approach to address this challenge. In this paper, a comprehensive review of recent studies of oral cancer detection across varied data modalities that utilise technologies such as computer vision, natural language processing, acoustics analysis, Internet of Things, and machine learning and Deep Learning (DL) is presented. Across the reviewed literature, unique datasets spanning imaging, histopathology, spectroscopy, and clinical text are identified and represented. Reported performance metrics vary by modality, such as image‐based DL methods, which achieved accuracies between 91% and 99% and area under the curve values up to 0.95, spectroscopy‐based approaches reported accuracies above 92%. These results highlight the diagnostic potential of varied data modalities for future research direction, and small, imbalanced datasets, lack of external validation, and personalisation are major concerns to be addressed.
A Novel Soft Deep Learning Technique to Enhance Fashion Image Retrieval from Text Query Geeta Kasana, Sonal Kasana, Seema Wazarkar 2025 IEEE 2nd International Conference on Green Industrial Electronics and Sustainable Technologies Giest 2025, 2025 The necessity for robust text-to-image retrieval systems has been brought a focus due to the increasing demand for intellectual fashion support in digital wardrobes. In order to maintain semantic fidelity, the current experimental work studies a novel wardrobe retrieval system that allows users to look for specific fashion items using natural language queries like “black top” or “red jacket”. The two experiments are carried out. In the first, the Contrastive Language-Image Pretraining (CLIP) model from OpenAI works by converting text and images into 512-dimensional vectors and figuring out how similar they are cosine-wise. Although CLIP’s high-dimensional embeddings are good at capturing semantic alignment, they come with a hefty processing cost and could contain duplicate or irrelevant characteristics. To get around these restrictions, the other experiment combines CLIP features with Rough Set Theory (RST). RST reduces dimensionality while maintaining semantic fidelity using entropy-based feature selection. Based on similarity scores, this hybrid CLIP+RST model divides retrieved images into three groups: lower, upper boundary region, and outside upper approximation. Regardless of its relevance, this classification allows for multi-perspective retrieval to meet various user search thrusts and sustainable computing.
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers 2022 Language Resources and Evaluation Conference Lrec 2022, 2022
Social Image Content Analysis for Fashion using Deep Learning Seema Wazarkar, Ketan Kotecha, Shruti Patil, Nidhi Kalra Proceedings 2022 IEEE World Conference on Applied Intelligence and Computing Aic 2022, 2022 Social image content analysis is one of the important tasks for fashion analysis. Use of proposed system in fashion industries will uplift their business as social visual perception is very useful for the decision making in fashion industries. It supports growth in business and helps in minimizing loss or provides prior knowledge of risks. Analysis of social content data is a challenging task due to the nature of social data. Social content data is unstructured and full of ambiguity. But, this source of data is very important because it keeps updating continuously so that current data is being available for analysis. There is a necessity of current data for applications related to fashion as fashion trends keep changing. Therefore, in this paper convolutional neural networks is applied along with our machine learning approaches to find optimal fashion analyzing approach where social media is utilized to predict fashion style. Deep learning approach with Softsign and Softplus function performed well.
Enhancing surface fault detection using machine learning for 3d printed products Vaibhav Kadam, Satish Kumar, Arunkumar Bongale, Seema Wazarkar, Pooja Kamat, Shruti Patil Applied System Innovation, 2021 In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.
Social image mining for fashion analysis and forecasting Seema Wazarkar, Bettahally N. Keshavamurthy Applied Soft Computing Journal, 2020 Fashion industries need to be attentive towards the changing fashion and its upcoming market demands to grow their business, optimally. This paper describes research work involved in image mining for fashion analysis and forecasting using fashion-related images collected from the social network. A novel soft clustering technique is proposed for grouping the social fashion images. This technique is robust against uncertainty found in given images. The proposed clustering approach is compared with existing soft clustering approaches. It is found that the proposed approach performs well. Attributes of fashion items found in each cluster are analyzed through correlation, causal analysis, and fashion cycle visualization. Predictive models are applied to the clustered fashion items for style forecasting. A comparative study of predictive models is also done to find an optimal technique for various fashion items. As social visual perception is helpful for decision making, the proposed system is very useful in fashion industries to uplift their business.
A Bibliometric Survey of Fashion Analysis using Artificial Intelligence Library Philosophy and Practice, 2020
Semi-supervised model for feature extraction and classification of fashion images Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
A multimodal AI-enabled system using advanced visual transformer and data science approaches for early oral cancer detection S Sharma, G Kasana, S Wazarkar, SS Kasana Eighth International Conference on Image Processing and Machine Vision (IPMV … , 2026 2026
A Novel Soft Deep Learning Technique to Enhance Fashion Image Retrieval from Text Query G Kasana, S Kasana, S Wazarkar 2025 IEEE 2nd International Conference on Green Industrial Electronics and … , 2025 2025
AFS-MHMD: Adaptive Fusion Strategies for Multi-modal Hateful Meme Detection S Wazarkar, H Singh, A Jain, M Singh IMVIP 2025, 136 , 2025 2025 Citations: 1
Aqua Check: The Water Quality Assessment System For Public Health And Environmental Stability SW S. Trivedi, ..., S. Bawa DST PURSE" Sponsored NCSPER2025 , 2025 2025
Exploring Data Modalities and Advances in Related AI Technologies for Oral Cancer Detection S Sharma, S Wazarkar, G Kasana IET Image Processing 19 (1), e70223 , 2025 2025
Personal Credit Score Generator using Federated Learning for Financial Stress Management R Kaur, S Wazarkar, et al. International Conference on Artificial Intelligence and Networking, ICAIN 2024 , 2024 2024 Citations: 3
Socio-fashion Dataset: A Fashion Attribute Data Generated Using Fashion-Related Social Images S Wazarkar, BN Keshavamurthy, ED Sequeira International Conference on Hybrid Intelligent Systems, 350-356 , 2023 2023
Advanced fashion recommendation system for different body types using deep learning models S Wazarkar, S Patil, PS Gupta, K Singh, M Khandelwal, CVS Vaishnavi, ... 2022 Citations: 10
Erosion model for abrasive water jet machining of composite materials A Dhanawade, S Wazarkar, S Kumar Journal of the Brazilian Society of Mechanical Sciences and Engineering 44 … , 2022 2022 Citations: 14
Social Image Content Analysis for Fashion using Deep Learning S Wazarkar, K Kotecha, S Patil, N Kalra 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), 714-718 , 2022 2022
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers M Garg, S Wazarkar, M Singh, O Bojar LREC 2022 , 2022 2022 Citations: 31
Intelligent Machining A Dhanawade, S Wazarkar, V Naranje Industry 4.0 in Small and Medium-Sized Enterprises (SMEs), 139-154 , 2022 2022
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products SP V Kadam, S Kumar, A Bongale, S Wazarkar, P Kamat Applied System Innovation 4 (2), 34 , 2021 2021 Citations: 124
Enhancing sur-face fault detection using machine learning for 3d printed products. Applied System Innovation, 4 (2) V Kadam, S Kumar, A Bongale, S Wazarkar, P Kamat, S Patil 2021 Citations: 5
A Bibliometric Survey of Fashion Analysis using Artificial Intelligence S Wazarkar, S Patil, S Kumar Library Philosophy and Practice (Scopus) , 2020 2020 Citations: 15
Social image mining for fashion analysis and forecasting S Wazarkar, BN Keshavamurthy Applied Soft Computing (SCIE, Impact Factor: 5.472), 106517 , 2020 2020 Citations: 29
A soft clustering technique with layered feature extraction for social image mining S Wazarkar, BN Keshavamurthy Multimedia Tools and Applications (SCIE INDEXED), 1-28 , 2019 2019 Citations: 5
A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users A Hussain, BN Keshavamurthy, S Wazarkar International Journal of Web Services Research 16 (2) , 2019 2019
An Efficient Approach for Classifying Social Network Events using Convolution Neural Networks A Hussain, BN Keshavamurthy, S Wazarkar Springers's International Conference on Data and Information Sciences (ICDIS … , 2019 2019 Citations: 7
Sentiment Analysis of Sub-Events extracted out of an Event using Word2vec B Keshavamurthy, S Srivastava, J Haris, A Kumar, S Wazarkar 18th IEEE International Conference on Advanced Learning Technologies (ICALT … , 2018 2018
MOST CITED SCHOLAR PUBLICATIONS
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products SP V Kadam, S Kumar, A Bongale, S Wazarkar, P Kamat Applied System Innovation 4 (2), 34 , 2021 2021 Citations: 124
A Survey on Image Data Analysis through Clustering Techniques for Real World Applications S Wazarkar, B Keshavamurthy Journal of Visual Communication and Image Representation(SCIE, Scopus … , 2018 2018 Citations: 77
Region-based Segmentation of Social Images Using Soft KNN Algorithm S Wazarkar, BN Keshavamurthy, A Hussain Procedia Computer Science (Scopus indexed) 125, 93-98 , 2018 2018 Citations: 39
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers M Garg, S Wazarkar, M Singh, O Bojar LREC 2022 , 2022 2022 Citations: 31
Social image mining for fashion analysis and forecasting S Wazarkar, BN Keshavamurthy Applied Soft Computing (SCIE, Impact Factor: 5.472), 106517 , 2020 2020 Citations: 29
Artificial Neural Networks and Machine Learning–ICANN 2017: 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings A Lintas, S Rovetta, PFMJ Verschure, AEP Villa Springer 10614, 786-787(Wazarkar,Keshavamurthy, Prasad) , 2017 2017 Citations: 25
Text clustering using HFRECCA and rough K-means clustering algorithm SV Wazarkar, AA Manjrekar Discovery 15 (40), 44-47 , 2014 2014 Citations: 17
Feature Extraction Model for Social Images S Wazarkar, BN Keshavamurthy Smart Computing and Informatics, 669-677 , 2018 2018 Citations: 16
A Bibliometric Survey of Fashion Analysis using Artificial Intelligence S Wazarkar, S Patil, S Kumar Library Philosophy and Practice (Scopus) , 2020 2020 Citations: 15
Erosion model for abrasive water jet machining of composite materials A Dhanawade, S Wazarkar, S Kumar Journal of the Brazilian Society of Mechanical Sciences and Engineering 44 … , 2022 2022 Citations: 14
Fashion Image Classification using Matching Points with Linear Convolution S Wazarkar, BN Keshavamurthy Multimedia Tools and Applications -Springer (SCIE, Science Edition, SCOPUS … , 2018 2018 Citations: 12
Advanced fashion recommendation system for different body types using deep learning models S Wazarkar, S Patil, PS Gupta, K Singh, M Khandelwal, CVS Vaishnavi, ... 2022 Citations: 10
An Efficient Approach for Classifying Social Network Events using Convolution Neural Networks A Hussain, BN Keshavamurthy, S Wazarkar Springers's International Conference on Data and Information Sciences (ICDIS … , 2019 2019 Citations: 7
Probabilistic Classifier for Fashion Image Grouping using Multi-layer Feature Extraction Model S Wazarkar, BN Keshavamurthy, A Hussain International Journal of Web Services Research 15(2): 89-104 (SCIE, SCOPUS … , 2018 2018 Citations: 6
Enhancing sur-face fault detection using machine learning for 3d printed products. Applied System Innovation, 4 (2) V Kadam, S Kumar, A Bongale, S Wazarkar, P Kamat, S Patil 2021 Citations: 5
A soft clustering technique with layered feature extraction for social image mining S Wazarkar, BN Keshavamurthy Multimedia Tools and Applications (SCIE INDEXED), 1-28 , 2019 2019 Citations: 5
HFRECCA for clustering of text data from travel guide articles SV Wazarkar, AA Manjrekar Advances in Computing, Communications and Informatics (ICACCI 2014 IEEE … , 2014 2014 Citations: 5
Personal Credit Score Generator using Federated Learning for Financial Stress Management R Kaur, S Wazarkar, et al. International Conference on Artificial Intelligence and Networking, ICAIN 2024 , 2024 2024 Citations: 3
AFS-MHMD: Adaptive Fusion Strategies for Multi-modal Hateful Meme Detection S Wazarkar, H Singh, A Jain, M Singh IMVIP 2025, 136 , 2025 2025 Citations: 1
A Review of Hierarchical Fuzzy Text Clustering S Wazarkar, BN Keshavamurthy, A Manjrekar 9th IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICoAC 2017), 516-519 , 2017 2017 Citations: 1
