Disha D N

@nmamit.nitte.edu.in

Assistant Professor II, Artificial Intelligence and Machine Learning
NMAM Institute of Technology

Disha D N


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https://researchid.co/dishadn

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science Applications, Genetics, Computer Vision and Pattern Recognition
15

Scopus Publications

37

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Autism spectrum disorder classification using machine learning with factor analysis
    Disha Devidas Nayak, Seema Shedole, Archana Mathur
    Iaes International Journal of Artificial Intelligence, 2025
    <p>Due to the complexity and heterogeneity of autism spectrum disorder (ASD), diagnosis and categorization have attracted a lot of interest. To improve the robustness of ASD classification across the toddler age group, this work proposes an integrated strategy that integrates machine learning approaches with factor analysis and correlation validation. Benchmark dataset representing toddlers used to test this strategy’s efficiency. To first find the latent variables behind the ASD features in each dataset, factor analysis is used. We intend to capture the shared variance between variables and lower the dimensionality of the initial feature space by identifying these latent components. The subsequent machine-learning classification models used the retrieved components as input features. To validate the categorization results, correlation analyses were carried out in addition to factor analysis. The associations between the latent components discovered by factor analysis and the diagnostic labels were examined using Pearson correlation, a measure of linear association. The results highlight the method’s potential to improve diagnostic precision and shed light on the intricate connections between characteristics and diagnostic labels on the autism spectrum for toddlers.</p>
  • Improving Detection of Autism Spectrum Disorder (ASD) by Using mRMR Feature Selection and Genetic Optimization Based CES Model for Computing Autism Severity Score
    D. N. Disha, S. Seema, Archana Mathur
    SN Computer Science, 2025
  • Data analytics and cognitive computing for digital health: A generic approach and a review of emerging technologies, challenges, and research directions
    K. Aditya Shastry, D.N. Disha
    Explainable AI in Healthcare Imaging for Medical Diagnoses Digital Revolution of Artificial Intelligence, 2025
  • Deep Learning Approaches for Enhanced Automatic Text Summarization
    Rakshitha, Pushpa Mohan, Rashmitha Shettigar, Disha D N, Sudesh Rao
    2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025
    Although there is a wealth of information on the internet these days, it can be challenging to find it quickly and effectively. You may have trouble finding the precise information you need to comprehend a given subject. The summary of a huge text document is exceedingly hard for humans to manually extract. The Internet is a great source of textual content. Searching through the large number of papers accessible and extracting pertinent information from them is therefore a challenge. To address the two issues mentioned above, automatic text summarizing is essential. One famous method for distilling a document to its essential points is automatic summarization. It refers to the technique of condensing a lengthy text document into a succinct and well-written summary that captures the essential information and main ideas of the original text, achieved by highlighting the significant points of the document. Presenting a condensed, semantically sound version of the original text is the aim of automatic text summarization. The main benefit is that reading time is decreased.
  • Unveiling Media Bias: Investigating Influences, Politics, and Solutions through Advanced Classification Techniques
    Amogh K Kudva, Disha D N, Soumya C S
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    This research investigates the common problem of media bias, focusing on how it affects what journalists and news producers choose to cover. Media bias is when news does not follow the usual rules, going beyond personal opinions. The level and direction of bias can differ across countries because journalists cannot cover all aspects and they need to construct a concise story with selected facts. In almost every country government influence on the media outlets and by people in power can introduce bias. The study also looks at the tricky relationship between politics and media bias, understanding how they affect each other. Things like who owns the media and how they pick their staff can also add to bias. The main point is that summarizing articles is crucial to quickly get the point across without reading the whole thing. The proposed system for classifying news articles pulls information from different online newspapers using encoding techniques. Use of web-scraping to find articles, and smart technology like Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks to figure out media bias. This new way of doing things aims to help us understand bias in media better, making it easier to get what is going on in the digital age.
  • Revolutionizing Healthcare Triage: A Comparative Analysis of Machine Learning-Driven Symptom Checkers and Triage Bots for Common Diseases and Skin Conditions
    Rakshitha, Disha D N, Sudesh Rao, Meghana Bhat P, Kavana Pai, Anirudh Pandit
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    Symptoms Checker and Triage Bot application built using Streamlit, offering users a comprehensive tool for diagnosing common diseases and skin conditions. Leveraging machine learning models, the application provides two main modules: the Common Diseases Checker and the Skin Disease Classifier. The Common Diseases Checker utilizes a decision tree classifier trained on symptom-disease datasets to predict potential health conditions based on user-provided symptoms, offering detailed disease descriptions and precautionary measures. Meanwhile, the Skin Disease Classifier employs a pretrained ResNet-50 convolutional neural network to analyze uploaded images of skin lesions and classify them as either melanoma or allergy, aiding in early diagnosis. The Streamlit interface enables seamless navigation between modules via sidebar buttons, ensuring a user-friendly experience. Overall, the application aims to empower users with efficient and accurate health assessments, facilitating informed decision- making and timely medical intervention. ResNet50 outperformed and attained 97.23% accuracy than other baseline models in skin disease prediction. In common disease prediction, K- nearest neighbour model attained 98.87% accuracy than other models.
  • TechAsanaAdvancer Technical Mastery of Yoga Poses with AI
    Sudesh Rao, Nishmitha, Bhat Aditi Dinamani, Chidananda T, Disha D N, Rakshitha
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    Yoga is one of the best at-home exercises for maintaining our physical health. However, yoga is all about successfully performing the 82 Yoga Asanas throughout six classes. Lamentably, not everyone knows or can perform yoga accurately. So, acquiring the guidance of a yoga instructor for mastering yoga poses can be challenging and costly, especially when factoring in various scenarios and circumstances. This paper introduces an intelligent yoga trainer application utilizing computer vision and deep learning techniques. The system employs a pretrained TensorFlow MoveNet model for real-time detection of key body points, enabling assessment of yoga pose accuracy. Two model variants, “lightning” and “thunder,” balance speed and accuracy based on image input sizes. The architecture incorporates MobileNet v2 and a custom prediction head for pose classification. Data preprocessing involves generating feature vectors from annotated pose images, with a focus on pose-centric coordinate transformations for improved accuracy. Neural network design includes dense layers with ReLU activation and dropout regularization to prevent overfitting. Experimental results show promising accuracy rates, suggesting potential enhancements through data augmentation or angle-based features. This work contributes to AI-driven fitness applications, enhancing yoga practice with real-time feedback and personalized guidance.
  • Enhancing Road Safety: A Deep Learning Approach to Accurate Traffic Sign Classification and its Implications for Autonomous Vehicles
    Disha D N, Sudesh Rao, Rakshitha, Soumya C S, Adithi Ram, Aditi Rao
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    Traffic signs play a vital role in providing safety while driving by providing important information for drivers. It is essential for adults to responsibly follow traffic laws and be aware of signs indicating speed limits, road conditions, and potential dangers. Further, they must also learn to control their driving behavior by strictly following the traffic rules and regulations at all times. Speed limit signs, left or right turns, no entry restrictions, and kids crossing are a few examples of traffic signs. Traffic sign classification is used to identify and distinguish between different types of traffic signs to forewarn and notify drivers of impending violations of the law. To develop the system, we implemented a deep neural network model that classifies traffic signs found within the image into distinct categories by utilizing TensorFlow and the Keras library. This model aims to improve upon existing systems by addressing issues like inaccurate predictions and high costs. By using this model, we are able to analyze and interpret different traffic signs which is an essential prerequisite for all autonomous vehicles. Ultimately, it contributes to the development of autonomous vehicles that can effectively understand and respond to traffic signs.
  • Predictive Modelling of Bipolar Disorder Utilizing Advanced Machine Learning Techniques
    Disha D N, Seema S, Soumya C S, Sudesh Rao, Rakshitha
    2023 2nd International Conference on Futuristic Technologies Incoft 2023, 2023
    A complex mental health disease called bipolar disorder is characterised by recurrent manic and depressive episodes. For effective treatment planning and management, bipolar illness must be predicted accurately and promptly. Recently, machine learning approaches have popularity in the healthcare industry and have exhibited encouraging results in the diagnosis of certain medical conditions. This paper offers a thorough examination of machine learning methods for bipolar disorder prediction modelling. This study's objective is to look at the potential of cutting-edge machine learning models for more accurate and reliable bipolar disorder prediction. Numerous machine learning techniques, which are not restricted to decision trees, support vector machines, random forests, logistic regression, and neural networks are utilised to develop prediction models. The models are trained and evaluated using a sizable and representative dataset that includes characteristics of those with bipolar disorder, both clinically and demographically.
  • A Novel Approach For Prediction Of Diabetes Using Genetic Algorithm And Entropy Technique
    Sudesh Rao, Sudesh Rao, Sanjeev D Kulkarni, T Chidananda, D N Disha, Rakshitha
    International Conference on Integrated Intelligence and Communication Systems Iciics 2023, 2023
    The age-old adage, “Prevention is better than Cure,” underscores the significance of early disease detection. Diabetes, a burgeoning global health concern, ranks as a leading cause of mortality and is poised to escalate in prevalence. Proactive measures are vital to curb its impact. Anticipating the risk of diabetes in future generations can facilitate early preventive actions. This paper introduces an innovative algorithm that harnesses Data Mining techniques and Genetic Algorithms for diabetes prediction. Data mining plays a pivotal role in scrutinizing the data for diabetes prognosis, enabling the reduction of unnecessary tests. The proposed algorithm streamlines the diagnostic process by trimming down the requisite attributes for prediction by using entropy technique. This not only curtails costs but also yields highly accurate predictions of 96%. By embracing this cutting-edge approach, we can revolutionize disease detection, safeguarding the health of future generations and promoting a healthier society.
  • Advances in Natural Language Processing and Deep Learning for Document Summarization
    Rakshitha, Pushpa Mohan, M B Shanthi, D N Disha, Sudesh Rao
    International Conference on Integrated Intelligence and Communication Systems Iciics 2023, 2023
  • Prediction of Bipolar Disorder Using Machine Learning Techniques
    Disha D N, Seema S, Sharada U Shenoy, Sudesh Rao
    2022 2nd International Conference on Intelligent Technologies Conit 2022, 2022
  • Pushing the Boundaries of Combinatorial Graph Isomorphism Algorithms
    Iaeng International Journal of Computer Science, 2022
  • Geofencing-based accident avoidance notification for road safety
    Bhavyashree Nayak, Priyanka S. Mugali, B. Raksha Rao, Saloni Sindhava, D. N. Disha, K. S. Swarnalatha
    Advances in Intelligent Systems and Computing, 2019
  • An efficient framework of data mining and its analytics on massive streams of big data repositories
    D N Disha, B J Sowmya, Chetan, S Seema
    2016 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2016 Proceedings, 2016

RECENT SCHOLAR PUBLICATIONS

  • Autism spectrum disorder classification using machinelearning with factor analysis
    AM Disha Devidas Nayak, Seema Shedole
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (3), pp2185 … , 2025
    2025
  • Autism spectrum disorder classification using machinelearning with factor analysis
    AM Disha Devidas Nayak, Seema Shedole
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (3), pp2185 … , 2025
    2025
  • Improving Detection of Autism Spectrum Disorder (ASD) by Using mRMR Feature Selection and Genetic Optimization Based CES Model for Computing Autism Severity Score
    DN Disha, S Seema, A Mathur
    SN Computer Science 6 (4), 309 , 2025
    2025
  • Data analytics and cognitive computing for digital health: A generic approach and a review of emerging technologies, challenges, and research directions
    KA Shastry, DN Disha
    Explainable AI in Healthcare Imaging for Medical Diagnoses, 477-501 , 2025
    2025
  • Revolutionizing Healthcare Triage: A Comparative Analysis of Machine Learning-Driven Symptom Checkers and Triage Bots for Common Diseases and Skin Conditions
    DN Disha, S Rao, M Bhat, K Pai, A Pandit
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
    Citations: 3
  • Enhancing Road Safety: A Deep Learning Approach to Accurate Traffic Sign Classification and its Implications for Autonomous Vehicles
    DN Disha, S Rao, CS Soumya, A Ram, A Rao
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
  • Unveiling Media Bias: Investigating Influences, Politics, and Solutions through Advanced Classification Techniques
    AK Kudva, DN Disha, CS Soumya
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
    Citations: 1
  • TechAsanaAdvancer Technical Mastery of Yoga Poses with AI
    S Rao, BA Dinamani
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
    Citations: 1
  • TechAsanaAdvancer Technical Mastery of Yoga Poses with AI," 2024 Second International Conference on Advances in Information Technology (ICAIT)
    DDNR S. Rao, Nishmitha, B. A. Dinamani, C. T
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
  • Aid for Visually Challenged People
    GK Dayananda, SS JG, M Vayusutha, S Acharya, S Shenoy, YR Rai, ...
    2024 IEEE International Conference for Women in Innovation, Technology … , 2024
    2024
    Citations: 1
  • Predictive Modelling of Bipolar Disorder Utilizing Advanced Machine Learning Techniques
    DN Disha, S Seema, CS Soumya
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-6 , 2023
    2023
  • Advances in Natural Language Processing and Deep Learning for Document Summarization
    P Mohan, MB Shanthi, DN Disha, S Rao
    2023 International Conference on Integrated Intelligence and Communication … , 2023
    2023
    Citations: 4
  • A Novel Approach For Prediction Of Diabetes Using Genetic Algorithm And Entropy Technique
    S Rao, SD Kulkarni, T Chidananda, DN Disha
    2023 International Conference on Integrated Intelligence and Communication … , 2023
    2023
  • HYBRID SUMMARIZATION TECHNIQUES FOR SINGLE OR MULTIPLE DOCUMENTS USING ENSEMBLE DEEP LEARNING TECHNIQUES
    S Rakshitha, P Mohan, MB Shanthi, DN Disha, S Rao
    Authorea Preprints , 2023
    2023
  • DIFFERENT APPROACHES TO CLASSIFY AND PREDICT THE DIABETES OF OUR CURRENT AND FUTURE GENERATION
    S Rao, T Chiddananda, DN Disha
    Lat. Am. J. Pharm 42, 3 , 2023
    2023
  • Prediction of Bipolar Disorder Using Machine Learning Techniques
    SR Disha D N, Seema, S, Sharada U shenoy
    International Conference on Intelligent Technologies (CONIT) , 2022
    2022
    Citations: 4
  • Prediction of Autism in children with down’s syndrome using machine learning algorithms
    DN Disha, S Seema, KA Shastry
    Recent Advances in Artificial Intelligence and Data Engineering: Select … , 2021
    2021
    Citations: 1
  • Geofencing-based accident avoidance notification for road safety
    B Nayak, PS Mugali, BR Rao, S Sindhava, DN Disha, KS Swarnalatha
    Emerging Research in Computing, Information, Communication and Applications … , 2019
    2019
    Citations: 11
  • DEDUPLICATION IN CLOUD COMPUTING USING HYBRID CLOUD
    CHR Disha D N
    International Journal of Computer Science and Engineering (IJCSE) 6 (2), 1-10 , 2017
    2017
  • An efficient framework of data mining and its analytics on massive streams of big data repositories
    DN Disha, BJ Sowmya, S Seema
    2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics … , 2016
    2016
    Citations: 9

MOST CITED SCHOLAR PUBLICATIONS

  • Geofencing-based accident avoidance notification for road safety
    B Nayak, PS Mugali, BR Rao, S Sindhava, DN Disha, KS Swarnalatha
    Emerging Research in Computing, Information, Communication and Applications … , 2019
    2019
    Citations: 11
  • An efficient framework of data mining and its analytics on massive streams of big data repositories
    DN Disha, BJ Sowmya, S Seema
    2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics … , 2016
    2016
    Citations: 9
  • Advances in Natural Language Processing and Deep Learning for Document Summarization
    P Mohan, MB Shanthi, DN Disha, S Rao
    2023 International Conference on Integrated Intelligence and Communication … , 2023
    2023
    Citations: 4
  • Prediction of Bipolar Disorder Using Machine Learning Techniques
    SR Disha D N, Seema, S, Sharada U shenoy
    International Conference on Intelligent Technologies (CONIT) , 2022
    2022
    Citations: 4
  • Revolutionizing Healthcare Triage: A Comparative Analysis of Machine Learning-Driven Symptom Checkers and Triage Bots for Common Diseases and Skin Conditions
    DN Disha, S Rao, M Bhat, K Pai, A Pandit
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
    Citations: 3
  • Phishing & Anti-Phishing: A Review
    DN Disha, NB Rachana, NSG Kumari Deepika
    Int. J. Eng. Tech. Res.(IJETR) 2, 278-283 , 2014
    2014
    Citations: 2
  • Unveiling Media Bias: Investigating Influences, Politics, and Solutions through Advanced Classification Techniques
    AK Kudva, DN Disha, CS Soumya
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
    Citations: 1
  • TechAsanaAdvancer Technical Mastery of Yoga Poses with AI
    S Rao, BA Dinamani
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
    Citations: 1
  • Aid for Visually Challenged People
    GK Dayananda, SS JG, M Vayusutha, S Acharya, S Shenoy, YR Rai, ...
    2024 IEEE International Conference for Women in Innovation, Technology … , 2024
    2024
    Citations: 1
  • Prediction of Autism in children with down’s syndrome using machine learning algorithms
    DN Disha, S Seema, KA Shastry
    Recent Advances in Artificial Intelligence and Data Engineering: Select … , 2021
    2021
    Citations: 1
  • Autism spectrum disorder classification using machinelearning with factor analysis
    AM Disha Devidas Nayak, Seema Shedole
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (3), pp2185 … , 2025
    2025
  • Autism spectrum disorder classification using machinelearning with factor analysis
    AM Disha Devidas Nayak, Seema Shedole
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (3), pp2185 … , 2025
    2025
  • Improving Detection of Autism Spectrum Disorder (ASD) by Using mRMR Feature Selection and Genetic Optimization Based CES Model for Computing Autism Severity Score
    DN Disha, S Seema, A Mathur
    SN Computer Science 6 (4), 309 , 2025
    2025
  • Data analytics and cognitive computing for digital health: A generic approach and a review of emerging technologies, challenges, and research directions
    KA Shastry, DN Disha
    Explainable AI in Healthcare Imaging for Medical Diagnoses, 477-501 , 2025
    2025
  • Enhancing Road Safety: A Deep Learning Approach to Accurate Traffic Sign Classification and its Implications for Autonomous Vehicles
    DN Disha, S Rao, CS Soumya, A Ram, A Rao
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
  • TechAsanaAdvancer Technical Mastery of Yoga Poses with AI," 2024 Second International Conference on Advances in Information Technology (ICAIT)
    DDNR S. Rao, Nishmitha, B. A. Dinamani, C. T
    2024 Second International Conference on Advances in Information Technology … , 2024
    2024
  • Predictive Modelling of Bipolar Disorder Utilizing Advanced Machine Learning Techniques
    DN Disha, S Seema, CS Soumya
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-6 , 2023
    2023
  • A Novel Approach For Prediction Of Diabetes Using Genetic Algorithm And Entropy Technique
    S Rao, SD Kulkarni, T Chidananda, DN Disha
    2023 International Conference on Integrated Intelligence and Communication … , 2023
    2023
  • HYBRID SUMMARIZATION TECHNIQUES FOR SINGLE OR MULTIPLE DOCUMENTS USING ENSEMBLE DEEP LEARNING TECHNIQUES
    S Rakshitha, P Mohan, MB Shanthi, DN Disha, S Rao
    Authorea Preprints , 2023
    2023
  • DIFFERENT APPROACHES TO CLASSIFY AND PREDICT THE DIABETES OF OUR CURRENT AND FUTURE GENERATION
    S Rao, T Chiddananda, DN Disha
    Lat. Am. J. Pharm 42, 3 , 2023
    2023