Dr. Muskan Chawla

@chitkara.edu.in

Assistant Professor / DCSE
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, india

Dr. Muskan Chawla


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https://researchid.co/muskanchawla07
Dr. Muskan Chawla is a distinguished researcher, academician, and innovator in Computer Science and Engineering at Chitkara University, Punjab, India. She holds a Ph.D. and Master’s degree in Computer Science and has specialized expertise in Artificial Intelligence, Machine Learning, Internet of Things (IoT), assistive technologies, and rehabilitation systems. Her research primarily focuses on developing advanced AI-driven solutions for communication disorders, emotional analysis, and inclusive technologies for individuals with special . Chawla has an impressive portfolio of publications in SCI and Scopus-indexed journals, covering topics such as social communication disorder screening, deep learning applications, augmented reality interventions, and federated learning in healthcare. Her work demonstrates a strong commitment to using emerging technologies for social impact and healthcare improvement.

RESEARCH, TEACHING, or OTHER INTERESTS

Psychiatry and Mental health, Rehabilitation, Human-Computer Interaction, Computer Engineering
16

Scopus Publications

33

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Communication Disorders and Artificial Intelligence: A Short Biblio-metric Review
    Muskan Chawla, Surya Narayan Panda, VIKAS KHULLAR
    Disability Cbr and Inclusive Development, 2025
  • Deep learning and robotics enabled approach for audio based emotional pragmatics deficits identification in social communication disorders
    Muskan Chawla, Surya Narayan Panda, Vikas Khullar
    Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine, 2025
    The aim of this study is to develop Deep Learning (DL) enabled robotic systems to identify audio-based emotional pragmatics deficits in individuals with social pragmatic communication deficits. The novelty of the work stems from its integration of deep learning with a robotics platform for identifying emotional pragmatics deficits. In this study, the proposed methodology utilizes the implementation of machine and DL-based classification techniques, which have been applied to a collection of open-source datasets to identify audio emotions. The application of pre-processing and converting audio signals of different emotions utilizing Mel-Frequency Cepstral Coefficients (MFCC) resulted in improved emotion classification. The data generated using MFCC were used for the training of machine or DL models. The trained models were then tested on a randomly selected dataset. DL has been proven to be more effective in the identification of emotions using robotic structure. As the data generated by MFCC is of a single dimension, therefore, one-dimensional DL algorithms, such as 1D-Convolution Neural Network, Long Short-Term Memory, and Bidirectional-Long Short-Term Memory, were utilized. In comparison to other algorithms, bidirectional Long Short-Term Memory model has resulted in higher accuracy (96.24%), loss (0.2524 in value), precision (92.87%), and recall (92.87%) in comparison to other machine and DL algorithms. Further, the proposed model was deployed on the robotic structure for real-time detection for improvement of social-emotional pragmatic responses in individuals with deficits. The approach can serve as a potential tool for the individuals with pragmatic communication deficits.
  • Augmented Reality Based Learning Intervention for Impaired Individuals
    Muskan Chawla, Suhasini Monga, Sanya
    2025 IEEE 4th World Conference on Applied Intelligence and Computing Aic 2025, 2025
    Inclusive education method not only limits the abilities of disabled individuals, but it also does not use the valuable point of view that can make learning better for all individuals. Though inclusive education aims to establish fair learning settings for every individual, who need flexible, individualized instruction to successfully interact with the curriculum may find the approach disadvantageous. Individuals might be physically present in the classroom but excluded from meaningful engagement and learning without adaptive pedagogies, assistive technologies, or tailored support measures. The aim of the paper is to develop and explore Augmented Reality (AR) technology applications to enhance educational opportunities for individuals with visual, auditory, cognitive, and motor disabilities. The proposed system has been designed considering the individuals with communication deficits. The system has been tested on the pre and post intervention performance of the individuals. The prototype application has been evaluated on usability, engagement, and learning results. The findings reveal that in comparison to conventional teaching methods, individual knowledge, memory retention, and motivation enhanced by utilizing the AR based learning. The intervention was found to be 91 % effective. The study underlines how, by lowering need on human support, augmented reality might enable autonomous learning and increase self-confidence in persons with communication impairments. The suggested AR-based approach underlines the need of more research on customizing new technologies for underprivileged populations and hints to an interesting future for inclusive education.
  • Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection
    Muskan Chawla, Surya Narayan Panda, Vikas Khullar, Isha Kansal, Rajeev Kumar
    Recent Advances in Electrical and Electronic Engineering, 2025
    Emotion recognition, both verbal and non-verbal, is a crucial component of artificial intelligence, psychology, and human-computer interaction. Emotion recognition is an integral component that significantly contributes to the improvement of communication and interaction. The research endeavors to conduct a thorough analysis and synthesis of the most recent developments in Deep Learning (DL) and Machine Learning (ML) techniques. Specifically, the study concentrates on the recognition of both verbal and non-verbal emotions. In contrast to previous research concentrated on verbal or non-verbal emotion detection separately, the study attempts to reconcile the gap between the two by demonstrating how ML and DL can be utilized effectively to detect emotions. The study also examines new methods, including multimodal data and integration of contextual information. Additionally, the research has examined the ethical implications and difficulties associated with emotion detection technologies. Findings have also revealed the wide-reaching implications for various sectors, including healthcare, education, customer service, and entertainment, where comprehending human emotions plays a crucial role in enhancing user experience and outcomes. In conclusion, the study provides invaluable knowledge to practitioners and researchers, which may facilitate the development of more advanced and accurate systems.
  • SMILEY—assistive application to support social and emotional skills in SPCD individuals
    Muskan Chawla, Surya Narayan Panda, Vikas Khullar
    Medical and Biological Engineering and Computing, 2024
  • A lightweight and privacy preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases
    Muskan Chawla, Surya Narayan Panda, Vikas Khullar, Sushil Kumar, Shyama Barna Bhattacharjee
    Measurement Sensors, 2024
    The lack of vocal emotional expression is a major deficit in social communication disorders. The current scenario of artificial intelligence focuses on collaborative training of deep learning models without losing data privacy. The primary objective of this paper is to propose a federated learning-based classification model to identify and analyze the emotional capabilities of individuals with vocal emotion deficits. The methodology has developed a collaborative and privacy-preserved approach using federated learning for training the deep learning models. The proposed methodology utilizes Mel-frequency Cepstral Coefficients (MFCC) to preprocess audio recordings. The four datasets (RAVDESS, CREMA, TESS, SAVEE) including emotion-based classified audio recordings were collected from open sources. The collected audio recordings are 3 s each and the total data set has 668376 audio files with happy - 175119 files, sad – 172611 files, angry – 176346 files, and normal - 144300 files. Further, the input audio was pre-processed to generate MFCC features. The study began with extracting features from multiple pre-trained DL models as its base model. Then, the performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. Further, this paper presents a federated deep learning-based multimodal system for verbal communication emotions classification that uses audio datasets to meet data privacy requirements by DL on the FL ecosystem. As per the findings, the federated learning trained model provides nearly similar parametric results in comparison to base model training. For IID data, the model had 99.71 % validation accuracy, precision (99.73 %), recall (99.69 %), and validation loss (0.01). The FL architecture with non-IID data outperformed these measures with validation accuracy (99.97 %), precision (99.97 %), recall (99.97 %), and least loss (0). Hence the acquired results support the utilization of federated learning ecosystem-based trained models with identically and non-identically distributed audio features from emotion identification without losing parametric results. In conclusion, the proposed techniques could be applied to identify verbal emotional deficits in individuals and could support developing emerging technological interventions for their well-being.
  • Deep learning based next word prediction aided assistive gaming technology for people with limited vocabulary
    Muskan Chawla, Surya Narayan Panda, Vikas Khullar, Kamal Deep Garg, Mohit Angurala
    Entertainment Computing, 2024
  • A bibliometric analyses on emerging trends in communication disorder
    Applied Data Science and Smart Systems, 2024
  • Mapping the psoriasis research landscape: A comprehensive bibliometric analysis from 2012-2023
    Sneha Garg, Muskan Chawla, Muskan Dixit, Arushal Sharma, Manjinder Singh, Varinder Singh, Sheikh F Ahmad, Sabry M Attia
    International Journal of Immunopathology and Pharmacology, 2024
    An extensive investigation explores the complex terrain of psoriasis, a persistent inflammatory dermatological disorder that impacts between 1% and 3% of the worldwide populace. Acknowledging the intricate interplay between environmental, genetic, and immunological influences on the etiology of psoriasis, the study utilizes sophisticated bibliometric techniques to investigate patterns, gaps in knowledge, and emergent trends within the field. The study utilizes advanced bibliometric techniques to analyze patterns, gaps in knowledge, and emerging trends in the field while acknowledging the intricate interplay between environmental, genetic, and immune-related influences on the etiology of psoriasis. An examination of 18,765 documents from December 2012 to December 2023 was conducted using machine learning techniques and the Scopus database. The explanation for conducting analysis is rooted in its capacity to provide significant perspectives on the dynamic progression of psoriasis research. The study facilitates the identification of significant subject areas, exposes patterns in publication trends, emphasizes influential authors and journals, and outlines the worldwide contributions to the field. The study demonstrates a steady and progressive increase in publications, with significant contributions from the Journal of the American Academy of Dermatology, the British Journal of Dermatology, and the Journal of the European Academy of Dermatology and Venereology. Prominent scholars in research output, such as the United States, China, and Germany, as well as authors including Feldman, Wu, Griffiths, Puig, and Reich K., are identified. Biochemistry, genetics, and molecular biology come to the forefront as esteemed fields that make substantial contributions to the study of psoriasis alongside medicine. This research highlights the interdisciplinary aspects of psoriasis by uncovering knowledge hubs and international collaborations between authors and organizations. The findings highlight the global reach of research on psoriasis and the importance of international cooperation.
  • An Improved Binomial Distribution-Based Trust Management Algorithm for Remote Patient Monitoring in WBANs
    Sunny Singh, Muskaan Chawla, Devendra Prasad, Divya Anand, Abdullah Alharbi, Wael Alosaimi
    Sustainability Switzerland, 2022
    A wireless body area network (WBAN) is a technology that is widely employed in the medical sector. It is a low-cost network that allows for mobility and variation. It can be used for long-distance, semiautonomous remote monitoring without interfering with people’s regular schedules. Detection devices are embedded in the human body in a simple WBAN configuration to continuously screen physiological boundaries or critical pointers. Confidence among shareholders (for example, medical care suppliers, clients, and medical teachers) is recognized as an essential achievement factor for data stream reliability in such an organization. Given the inherent characteristics of remote locations, it is critical to exercise confidence and security when conducting remote comprehension testing. In the present scenario, WBAN has majorly contributed towards healthcare and its application in medical services. Solid correspondence systems are frequently used to address trust and security concerns on WBANs. In terms of purpose, we present in this study a communication approach built on trust to protect the WBAN’s integrity and confidentiality. For ensuring authenticity, an enhanced bilingual distribution-based trust-management system (PDATMS) approach is used, while a cryptographic system is used to maintain anonymity. A MATLAB simulator is used to evaluate the performance of the recommended program. The recommended approach, according to the release information, improves accuracy by 96%, service delivery rate by 99%, throughput by 99%, as well as confidence, while reducing average latency.
  • Technological Intervention for Supporting Individuals for Social (Pragmatic) Communication Disorders
    Muskan Chawla, S.N. Panda, Vikas Khullar
    2022 10th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2022, 2022
  • Assistive Technologies for Individuals with Communication Disorders
    Muskan Chawla, S. N. Panda, Vikas Khullar
    2022 10th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2022, 2022
  • Challenges and Risks Associated with Public Key Infrastructure
    Muskaan, Sarvesh Tanwar, Sunny Singh
    Lecture Notes in Electrical Engineering, 2022
  • Prediction of Currency Exchange Rate: Performance Analysis Using ANN-GA and ANN-PSO
    Muskaan, Pradeepta Kumar Sarangi, Sunny Singh, Soumya Ranjan Nayak, Akash Kumar Bhoi
    Lecture Notes in Networks and Systems, 2022
  • Comparison and Performance Evaluation of Software-Defined Networking Controllers
    Shanu Bhardwaj, S. N. Panda, Muskaan, Priyanka Datta
    2020 International Conference on Emerging Smart Computing and Informatics Esci 2020, 2020
  • FOREX trend analysis using machine learning techniques: INR vs USD currency exchange rate using ANN-GA hybrid approach
    Pradeepta Kumar Sarangi, Muskaan Chawla, Pinaki Ghosh, Sunny Singh, P.K. Singh
    Materials Today Proceedings, 2020

RECENT SCHOLAR PUBLICATIONS

  • Communication Disorders and Artificial Intelligence: A Short Bibliometric Review
    M Chawla, SN Panda, V Khullar
    Disability, CBR & Inclusive Development 36 (4), 69-76 , 2025
    2025
  • Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection
    M Chawla, SN Panda, V Khullar, I Kansal, R Kumar
    Recent Advances in Electrical & Electronic Engineering 18 (7), 903-922 , 2025
    2025
  • Augmented Reality Based Learning Intervention for Impaired Individuals
    M Chawla, S Monga
    2025 IEEE 4th World Conference on Applied Intelligence and Computing (AIC … , 2025
    2025
  • Deep learning and robotics enabled approach for audio based emotional pragmatics deficits identification in social communication disorders
    M Chawla, SN Panda, V Khullar
    Proceedings of the Institution of Mechanical Engineers, Part H: Journal of … , 2025
    2025
    Citations: 1
  • SMILEY—assistive application to support social and emotional skills in SPCD individuals
    M Chawla, SN Panda, V Khullar
    Medical & Biological Engineering & Computing 62 (11), 3507-3529 , 2024
    2024
    Citations: 1
  • Mapping the psoriasis research landscape: A comprehensive bibliometric analysis from 2012-2023
    S Garg, M Chawla, M Dixit, A Sharma, M Singh, V Singh, SF Ahmad, ...
    International Journal of Immunopathology and Pharmacology 38, 03946320241290341 , 2024
    2024
    Citations: 3
  • A lightweight and privacy preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases
    M Chawla, SN Panda, V Khullar, S Kumar, SB Bhattacharjee
    Measurement: Sensors 34, 101268 , 2024
    2024
    Citations: 8
  • A bibliometric analyses on emerging trends in communication disorder
    M Chawla, SN Panda, V Khullar
    Applied Data Science and Smart Systems, 246-255 , 2024
    2024
  • Deep learning based next word prediction aided assistive gaming technology for people with limited vocabulary
    M Chawla, SN Panda, V Khullar, KD Garg, M Angurala
    Entertainment Computing 50, 100661 , 2024
    2024
    Citations: 10
  • Technological Intervention for Supporting Individuals for Social (Pragmatic) Communication Disorders
    M Chawla, SN Panda, V Khullar
    2022 10th International Conference on Reliability, Infocom Technologies and … , 2022
    2022
    Citations: 2
  • Assistive technologies for individuals with communication disorders
    M Chawla, SN Panda, V Khullar
    2022 10th international conference on reliability, Infocom technologies and … , 2022
    2022
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • Deep learning based next word prediction aided assistive gaming technology for people with limited vocabulary
    M Chawla, SN Panda, V Khullar, KD Garg, M Angurala
    Entertainment Computing 50, 100661 , 2024
    2024
    Citations: 10
  • A lightweight and privacy preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases
    M Chawla, SN Panda, V Khullar, S Kumar, SB Bhattacharjee
    Measurement: Sensors 34, 101268 , 2024
    2024
    Citations: 8
  • Assistive technologies for individuals with communication disorders
    M Chawla, SN Panda, V Khullar
    2022 10th international conference on reliability, Infocom technologies and … , 2022
    2022
    Citations: 8
  • Mapping the psoriasis research landscape: A comprehensive bibliometric analysis from 2012-2023
    S Garg, M Chawla, M Dixit, A Sharma, M Singh, V Singh, SF Ahmad, ...
    International Journal of Immunopathology and Pharmacology 38, 03946320241290341 , 2024
    2024
    Citations: 3
  • Technological Intervention for Supporting Individuals for Social (Pragmatic) Communication Disorders
    M Chawla, SN Panda, V Khullar
    2022 10th International Conference on Reliability, Infocom Technologies and … , 2022
    2022
    Citations: 2
  • Deep learning and robotics enabled approach for audio based emotional pragmatics deficits identification in social communication disorders
    M Chawla, SN Panda, V Khullar
    Proceedings of the Institution of Mechanical Engineers, Part H: Journal of … , 2025
    2025
    Citations: 1
  • SMILEY—assistive application to support social and emotional skills in SPCD individuals
    M Chawla, SN Panda, V Khullar
    Medical & Biological Engineering & Computing 62 (11), 3507-3529 , 2024
    2024
    Citations: 1
  • Communication Disorders and Artificial Intelligence: A Short Bibliometric Review
    M Chawla, SN Panda, V Khullar
    Disability, CBR & Inclusive Development 36 (4), 69-76 , 2025
    2025
  • Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection
    M Chawla, SN Panda, V Khullar, I Kansal, R Kumar
    Recent Advances in Electrical & Electronic Engineering 18 (7), 903-922 , 2025
    2025
  • Augmented Reality Based Learning Intervention for Impaired Individuals
    M Chawla, S Monga
    2025 IEEE 4th World Conference on Applied Intelligence and Computing (AIC … , 2025
    2025
  • A bibliometric analyses on emerging trends in communication disorder
    M Chawla, SN Panda, V Khullar
    Applied Data Science and Smart Systems, 246-255 , 2024
    2024