Rashi Agarwal

@kanpuruniversity.org

Head. Department of Information Technology
UIET, CSJMU, Kanpur



                          

https://researchid.co/rashiagarwal

RESEARCH INTERESTS

Computer Vision, Machine Learning, Image processing, Deep Learning

38

Scopus Publications

504

Scholar Citations

11

Scholar h-index

17

Scholar i10-index

Scopus Publications


  • NEMAEP: A NOVEL ENSEMBLE MACHINE LEARNING FRAMEWORK FOR ACCURATE EFFORT ESTIMATION IN SOFTWARE PROJECTS


  • ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization
    Vishal Awasthi, Namita Awasthi, Hemant Kumar, Shubhendra Singh, Prabal Pratap Singh, Poonam Dixit, and Rashi Agarwal
    Elsevier BV

  • Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm
    Hemant Kumar, Abhishek Dwivedi, Abhishek Kumar Mishra, Arvind Kumar Shukla, Brajesh Kumar Sharma, Rashi Agarwal, and Sunil Kumar
    Elsevier BV

  • Unmasking VGG16: LIME Visualizations for Brain Tumor Diagnosis
    Richa Tiwari and Rashi Agrawal
    IEEE
    Brain tumors are complex and potentially life-threatening conditions requiring accurate diagnosis. This study explores using VGG16 CNN architecture to classify brain tumors in MRI scans. CNNs excel in image classification but lack transparency, raising concerns in critical applications like medical diagnosis. To address this, we use Explainable AI (XAI) techniques. We enhance interpretability using Superpixel images, Heatmaps, and Image Masks to visualize important features for predictions. Our approach achieves 98.8% accuracy, with visualizations aiding in understanding the model’s decisions and providing insights for medical professionals. This research contributes to XAI in medical imaging, showing the potential of combining deep learning with interpretable methods for healthcare decision-making.



  • Can ChatGPT Help in the Awareness of Diabetes?
    Imran Khan and Rashi Agarwal
    Springer Science and Business Media LLC


  • Diabetic Retinopathy Segmentation in IDRiD using Enhanced U-Net
    Rashi Agarwal
    IEEE
    Diabetes Retinopathy is the primary reason for avoidable vision loss, especially among people of working age. Diabetes is expected to affect 600 million people worldwide by 2040, with one-third developing Diabetic Retinopathy [1] Recent studies have highlighted the need for more cost-effective and efficient methods of diagnosing, managing, and treating retinal diseases in clinical eye care settings. Before the development of a computer-aided diagnosis tool, the difficulty of achieving an accurate early diagnosis at an affordable cost must be considered. It may be possible to expand diabetic retinopathy screening efforts and allow physicians to use their time more efficiently by utilizing computer-aided disease detection in retinal image processing. Biomedical engineers and computer scientists now have a unique opportunity to meet the demands of clinical practice, thanks to advances in edge computing and machine learning techniques. We have used Enhanced U-Net to segment typical pathological features of DR which include hard exudates (EX), soft exudates (SE), microaneurysms (MA), and haemorrhages (HE) in the Indian Diabetic Retinopathy Image Dataset (IDRiD) with better accuracy than other techniques used in this domain. The Enhanced U-Net is able to segment all the features at an early stage, hence, this method can be used to aid diagnosis of DR at an early stage, thereby preventing vision loss at later stages.

  • Incorporating Requirement Engineering into Agile Methodologies: Challenges and Proposed Solutions
    Prateek Srivastava, Nidhi Srivastava, Rashi Agarwal, and Pawan Singh
    IEEE
    Agile software development has a good track record, but only when the project size is limited. Requirement engineering (RE) is critical because “Requirements” play a critical role at each stage of the lifecycle of software. Agile supports changing customer requirements while adding value to the business, it also comes with its own set of obstacles, many of which are connected to requirement constraints. This study seeks to uncover the issues that agile project requirements engineers confront. Many research studies on requirement issues have been undertaken, all of which are compromised in some way, offer no suggestions for improving the agile development process, and fail to reveal massive agile development hurdles. This paper addresses all the issues raised above and suggests possible solutions from the standpoint of requirement engineering and results can be extremely beneficial to the software business in terms of improving development processes, as well as individuals who would like to continue working in this area.

  • Diagnosis of COVID-19 Using Deep Learning Augmented with Contour Detection on X-rays
    Rashi Agarwal and S. Hariharan
    Springer Nature Singapore

  • Integrated learning algorithms-based epileptologist assistive tool for seizure detection and prediction
    Sripada Rama Sree, Rashi Agarwal, S. Markkandan, Suraya Mubeen, Manoj Ashok Wakchaure, and Bal Krishna Saraswat
    Springer Science and Business Media LLC

  • Using Deep Learning Approach for Land-Use and Land-Cover Classification based on Satellite images
    Rashi Agarwal, Silky Goel, and Rahul Nijhawan
    IEEE
    The land cover is the apparent (bio)physical cover, and land use alludes to how the actual land type is being utilized. This research is fundamental to survey the degree to which social, monetary, and natural factors influence urbanization. This will likewise assist with urban planning. As laborious process of handcrafted feature extraction has not helped obtain high accuracies, this paper proposes use of Deep Learning approach that explores different Image Recognition Models using various ML classifiers on remote sensing images classifying the images from large Landsat satellite dataset into 9 different classes. It was observed that the highest accuracy of 97.4% was achieved by the Logistic Regression algorithm coupled with Inceptionv3 model. The proposed model shows the capability of increasing the accuracy of existing state-of-art-algorithms low resolution land classification maps. Thus, the improved results will contribute to better land maps helping with the growing demand of LULC information concerning climate change and sustainable development.

  • Human Disease Prognosis and Diagnosis Using Machine Learning
    Sunil Kumar, Harish Kumar, Rashi Agarwal, and V. K. Pathak
    Springer Nature Singapore

  • Estimation in Agile Software Development Using Artificial Intelligence
    Prateek Srivastava, Nidhi Srivastava, Rashi Agarwal, and Pawan Singh
    Springer Singapore

  • Audio-Visual Emotion Recognition System Using Multi-Modal Features
    Anand Handa, Rashi Agarwal, and Narendra Kohli
    IGI Global
    Due to the highly variant face geometry and appearances, Facial Expression Recognition (FER) is still a challenging problem. CNN can characterize 2-D signals. Therefore, for emotion recognition in a video, the authors propose a feature selection model in AlexNet architecture to extract and filter facial features automatically. Similarly, for emotion recognition in audio, the authors use a deep LSTM-RNN. Finally, they propose a probabilistic model for the fusion of audio and visual models using facial features and speech of a subject. The model combines all the extracted features and use them to train the linear SVM (Support Vector Machine) classifiers. The proposed model outperforms the other existing models and achieves state-of-the-art performance for audio, visual and fusion models. The model classifies the seven known facial expressions, namely anger, happy, surprise, fear, disgust, sad, and neutral on the eNTERFACE’05 dataset with an overall accuracy of 76.61%.

  • WEED IDENTIFICATION USING K-MEANS CLUSTERING WITH COLOR SPACES FEATURES IN MULTI-SPECTRAL IMAGES TAKEN BY UAV
    Rashi Agarwal, S Hariharan, M. Nagabhushana Rao, and Abhishek Agarwal
    IEEE
    Food security is a pertinent global challenge that has plagued the nations over time immemorial. Maize is one of the world's most significant consumption crop, based on production volume. Maize supply can vary according to the cultivation area, climatic condition, and disease. Modern precision weed management relies on site-specific management tactics to maximize resource use efficiency and yield, while reducing unintended environmental impacts caused by herbicides. Recent advancements in Unmanned Aircraft Systems (UAS)-based tools and geospatial information technology have created enormous applications for efficient and economical assessment of weed infestations as well as site-specific weed management. This paper explores the possibility of extracting features from color spaces and combining them with vegetation indices (NDVI, VARI, and TGI) to be clustered using K-means classifier to identify the weed population from a multispectral imagery. The results give a clear indication that the NDVI performance is better than VARI; It also shows that TGI is not acceptable for the classification.

  • Covid-19 Analysis using Deep Learning Methods and Computed Tomography Scans
    Rashi Agarwal, Rahul Nijhawan, Siddharth Gupta, and Silky Goel
    IEEE
    Covid-19 has quickly emerged as a global threat, tipping the world into a new phase. The delay in medical care because of the quickly rising Covid-19 cases makes it necessary to overcome the manual and time taking technique such as RTPCR. This paper implements different pre-trained CNN feature extraction models using various Machine Learning (ML) classifiers on chest CT scans to analyze Covid-19 infected patients. It may be observed from the obtained results that accuracy of 96.4% was obtained using the VGG16 model and neural network classifier. The implementation of pre-trained models and classifiers reduce the time taken for manual detection of disease and helps doctors to prevent life of a patient.


  • Incremental approach for multi-modal face expression recognition system using deep neural networks
    Anand Handa, Rashi Agarwal, and Narendra Kohli
    Inderscience Publishers

  • A multimodel keyword spotting system based on lip movement and speech features
    Anand Handa, Rashi Agarwal, and Narendra Kohli
    Springer Science and Business Media LLC
    The spoken keyword recognition and its localization are one of the fundamental aspects of speech recognition and known as keyword spotting. In automatic keyword spotting systems, the Lip-reading (LR) methods have a broader role when audio data is not present or has corrupted information. The available works from the literature have focussed on recognizing a limited number of words or phrases and require the cropped region of face or lip. Whereas the proposed model does not require the cropping of the video frames and it is recognition free. The proposed model is utilizing Convolutional Neural Networks and Long Short Term Memory networks to improve the overall performance. The model creates a 128-dimensional subspace to represent the feature vectors for speech signals and corresponding lip movements (focused viseme sequences). Thus the proposed model can tackle lip reading as an unconstrained natural speech signal in the video sequences. In the experiments, different standard datasets as LRW (Oxford-BBC), MIRACL-VC1, OuluVS, GRID, and CUAVE are used for the evaluation of the proposed model. The experiments also have a comparative analysis of the proposed model with current state-of-the-art methods for Lip-Reading task and keyword spotting task. The proposed model obtain excellent results for all datasets under consideration.

  • Facial expression recognition using local binary pattern and modified hidden Markov model
    Mayur Rahul, Narendra Kohli, and Rashi Agarwal
    Inderscience Publishers

  • A comprehensive video dataset for multi-modal recognition systems
    Anand Handa, Rashi Agarwal, and Narendra Kohli
    Ubiquity Press, Ltd.
    This paper presents a comprehensive, highly defined and fully labelled video dataset. This dataset consists of videos related to 67 different subjects. The videos contain similar text and the text contains digits from 1 to 20 recited by 67 different subjects using the same experimental setup. This dataset can be used as a unique resource for researchers and analysts for training deep neural networks to build highly efficient and accurate recognition models in various domains of computer vision such as face recognition model, expression recognition model, speech recognition model, text recognition, etc. In this paper, we also train models related to face recognition and speech recognition on our dataset and also compare the results with the publically available datasets to show the effectiveness of our dataset. The experimental results show that our comprehensive dataset is more accurate than other dataset on which the models are tested.

  • Facial expression recognition using geometric features and modified hidden Markov model
    Mayur Rahul, Narendra Kohli, Rashi Agarwal, and Sanju Mishra
    Inderscience Publishers

RECENT SCHOLAR PUBLICATIONS

  • Bio-inspired gloden jackal optimization of XGBoost model enhances 30-day sepsis mortality predictions
    H Kumar, R Agarwal, A Yadav
    Journal of critical care 87, 155013 2025

  • A Hybrid EfficientNetB0-XGBoost Framework for Efficient Brain Tumor Classification Using MRI Images
    H Kumar, D Pandey, A Yadav, R Agarwal, S Tripathi, SK Malhotra
    Interdisciplinary Approaches to AI, Internet of Everything, and Machine 2025

  • NEMAEP: A NOVEL ENSEMBLE MACHINE LEARNING FRAMEWORK FOR ACCURATE EFFORT ESTIMATION IN SOFTWARE PROJECTS
    P SRIVASTAVA, N SRIVASTAVA, R AGARWAL, P SINGH
    Journal of Theoretical and Applied Information Technology 102 (24) 2024

  • ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization
    V Awasthi, N Awasthi, H Kumar, S Singh, PP Singh, P Dixit, R Agarwal
    MethodsX 13, 103018 2024

  • Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm
    H Kumar, A Dwivedi, AK Mishra, AK Shukla, BK Sharma, R Agarwal, ...
    MethodsX 13, 102839 2024

  • Unmasking VGG16: LIME Visualizations for Brain Tumor Diagnosis
    R Tiwari, R Agrawal
    2024 IEEE International Conference on Computer Vision and Machine 2024

  • An Insight on Artificial Intelligence (AI) and Internet of things (IOT) driven Hydroponics farming
    NS Bhandari, N Bhandari, R Agarwal, PK Sharma
    2024 5th International Conference on Image Processing and Capsule Networks 2024

  • Advances in Deep Learning for the Detection of Alzheimer’s Disease Using MRI—A Review
    S Hariharan, R Agarwal
    Computational Intelligence in Healthcare Informatics, 363-388 2024

  • Tomato Disease Detection Using Vision Transformer with Residual L1-Norm Attention and Deep Neural Networks
    M Tiwari, H Kumar, N Prakash, S Kumar, R Neware, S Tripathi, R Agarwal
    International Journal of Intelligent Engineering & Systems 17 (1) 2024

  • Explainable Bayesian-Optimized XGBoost Model for Component Failure Detection in Predictive Maintenance
    H Kumar, KK Bhartiy, D Dhabliya, R Agarwal, S Kumar, S Tripathi
    AI and Machine Learning Impacts in Intelligent Supply Chain, 137-155 2024

  • Incorporating Requirement Engineering into Agile Methodologies: Challenges and Proposed Solutions
    P Srivastava, N Srivastava, R Agarwal, P Singh
    2023 9th International Conference on Signal Processing and Communication 2023

  • Diabetic retinopathy segmentation in IDRiD using enhanced U-Net
    R Agarwal
    2023 International Conference on Ambient Intelligence, Knowledge Informatics 2023

  • Detection of Brain Tumor from MRI Samples Using Deep Learning Algorithms
    R Agarwal, P Sarma, N Dev, PP Mazumder
    2023 First International Conference on Advances in Electrical, Electronics 2023

  • Chaos cryptosystem with optimal key selection for image encryption
    S Khaitan, S Sagar, R Agarwal
    Multimedia Tools and Applications 82 (25), 39653-39668 2023

  • Can ChatGPT help in the awareness of diabetes?
    I Khan, R Agarwal
    Annals of biomedical engineering 51 (10), 2125-2129 2023

  • Software development estimation using soft computing techniques
    P Srivastava, N Srivastava, R Agarwal, P Singh
    Emerging Trends in IoT and Computing Technologies, 321-325 2023

  • Diagnosis of COVID-19 Using Deep Learning Augmented with Contour Detection on X-rays
    R Agarwal, S Hariharan
    Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, 191-204 2023

  • An Intelligent Framework for Estimating Software Development Projects using Machine Learning
    P Srivastava, N Srivastava, R Agarwal, P Singh
    Int. J. Recent Innov. Trends Comput. Commun. 11, 160-169 2023

  • Integrated learning algorithms-based epileptologist assistive tool for seizure detection and prediction
    SR Sree, R Agarwal, S Markkandan, S Mubeen, MA Wakchaure, ...
    Soft Computing, 1-10 2023

  • Transfer Learning and Supervised Machine Learning Approach for Detection of Skin Cancer: Performance Analysis and Comparison
    SK H Kumar, A Virmani, S Tripathi, R Agrawal
    DCTH - Drugs and Cells Therapies in Hematology 10 (1), 1845 - 1860 2023

MOST CITED SCHOLAR PUBLICATIONS

  • Effect of stochastic noise on superior Julia sets
    M Rani, R Agarwal
    Journal of Mathematical Imaging and Vision 36, 63-68 2010
    Citations: 60

  • Detection of coal mine fires in the Jharia coal field using NOAA/AVHRR data
    R Agarwal, D Singh, DS Chauhan, KP Singh
    Journal of Geophysics and engineering 3 (3), 212-218 2006
    Citations: 51

  • Facial expression recognition using geometric features and modified hidden Markov model
    M Rahul, N Kohli, R Agarwal, S Mishra
    International Journal of Grid and Utility Computing 10 (5), 488-496 2019
    Citations: 41

  • Antibacterial finish of textile using papaya peels derived silver nanoparticles
    R Agarwal, N Garg, SR Kashyap, RP Chauhan
    Indian Journal of Fibre & Textile Research (IJFTR) 40 (1), 105-107 2015
    Citations: 21

  • Nano surface modification of poly (ethylene terephthalate) fabrics for enhanced comfort properties for activewear
    R Agarwal, M Jassal, AK Agrawal
    Journal of Industrial and Engineering Chemistry 98, 217-230 2021
    Citations: 19

  • Weed identification using K-means clustering with color spaces features in multi-spectral images taken by UAV
    R Agarwal, S Hariharan, MN Rao, A Agarwal
    2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 7047 2021
    Citations: 18

  • Durable functionalization of polyethylene terephthalate fabrics using metal oxides nanoparticles
    R Agarwal, M Jassal, AK Agrawal
    Colloids and Surfaces A: Physicochemical and Engineering Aspects 615, 126223 2021
    Citations: 15

  • Human disease prognosis and diagnosis using machine learning
    S Kumar, H Kumar, R Agarwal, VK Pathak
    Emerging Technologies for Computing, Communication and Smart Cities 2022
    Citations: 14

  • Facial expression recognition using local binary pattern and modified hidden Markov model
    M Rahul, N Kohli, R Agarwal
    International Journal of Advanced Intelligence Paradigms 17 (3-4), 367-378 2020
    Citations: 12

  • An approach for congestion control in mobile ad hoc networks
    S Sharma, D Jindal, R Agarwal
    International Journal of Emerging Trends in Engineering and Development 3 (7 2017
    Citations: 12

  • Can ChatGPT help in the awareness of diabetes?
    I Khan, R Agarwal
    Annals of biomedical engineering 51 (10), 2125-2129 2023
    Citations: 11

  • Analysing mobile random early detection for congestion control in mobile ad-hoc network
    S Sharma, D Jindal, R Agarwal
    International Journal of Electrical and Computer Engineering 8 (3), 1305 2018
    Citations: 11

  • A review and a comparative study of various plant recognition and classification techniques using leaf images
    A Handa, R Agarwal
    International Journal of Computer Applications 123 (2), 20-25 2015
    Citations: 11

  • Bit plane average filtering to remove Gaussian noise from high contrast images
    R Agarwal
    2012 International Conference on Computer Communication and Informatics, 1-5 2012
    Citations: 11

  • Minimum relevant features to obtain ai explainable system for predicting breast cancer in WDBC
    R Agarwal, M Revanth
    International Journal of Health Sciences 6 (!X) 2022
    Citations: 10

  • A multimodel keyword spotting system based on lip movement and speech features
    A Handa, R Agarwal, N Kohli
    Multimedia Tools and Applications 79 (27), 20461-20481 2020
    Citations: 10

  • An easy method for leaf area estimation based on digital images
    M Jadon, R Agarwal, R Singh
    2016 International Conference on Computational Techniques in Information and 2016
    Citations: 10

  • Bit planes histogram equalization for tone mapping of high contrast images
    R Agarwal
    2011 Eighth International Conference Computer Graphics, Imaging and 2011
    Citations: 9

  • Incremental approach for multi-modal face expression recognition system using deep neural networks
    A Handa, R Agarwal, N Kohli
    International journal of computational vision and robotics 11 (1), 1-20 2021
    Citations: 8

  • A survey of face recognition techniques and comparative study of various bi-modal and multi-modal techniques
    A Handa, R Agarwal, N Kohli
    2016 11th International Conference on Industrial and Information Systems 2016
    Citations: 8