Born in 1969 at Indore a city in Madhya Pradesh, a central India Province, Ms. Sunita has brought up and educated in the same city. She obtained B.E. (Electronics & Telecommunication, 1991) and M.E. (Computer Engineering, 1998) from a very reputed Engineering college, Shri. G.S. Institute of Technology and Science, Indore. She had been awarded Doctoral degree from the local university named as Devi Ahilya university, Indore in 2013.
At present she is working as an Professor and head in the Department of Information Tehnology at Shri. G.S. Institute of Technology and science, Indore. Her total teaching experience, however, is about thirty years. She has taught to undergraduate and post graduate classes, subjects like computer networks, Mobile Computing, Cloud Computing, Big Data anayltics etc. Her field of interest is Mobile Computing and Communication, Cloud Computing, Big Data Science and Artificial Intelligence She has supervised the various research projects of UG, PG stu
EDUCATION
Ph.D. in Computer Engineering
RESEARCH INTERESTS
Cloud Computing, Big Data, Data Science, Artificial Intelligence
Detecting Abnormal Phosphorylation in Leukemia Using Deep Learning and Mass Spectrometry Shraddha Kumar, Anuradha Purohit, Sunita Varma 2025 IEEE International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2025, 2025 Leukemia (medical term referring to blood cancer) encompasses various malignancies affecting blood-forming tissues, with multiple genetic and epigenetic disruptions underpinning disease onset and progression. Central to these disruptions are post-translational modifications (PTMs), particularly phosphorylation, which fine-tune protein function and signaling networks. This paper explores the critical role of phosphorylation and other post-translational modifications (PTMs) in leukemia, development and progression. We emphasize how abnormal phosphorylation, particularly through dysregulated kinase signaling, acts as a hallmark of leukemic transformation and a cornerstone for targeted therapy. Furthermore, we develop a Hybrid CNN-Transformer model that combines the power of CNNs for local feature extraction and transformers for capturing long-range dependencies, making it highly suited for complex sequence tasks such as phosphorylation detection from mass spectrometry data. The proposed deep learning model detects abnormal phosphorylation in leukemia, offering a roadmap for personalized medicine approaches in leukemia treatment.
Secure internet of medical things based electronic health records scheme in trust decentralized loop federated learning consensus blockchain Megha Kuliha, Sunita Verma International Journal of Intelligent Networks, 2024 Electronic Health Records (EHRs) have become an increasingly significant source of information for healthcare professionals and researchers. Two technical challenges are addressed: motivating federated learning members to contribute their time and effort, and ensuring accurate aggregation of the global model by the centralized federated learning server. To overcome these issues and establish a decentralized solution, the integration of blockchain and federated learning proves effective, offering enhanced security and privacy for smart healthcare. The proposed approach includes a gamified element to incentivize and recognize contributions from federated learning members. This research work offers a solution involving resource management within the Internet of Medical Things (IoMT) using a newly proposed trust decentralized loop federated learning consensus blockchain. The obtained raw data is pre-processed by using handling missing values and adaptive min-max normalization. The appropriate features are selected with the aid of hybrid weighted-leader exponential distribution optimization algorithm. Because, data with multiple features exhibits varying levels of variation across each feature. The selected features are then forwarded to the training phase through the proposed pyramid squeeze attention generative adversarial networks to classify the EHR as positive and negative. The proposed classification model demonstrates high flexibility and scalability, making it applicable to a wide range of network architectures for various computer vision tasks. The introduced model provides better outcomes in terms of 98.5% in the training accuracy and 99% in the validation accuracy over Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which is more efficient than the other traditional methods.
TMT-Labeled Annotated Spectra Prediction using Deep Neural Network Shraddha Kumar, Anuradha Purohit, Sunita Varma Proceedings 2024 IEEE 16th International Conference on Communication Systems and Network Technologies Cicn 2024, 2024 Peptides offer a promising avenue for the treatment and prevention of cancer by enabling highly targeted, effective, and safe approaches. Identification of peptides is mainly done through database searching and spectral library matching methods. However, database size and spectrum complexity respectively limit the effectiveness of peptide identification. The competency to precisely predict spectra from peptide sequences can strengthen peptide identification and protein quantification in proteomics. This work introduces a Deep Neural Network (DNN) designed to predict Tandem Mass Tag (TMT)-labeled annotated spectra to support peptide identification experiments in proteomics. The proposed DNN model was trained on the COREAD (COloREctal cAncer cell lines Data) experimental datasets. The model is trained on the annotated spectra in which TMT peaks are preserved. It incorporates TMT modifications, allowing them to predict spectra with annotated peptides. The architecture uses a sequence-to-sequence model based on a Convolution Recurrent Neural Network with Attention mechanism (CRNNA), capturing correlations between peptide sequences and their spectra. The model's performance is evaluated using cosine similarity between predicted and experimental spectra, showing remarkable performance in spectrum prediction to identify peptides that were not identified earlier from the data. The study emphasizes the utility of machine learning, particularly deep learning, for spectral prediction and the importance of accurately predicting TMT-labeled annotated spectra in proteomics.
Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM Upendra Singh, Puja Gupta, Mukul Shukla, Varsha Sharma, Sunita Varma, Sumit Kumar Sharma Concurrency and Computation Practice and Experience, 2023 Recognizing patient activity in real-time from video or images collected by a CCTV camera available in the hospital during a Covid-19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre-trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre-trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID-19 patients as input and looks for matches inside the hold-on images.
Cancer Prediction Using Random Forest and Deep Learning Techniques Muskan Ranjan, Akanksha Shukla, Kartik Soni, Sunita Varma, Megha Kuliha, Upendra Singh Proceedings 2022 IEEE 11th International Conference on Communication Systems and Network Technologies Csnt 2022, 2022
Detecting Abnormal Phosphorylation in Leukemia Using Deep Learning and Mass Spectrometry S Kumar, A Purohit, S Varma 2025 IEEE International Conference on Advances in Computing Research On … , 2025 2025
Leveraging Vision Transformers for Early Detection of Oral Cancer: A Deep Learning Approach to Medical Imaging M Soni, S Negi, S Varma, A Jain, A Parihar International Conference on Intelligent and Fuzzy Systems, 472-487 , 2025 2025
TMT-Labeled Annotated Spectra Prediction using Deep Neural Network S Kumar, A Purohit, S Varma 2024 IEEE 16th International Conference on Computational Intelligence and … , 2024 2024
Classify-Imbalance Data Sets in IoT Framework of Agriculture Field with Multivariate Sensors Using Centroid-Based Oversampling Method: N. Bhatt, S. Varma N Bhatt, S Varma National Academy Science Letters 46 (6), 585-590 , 2023 2023 Citations: 3
Intelligent Security System Based on the Internet of Things (IoT) P Gupta, S Varma, N Arya, R Bhagel Intelligent Sensor Node-Based Systems, 177-191 , 2023 2023 Citations: 1
A comprehensive survey on network resource management in SDN enabled data centre network A Sharma, S Tokekar, S Varma 6G Enabled Fog Computing in IoT: Applications and Opportunities, 333-353 , 2023 2023 Citations: 3
Study of cloud providers (azure, amazon, and oracle) according to service availability and price A Rajput, P Gupta, P Ghodeshwar, S Varma, KK Sharma, U Singh 2023 3rd International conference on pervasive computing and social … , 2023 2023 Citations: 9
A reliable method for detecting false alarm notification in vanet P Shukla, R Patel, S Varma 2023 International Conference on Sustainable Computing and Smart Systems … , 2023 2023 Citations: 3
An enhanced light GBM model with data analytical approach for crop recommendation N Bhatt, S Varma 2023 Second international conference on electronics and renewable systems … , 2023 2023 Citations: 8
Recommendation system for crops integrating with specific soil parameters by machine learning techniques N Bhatt, S Varma 2023 IEEE International Students' Conference on Electrical, Electronics and … , 2023 2023 Citations: 5
A Reliable Method for Establishing a Common Time synchronization in Mobile Ad Hoc Networks in VANET. P Shukla, R Patel, S Varma 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023 Citations: 4
Meta-reinforcement learning based resource management in software defined networks using bayesian network A Sharma, S Tokekar, S Varma 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023 Citations: 7
Review of spectral clustering algorithms used in proteomics S Kumar, A Purohit, S Varma International Journal of Data Science 8 (1), 16-38 , 2023 2023 Citations: 1
Effective congestion control mechanism for smart vehicles using edge computing in VANET P Shukla, S Varma, R Petel Autonomous Vehicles Volume 2: Smart Vehicles, 105-121 , 2022 2022 Citations: 2
Analysis of crowd features based on deep learning P Gupta, V Sharma, S Varma 2022 international conference on automation, computing and renewable systems … , 2022 2022 Citations: 5
An Algorithm for Counting People using Dense Nets and Feature Fusion P Gupta, V Sharma, S Varma 2022 4th International Conference on Inventive Research in Computing … , 2022 2022 Citations: 1
Cancer prediction using random forest and deep learning techniques M Ranjan, A Shukla, K Soni, S Varma, M Kuliha, U Singh 2022 IEEE 11th International Conference on Communication Systems and Network … , 2022 2022 Citations: 13
Adaptive Load Balancing Scheme for Software-Defined Networks Using Fuzzy Logic Based Dynamic Clustering A Sharma, S Tokekar, S Varma Sustainable Communication Networks and Application: Proceedings of ICSCN … , 2022 2022 Citations: 2
The future of Internet of Vehicle: Challenges and applications R Dhanare, KK Nagwanshi, S Varma, S Pathak 2021 International Conference on Computational Performance Evaluation (ComPE … , 2021 2021 Citations: 7
Blockchain Architecture to Meet Challenges in Management of Electronic Health Records in IoT based Healthcare Systems M Arif, M Kuliha, S Sunita Varma CS & IT Conference Proceedings 11 (12) , 2021 2021
MOST CITED SCHOLAR PUBLICATIONS
Development and design strategies of evidence collection framework in cloud environment Y Khan, S Varma Social Networking and Computational Intelligence: Proceedings of SCI-2018, 27-37 , 2020 2020.0 Citations: 15
Cancer prediction using random forest and deep learning techniques M Ranjan, A Shukla, K Soni, S Varma, M Kuliha, U Singh 2022 IEEE 11th International Conference on Communication Systems and Network … , 2022 2022.0 Citations: 13
Study of cloud providers (azure, amazon, and oracle) according to service availability and price A Rajput, P Gupta, P Ghodeshwar, S Varma, KK Sharma, U Singh 2023 3rd International conference on pervasive computing and social … , 2023 2023.0 Citations: 9
An enhanced light GBM model with data analytical approach for crop recommendation N Bhatt, S Varma 2023 Second international conference on electronics and renewable systems … , 2023 2023.0 Citations: 8
Meta-reinforcement learning based resource management in software defined networks using bayesian network A Sharma, S Tokekar, S Varma 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023.0 Citations: 7
The future of Internet of Vehicle: Challenges and applications R Dhanare, KK Nagwanshi, S Varma, S Pathak 2021 International Conference on Computational Performance Evaluation (ComPE … , 2021 2021.0 Citations: 7
An efficient cloud forensic approach for IaaS, SaaS and PaaS model Y Khan, S Varma 2nd International Conference on Data, Engineering and Applications (IDEA), 1-6 , 2020 2020.0 Citations: 7
Recommendation system for crops integrating with specific soil parameters by machine learning techniques N Bhatt, S Varma 2023 IEEE International Students' Conference on Electrical, Electronics and … , 2023 2023.0 Citations: 5
Analysis of crowd features based on deep learning P Gupta, V Sharma, S Varma 2022 international conference on automation, computing and renewable systems … , 2022 2022.0 Citations: 5
A Reliable Method for Establishing a Common Time synchronization in Mobile Ad Hoc Networks in VANET. P Shukla, R Patel, S Varma 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023.0 Citations: 4
Classify-Imbalance Data Sets in IoT Framework of Agriculture Field with Multivariate Sensors Using Centroid-Based Oversampling Method: N. Bhatt, S. Varma N Bhatt, S Varma National Academy Science Letters 46 (6), 585-590 , 2023 2023.0 Citations: 3
A comprehensive survey on network resource management in SDN enabled data centre network A Sharma, S Tokekar, S Varma 6G Enabled Fog Computing in IoT: Applications and Opportunities, 333-353 , 2023 2023.0 Citations: 3
A reliable method for detecting false alarm notification in vanet P Shukla, R Patel, S Varma 2023 International Conference on Sustainable Computing and Smart Systems … , 2023 2023.0 Citations: 3
Replica Allocation in MANETs for Eliminating Selfish Node MR Dhanare, DS Varma International Journal of Scientific & Engineering Research, ISSN, 2229-5518 , 0 Citations: 3
Effective congestion control mechanism for smart vehicles using edge computing in VANET P Shukla, S Varma, R Petel Autonomous Vehicles Volume 2: Smart Vehicles, 105-121 , 2022 2022.0 Citations: 2
Adaptive Load Balancing Scheme for Software-Defined Networks Using Fuzzy Logic Based Dynamic Clustering A Sharma, S Tokekar, S Varma Sustainable Communication Networks and Application: Proceedings of ICSCN … , 2022 2022.0 Citations: 2
A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN) NK Rathore, V Jaiswal, V Sharma, S Varma Tech. Rep. RS-3. RS-621258 , 2021 2021.0 Citations: 2
An interference graph based MAC protocol for multi rate ad hoc networks S Varma, V Tokekar 2011 World Congress on Information and Communication Technologies, 581-586 , 2011 2011.0 Citations: 2
Intelligent Security System Based on the Internet of Things (IoT) P Gupta, S Varma, N Arya, R Bhagel Intelligent Sensor Node-Based Systems, 177-191 , 2023 2023.0 Citations: 1
Review of spectral clustering algorithms used in proteomics S Kumar, A Purohit, S Varma International Journal of Data Science 8 (1), 16-38 , 2023 2023.0 Citations: 1
