Patent Filed :

Inventor/s Name Title of the Patent Patent Application No. Patent Published Date / Granted Date  & Department
1. Sudip Kumar Sahana
2. Sankar Mukherjee
3. Milan Kumar Dholey		 IOT BASED AUTOMATED REAL TIME MONITORING SYSTEM FOR PATIENTS AND METHOD THEREOF 202331013273 21-04-2023 CSE
1. Sanskriti
2. Dr. K. Sridhar Patnaik
3. Saumya Raj
4. Saraswati Rai
5. Sinha, M.K
6. Amresh, A.K.		 AN AUTOMATIC TOILET DOOR SYSTEM AND METHOD THEREOF FOR MAINTAINING HYGIENE IN A TOILET 202331090139 26-01-2024 CSE
1. Dr. Shamama Anwar
2. Dr. Prashant Pranav
3. Dr. Sandip Dutta		 AN ENCRYPTION BASED PORTABLE DEVICE FOR GENERATING ELECTRONIC HEALTH RECORDS (EHRS) AND METHOD THEREOF 202431032969 03-05-2024 CSE
1. Komal Naaz
2. Dr. Niraj Kumar Singh		 A DIGITAL HINDI LITERARY CRITIC DEVICE AND METHOD THEREOF 202431039960 31-05-2024 CSE
1. Mamata Pandey
2. Dr. Anup Kumar Keshri
3. Dr. Rakesh Kumar  Sinha		 AN ECG-BASED AUTHENTICATION SYSTEM AND METHOD THEREOF 202431049057 05-07-2024 CSE

List of Publications :

Journal/Conference/Book Chapter Paper Title Author List Current Status
Sustainibility (MDPI) TeaNet: A Temporal Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections. Prashant Parasar, Poonam Moral, et al. Published
2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Forest Fire Forecasting Leveraging Modis Satellite Fire Data Using Machine Learning For Jharkhand State, India Poonam Moral, Prashant Parasar, et al. Published
Grenze International Journal of Engineering & Technology (GIJET) Deep Learning for Post-Hurricane Damage Detection with SAR-based Analysis using DenseNet201 and SVM Neha Kumari, Poonam Moral, et al. Published
UAV Applications in Natural Resource Management (Book Chapter) A Machine Learning Approach to Improve DEM Accuracy for Hydrological and Topographic Applications Prashant Parasar, Poonam Moral, et al. Accepted
Discover Environmental A Data-Driven Approach to Forest Fire Prediction: Evaluating the STV-FF Model for Fire Susceptibility Mapping Poonam Moral, Prashant Parasar, et al. Under Review
IETE Journal of Research Enhanced UNet Model for Segmentation and Pesudo-Colorization of Water Bodies in Satellite Images Neha Kumari, Abhijit Mustafi et al. Under Review
Scientific Reports CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images Poonam Moral, Debjani Mustafi, et al. Published
Neural Computing and Applications PODBoost: an explainable AI model for polycystic ovarian syndrome detection using grey wolf-based feature selection approach Poonam Moral, Debjani Mustafi, et al. Published
Lecture Notes in Networks and System Anomaly Detection in IoT Networks using WGAN-GP: A Novel Approach for Robust IoT Security Purushottam Singh, Sandip Dutta, et al. Published
International Conference on Artificial Intelligence and Emerging Technologies (ICAIET 2025) Machine Learning Approach for Predicting Forest Fire Using Autoencoder Smiriti Bharti , Debjani Mustafi, et al. Accepted
2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Gmm assisted clustering for long-term meteorological

Drought prediction in Jharkhand using LSTM

 Neha Kumari, Nikita Roy Mukherjee, et al. Accepted
Machine Vision and Applications Bidirectional cascaded multimodal attention for multiple choice visual question answering Sushmita Upadhyay, Sanjaya Shankar Tripathy Published
International Journal of Computers and Applications A threefold chaotic image encryption technique for efficacious image data protection Shamama Anwar, Anshuman Srivastava, et al. Published
International Conference on Machine Learning, Image Processing, Network Security, and Data Sciences  2024 Novel Hybrid Deep Learning-based ERCNet Framework for Enhanced Breast Cancer Identification Using Multi-Modal Imaging Techniques Saket Kumar Singh, K. Sridhar Patnaik, et al. Accepted
Discover Computing MeDiStore Trust Protocol Integrating Reputation based Proof of Stake Consensus for Enhanced Security of Blockchain Networks in Electronic Health Record Management Adla Sanober, Shamama Anwar, et al. Accepted
International Conference on Artificial Intelligence and Emerging Technologies (ICAIET 2025) Variational Autoencoders for Approximation of reduced Feature Space in Context of Groundwater Depth Prediction Chirashri Bhattacharya, Abhijit Mustafi, et al. Accepted
Progress in Artificial Intelligence Detecting Cervical Cancer from Pap Smear Images using e-TransCoder based Deep Learning Technique Poonam Moral, Debjani Mustafi, et al. Under Revision
International Conference on Advanced Computing and Communication Paradigms Prediction of Indian Tropical Cyclones using Machine Learning Techniques Anish Kishore, Pratyush Ranjan, et al. Under Review
IEEE Transactions on Intelligent Transportation Systems Enhancing Traffic Surveillance using Lightweight Framework for UAV-Based Vehicle Classification in Adverse Weather Condition Mayank Kumar, Shamama Anwar, et al. Under Review
Knowledge-Based Systems Extraction under Multi-Level Magnification using Adaptive Texture Transformer Optimization Method for Histopathology Image Classification Poonam Moral, Abhijit Mustafi, et al. Under Review
Discover Computing A two stage deep supervised learning model with inter-layer weight sharing for multiclass medical image classification Poonam Moral, Debjani Mustafi Under Review
IEEE Transactions on Emerging Topics in Computational Intelligence Towards Robust Adversarial Detection Using Enhanced Spiking Neural Networks Shamama Anwar, Mohammad Abdul Mujeeb Khan Under Review

RACHEL HPC FACILITY Resource Usage Policy

Policy Overview

Date: 11 March 2026

The Rachel High Performance Computing (HPC) Facility provides shared computational infrastructure to support the research, teaching, and scholarly activities of the institution. To ensure equitable access and the efficient utilization of these resources, all registered users are required to read, understand, and comply with the provisions set out in this Policy. These guidelines are effective immediately upon account activation and apply to all submitted workloads, stored data, and system interactions.

ParameterLimit / Requirement
Maximum Job Runtime120 hours (5 consecutive days)
Per-User Storage Quota150 GB (unauthorised use prohibited)
Job MonitoringUser's ongoing responsibility
Storage MaintenancePeriodic review & cleanup required
Non-Compliance ActionJob termination / access suspension

Job Runtime Limit

No user shall be permitted to execute any computational job on the Rachel HPC cluster for a continuous period exceeding total 120 hours (CPU-5 consecutive days or GPU-3 consecutive days). Jobs that approach or exceed this threshold will be subject to administrative intervention, including automatic or manual termination, to preserve system availability for all users.

Users requiring extended runtime beyond the standard limit must submit a written request to the System Administrator prior to job submission, providing scientific justification and an estimated resource budget.

Storage Utilization Limit

Individual users shall not utilize more than total 150 GB (for CPU nCPU-12 Core & for GPU nGPU-6 Core) of storage on the Rachel HPC system without prior written authorization from the System Administration team. This quota encompasses all personal directories, project workspace, and temporary scratch areas associated with the user account.

The following storage hygiene practices are mandatory:

  • Removal of completed job output files that are no longer required for active analysis.
  • Deletion of temporary, intermediate, and scratch files upon job completion.
  • Regular archival of results to institutional long-term storage systems.
  • Prompt response to storage-usage alerts issued by System Administration.

Fair Resource Usage

All users are expected to exercise good judgement in their use of Rachel HPC resources. Users shall not monopolies CPUs, memory, network bandwidth, or storage in a manner that disrupts or prevents other researchers, faculty members, or students from accessing the facility.

Submitting large numbers of simultaneous jobs that saturate the scheduler queue without scientific necessity, or deliberately circumventing resource allocation controls, constitutes a violation of this Policy.

Job Monitoring Responsibility

Users bear full responsibility for monitoring all jobs they submit to the Rachel HPC scheduler. This responsibility includes:

  • Regularly checking job status, output logs, and resource-consumption metrics.
  • Promptly terminating any job found to be stalled, malfunctioning, or producing incorrect results.
  • Ensuring that submitted jobs do not run beyond their stated resource requirements.

Users who are unable to actively monitor a job due to absence or other commitments should make arrangements for a designated colleague to assume monitoring responsibility, or should limit job scope accordingly.

Storage Maintenance

Users shall periodically review their allocated storage space and proactively remove unnecessary files, redundant datasets, temporary data, and intermediate outputs. Maintaining adequate free capacity on the Rachel HPC storage systems is a shared responsibility that directly impacts the productivity of all users.

System Administration may issue storage-usage warnings when a user's allocation approaches the permitted quota. Users are expected to respond to such warnings within five (5) working days by reducing their storage footprint to within the authorized limit.

Policy Compliance

All users of the Rachel HPC facility are required to comply with the provisions of this Policy as a condition of continued access. Compliance is the personal responsibility of every account holder.

âš   Non-Compliance & Administrative Authority Failure to comply with any provision of this Policy may result in the immediate termination of running jobs, restriction of computational resource allocations, or temporary suspension of access privileges to the Rachel HPC facility. The System Administrator reserves the right to terminate any user process deemed to be making undue or excessive use of system resources, without prior notice, in order to maintain overall system stability and equitable access for all users.

                                                                                                                              

Signature of HOD (CSE)