MBA, BE, Project Management Professional (PMI-USA)
RESEARCH, TEACHING, or OTHER INTERESTS
Marketing, Multidisciplinary
41
Scopus Publications
Scopus Publications
Joint Deraining and Dehazing Using a CNN with Dark Channel Prior and Atmospheric Light Hybrid Model for Robust Image Restoration International Journal of Intelligent Engineering and Systems, 2025 Rain, fog and haze severely downgrade visibility conditions, making it difficult for vision-based systems, such as autonomous driver and traffic monitoring systems to function, as they not only compromise the quality of the image, but introduce artifacts.Improving the robustness of these systems in such scenarios is imperative for road safety and dependable decision making.In this work, we propose a new CNN for concurrent image deraining and dehazing, which is built by unifying the power of deep learning with the main concepts from image processing for better traditional image processing.The most remarkable novelty of the method presented in the paper is the fact that it benefits from the estimation of the so-called Dark Channel Prior (DCP), a hand-crafted prior that tells the CNN what a clear image looks like whenever the input is affected by rain streaks as well as haze, since both these atmospheric phenomena occur in outdoor scenarios.The approach thus effectively provides a more robust solution to these complex tasks by combining data-driven learning with handcrafted priors.A novel Atmospheric Light Condition Hybrid model is presented, which exploits the advantages of K-means intensity clustering, CNN-based processing and saliency maps to improve the atmospheric light estimation accuracy, a crucial step in the haze removal process.It also makes corrections for errors in atmospheric light estimation, giving the final restoration a better quality.The architecture of the proposed CNN is a multi-stage decomposition, with each stage tailored to a specific degradation item: rain streak separation, haze-free region recovery, and clean image reconstruction.Large amounts of experimental data show that PSNR and SSIM of 28.14 dB and 0.901 are achieved under light rain in the Rain L dataset, and PSNR of 25.99 dB and SSIM of 0.873 are obtained under moderate haze by the I-Haze dataset, outperforming significantly compared with existing state-of-the-art methods.Besides, our model keeps robustness in more heavy degradation: PSNR=22.097dB,SSIM=0.813onRain 1400 dataset and PSNR=21.90,SSIM=0.812 on NH-Haze datasets.These results demonstrate the model's robustness in processing diverse rain and haze conditions, leading to considerable improvements in restoring images over the best previously published results.The approach presents a new state of the art for image restoration tasks in harsh weather conditions.
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System Shubhi Srivastava, Roopesh Gupta, Somnath Banerjee, Alpha Vijayan, Chintan Thacker, Abhijit Vasmatkar 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2025, 2025 Healthcare IoT systems have been plagued with significant challenges with regard to maintaining an optimum QoS due to the dynamic conditions of the network, diverse device capabilities, and stringent real-time constraints imposed by patient monitoring-type applications. Traditional QoS mechanisms are basically static; they do not take into account changes within the network. Hence, service delivery experiences degradation, with attendant risk to patients' safety. As a solution, this research proposes an adaptive QoS approach employing Deep Q-Network (DQN) reinforcement learning algorithms to dynamically control resource allocation and traffic prioritization in healthcare IoT networks. This system involves multi-agent reinforcement learning architecture where continuous state-action space mapping is utilized for adjusting bandwidth allocation, latency management, and packet prioritization automatically based on network conditions and the criticality levels of applications in real-time. Experimentally, the solution has attained an accuracy of 94.7 percent in QoS prediction, an 87.3 percent reduction in average latency to critical healthcare applications, 91.2 percent improvement in network throughput utilization, and an 89.6 percent success rate in adhering to service level agreements in peak traffic conditions. Through reinforcement learning-based decision making, the adaptive QoS mechanism dynamically accommodates the requirements of healthcare IoT, ensuring reliable service delivery while optimizing the usage of network resources.
AgroInnovate: A Framework for Sustainable and Smart Irrigation in Agriculture Aruna Singh, Roopam Gupta 2023 Global Conference on Information Technologies and Communications Gcitc 2023, 2023 Agriculture is a fundamental global practice, reliant on optimal conditions for crop productivity. Agriculture, a globally embraced practice, demands optimal conditions for efficient crop production and yield improvement. This research introduces an intelligent irrigation monitoring and control system, employing Internet of Things (IoT) technology. The system leverages soil pH, moisture, and temperature data obtained from field sensors, storing it in the cloud. It allows manual and automated operation via a mobile app, with automated mode activating pumps based on soil conditions. Key parameters, including soil pH, moisture, and temperature, are sensed using on-field sensors, with data stored in the cloud. The system introduces dual operational modes, manual and automated, to cater to varying farmer requirements. In manual mode, farmers retain remote control, enabling the activation or deactivation of critical components such as the water motor pump, water sprinkler pump, and fertilizer pump, all via a user-friendly mobile application. In automated mode, pumps are triggered based on soil conditions, optimizing water and fertilizer use. The system also maintains ideal soil pH, promoting crop growth. Additionally, it manages soil pH by automatically adjusting fertilizer injection. The proposed system offers remote monitoring and control via a website or mobile app, aiming to maximize crop production while adhering to standard parameters. This research proposes a sensor module for parameter measurement and actuation. The system facilitates remote monitoring and control through a website or mobile app, aiming to maximize crop production while adhering to standard parameters. Upon detecting soil conditions falling outside the predefined threshold values, the system initiates automatic control actions. When soil moisture registers below the established threshold, the system automatically activates the water supply pump to ensure adequate irrigation. Conversely, when the soil temperature surpasses the defined threshold, the system triggers the water sprinkle pump to mitigate excessive heat and optimize crop growth conditions. This dynamic response mechanism, orchestrated by the automated mode, serves as an indispensable feature of the proposed system, enhancing its ability to provide timely and precise irrigation support based on real-time soil conditions.
Comparative Error Rate Performance Analysis of Quasi Cyclic LDPC Codes for 5G Radio Channel Sonu Lal, Roopam Gupta, Rakesh Kumar Arya 1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing Ihcsp 2023, 2023 Fifth Generation New radio (5G) is in final stage for freezing frequency band by Release 16in between 1 GHz to 43 GHz. It is also attracting much attention because of short length and long length data transfer with high speed and low latency time resulting error rate performance will degrade. Researchers are thus in continuous working on construction of more efficient regular Quasi Cyclic LDPC Code with parity check matrices of column weight two on half code rate. We suggest is a construction of regular quasi-cyclic LDPC codes with parity-check matrices of column weight two have good capability to correct bursts errors of erasures that provides benefits in terms of the error rate performance. This paper presents fundamental concepts, review study and comparative analysis which carried out by different methods of constructions QC-LDPC codes for better BER, FER calculation and improved coding gain using latest techniques.
Design and Development of an Efficient Network Intrusion Detection System Using Machine Learning Techniques Thomas Rincy N, Roopam Gupta Wireless Communications and Mobile Computing, 2021 Today’s internets are made up of nearly half a million different networks. In any network connection, identifying the attacks by their types is a difficult task as different attacks may have various connections, and their number may vary from a few to hundreds of network connections. To solve this problem, a novel hybrid network IDS called NID‐Shield is proposed in the manuscript that classifies the dataset according to different attack types. Furthermore, the attack names found in attack types are classified individually helping considerably in predicting the vulnerability of individual attacks in various networks. The hybrid NID‐Shield NIDS applies the efficient feature subset selection technique called CAPPER and distinct machine learning methods. The UNSW‐NB15 and NSL‐KDD datasets are utilized for the evaluation of metrics. Machine learning algorithms are applied for training the reduced accurate and highly merit feature subsets obtained from CAPPER and then assessed by the cross‐validation method for the reduced attributes. Various performance metrics show that the hybrid NID‐Shield NIDS applied with the CAPPER approach achieves a good accuracy rate and low FPR on the UNSW‐NB15 and NSL‐KDD datasets and shows good performance results when analyzed with various approaches found in existing literature studies.
A review on 5G technology: A heterogeneous architecture, OSI protocol model & future challenges 11th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2020, 2020
Fintech making ESG Impact on the Financial Market: Way to Sustainable and Inclusive Finance RG Anita S Mathapati, Gaurav Bhalla Chetana’s Journal of Management 17 (2), 118-135 , 2025 2025.0
Fintech and Its ESG Impact on the Financial Market: A Pathway to Sustainable and Inclusive Finance AS Mathapati, G Bhalla, R Gupta Journal of Management Research 17 (2), 118-135 , 2025 2025.0
Behavioral Nudges and Consumer Decisions in India’s Quick Commerce Industry - A Sustainability Focused Framework Integrating ESG and Gig Worker Welfare DHB Roopesh Gupta, Dr. Ranu Gupta Chetana’s Journal of Management 17 (2), 17-33 , 2025 2025.0
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System S Srivastava, R Gupta, S Banerjee, A Vijayan, C Thacker, A Vasmatkar 2025 2nd International Conference on Intelligent Algorithms for … , 2025 2025.0
Contemporary Trends in Commerce, Management & Economics Vol:2 RGPMP Shabnam Ashrat Mehta, Dr. Neha, Dr. G. Usha Sree, Dr. Vasudha Kurikala ISBN:978-81-984646-2-0 2 (ISBN:978-81-984646-2-0), 93 , 2025 2025.0
Contemporary Trends in Commerce, Management & Economics Vol:1 DAKJA Board of Editors - Dr. S. Thilaka, Dr. Mohd Shahid Ali, Roopesh Gupta ... ISBN:978-81-984646-2-0 1 (https://doi.org/10.5281/zenodo.14777879), 106 , 2025 2025.0
Commerce Management & Economics S Thilaka, A Kalaiya, MS Ali, RG PMP Holy Cross College , 0
MOST CITED SCHOLAR PUBLICATIONS
Fintech making ESG Impact on the Financial Market: Way to Sustainable and Inclusive Finance RG Anita S Mathapati, Gaurav Bhalla Chetana’s Journal of Management 17 (2), 118-135 , 2025 2025.0
Fintech and Its ESG Impact on the Financial Market: A Pathway to Sustainable and Inclusive Finance AS Mathapati, G Bhalla, R Gupta Journal of Management Research 17 (2), 118-135 , 2025 2025.0
Behavioral Nudges and Consumer Decisions in India’s Quick Commerce Industry - A Sustainability Focused Framework Integrating ESG and Gig Worker Welfare DHB Roopesh Gupta, Dr. Ranu Gupta Chetana’s Journal of Management 17 (2), 17-33 , 2025 2025.0
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System S Srivastava, R Gupta, S Banerjee, A Vijayan, C Thacker, A Vasmatkar 2025 2nd International Conference on Intelligent Algorithms for … , 2025 2025.0
Contemporary Trends in Commerce, Management & Economics Vol:2 RGPMP Shabnam Ashrat Mehta, Dr. Neha, Dr. G. Usha Sree, Dr. Vasudha Kurikala ISBN:978-81-984646-2-0 2 (ISBN:978-81-984646-2-0), 93 , 2025 2025.0
Contemporary Trends in Commerce, Management & Economics Vol:1 DAKJA Board of Editors - Dr. S. Thilaka, Dr. Mohd Shahid Ali, Roopesh Gupta ... ISBN:978-81-984646-2-0 1 (https://doi.org/10.5281/zenodo.14777879), 106 , 2025 2025.0
Commerce Management & Economics S Thilaka, A Kalaiya, MS Ali, RG PMP Holy Cross College , 0
