Computer Science, Computational Theory and Mathematics
17
Scopus Publications
123
Scholar Citations
5
Scholar h-index
2
Scholar i10-index
Scopus Publications
RETRACTION:Enhanced radial base deep learning algorithm for predicting multimedia security issues V. Haripriya, Mohan Vishal Gupta, Nikita Nadkarni, Suraj Malik, Aditya Yadav, et al. Journal of Intelligent and Fuzzy Systems, 2024 From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments.
Sensor-based human activity detection using a novel machine learning algorithm in home automation R. Raghavendra, Vaishali Singh, Sonia Arora, Mohan Vishal Gupta Multidisciplinary Science Journal, 2024 The identification of anomalous behavior, the administration of home automation, and a broad variety of real-world applications that depend on human activity detection (HAD). HAD systems make use of intelligent human-computer interfaces, a component of proactive and proactive surveillance systems. Accurately detecting HAD from a sequence of still images is a challenging task because of factors like as the distracting background, the many viewpoints, the low resolution, and the partial occlusion.In this study, we propose a Radiant Moth Flame-Optimized Probabilistic Decision Tree Algorithm (RMFO-PDTA) method to detect human activities.Machine learning (ML) algorithms, according to recent research, are better suited to automate the feature extraction procedure from basic sensor information. The goal of this research is to improve home automation systems by adding HAD based on sensors with the help of a cutting-edge ML algorithm.Initially, the research acquired the HAD dataset and a Min-max normalization technique was employed to preprocess the data. The feature extraction method uses a Fast Fourier transform (FFT) technique which tends to extract the data features. To make a precise prediction of the HAD while simultaneously improving the speed of the RMFO-PDTA model's performance. Accuracy (97.3%), recall (97%), f1-score (97.5%), and precision (97.01%) are used to examine the outcomes of the experiment. The result of the research reveals that the suggested RMFO-PDTA technique works better than conventional approaches in terms of detecting the HAD levels efficiently.
An innovative transfer-learning-based deep learning approach to identify agriculture pest Trapty Agarwal, Amita Shukla, Mohan Vishal Gupta, A. Kannagi Multidisciplinary Science Journal, 2024 Agricultural pests are living organisms that cause damage or hinder the typical growth and development of crops, resulting in reduced yields and financial losses for the farming industry. Agriculture has been a crucial source of nutrition across all of human history. It is the foundation of the economy in many nations, with over 90% of the population depending on it for a living. Outbreaks of pests increase the problems with agricultural crop production. This study suggested a deep learning (DL) architecture based on an Attentive deep convolutional generative adversarial network (ADCGAN) that includes transfer learning to identify agricultural pest that helps farmers for cultivation. For this study, the IP102 insect pest dataset was used. The preprocessing stage comprises the sliding window cropping method, an essential tool in modern agriculture. We use Gabor Filter for feature extraction, enabling pattern identification and image enhancement. To detect pests in agriculture, the target data training network uses the initial values of the parameters learned using ResNet networks on the accessible ImageNet dataset. Realistic images can be produced with ADCGANs with simplicity. With a collection of pest images as training data, the generator can generate artificially manufactured once incredibly real images of pests. Training the strong pest detection models using this enhanced dataset could be beneficial. We evaluate the effectiveness of the suggested method by contrasting it with existing identification approaches that employ evaluation parameters such as the AUC score, f1-score, recall, accuracy and precision. The results indicate that the suggested ADCGAN performs more effectively than the conventional techniques. The flexibility of the ADCGAN method enables customized pest control plans. It can adapt its identification method to various agricultural settings by taking inputs from particular agro-ecosystems.
Technologies based on the IoT and Artificial and Natural Intelligence for Sustainable Agriculture Data Driven Mathematical Modeling in Agriculture Tools and Technologies, 2024
A Novel Hybrid Optimization Approach for Securing Health Information using Block Chain International Journal of Intelligent Systems and Applications in Engineering, 2023
Detection of Copy-Move Forgery (CMF) in Videos through the Application of a Machine Learning Algorithm International Journal of Intelligent Systems and Applications in Engineering, 2023
Utilization of Random Forest Classifier (RFC) To Predict the Quality of Beverages Mohan Vishal Gupta, Soumya K 2023 International Conference on Communication Security and Artificial Intelligence Iccsai 2023, 2023 In contemporary times, individuals strive to attain a lifestyle characterized by opulence and extravagance. Individuals often utilize these items either for ostentation or for their routine activities. Currently, the ingestion of fizzy beverages is widely prevalent among the general population. This study pertains to the prediction of beverage quality through the analysis of its diverse attributes. The dataset utilized in this study was obtained from publicly available sources, including Kaggle. The Random Forest algorithm was employed as a technique for analysis. Various performance measures are calculated and then compared across the training and testing sets to predict the overall quality of the beverage.
Extracting Features Using Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) to Produce Colorization of Images A. Rengarajan, Mohan Vishal Gupta, R. Kishore Kanna 2023 3rd International Conference on Advancement in Electronics and Communication Engineering Aece 2023, 2023 The act of imbuing monochromatic images or videos with hues is commonly referred to as gray scale image colorization. The incorporation of colors not only serves to augment the aesthetic appeal of the image, but also serves to amplify its inherent characteristics. The process of colorization has various applications, including but not limited to medical imaging and scientific illustrations. The process entails the allocation of three-dimensional pixel values to a monochromatic image that solely possesses a single dimension, namely luminance or intensity. Given that distinct colors can possess identical luminance values but differ in terms of hue or saturation, human intervention is typically necessary. However, with the increasing demands of time, it is imperative to reduce the amount of human labour involved. Hence, there is a requirement to devise effective methodologies that facilitate the proficient mapping of colors. The paper outlines a proposed methodology for the implementation of an automated colorization system. The resulting output would exhibit superior performance in terms of evaluation metrics namely PSNR (peak signal to noise ratio) to show colorization quality.
Rabble Based Autonomous Assistance Using Machine Learning Algorithms Kamal Sutariya, Mohan Vishal Gupta, Bechoo Lal, Sura Rahim Alatba, G.V. Sriramakrishnan, et al. 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2023, 2023
Introduction to GUI Programming in Python DASB Mr. Anurag Tripathi, Dr. Mohan Vishal Gupta The Future of Engineering Innovation: Smart Technologies and Advanced Design … , 2026 2026
A Machine Learning-Based Framework for Assignment Evaluation and Feedback Generation SJ Kanika Jain , Mohan Vishal Gupta , Amit Kumar , Namit Gupta Journal of European Economic History 7 (2), 105-113 , 2026 2026
An Adaptive AI Framework for Personalized Meal Preparation Assistance GJ Vanshika Jain, Mohan Vishal Gupta, Amit Kumar, Namit Gupta Journal of International Commercial Law and Technology 7 (1), 954-962 , 2026 2026
Artificial Intelligence in Cyber Security DHS Dr. Indradeep Verma, Dr. Mohan Vishal Gupta, Dr. Navneet Vishnoi 2026
AI and ML Applications in Industry 4.0: Opportunities, Challenges, and Future Directions HS Nida Siddiqui, Bhupender Singh Rawat, Mohan Vishal Gupta INDUSTRY 4.0 EMERGING TECHNOLOGIES IN DIGITAL ERA 1, 17-40 , 2026 2026
TRANSFORMING SCHOOL LIBRARIES WITH ICT: ASSESSING DIGITAL LITERACY AND THE EVOLVING ROLE OF LIBRARIANS IN THE CONTEXT OF NEP 2020 M GUPTA, R SINGH LIS TODAY Учредители: ACS Publisher 10 (2), 26-36 , 2025 2025
Reliable person identification using a novel multibiometric image sensor fusion architecture P Amin, R Murugan, M patel, MV Gupta International Journal of System Assurance Engineering and Management, 1-10 , 2024 2024 Citations: 1
Technologies based on the IoT and Artificial and Natural Intelligence for Sustainable Agriculture SKM Bargavi, MV Gupta, R Kumar, A Afaq, P Sikchi, DN Le Data Driven Mathematical Modeling in Agriculture, 13-32 , 2024 2024 Citations: 1
An innovative transfer-learning-based deep learning approach to identify agriculture pest T Agarwal, A Shukla, MV Gupta, A Kannagi Multidisciplinary Science Journal 6 , 2024 2024
RETRACTED: Enhanced radial base deep learning algorithm for predicting multimedia security issues V Haripriya, M Vishal Gupta, N Nadkarni, S Malik, A Yadav, A Joshi Journal of Intelligent & Fuzzy Systems 46 (2), 4829-4840 , 2024 2024 Citations: 3
Sentiment analysis of online customer feedbacks using NLP and supervised learning algorithm K Anbazhagan, P Singhal, M Gupta, K Saxena International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024 Citations: 8
Sensor-based human activity detection using a novel machine learning algorithm in home automation R Raghavendra, V Singh, S Arora, MV Gupta Multidisciplinary Science Journal 6 , 2024 2024 Citations: 2
Utilization of Random Forest Classifier (RFC) To Predict the Quality of Beverages MV Gupta, K Soumya 2023 International Conference on Communication, Security and Artificial … , 2023 2023
Extracting features using discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) to produce colorization of images A Rengarajan, MV Gupta, RK Kanna 2023 3rd International Conference on Advancement in Electronics … , 2023 2023 Citations: 7
Comparative Analysis of Various Statistical Learning Algorithms in Classification of Satellite Imagery MV Gupta, RK Dwivedi, A Kumar 2023 3rd International Conference on Technological Advancements in … , 2023 2023
Employing anfis to perform an efficient energy routing in wireless sensor networks MV Gupta, K Anbazhagan 2023 6th International Conference on Contemporary Computing and Informatics … , 2023 2023 Citations: 2
Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment: M. Thomas et al. M Thomas, MV Gupta, V Gokul Rajan, R Rajalakshmi, RS Dixit, ... Soft Computing, 1-12 , 2023 2023 Citations: 20
Rabble based autonomous assistance using machine learning algorithms K Sutariya, MV Gupta, B Lal, SR Alatba, GV Sriramakrishnan, V Tripathi 2023 3rd International Conference on Advance Computing and Innovative … , 2023 2023 Citations: 3
Rajalakshmi R, Shailee Lohmor Choudhary,“ RS Dixit, M Thomas, MV Gupta, G Rajan Soft computing in computer network security protection system with machine … , 2023 2023 Citations: 8
Spoken emotion recognition through human-computer interaction using a novel deep learning technology MB SK, P Bhambu, MV Gupta Multidisciplinary Science Journal 5 , 2023 2023 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Electrocatalytic properties of perovskite-type La1-xSrxCoO3 (0⩽ x⩽ 0.4) obtained by a novel stearic acid sol–gel method for electrocatalysis of O2 evolution in KOH solutions B Lal, MK Raghunandan, M Gupta, RN Singh International journal of hydrogen energy 30 (7), 723-729 , 2005 2005 Citations: 61
Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment: M. Thomas et al. M Thomas, MV Gupta, V Gokul Rajan, R Rajalakshmi, RS Dixit, ... Soft Computing, 1-12 , 2023 2023 Citations: 20
Sentiment analysis of online customer feedbacks using NLP and supervised learning algorithm K Anbazhagan, P Singhal, M Gupta, K Saxena International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024 Citations: 8
Rajalakshmi R, Shailee Lohmor Choudhary,“ RS Dixit, M Thomas, MV Gupta, G Rajan Soft computing in computer network security protection system with machine … , 2023 2023 Citations: 8
Extracting features using discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) to produce colorization of images A Rengarajan, MV Gupta, RK Kanna 2023 3rd International Conference on Advancement in Electronics … , 2023 2023 Citations: 7
RETRACTED: Enhanced radial base deep learning algorithm for predicting multimedia security issues V Haripriya, M Vishal Gupta, N Nadkarni, S Malik, A Yadav, A Joshi Journal of Intelligent & Fuzzy Systems 46 (2), 4829-4840 , 2024 2024 Citations: 3
Rabble based autonomous assistance using machine learning algorithms K Sutariya, MV Gupta, B Lal, SR Alatba, GV Sriramakrishnan, V Tripathi 2023 3rd International Conference on Advance Computing and Innovative … , 2023 2023 Citations: 3
Review of Various Learning Algorithms Applied to Satellite Image Classification MV Gupta, RK Dwivedi, A Kumar 2021 10th International Conference on System Modeling & Advancement in … , 2021 2021 Citations: 3
Sensor-based human activity detection using a novel machine learning algorithm in home automation R Raghavendra, V Singh, S Arora, MV Gupta Multidisciplinary Science Journal 6 , 2024 2024 Citations: 2
Employing anfis to perform an efficient energy routing in wireless sensor networks MV Gupta, K Anbazhagan 2023 6th International Conference on Contemporary Computing and Informatics … , 2023 2023 Citations: 2
Spoken emotion recognition through human-computer interaction using a novel deep learning technology MB SK, P Bhambu, MV Gupta Multidisciplinary Science Journal 5 , 2023 2023 Citations: 2
Evaluation Of Metrics And Assessment Of Quality Of Object Oriented Software N Chauhan, MV Gupta IJRET: International Journal of Research in Engineering and Technology 3 , 2014 2014 Citations: 2
Reliable person identification using a novel multibiometric image sensor fusion architecture P Amin, R Murugan, M patel, MV Gupta International Journal of System Assurance Engineering and Management, 1-10 , 2024 2024 Citations: 1
Technologies based on the IoT and Artificial and Natural Intelligence for Sustainable Agriculture SKM Bargavi, MV Gupta, R Kumar, A Afaq, P Sikchi, DN Le Data Driven Mathematical Modeling in Agriculture, 13-32 , 2024 2024 Citations: 1
Introduction to GUI Programming in Python DASB Mr. Anurag Tripathi, Dr. Mohan Vishal Gupta The Future of Engineering Innovation: Smart Technologies and Advanced Design … , 2026 2026
A Machine Learning-Based Framework for Assignment Evaluation and Feedback Generation SJ Kanika Jain , Mohan Vishal Gupta , Amit Kumar , Namit Gupta Journal of European Economic History 7 (2), 105-113 , 2026 2026
An Adaptive AI Framework for Personalized Meal Preparation Assistance GJ Vanshika Jain, Mohan Vishal Gupta, Amit Kumar, Namit Gupta Journal of International Commercial Law and Technology 7 (1), 954-962 , 2026 2026
Artificial Intelligence in Cyber Security DHS Dr. Indradeep Verma, Dr. Mohan Vishal Gupta, Dr. Navneet Vishnoi 2026
AI and ML Applications in Industry 4.0: Opportunities, Challenges, and Future Directions HS Nida Siddiqui, Bhupender Singh Rawat, Mohan Vishal Gupta INDUSTRY 4.0 EMERGING TECHNOLOGIES IN DIGITAL ERA 1, 17-40 , 2026 2026
TRANSFORMING SCHOOL LIBRARIES WITH ICT: ASSESSING DIGITAL LITERACY AND THE EVOLVING ROLE OF LIBRARIANS IN THE CONTEXT OF NEP 2020 M GUPTA, R SINGH LIS TODAY Учредители: ACS Publisher 10 (2), 26-36 , 2025 2025
