Data Science, Machine Learning, Natural Language Processing
7
Scopus Publications
51
Scholar Citations
3
Scholar h-index
1
Scholar i10-index
Scopus Publications
WSN- Based Optimal Crude Oil Storage Health Monitoring Framework Ismail Keshta, Mukesh Soni, Sagar Dhanraj Pande, Roshan Rajendrakumar Karwa, Haewon Byeon AI and Machine Learning for Mechanical and Electrical Engineering, 2025 The major goal of optimizing WSN health monitoring decisions is to guarantee that users encounter less repair time. The confidence you have in the WSN determines how well these judgements turn out. We propose a probabilistic sensor system (PSS) as a solution to problems like complex system dynamics and short detection data sets. The system’s probabilistic decision-making architecture helps provide the best possible health monitoring results. Evaluation of the current health monitoring state and prediction of future health monitoring status are its two primary operational areas. The PSS framework determines when it is best to do health monitoring by looking at the network’s present and future health monitoring and using the Kalman process. The experts help with this by setting the bar for the lowest possible network health monitoring state within the Kalman process-based prediction framework. Research on optimizing crude oil storage using wireless WSNs is being carried out to verify the effectiveness of this proposal.
Generative Modeling Techniques for Simulating Rare Agricultural Events on Prediction of Wheat Yield Production Yogita Dhumale, Gajendra Bamnote, Ruchita Kale, Gaurav Sawale, Anand Chaudhari, Roshan Karwa 2024 2nd Dmiher International Conference on Artificial Intelligence in Healthcare Education and Industry Idicaiei 2024, 2024 Wheat yield prediction is key to food security and optimal resource use. In this paper, Generative AI is use for specifically GANs and VAEs, to predict wheat yield under different weather conditions, uses the crop yield prediction dataset from Kaggle which has temperature, rainfall and soil quality as parameters and build models that beat traditional statistical methods. By leveraging advanced AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), the model generates synthetic data that mimics these rare occurrences. The GAN generates synthetic data that enriches the training set and improves prediction accuracy. The VAE captures the complex non-linear relationships between input features and output. The combination of both models gives us a robust framework for yield prediction. Experimental results shows that the GAN dataset gives 14% improvement over baseline models. VAE model gives 13% improvement on its own, showing its ability to handle the variability and complexity of agricultural data. This dual model approach is a big leap in agricultural analytics and will enable more robust and adaptive farming in the face of climate change.
Predictive Maintenance: Machine Learning Approaches for Enhanced Equipment Reliability Roshan R. Karwa, Gajendra R. Bamnote, Yogita A. Dhumale, Pranita P. Deshmukh, Rupali A. Meshram, Sumera M. Iqbal 2024 2nd Dmiher International Conference on Artificial Intelligence in Healthcare Education and Industry Idicaiei 2024, 2024 Predictive maintenance is an innovative approach that predicts equipment failure before it happens to allow organizations to schedule the maintenance activities at optimal times. The strategy eliminates unexpected downtime and overall operational costs, so it is one of the most effective solutions in industries with on-going continuous machinery operation. In this paper, machine learning techniques in predictive maintenance using the publicly available data from Kaggle is examined. The proposed work investigates critical steps undertaken for the development of PdM systems-including data preprocessing, feature engineering, model selection, and evaluation. Due care is taken at every stage to enhance predictive accuracy and reliability as effective data preparation and relevant feature extraction are essential to the success of a model. Several models are tested and compared, the best one coming out to be the Random Forest classifier. This model achieves the highest accuracy at 96 percent, though its precision, at 93 percent, is slightly less than that of some other models. These results show that it is indispensable to balance accuracy and precision to realize the potential of PdM. This study has shown how predictive analytics based on ML significantly strengthen maintenance strategies by making decisions based on data, thereby saving time, money, and preventing machinery from breaking down when least expected.
Automated Hybrid Deep Neural Network Model for Fake News Identification and Classification in Social Networks Roshan R. Karwa, Sunil Gupta International Journal of Automation and Smart Technology, 2023 The rapid growth of social media has far-reaching impacts on civilization, traditions, and economics, including both beneficial and unfavourable implications. Since social networking sites have become more frequently utilized for transmitting data, they have also become a gateway for the distribution of fake news for diverse financial and legislative goals. Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches have a lot of ability for academics who wish to design models that can recognize fake news automatically. On the other hand, identifying fake news is a difficult issue because it demands systems that describe the news and then contrast it to the actual news to categorize it as fake. Thus, to overcome this, this paper introduces Hybrid Deep Neural Network Model, in which C-DSSM and Deep CNN models have been utilized. It identifies and classifies fake news using the LIAR dataset. According to experimental results, the proposed model obtained an accuracy of 92.60%, a recall of 92.40%, a precision of 92.50%, and an F1 score of 92.50%. Furthermore, the proposed model is compared to earlier studies for fake news identification using the LIAR dataset, and the proposed model's performance is remarkable. As a result, the proposed hybrid model gives better results in detecting and classifying fake news on social networks.
Automated hybrid Deep Neural Network model for fake news identification and classification in social networks Journal of Integrated Science and Technology, 2022
Artificial intelligence based approach to validate the authenticity of news Roshan R. Karwa, Sunil R. Gupta Proceedings of the 2021 1st International Conference on Advances in Electrical Computing Communications and Sustainable Technologies Icaect 2021, 2021 Misinformation isn’t definitely new thing; it is way before the inception of social media. It is evolving since 14th century but the term like "fake news", "post truth" are used commonly during movement of 2016 US presidential election. People use social media to read news as it is lost cost and user friendly platform; also it is possible to share news on social media with one click. With this merit, it is also having major disadvantage. If the news is false or misleading news then spread of such news will have adverse consequence on civilization. Therefore, battling fake news is important and has now become developing area of research. Researchers are using Artificial Intelligence based approach such as machine learning and natural language processing to battle with the fake news. This paper presents a comprehensive overview of the earlier detection techniques as well as proposes mathematical model and methodology to improve the result.
RECENT SCHOLAR PUBLICATIONS
Predictive maintenance: Machine learning approaches for enhanced equipment reliability RR Karwa, GR Bamnote, YA Dhumale, PP Deshmukh, RA Meshram, ... 2024 2nd DMIHER International Conference on Artificial Intelligence in … , 2024 2024.0 Citations: 9
Generative Modeling Techniques for Simulating Rare Agricultural Events on Prediction of Wheat Yield Production Y Dhumale, G Bamnote, R Kale, G Sawale, A Chaudhari, R Karwa 2024 2nd DMIHER International Conference on Artificial Intelligence in … , 2024 2024.0 Citations: 3
Cuttlefish algorithm-based deep learning model to predict the missing data in healthcare application A Sasi Kumar, T Rajesh Kumar, R Balamanigandan, R Meganathan, ... International Conference on Data Analytics & Management, 513-528 , 2023 2023.0 Citations: 3
Automated hybrid Deep Neural Network model for fake news identification and classification in social networks RR Karwa, SR Gupta Journal of Integrated Science and Technology 10 (2), 110-119 , 2022 2022.0 Citations: 27
Artificial intelligence based approach to validate the authenticity of news RR Karwa, SR Gupta 2021 International Conference on Advances in Electrical, Computing … , 2021 2021.0 Citations: 3
Detect misinformation using two stage semantic extractor based on neural network classification RR Karwa, SR Gupta Webology 18 (6) , 2021 2021.0 Citations: 1
Identifying Tags and Trends by Opinion Analysis of Social Media Data about current Indian Economy: Text Mining Approach using Word Cloud DSRG Roshan Karwa Test Engineering and Management, 8863 - 8870 , 2020 2020.0
Drowsy Driver Detection and Alert System PRRK Gauri Thakare, Vaishnavi Shirbhate, Tanmay Fuse, Sanjiv Sharma International Journal of Advanced Research in Computer and Communication … , 2018 2018.0
Database Cache Management System PRRK H. V. Chainani, S.S.Kale, R. R. Sarad International Journal for Scientific Research & Development 6 (2), 2334-2336 , 2018 2018.0
Pulling social media twitter data into R ARD Roshan R. Karwa,Vishal V. Rath,Parag P.Kadu International Journal of Electronics,Communication and Soft Computing … , 2018 2018.0
A Review on Web Sentiment Analysis for Scoring Positive or Negative Words using Social Networking Sites GBS Parag Kadu, V.S.Sakharkar, S.V.Baghel,R.R.Karwa International Journal of Advance Research in Engineering, Science … , 2017 2017.0
Review: Web Semantics in Cloud Computing RK Rupali Meshram, Komal Hole, Pranita Deshmukh IJSER 7 (2), 378-381 , 2016 2016.0
Word sense disambiguation: hybrid approach with annotation up to certain level–a review RR Karwa, MB Chandak International Journal of Engineering Trends and Technology 18 (7), 328-330 , 2014 2014.0 Citations: 5
A NOVEL AUTOMATED BLOOD BANK SYSTEM USING ARDUINO VV Rathi, R Karwa GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES , 0
Approaches of Resolving the Ambiguities of Word in Sentences R Karwa
A Knowledge based Approach to Resolve Word Level Ambiguity for Machine Translation RR KarwaA, MB ChandakB, D PandeC
AN HYBRID APPROACH TO WORD SENSE DISAMBIGUATION WITH AND WITHOUT LEARNED KNOWLEDGE R Karwa, M Chandak
MOST CITED SCHOLAR PUBLICATIONS
Automated hybrid Deep Neural Network model for fake news identification and classification in social networks RR Karwa, SR Gupta Journal of Integrated Science and Technology 10 (2), 110-119 , 2022 2022.0 Citations: 27
Predictive maintenance: Machine learning approaches for enhanced equipment reliability RR Karwa, GR Bamnote, YA Dhumale, PP Deshmukh, RA Meshram, ... 2024 2nd DMIHER International Conference on Artificial Intelligence in … , 2024 2024.0 Citations: 9
Word sense disambiguation: hybrid approach with annotation up to certain level–a review RR Karwa, MB Chandak International Journal of Engineering Trends and Technology 18 (7), 328-330 , 2014 2014.0 Citations: 5
Generative Modeling Techniques for Simulating Rare Agricultural Events on Prediction of Wheat Yield Production Y Dhumale, G Bamnote, R Kale, G Sawale, A Chaudhari, R Karwa 2024 2nd DMIHER International Conference on Artificial Intelligence in … , 2024 2024.0 Citations: 3
Cuttlefish algorithm-based deep learning model to predict the missing data in healthcare application A Sasi Kumar, T Rajesh Kumar, R Balamanigandan, R Meganathan, ... International Conference on Data Analytics & Management, 513-528 , 2023 2023.0 Citations: 3
Artificial intelligence based approach to validate the authenticity of news RR Karwa, SR Gupta 2021 International Conference on Advances in Electrical, Computing … , 2021 2021.0 Citations: 3
Detect misinformation using two stage semantic extractor based on neural network classification RR Karwa, SR Gupta Webology 18 (6) , 2021 2021.0 Citations: 1
Identifying Tags and Trends by Opinion Analysis of Social Media Data about current Indian Economy: Text Mining Approach using Word Cloud DSRG Roshan Karwa Test Engineering and Management, 8863 - 8870 , 2020 2020.0
Drowsy Driver Detection and Alert System PRRK Gauri Thakare, Vaishnavi Shirbhate, Tanmay Fuse, Sanjiv Sharma International Journal of Advanced Research in Computer and Communication … , 2018 2018.0
Database Cache Management System PRRK H. V. Chainani, S.S.Kale, R. R. Sarad International Journal for Scientific Research & Development 6 (2), 2334-2336 , 2018 2018.0
Pulling social media twitter data into R ARD Roshan R. Karwa,Vishal V. Rath,Parag P.Kadu International Journal of Electronics,Communication and Soft Computing … , 2018 2018.0
A Review on Web Sentiment Analysis for Scoring Positive or Negative Words using Social Networking Sites GBS Parag Kadu, V.S.Sakharkar, S.V.Baghel,R.R.Karwa International Journal of Advance Research in Engineering, Science … , 2017 2017.0
Review: Web Semantics in Cloud Computing RK Rupali Meshram, Komal Hole, Pranita Deshmukh IJSER 7 (2), 378-381 , 2016 2016.0
A NOVEL AUTOMATED BLOOD BANK SYSTEM USING ARDUINO VV Rathi, R Karwa GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES , 0
Approaches of Resolving the Ambiguities of Word in Sentences R Karwa
A Knowledge based Approach to Resolve Word Level Ambiguity for Machine Translation RR KarwaA, MB ChandakB, D PandeC
AN HYBRID APPROACH TO WORD SENSE DISAMBIGUATION WITH AND WITHOUT LEARNED KNOWLEDGE R Karwa, M Chandak
