Ajit Muzumdar

Scopus Publications

Social Media Sentiment Analysis Using Machine Learning
Om Rajendra Deokar, Tushar Raju Gaikwad, Kaushal Ramesh Gawali, Piyush Dyaneshwar Ghanghav, Sandip Ashok Shivarkar, Ajit Ashok Muzumdar
Icdt 2025 3rd International Conference on Disruptive Technologies, 2025
In recent years, social media has emerged as a crucial platform for public expression, enabling individuals to share opinions and sentiments on a wide range of topics. This study explores the application of machine learning techniques-Support Vector Machines (SVM), Decision Trees, Naive Bayes, and Random Forest for sentiment analysis of social media content. By leveraging a diverse dataset of user-generated posts, we aim to classify sentiments into positive, negative, and neutral categories. We evaluate the performance of each algorithm based on accuracy, providing a comparative analysis of their effectiveness. Our findings reveal that SVM significantly outperforms the other methods in terms of accuracy establishing it as the most effective model for sentiment classification in this domain. The superior performance of SVM is attributed to its capability to manage high-dimensional data and create optimal hyperplanes for classification. These results emphasize the critical importance of selecting appropriate machine learning models for sentiment analysis, especially in the dynamic landscape of social media.
Designing a blockchain-enabled privacy-preserving energy theft detection system for smart grid neighborhood area network
Ajit Muzumdar, Chirag Modi, C. Vyjayanthi
Electric Power Systems Research, 2022
Designing a Robust and Accurate Model for Consumer-Centric Short-Term Load Forecasting in Microgrid Environment
Ajit A. Muzumdar, Chirag N. Modi, Madhu G. M, Chintamani Vyjayanthi
IEEE Systems Journal, 2022
The consumer’s short-term load forecasting plays an essential role in microgrid energy distribution. However, the load forecasting at consumer level is more challenging than at substation level due to high volatility and uncertainty in energy consumption. In literature, many machine learning-based forecasting models have been explored. However, there is a need of developing a robust and accurate model to handle highly inconsistent energy consumption. In this article, we propose a robust and accurate model for consumer’s short-term load forecasting, which uses feasible techniques such as random forest, support vector regressor, and long short-term memory as base predictors to handle varying traits of energy consumption. For the final decision-making on forecasting result of these predictors, it assigns weights to each predictor dynamically as per the forecasting efficacy. The proposed model is tested on different consumer’s varying traits of energy consumption. The experimental results show that the proposed model achieves forecasting error reduction by 3.46 and 2.53 in terms of average RMSE and MAE, respectively, in comparison with the existing models. It is robust and accurate even in presence of highly volatile and uncertain load patterns, and thus, it can better fit for microgrid energy management.
A permissioned blockchain enabled trustworthy and incentivized emission trading system
Ajit Muzumdar, Chirag Modi, C. Vyjayanthi
Journal of Cleaner Production, 2022
Designing a Trustworthy and Secured House Rental System using Blockchain and Smart Contracts
Pooja Yadav, Shubham Sharma, Ajit Muzumdar, Chirag Modi, C. Vyjayanthi
Indicon 2022 2022 IEEE 19th India Council International Conference, 2022
The traditional process of renting the house has several issues such as data security, immutability, less trust and high cost due to the involvement of third party, fraudulent agreement, payment delay and ambiguous contracts. To address these challenges, a blockchain with smart contracts can be an effective solution. This paper leverages the vital features of blockchain and smart contract for designing a trustworthy and secured house rental system. The proposed system involves off-chain and on-chain transactions on hyperledger blockchain. Off-chain transaction includes the rental contract creation between tenant and landlord based on their mutual agreement. On-chain transactions include the deposit and rent payment, digital key generation and contract dissolution, by considering the agreed terms and conditions in the contract. The functional and performance analysis of the proposed system is carried out by applying the different test cases. The proposed system fulfills the requirements of house rental process with high throughput (>92 tps) and affordable latency (<0.7 seconds).
NITG Chain: A Scalable, Private and Permissioned Blockchain with Proof of Reputation Consensus Method
Alok Jaiswal, Sheetal Chandel, Ajit Muzumdar, Chirag Modi, Madhu G. M., C. Vyjayanthi
Lecture Notes in Electrical Engineering, 2022
A trustworthy and incentivized smart grid energy trading framework using distributed ledger and smart contracts
Ajit Muzumdar, Chirag Modi, Madhu G.M., C. Vyjayanthi
Journal of Network and Computer Applications, 2021
An Efficient Regional Short-Term Load Forecasting Model for Smart Grid Energy Management
Ajit Muzumdar, Chirag Modi, C Vyjayanthi
IECON Proceedings Industrial Electronics Conference, 2020
The conventional grid has experienced a transition towards smart grid with the advancements in metering infrastructure and increasing usage of renewable energy sources. In smart grid, the energy management system relies heavily on an accurate short-term load forecasting at regional level for an efficient planning and operations of grid.In this paper, we propose an efficient model for regional short-term load forecasting using machine learning techniques in parallel. This model uses feasible machine learning techniques such as support vector regressor (SVR) and random forest (RF) as base predictors. The forecasting results of RF and SVR are averaged to derive final outcome. The performance of the proposed model is validated using load data collected from different regions such as Goa, Maharashtra and Mumbai in India.
A conceptual framework for trustworthy and incentivized trading of food grains using distributed ledger and smart contracts
Alok Jaiswal, Sheetal Chandel, Ajit Muzumdar, Madhu G.M., Chirag Modi, C. Vyjayanthi
2019 IEEE 16th India Council International Conference Indicon 2019 Symposium Proceedings, 2019
Blockchain has transformed business processes from centralized to decentralized. It can better help in making food grain supply chain fully decentralized with peer to peer (P2P) business by removing intermediaries, and thus reducing the overall cost at end user side with better returns to farmers. In this paper, we explore a blockchain technology and the required smart contracts for trustworthy and incentivized P2P trading of food grains. We propose different smart contracts such as food grain supply, bidding, trading and utilization, which are deployed on ethereum blockchain for the decentralized trading of food grains. We use Vickrey-Clarkegrove (Vickrey auction) method for achieving the incentivized trading for both farmers and end users. The proposed framework offers P2P trading, security of food grain data, data transparency, user's anonymity and incentives in the trading process. The performance of the proposed framework is evaluated and analyzed in terms of fulfilling the requirements of food grain supply management.
Analyzing the Feasibility of Different Machine Learning Techniques for Energy Imbalance Classification in Smart Grid
Ajit Muzumdar, Chirag N. Modi, G M Madhu, C. Vyjayanthi
2019 10th International Conference on Computing Communication and Networking Technologies Icccnt 2019, 2019
Smart grid involves distributed energy resources (DERs) such as Solar PV, Wind, Battery generations, etc to meet the increasing demand of energy. However, due to the increasing demand of energy, there is need of generating more energy from the available resources to avoid the energy imbalance. Energy imbalance is the core reason behind poor power quality and energy outage problems, and therefore it is required to predict the energy demand and supply on a daily basis for better preparedness to avoid energy crisis. This leads to the energy imbalance classification problem. In literature, many machine learning techniques have been used to address this problem. However, it is required to perform the feasibility analysis of such techniques. In this paper, we have investigated well-known machine learning techniques for energy imbalance classification in smart grid. For the feasibility analysis of different machine techniques, we have collected the energy generation and consumption data from Maharashtra region, India and preprocessed these data using the principal component analysis (PCA) which can help in improving the classification accuracy. For energy imbalance classification, we have considered different machine learning techniques such as Naive bayesian, neural network, support vector machine, decision tree, random forest, bagging and boosting. We have evaluated the performance results in terms of accuracy, root mean square error and mean absolute error.
A Novel Framework for Pharmaceutical Supply Chain Management using Distributed Ledger and Smart Contracts
Sandip Jangir, Ajit Muzumdar, Alok Jaiswal, Chirag N. Modi, Sheetal Chandel, C. Vyjayanthi
2019 10th International Conference on Computing Communication and Networking Technologies Icccnt 2019, 2019