M
Muhammad Umar Javed
Researcher at COMSATS Institute of Information Technology
Publications - 25
Citations - 451
Muhammad Umar Javed is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Computer science & Mean absolute percentage error. The author has an hindex of 6, co-authored 16 publications receiving 133 citations.
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Journal ArticleDOI
A Blockchain-Based Load Balancing in Decentralized Hybrid P2P Energy Trading Market in Smart Grid
TL;DR: A model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio of electricity is proposed and three smart contracts are proposed to implement the hybrid electricity trading market.
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Blockchain-Based Secure Data Storage for Distributed Vehicular Networks
Muhammad Umar Javed,Mubariz Rehman,Nadeem Javaid,Abdulaziz Aldegheishem,Nabil Alrajeh,Muhammad Tahir +5 more
TL;DR: Both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism and a trust management mechanism is also proposed in this work to calculate the nodes’ reputation values based upon their trust values.
Journal ArticleDOI
Blockchain Based Data and Energy Trading in Internet of Electric Vehicles
TL;DR: Wang et al. as discussed by the authors exploited consortium blockchain to maintain transparency and trust in trading activities in Internet of Electric Vehicles (IoEV), where smart contracts are used to tackle trading disputes and illegal actions.
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Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids
Aqdas Naz,Muhammad Umar Javed,Nadeem Javaid,Tanzila Saba,Musaed Alhussein,Khursheed Aurangzeb +5 more
TL;DR: The proposed techniques efficiently increased the prediction accuracy of load and price and the computational time is increased in both scenarios.
Journal ArticleDOI
An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids
TL;DR: In this paper, a robust big data analytics technique is proposed to resolve the electricity theft problem by using adaptive synthesis (ADASYN) and convolutional neural network (CNN) and long short term memory (LSTM) integrated deep siamese network (DSN).