Research Projects

TEQIP-III Seed Grant:
Amount: 1,50,000 INR
Title of the project: Machine Learning/AI Based Solutions to minimize Power misuse using smart meters and IoT.
Equipments Bought under this project: IoT based Smart Meter, High End HP Laptop (Intel i7 10th Gen, 32 GB RAM)
Software: Open Source software is used, mostly Python.
Publications based on this project:

  1. Shahid M Shah, "Modelling energy consumption of domestic households via supervised and unsupervised learning: A Case Study", Accepted for publication in proceedings for Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications (SoMMA'20) .

  2. S. Dyuthi , Shahid M Shah, "Activity Modeling of individuals in domestic households using fuzzy logic", Accepted for publication in proceedings for Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications (SoMMA'20) .

  3. M. Shahnawaz, Shahid M Shah, "Supervised Machine Learning Approaches for Attack Detection in the IoT Network" accepted in International Conference on IoT and its Applications (ICIA-2020)

  4. Shahnawaz, M, Shahid M Shah, "Mitigating malicious insider attacks in the Internet of Things using supervised machine learning techniques", accepted for publication in scalable computing: practice and experience Journal. (Scopus, WoS Indexed, E-SCI).

  5. Shahid M Shah, A. Anand, N. Islam, R. Junaid, "LSTM and CNN Neural Net Based prediction in time series data from smart meters", accepted for International Conference on Mathematical Sciences (ICMS-2021), NIT Surat.

(Under Review)

1. Nida Ul Islam, Shahid M Shah, “Gaussian Mixture Model-based Clustering for Smart Meter Time Series Data.”, Proposal accepted for upcoming book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, CRC Publishers

2. Mir Shahnawaz, Shahid M Shah, “Attack Detection in IoT: A Machine Learning Approach”, Proposal accepted for upcoming book AI in Security (part of AAP Advances in Artificial Intelligence and Robotics), CRC Publishers

Man-power involved in this project:

PhD Scholars:

  1. Nida Ul Islam

  2. Mir Shahnawaz

BTech Students:

  1. Raheen Junaid

  2. Anurag Anand

  3. Randhir

  4. Numair

  5. Abrar