Jyotir Moy Chatterjee
@geu.ac.in
Assistant Professor
Graphic Era University
I'm an Assistant Professor (Visiting Faculty) at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation) in Kathmandu, Nepal. I've also held roles as an Assistant Professor and Program Leader (B.Sc. IT) at the same institution and as an Assistant Professor (CSE) at GD-Rungta College of Engineering & Technology (Chhattisgarh Swami Vivekananda Technical University) Bhilai, India. I received an M. Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha in 2016, and a B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Maduravoyal, Chennai in 2013. My research interests include Machine Learning and Deep Learning. My research focuses on Deep Learning and Machine Learning.
EDUCATION
2014-2016 M. Tech (Computer Science & Engineering) Kalinga Institute of Industrial Technology, Bhubaneswar-24
2009-2013 B. Tech (Computer Science & Engineering) Dr. MGR Educational and Research Institute, Chennai-95
2009 Intermediate (Science) Central Board of Secondary Education, New Delhi
2006 Matriculation Central Board of Secondary Education, New Delhi
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Engineering, Information Systems, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, Sangeeta Yadav, and Jyotir Moy Chatterjee
Springer Science and Business Media LLC
Kailash P. Dewangan, Padma Bonde, Rohit Raja, and Jyotir Moy Chatterjee
Springer Science and Business Media LLC
Amit Sagu, Nasib Singh Gill, Preeti Gulia, Ishaani Priyadarshini, and Jyotir Moy Chatterjee
Institute of Electrical and Electronics Engineers (IEEE)
R. Sujatha, K. Mahalakshmi, and Jyotir Moy Chatterjee
Wiley
Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, and Jyotir Moy Chatterjee
Springer Science and Business Media LLC
Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, and Jyotir Moy Chatterjee
Springer Science and Business Media LLC
T. Muthamilselvan, K. Brindha, Sudha Senthilkumar, Saransh, Jyotir Moy Chatterjee, and Yu-Chen Hu
Springer Science and Business Media LLC
Yamuna Mundru, Manas Kumar Yogi, and Jyotir Moy Chatterjee
Elsevier
R. Sujatha and Jyotir Moy Chatterjee
Elsevier
Yamuna Mundru, Manas Kumar Yogi, Jyotir Moy Chatterjee, Madhur Meduri, and Ketha Dhana Veera Chaitanya
Elsevier
Sudha Senthilkumar, K. Brindha, Jyotir Moy Chatterjee, Anannya Popat, Lakshya Gupta, and Abhimanyu Verma
Springer Science and Business Media LLC
Palak, Sangeeta, Preeti Gulia, Nasib Singh Gill, and Jyotir Moy Chatterjee
Springer International Publishing
R. Sujatha, Jyotir Moy Chatterjee, A. Rohith, and Rabie A. Ramadan
IEEE
People take various types of cereals every day in their regular meals, but most people do not know their importance while consuming them. Each cereal has its benefit. One such cereal content is dry beans. Dry beans are a variety of beans that are produced in pods. These dry beans can be cooked and eaten, and it has numerous proteins and vitamins and are highly beneficial for our health. It also provides excellent immunity. Each bean has its characteristics, which can be identified with its unique features. These can be oval or kidney-shaped or even without a proper shape. According to their shape and features, they are classified separately. These nutrient-rich beans have to be classified based on their shapes. Human eyes sometimes may oversee or misclassify these tiny cereals for classification. This work involves the classification of 7 such dry bean varieties utilizing a deep learning context. The dataset utilized in this paper includes 13611 dry bean data for seven different UCI Machine learning repositories. In this work, we have taken seven different categorical labels: the dry bean varieties. The classification is done using deep learning techniques, and here we have utilized the Keras Sequential Algorithm for the classification. It is a supervised learning concept of machine learning used to predict two or more categorical labels. With the help of the deep learning approach, these dry beans are classified and obtained an accuracy of 94.88%.
Rishikesh Bhupendra Trivedi, Daksh Mittal, Anuj Sahani, Clely Voyena Fernandes, Somya Goyal, Jyotir Moy Chatterjee, and Vanshika Mehta
Springer Nature Singapore
M. L. S. N. S. Lakshmi, S. Prasad Jones Christydass, S. Kannadhasan, K. Anguraj, and Jyotir Moy Chatterjee
Hindawi Limited
An efficient triband metamaterial absorber is presented for X- and K-band applications. The unit cell is of simple shape. The absorber is fabricated on a thin polyamide, which makes it flexible. The parameters of the designed absorber are optimized. The simulated results show that it has good absorption rate and polarization stability. The stability is exhibited over a wide range in both TE and TE modes of the incident waves. The measured results are on par with the simulated results. The measurement is carried out with the waveguide measurement method.
Avishek Choudhuri, R. Sujatha, Chhazed Shreyans Nitin, Jyotir Moy Chatterjee, and R. N. Thakur
Springer Nature Singapore
R. Sujatha, Jyotir Moy Chatterjee, Ishaani Priyadarshini, Aboul Ella Hassanien, Abd Allah A. Mousa, and Safar M. Alghamdi
Springer Science and Business Media LLC
AbstractAny nation’s growth depends on the trend of the price of fuel. The fuel price drifts have both direct and indirect impacts on a nation’s economy. Nation’s growth will be hampered due to the higher level of inflation prevailing in the oil industry. This paper proposed a method of analyzing Gasoline and Diesel Price Drifts based on Self-organizing Maps and Bayesian regularized neural networks. The US gasoline and diesel price timeline dataset is used to validate the proposed approach. In the dataset, all grades, regular, medium, and premium with conventional, reformulated, all formulation of gasoline combinations, and diesel pricing per gallon weekly from 1995 to January 2021, are considered. For the data visualization purpose, we have used self-organizing maps and analyzed them with a neural network algorithm. The nonlinear autoregressive neural network is adopted because of the time series dataset. Three training algorithms are adopted to train the neural networks: Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization. The results are hopeful and reveal the robustness of the proposed model. In the proposed approach, we have found Levenberg-Marquard error falls from − 0.1074 to 0.1424, scaled conjugate gradient error falls from − 0.1476 to 0.1618, and similarly, Bayesian regularization error falls in − 0.09854 to 0.09871, which showed that out of the three approaches considered, the Bayesian regularization gives better results.
Amit Sagu, Nasib Singh Gill, Preeti Gulia, Jyotir Moy Chatterjee, and Ishaani Priyadarshini
MDPI AG
With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data from the network and categorizes it as “Attack” or “Normal”. Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques.
M.O. Khairandish, M. Sharma, V. Jain, J.M. Chatterjee, and N.Z. Jhanjhi
Elsevier BV
Laying The
Wiley
The promise of smart city initiatives is difficult to ignore. From safer, less congested streets to a cleaner environment, smart cities can be more appealing places to live, work and play. Smart city solutions can also enable government to deliver services more efficiently, transparently and effectively. Whether it’s growing the tax base or making the best use of scarce resources, the case for investment is clear.