Computer Science & Engineering and Information Technology (CSE&IT)

Dr. Pardeep Kumar is currently working as Professor in the Department of Computer Science & Engineering at Jaypee University of Information Technology (JUIT), Wakanaghat and he has 17 plus years of extensive experience in Academics. Prior to joining Jaypee Group, he was associated with Mody University of Technology & Science (Formerly known as Mody Institute of Technology & Science) Laxmangarh, Sikar, Rajasthan. He has completed his Ph.D. (Computer Science and Engineering) from Uttarakhand Technical University, Dehradun. He obtained his M.Tech. (Computer Science & Engineering) from Guru Jambheshwar University of Science & Technology, Hisaar, Haryana. He obtained his B.Tech. (Information Technology) from Kurukshetra University, Kurukshetra, Haryana.

Dr. Kumar has served as TPC member for various conferences like I^st IEEE International Conference on Parallel, PDGC 2010, I^st IEEE International Conference on Image Information Processing, ICIIP 2011, 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, PDGC 2012, 2^nd IEEE International Conference on Advances in Computing, Communication and Informatics, ICACCI 2013, 3^rd International Conference on Advanced Computer Science & Information Technology ICAIT 2014, 3^rd International Conference on Digital Image Processing ICDIP 2014 etc. He has served as a member to organize Summer School on Parallel and Distributed Computing in 2012. He has served as Program Chair in 3^rd IEEE International Conference on Parallel, PDGC 2014. He has also served as Publicity Committee Chair in 3^rd International Conference on Image Information Processing (ICIIP-2015) held in December, 2015. He has actively participated in more than 8 workshops of international repute organized by IUCEE (Indo-US Collaboration for Engineering Education) Faculty Leadership Institute, Louisiana Tech University, LA, USA , Technion - Israel Institute of Technology etc. He has delivered invited talks in different institutions of repute across India in his area of expertise.

Dr. Kumar is also serving as Senior Member of ACM (Association for Computing Machinery), Life Member of IAENG (International Association of Engineers) and IAENG society of computer science and society of Data Mining. Dr. Kumar has published around 74 papers in peer reviewed Journals and Conferences of National and International repute with publishers like IEEE, ACM, Springer, Elsevier etc. He is serving as reviewer with publisher like by IEEE, ACM, Springer, Elsevier, Hindawi, Inderscience etc.

He has served as Executive General Chair of 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Guest Editor of Special Issue on "Robust and Secure Data Hiding Techniques for Telemedicine Applications", Multimedia Tools and Applications: An International Journal, Springer (SCI Indexed Journal, IF= 1.346), Lead Guest Editor of Special Issue on "Recent Developments in Parallel, Distributed and Grid Computing for Big Data", published in International Journal of Grid and Utility Computing, Inderscience (Scopus Indexed), Guest Editor of Special Issue on "Advanced Techniques in Multimedia Watermarking", published in International Journal of Information and Computer Security, Inderscience (Scopus Indexed). Dr. Kumar is appointed as Associate Editor of IEEE Access (SCI Indexed, IF = 3.5) Journal. Dr. Kumar has been enlisted in top 2% Computer Scientists (Artificial Intelligence & Image Processing) list of 2024 prepared and released by Stanford University, USA and Elsevier

Conference General Chair:- PDGC 2024

2024 Eighth International Conference on Parallel, Distributed and Grid Computing(PDGC): 18th - 20th December, 2024
Now Paper Submission is CLOSED in PDGC 2024

Regional Series Editor:

Dr. Kumar is appointed as Regional Book Series Editor (India and South Asia) in Book Series "Intelligent Data-Centric Systems: Sensor Collected Intelligence " published by Elsevier.

Call for Book Project Proposals:

Book Project Proposals are Welcome. The potential editors may send their proposals to pardeepkumarkhokhar@gmail.com

Managing Editor:

Dr. Kumar has been appointed as Managing Editor, Grid and Utility Computing, An International Journal (Indexed in ESCI, SCOPUS, DBLP and many more) on 16-06-2022.

Top 2% Scientist:

Dr. Kumar has been enlisted in top 2% Computer Scientists (Artificial Intelligence & Image Processing) list of 2024 prepared and released by Stanford University, USA and Elsevier

Qualifications:

• B.Tech. Information Technology, Kurukshetra University, Kurukshetra (Haryana), India
• M.Tech. Computer Science & Engineering, Guru Jambheshwar University of Science & Technology, Hisaar (Haryana) India
• Ph.D. Computer Science & Engineering, Uttarakhand Technical University, Dehradun (Uttarakhand), India

Odd Semester Teaching Assignment

Even Semester Teaching Assignment

Research Groups:

1. Computational and Machine Intelligence

2. Databases and Distributed Systems

Interest Areas:

Computational and Machine Intelligence

Machine Learning, Evolutionary Computing, Information Retrieval and Unstructured Data Mining, Intelligence Gathering based on Social Media

Databases and Distributed Computing

DBMS, Big Data, Distributed Databases, Data Warehousing, Data Mining, Social Media Data Analysis (Trends, Rumors, Gossips), Multimedia Analytics

Open Project Titles:

1. Robust and Secure Data Hiding Techniques for Digital Documents
Information hiding is the science of concealing a secret message or watermark inside a cover media (a host file/message) for providing various security purposes such as content authentication, integrity verification, covert communication, and so on. Robustness and Imperceptibility tradeoff is the crux of such techniques for securing digital documents. Fast Fourier Transform, Discrete Cosine Transform, Discrete Wavelet Transform etc are the important transform to be considered for robust and secure transmission of multimedia data over network.

2. Early Detection of Breast Cancer Using Deep Learning Models
Breast Cancer is most popular and growing disease in the world. Breast Cancer is mostly found in the women. Early detection is a way to control the breast cancer. There are many cases that are handled by the early detection and decrease the death rate. Deep learning techniques including different variants of CNNs including hybrid CNNs attract researchers and radiologists due to their high accuracy for early detection of breast cancer from analysis of mammograms.

3. Stock Market Prediction Using Deep Learning Models
Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). Four types of deep learning architectures i.e Multilayer Perception (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are used for predicting the stock price of a company based on the historical prices available. Hyper tuning of deep learning frameworks for stock market prediction is the main crux of such projects.

4. Thyroid Nodule Detection Using Deep Learning Models
Thyroid nodules are found in up to 68% of asymptomatic adults in the general population. Approximately 7–15% of thyroid nodules are thyroid cancer, which is the most rapidly increasing malignancy in all populations. The large number of thyroid nodules, with only a fraction being cancerous, calls for a reliable method to accurately differentiate malignant from benign nodules. A few studies have focused on a comparison of the diagnostic performance of AI with clinicians in thyroid nodule differentiation. Deep learning techniques including different variants of CNNs including hybrid CNNs attract researchers and radiologists due to their high accuracy for early detection of thyroid nodules from analysis of Ultrasound (USG) Images. Hyper tuning of newly developed model is the main crux in such types of projects.

5. Fake News Detection using Deep Learning Models
The explosion of social media allowed individuals to spread information without cost, with little investigation and fewer filters than before. This amplified the old problem of fake news, which became a major concern nowadays due to the negative impact it brings to the communities. In order to tackle the rise and spreading of fake news, automatic detection techniques have been researched building on artificial intelligence and machine learning. The recent achievements of deep learning techniques in complex natural language processing tasks, make them a promising solution for fake news detection too. Deep learning techniques including different variants of CNNs, LSTM and RNN including their hybrids attract researchers to get rid of fake news menace due to their high level of accuracy. Hyper tuning of such newly developed models is the main crux of fake news detection type of projects.

Doctoral Research Supervision:

Dr. Rajshree Srivastava (05 February, 2024) Title: Early Identification and Classification of Thyroid Nodule in Medical Ultrasound Images (Awarded) Discipline: Computer Science & Engineering

Dr. Raghavendra Kumar (26 August, 2022) Title: Design of Hybrid Classifiers for Time Series Prediction using Stock Market Data (Awarded) Discipline: Computer Science & Engineering

Dr. Chandan Kumar (27 May, 2020) Title: Improved Techniques of Robust and Secure Watermarking for Digital Documents (Awarded) Discipline: Computer Science & Engineering

Dr. Akash Punhani (02 August, 2018) Title: On Improving Throughput and Latency of Mesh Interconnection Network (Awarded) Discipline: Computer Science & Engineering

Two research scholars are pursuing Ph.D degrees under the supervision of Dr. Kumar at present.

M.Tech Theses (Supervised)

B.Tech Major Projects Guided:

Editorial Note:

Robust and Secure Data Hiding Techniques for Telemedicine Applications

Advanced Techniques in Multimedia Watermarking

Recent Developments in Parallel, Distributed and Grid Computing for Big Data

Recent Trends in Artificial Intelligence Techniques for Fault-Tolerance, Reliability and Availability in Mission-Critical Networks