CURRENT ACADEMIC ROLE & RESPONSIBILITIES
Dr. Ranjit Panigrahi received his Master of Technology in Computer Sciences & Engineering at Sikkim Manipal Institute of Technology, Sikkim and PhD degree in Computer Applications from Sikkim Manipal University. At present, he is deputed as Assistant Professor – Selection Grade in Department of Computer Applications in Sikkim Manipal Institute of Technology, Sikkim, India. His area of research includes machine learning, biomedical engineering. He is also a certified Microsoft Technology Specialist. At Sikkim Manipal Institute of technology his roles and responsibilities include -
- Teaching BCA and MCA students
- Teacher Guardian of 15 students.
- Advisor - SMIT IT Council
- Member – ERP Implementation Committee
SUBJECTS CURRENTLY TEACHING
| Subject |
Subject code |
Semester |
| ASP .NET Applications |
CA2403 |
MCA II Year / IV Semester |
| DATA WAREHOUSING AND DATA MINING |
CA2433 |
MCA II Year / IV Semester |
| .NET PROGRAMMING |
CA1506 |
BCA III Year / V Semester |
| PYTHON PROGRAMMING |
CA1603 |
BCA III Year / VI Semester |
ACADEMIC QUALIFICATIONS
| Degree |
Specialisation |
Institute |
Year of passing |
| PhD |
Design & Development of a Host Based Intrusion Detection Systems with Classification of Alerts |
Sikkim Manipal University, Sikkim, India |
2020 |
| M.Tech |
Computer Science and Engineering |
Sikkim Manipal Institute of Technology |
2013 |
Experience
| Institution / Organisation |
Designation |
Role |
Tenure |
| Sikkim Manipal Institute of Technology |
Assistant Professor - SG |
Teaching |
02-06-2020 to till date |
| Sikkim Manipal Institute of Technology |
Assistant Professor - I |
Teaching |
14-08-2015 to 01-06-2020 |
| Sikkim Manipal Institute of Technology |
Assistant Professor - II |
Teaching |
15-07-2010 to 13-08-2015 |
| Sikkim Manipal University |
System Analyst |
Software Development |
15-07-2010 to 13-08-2015 |
AREAS OF INTEREST, EXPERTISE AND RESEARCH
Data Mining, Machine Learning, Wireless Sensor Network
Pattern Recognition, Machine learning, Pattern Recognition
Machine learning, Intrusion Detection, Biomedical Engineering, Signal Processing
Professional Affiliations & Contributions
- International Association of Computer Science and Information Technology (IACSIT). Membership Number: 80348183, Url: http://www.iacsit.org/
Bhatt, T. V., Bhoi, A. K., Marques, G., & Panigrahi, R. (2021). A Fuzzy Logic Approach for Improved Simulation and Control Washing Machine System Variables. In Advances in Systems, Control and Automations: Select Proceedings of ETAEERE 2020 (pp. 699-715). Springer Singapore.
Chidinma, O. A., Borah, S., & Panigrahi, R. (2021). Suicidal Intent Prediction Using Natural Language Processing (Bag of Words) Approach. In Soft Computing Techniques and Applications (pp. 147-153). Springer, Singapore.
Panigrahi, R., Borah, S., & Mishra, D. (2021). A Proposal of Rule-Based Hybrid Intrusion Detection System Through Analysis of Rule-Based Supervised Classifiers. In Intelligent and Cloud Computing (pp. 623-633). Springer, Singapore.
Panigrahi, R., Borah, S., & Chakraborty, U. K. (2021). WEKA Result Reader—A Smart Tool for Reading and Summarizing WEKA Simulator Files. In Evolution in Computational Intelligence (pp. 159-167). Springer, Singapore.
Panigrahi, R., Borah, S., Bhoi, A. K., & Mallick, P. K. (2020). Intrusion detection systems (IDS)—an overview with a generalized framework. Cognitive Informatics and Soft Computing, 107-117.
Panigrahi, R., & Borah, S. (2020). A Statistical Analysis of Lazy Classifiers Using Canadian Institute of Cybersecurity Datasets. In Advances in Data Science and Management (pp. 215-222). Springer, Singapore.
Borah, S., Panigrahi, R., & Chakraborty, A. (2018). An enhanced intrusion detection system based on clustering. In Progress in Advanced Computing and Intelligent Engineering (pp. 37-45). Springer, Singapore.
Panigrahi, R., Sharma, K., & k Ghose, M. (2013). An Integrated Approach of Intrusion Detection System (IAIDS) for Wireless Sensor Networks. IJCA Proceedings on Computing Communication and Sensor Network, 5-8.
Panigrahi, R., Sharma, K., & Ghose, M. K. (2013, September). Wireless Sensor Networks–Architecture, Security Requirements, Security Threats and its Countermeasures. In Proc. Computer Science and Information Technology (CS&IT) (pp. 1-9).
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NEWS & ARTICLES
Big Data & Edge Intelligence for Enhanced Cyber Defense: Principles and Research
by CRC Press, Taylor & Francis Group
This book calls chapters based on recent state-of-the art edge artificial intelligence approaches used for enhanced cyber defense mechanisms to handle big data. Chapters should provide a glimpse of computational intelligence techniques and automated reasoning models capable of fast training and timely data processing of cybersecurity big data. Apart from edge artificial intelligence techniques, this book also calls chapters related to reviews of recent cyber threats and attacks and their countermeasures. Chapters solicited for this book are -
Chapter 1. A Brief introduction to cyber threats and related countermeasures
This chapter will be dealt with a brief introduction of Cyber Defense Systems that has been outlined highlighting the modern threats and attacks in various network environments such as host-based network-based, smart grid. An emphasis will be given to categorize and presents a detailed taxonomy of cyber threats according to the source of origination and location. A brief introduction of the latest available defense mechanisms is also discussed in this chapter. As an introductory chapter, these topics enlighten the readers with a brief idea of current threats found in the cyber arena.
Chapter 2. Edge Artificial Intelligence – An Overview
This chapter will be a brief introduction to Edge Artificial Intelligence. The chapter aims to deliver advantages of EdgeAI; viz., real-time operations, reduced power consumption, decreased data communication cost, and increased privacy. It is also intended to deliver the role of EdgeAI to handle cybercrimes and related threats. The role of EdgeAI will also be analyzed looking forward, how effectively it allows the network topology to be fault-tolerant even in the influence of attacks.
Chapter 3. Reviews of EdgeAI based Cyber Defense Mechanisms
This chapter will provide a brief survey of state-of-art EdgeAI based Cyber Defense Mechanisms (CDMs). Various CDMs are explored based on detection type and approaches. Emphasis will be given to those methods that integrate signature based CDMs in the process of detection. Moreover, many modern embedded EdgeAI based methods will be explored that acts as the front line of defense in the network environment. Further, a detailed taxonomy of CDMs datasets that are huge and contains recent attack trends will be explored elaborating the target environment.
Chapter 4 to Chapter 15. Contributory Chapters from prospective authors
All 11 chapters will be dealt with specific challenging areas pertaining to cyber defense through Edge AI. We are inviting chapters from you in the following 11 areas, including but not limited to
- Artificial Intelligence and Machine Learning for intrusion identification and prevention in vulnerable environments
- Improvising Digital forensic intelligence through EdgeAI techniques
- Digital Investigation, and forensic data analytics
- Proactive and Adaptive Defense of Network Infrastructure
- Artificial Intelligence (AI) based smart appliances and smart grid forensic
- Trustworthy infrastructures, services, and applications for sensing technologies
- Attacks detection and prediction based on deep and reinforcement learning
- Cryptocurrency-enabled crimes and countermeasures
- Cybersecurity Metrics and Assessment
- Bio-inspired cyber defense mechanisms
- AI-based security protocols to enhance cyber defense
Important Dates
Chapters Submission: August 30, 2021 (On or before)
Review Notification: September 30, 2021 (On or before)
Camera Ready Submission: October 30, 2021 (On or before)
Website | Indexing | Word Template | Submission Link
Do not hesitate to contact us at ranjit.panigrahi@gmail.com if you have any questions. Kindly mention "Big Data & Edge Intelligence for Enhanced Cyber Defense" on the subject line in all your email correspondences.
Healthcare Big Data Analytics:Computational Optimization and Cohesive Approaches
by Walter de Gruyter GmbH, Genthiner Str. 13, 10785 Berlin, Germany
This book highlights the latest and future trends and practices in healthcare systems. This book highlights various aspects of how cutting-edge smart optimized big data applications can be used for patient monitoring and clinical diagnosis. As the IoT-based applications are data-driven and mostly employ modern optimization techniques, therefore the book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges, and issues in data collection, data handling, and data collection set-up.
Call for chapters
The book calls chapters from the potentials authors in the following scope –
- Ambient Intelligence and Pervasive Computing in Health Care
- Artificial intelligence techniques in Healthcare Big Data
- Biomedical feature extraction and processing techniques
- Biomedical Signal Processing techniques
- Data Processing and Data Mining in e-Health Applications
- Decision support systems for medical decision making
- Deep learning based self-intelligent medical system
- E-Health Application Design for mobile devices
- Signal processing for healthcare IoT devices
- Expert Systems and class imbalance learning of medical Big data
- Healthcare data acquisition, cleaning and intelligent processing
- Healthcare telemedicine and teleservices
- Intelligent medical information systems
- Security System design on healthcare bigdata
- Knowledge extraction from healthcare big data
- Machine Intelligence and processing of Medical Big Data
- Machine learning and artificial intelligence for eHealth monitoring
- Machine Learning-based Medical Systems
- Massive healthcare data processing
- Medical Expert Systems and Applications
- Modern deep learning-based healthcare system
- Neural Network modelling and medical big data analysis
- Optimization algorithms for Healthcare Big data processing
- Recent developments of sensor devices for biomedical monitoring
- Remote diagnosis mechanisms
- Scientific discovery of large-scare healthcare data
- Standalone and Mobile e-Health Systems
- Visualization and design principles of medical big data
Important Dates
Chapters Submission: August 30, 2021 (On or before)
Review Notification: September 30, 2021 (On or before)
Camera Ready Submission: October 30, 2021 (On or before)
Website | Indexing | Submission Link
