The Department of Artificial Intelligence and Data Science started during the year 2020-2021. It is a four-year course. The Intake of students in the Department is 60. The Department has well-equipped laboratories to support students to explore themselves in the area of AI &DS. The Department has a Centre of Excellence, coloration with the AI Experts to teach students in the emerging technologies. A well-educated Staff member works in hand with students to impart quality education and set up a better career.
Vision
To impart quality education to stimulate academic excellence, research, and skills in students in the areas of data science and artificial intelligence at national and global levels to build an ecosystem to contribute significantly to society.
Mission
M1. develop technically competent and socially responsible professionals.
M2. inculcate professional ethics, human values, leadership qualities and lifelong learning.
M3. to prepare students to excel in the field of artificial intelligence through collaboration with the ai expertise via the centre of excellence.
M4. inspire students to develop their ideas into products and transforming them into aspirant ai professionals and entrepreneurs.
PROGRAM EDUCATIONAL OBJECTIVES (PEOs)
- To provide graduates with the proficiency to utilize the fundamental knowledge of basic sciences, mathematics, Artificial Intelligence, data science, and statistics to build systems that require management and analysis of a large volume of
- To enrich graduates with the necessary technical skills to pursue pioneering research in the field of AI and Data Science and create disruptive and sustainable solutions for the welfare of
- To enable graduates to think logically, pursue lifelong learning, and collaborate with an ethical attitude in a multidisciplinary
PROGRAM OUTCOMES (POs) ENGINEERING GRADUATES WILL BE ABLE TO:
- Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and Artificial Intelligence and Data Science basics to the solution of complex engineering
- Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using the first principles of mathematics, natural sciences, and engineering
- Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for public health and safety, and the cultural, societal, and environmental
- Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis, and interpretation of data, and synthesis of the information to provide validly
- Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the
- The engineer and society: Apply to reason informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering
- Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable
- Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering
- Individual and teamwork: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary
- Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear
- Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one‘s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
- Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological
Programme Specific Outcomes
- Graduates should be able to evolve AI-based efficient domain-specific processes for effective decision making in several domains such as business and governance domains.
- Graduates should be able to arrive at actionable Foresight, Insight, hindsight from data for solving business and engineering problems
- Graduates should be able to create, select and apply the theoretical knowledge of AI and Data Analytics along with practical industrial tools and techniques to manage and solve wicked societal problems
Paper Publications:
| S.No | Paper Publication Details |
|---|---|
| 1 | P.Ananthi “An Enhanced approach using Spectral Cluster-based Decision Tree Data Mining Technique for Analyzing Student Performance in Higher Education Institutions” Proteus Journal, Vol 11, issue 11, Nov 2020 |
| 2 | P.Ananthi “Evaluation of Mobile-Based Cloud Computing Using Destination-Sequenced Distance-Vector Routing Technique” IJSART, Vol 4, Issue 3, March 2018 |
| 3 | Patel, S., Thanikaiselvan, V., Pelusi, D., Nagaraj, B., Arunkumar, R. and Amirtharajan, R., 2021. Colour image encryption is based on customized neural networks and DNA encoding. Neural Computing and Applications, pp.1-18. |
| 4 | Selva, D., Nagaraj, B., Pelusi, D., Arunkumar, R. and Nair, A., 2021. Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms. Algorithms, 14(8), p.224. |
| 5 | Nagaraj, B., Arunkumar, R., Nisi, K. and Vijayakumar, P., 2020. Enhancement of fraternal K-median algorithm with CNN for high dropout probabilities to evolve optimal time-complexity. Cluster Computing, 23(3), pp.2001-2008. |
| 6 | Balakrishnan, N. and Rajendran, A., 2020. Computing WHERE-WHAT Classification Through FLIKM and Deep Learning Algorithms. In Advances in Electrical and Computer Technologies (pp. 593-608). Springer, Singapore. |
| 7 | Balakrishnan, N., Rajendran, A. and Palanivel, K., 2019. Meticulous fuzzy convolution C means for optimized big data analytics: adaptation towards deep learning. International Journal of Machine Learning and Cybernetics, 10(12), pp.3575-3586. |
| 8 | Nagaraj Balakrishnan, R.S., Arunkumar, R. and MS, P., 2018. Smart real-time rescue system for fishermen. Pak. J. Biotechnol, 15, pp.73-75. |
| 9 | Arunkumar, R. and Balakrishnan, N., 2018. Medical image classification for disease diagnosis by DBN methods. Pak. J. Biotechnol, 15(1), pp.107-110. |
| 10 | Nagaraj Balakrishnan, R.S., Arunkumar, R. and MS, P., 2017. A survey on border alert systems for fishermen. Pak. J. Biotechnol. Vol, 14(4), pp.829-831. |
| 11 | Arunkumar, R. and Karthigaikumar, P., 2017. Multi-retinal disease classification by reduced deep learning features. Neural Computing and Applications, 28(2), pp.329-334. |
| 12 | Rajendran, A., Balakrishnan, N., and Varatharaj, M., 2016. Malleable fuzzy local median C means algorithm for effective biomedical image segmentation. Sensing and Imaging, 17(1), p.24. |
| 13 | Soundharya, M. and Arunkumar, R., 2015, November. GDI-based area delay power-efficient carry select adder. In 2015 Online International Conference on Green Engineering and Technologies (IC-GET) (pp. 1-5). IEEE. |
| 14 | Vishnu, T., Saranya, K., Arunkumar, R. and Devi, M.G., 2015, November. Efficient and early detection of osteoporosis using trabecular region. In 2015 Online International Conference on Green Engineering and Technologies (IC-GET) (pp. 1-5). IEEE. |
| 15 | Rajendran, A. and Muthusamy, T., 2014. Adaptive unsupervised fuzzy C mean-based image segmentation. Sci. J. Circuits Syst. Signal Process., 3(6–1), pp.1-5. |
Patents Filed:
| S.NO | Faculty Name | Patent Title |
|---|---|---|
| 1 | R.Arunkumar | Building Quality Analyzer And A Life Predictor To Avoid Collapse /Disaster |
| 2 | R.Arunkumar | High Efficient Renewable Power Generator Drive For Portable Applications: A Portable Windmill |
| 3 | R.Arunkumar | Cost-efficient tyre inflation monitoring system for any vehicle |
| 4 | R.Arunkumar | Self Assessment Fuel Meter (Saf-Meter) |
| 5 | R.Arunkumar | Audience Behavioural Rating (Ab-Rating) |
| 6 | R.Arunkumar | Smart Automated Rubber Tree Tapping Machine |
| 7 | R.Arunkumar | Cost-Effective IR Based Anti-Piracy Theatre System |
| 8 | P.Ananthi | Soil Health Monitoring Using Drones And Augmented Reality In Agricultural Regions |
 |
Name:Dr.R.Arunkumar Designation: Associate Professor Qualification: M.E., Ph.D., Area of Interest: Deep Learning, Data Mining |
 |
Name:P.Ananthi Designation: Assistant Professor Qualification: M.E Area of Interest: Data Mining, Software Testing |
 |
Name: R.Karthika Designation: Assistant Professor Qualification: M.E Area of Interest: Computer Networks, Data Mining |
CENTRE OF EXCELLENCE FOR AI & ML
Artificial Intelligence and Machine Learning
Rough Outline
- Introduction to AI
- Introduction to ML
- Google Teachable Machines
- Python Basics
- Tensorflow 2.0
- Creating Analytics with tensor flow
- Applications of ML
- ML for 1D and 2D data
- Dataset management
- Clustering and Data mining
- Training ML
- Decision Making
- Deep Learning
PREREQUISITE
- Knowledge-based on Linear Algebra, Probability, and Statistics.
- Knowledge-based on General programming logic (not specific to any high-level language).
- Knowledge-based on Problem Solving Methodology.
LEARNING OUTCOMES
- Understand AI, its applications, and use cases.
- Transforming Industry to more intelligence.
- Understanding the terms of Machine Learning, Neural Networks, and Deep Learning.
- Hands-on training for the Tensorflow to implement AI
SYLLABUS
Foundation of AI
- Introduction to AI
- Is AI needed?
- Basics of AI
- Characteristics of Intelligent Agents
- Problem Solving approach
- Knowledge Representation & Reasoning
Reasoning under Uncertainty
- Bayesian Network
- Decision Tree
Planning
- Introduction to Planning
- Plan Space Planning
- Planning Graph and Graph Plan
Planning and Decision Making
- Practical Planning and Acting
- Sequential Decision Problem
- Making Complex Decisions
Learning Methodologies
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Machine Learning
- Introduction to Machine learning
- Decision Tree
- Linear Regression
- Support vector Machine
- Unsupervised Learning
- Reinforcement Learning
- Learning in Neural networks
- Genetic Algorithm
Deep Learning
- Recurrent Neural Networks
- Convolutional Neural Networks
- Autoencoders