AI Department

Artificial Intelligence Department

Department of Artificial Intelligence

faculty photo

Dr. K Thyagarajan

HOD, Dept of CSE(AI), AI

M.Tech., Ph.D

Department Profile

The Department of Artificial Intelligence at Sri Venkateswara College of Engineering and Technology (SVCET), Chittoor, was established in 2025 with an initial intake of 60 students. With academic autonomy, the department has designed its own innovative curriculum that blends fundamental computer science courses with advanced Artificial Intelligence concepts. The four-year B.Tech program aims to provide students with strong foundations in AI, machine learning, data science, and robotics, while preparing them to apply these skills to solve real-world problems across industries such as healthcare, automation, and data analytics.

The program follows a student-centric approach, combining classroom instruction, project-based learning, and industry internships. Courses such as Advanced Machine Learning, AI for Natural Language Processing, Recommender Systems, Predictive Analytics, AI for Computer Vision, and Cloud Computing for AI ensure that students gain both theoretical knowledge and practical expertise. Students are encouraged to participate in hackathons, technical clubs, and research projects in areas like deep learning, natural language processing, machine vision, and AI ethics, fostering innovation and hands-on experience.

The department is headed by Dr. M. Lavanya, who brings over 20 years of teaching and research experience with expertise in Data Mining, Web Mining, Machine Learning, and Data Analytics. She has published 25 research articles, organized technical events, guided projects, and holds patents in Artificial Neural Networks. With highly qualified faculty, research-driven initiatives, and collaborations with leading tech companies, the department ensures that students receive holistic training, global exposure, and industry readiness to excel in their careers or pursue higher education.

Our Objectives

  • To impart strong foundational knowledge in Artificial Intelligence, Machine Learning, Data Science, and related technologies.
  • To promote project-based learning, research, and innovation in emerging areas of AI.
  • To equip students with industry-relevant skills through internships, workshops, and industry collaborations.
  • To encourage participation in hackathons, technical clubs, and co-curricular activities for holistic development.
  • To nurture ethical values and responsible use of AI technologies for societal benefit.
  • To prepare graduates for successful careers in AI, data analytics, automation, and higher studies.
  • To provide hands-on experience with modern AI tools, frameworks, and cloud platforms.
  • To foster innovation and entrepreneurship in Artificial Intelligence applications.
  • To encourage interdisciplinary research in healthcare, robotics, finance, and other domains.
  • To build problem-solving and critical thinking skills through real-world case studies.
  • To strengthen collaboration with national and international institutions for research and knowledge exchange.

Department Strength

  • ✔ Academic Autonomy
  • ✔ Comprehensive Curriculum
  • ✔ Experienced Leadership and Faculty
  • ✔ Hands-On Learning Opportunities
  • ✔ Rapid Growth and High Demand
  • ✔ Industry-Focused Initiatives
  • ✔ Cutting-Edge Research and Innovation

Courses Offered

Level Course Intake Convenor Quota (70%) B Category (30%) PIO (15%)
Under Graduate Artificial Intelligence (B.Tech) 60 42 18 N/A

Vision and Mission

🎯 Vision

To be a center of excellence in Artificial Intelligence education and research, empowering students with innovation, ethics, and leadership to solve real-world problems for the betterment of society

🚀 Mission

  • Providing quality education in Artificial Intelligence and allied fields through a dynamic curriculum and innovative teaching- learning practices.

  • Fostering research and development by encouraging students and faculty to work on cutting-edge technologies and interdisciplinary projects.

  • Nurturing industry-academia collaboration that bridges theoretical knowledge with practical applications through internships, projects, and expert interactions.

  • Inculcating professional ethics, communication skills, and a sense of social responsibility to produce globally competent AI professionals.

  • Supporting entrepreneurial and lifelong learning mindsets that adapt to the evolving demands of technology and society.

Program Outcomes


Program Educational Objectives (PEOs):

PEO Code Outcome
PEO-1 Demonstrate strong foundational knowledge and technical expertise in Artificial Intelligence and related domains to solve complex engineering problems.
PEO-2 Pursue higher studies, research, or professional development through lifelong learning and stay updated with emerging AI technologies and tools applying innovative thinking, analytical skills, and AI-based solutions to address real-world challenges across various domains.
PEO-3 Exhibit leadership qualities, effective communication, and teamwork in multidisciplinary environments, contributing to organizational and societal development with practice professional ethics, contribute responsibly to society, and make data-driven decisions with an understanding of the societal and environmental impact.

Program Specific Outcomes (PSOs)

After successful completion of the program the graduates will be able to:

PSO Code Outcome
PSO-1 Design, develop, and deploy intelligent systems using machine learning, deep learning, natural language processing, and data analytics tools to solve complex real-world problems.
PEO-2 Apply Artificial Intelligence techniques to develop innovative solutions in domains such as healthcare, agriculture, smart cities, finance, cybersecurity, and education.
PEO-2 Demonstrate ethical responsibility, awareness of societal impacts, and sustainable practices in the development and implementation of AI technologies.

Program Outcomes (PO)

Engineering Graduates will be able to:

PO Code Description
PO-1 Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
PO-2 Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
PO-3 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 considerations.
PO-4 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 valid conclusions.
PO-5 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 limitations.
PO-6 The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal, and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
PO-7 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 development.
PO-8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
PO-9 Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
PO-10 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 instructions.
PO-11 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.
PO-12 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 change.

CSE(AI&ML) Faculty List

S.No

Name of the Faculty

Designation

Qualification

Experience (in Years)
1

Dr. M.LAVANYA

M.Tech, Ph.D

HoD & ASSOCIATE PROFESSOR

20
2

Dr. PRINCE W

M.Tech(IT), Ph.D

PROFESSOR

22
3

Dr. C.RAJASELVAN

M.Tech, Ph.D

PROFESSOR

23.6
4

Dr. K.NANDHA KUMAR

M.Tech, Ph.D

ASSOCIATE PROFESSOR

15
5

Dr. SWAROOP NAMBAKAM

M.Tech, Ph.D

ASSOCIATE PROFESSOR

16
6

Mr. GEORGE SEBASTIAN

M.Tech

ASSOCIATE PROFESSOR

27
7

Dr. NAGARJUNA E

M.Tech, Ph.D

ASSOCIATE PROFESSOR

9.6
8

Dr. VASANTHAN G

M.Tech, Ph.D

ASSOCIATE PROFESSOR

15
9

Dr. CHANDRA SHEKHAR NISHAD

M.Tech, Ph.D

ASSOCIATE PROFESSOR

13.4
10

Dr. PADETI MOUNIKA

M.Tech, Ph.D

ASSOCIATE PROFESSOR

6.8
11

Mr. MOHAMMAD IQBAL

M.Tech

ASST PROFESSOR

12
12

Mr. L.KARTHIKEYAN

M.Tech, Ph.D (Pursuing)

ASST PROFESSOR

10
13

Ms. D.GAYATHRI

M.Tech, Ph.D (Pursuing)

ASST PROFESSOR

16
14

Mr. K.JYOTHEESH

M.Tech

ASST PROFESSOR

2
15

Ms. S.MONICA

M.Tech

ASST PROFESSOR

3.6
16

Ms. B.MEENA

M.Tech

ASST PROFESSOR

4
17

Mr. K.ANJANEYULU

M.Tech

ASST PROFESSOR

20
18

Ms. DEEPA VARAMPATI

M.Tech

ASST PROFESSOR

12
19

Ms. SASIREKHA B

M.Tech

ASST PROFESSOR

6
20

Mr. GURUMOHAN JERRY

M.Tech

ASST PROFESSOR

9
21

Mr. U.RAVINDRAN

M.Tech(Pursuing)

ASST PROFESSOR

15
22

Mr. JAYACHANDRAN JAYERAJ

M.Tech

ASST PROFESSOR

6
23

Mr. KARTHI E

M.Tech

ASST PROFESSOR

8
24

Ms. GANTA KOMALI

M.Tech

ASST PROFESSOR

3
25

Mr. VELUSWAMY MAHESWARAN

M.E

ASST PROFESSOR

4
26

Mr. KARNAN S

M.Tech

ASST PROFESSOR

7
27

Ms. SANTHA LAKSHMI S

M.E

ASST PROFESSOR

6.6

Journal Publications:

1 Dr. M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled on “Advanced analytical framework for crop yield prediction leveraging diverse feature selection methods and machine learning classifiers in varied agricultural environments,” in Journal of emerging technologies and innovative research, ISSN 2349-5162, volume 11, issue 7, pp 158-163, 2024

2 Dr. K. Nandha Kumar, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled on “An Efficient Blockchain Assisted Electronic Health Record (HER) Authentication System” in Panamerican Mathematical Journal 35 No. 2. ISSN 1064, pp 57-71, 2025

3 Mr. George Sebastian, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled on “Circumvent the proliferation of counterfeited circulars using blockchain,” in International Journal of Critical Computer-Based Systems, 2024 Vol.11 No.3, pp.171 – 193, 2024.

Conference Publications:

1 1. Dr. M. Lavanya, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “Quantum Nanodevice Innovation for Smart Farming: Integrating Machine Learning and Blockchain Technology”. Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024), Available at SSRN: https://ssrn.com/abstract=5083248 or http://dx.doi.org/10.2139/ssrn.5083248, November 15, 2024

2 Dr. M. Lavanya, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “Comparative analysis of ransomware attacks: Tactics, impact, and response” in the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), eISBN: 9781003606185, held on March 29th -30th ,2024.

3 M. Lavanya, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents” in the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), , eISBN: 9781003606185, held on March 29th -30th, 2024.

4 S. Savitha, Assistant Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on “A Survey on text classification approaches based on NLP” in the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), eISBN: 9781003606185, held on March 29th-30th, 2024.

5 Dr. K. NandhaKumar, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on entitled “Brain Tumor Division for MR Brain Images utilizing Artificial Bee Colony with Interval Type-II Fuzzy Strategies”, organized by Sambhram University, Jizzax, Uzbekistan on November 11th ,2024.

6 Dr. K. NandhaKumar, Associate Professor, dept. of CSE(AI&ML), Sri Venkateswara College and Technology, has presented and published a paper on a National Conference entitled “An optimization Data analytics on real time application using Machine Learning techniques”, organized by VEMU Institute of Technology, Andhra Pradesh, ISBN: 978-93-48512-15-4, during February 27th to 28th, 2025.

Book Chapter Publications:

1 1. M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Comparative analysis of ransomware attacks: Tactics, impact, and response,” PP: 122|4 pages, ISBN9781003606185, 2025.

2 M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents” pp:119|4 pages, ISBN9781003606185, 2025.

3 3. S. Savitha, Assistant Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “A Survey on text classification approaches based on NLP, PP: 80|4 pages ISBN9781003606185, 2025.

Journal Publications:

1 Dr. M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Advanced analytical framework for crop yield prediction leveraging diverse feature selection methods and machine learning classifiers in varied agricultural environments,” in Journal of Emerging Technologies and Innovative Research, ISSN 2349-5162, Vol. 11, Issue 7, pp. 158–163, 2024.

2 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Fuzzy Heuristics in Content Based Image Retrieval,” in International Journal of Engineering Research and Science & Technology, ISSN: 2319-5991, Vol. 21, Issue 1, pp. 171–178, 2025.

3 Dr. K. Nandha Kumar, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “An Efficient Blockchain Assisted Electronic Health Record (EHR) Authentication System,” in Panamerican Mathematical Journal, Vol. 35, No. 2, ISSN 1064, pp. 57–71, 2025.

4 Dr. K. Nandha Kumar, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “A Hybrid Particle Swarm Optimization and C4.5 for Network Intrusion Detection and Prevention System,” in International Journal of Computing, Vol. 23, Issue 1, ISSN 2312-5381, DOI: 10.47839/ijc.23.1.3442, pp. 109–115, 2024.

5 Mr. George Sebastian, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Circumvent the proliferation of counterfeited circulars using blockchain,” in International Journal of Critical Computer-Based Systems, Vol. 11, No. 3, pp. 171–193, 2024.

6 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “A real-time environmental air pollution predictor model using dense deep learning approach in IoT infrastructure,” in Global NEST Journal, Vol. 26, No. 3, pp. 1–16, 2024.

7 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a paper entitled “Securing the Digital Commerce Spectrum and Cyber Security Strategies for Web, E-commerce, M-commerce, and E-mail Security,” in Journal of Cybersecurity and Information Management (JCIM), Vol. 14, No. 01, pp. 34–49, 2024.

Conference Publications:

1 Dr. M. Lavanya presented “Quantum Nanodevice Innovation for Smart Farming: Integrating Machine Learning and Blockchain Technology” at the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024), November 15, 2024. [SSRN | DOI]

2 Dr. M. Lavanya presented “Comparative analysis of ransomware attacks: Tactics, impact, and response” at the 1st International Conference on Digital Transformation & Sustainability of Business (ICDTSB), eISBN: 9781003606185, March 29–30, 2024.

3 Dr. R. Mohanraj presented “The artificial intelligence for NGO” at the 1st ICDTSB, eISBN: 9781003606185, March 29–30, 2024.

4 M. Lavanya presented “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents” at the 1st ICDTSB, eISBN: 9781003606185, March 29–30, 2024.

5 S. Savitha presented “A Survey on text classification approaches based on NLP” at the 1st ICDTSB, eISBN: 9781003606185, March 29–30, 2024.

6 Dr. R. Mohanraj presented “Fuzzy Heuristics in Content Based Image Retrieval” at the International Conference on Innovations and Recent Trends in Computer Science (ICIRTCS-24), St. Martin’s Engineering College, Hyderabad, December 17–18, 2024.

7 Dr. G. Kavitha presented “Food Calory Estimation and BMI Prediction Using ML” at the International Conference on Computational Intelligence, Emerging Technologies, and Smart Systems, RK University, Rajkot, March 28, 2025.

8 Dr. K. NandhaKumar presented “Brain Tumor Division for MR Brain Images utilizing Artificial Bee Colony with Interval Type-II Fuzzy Strategies” at Sambhram University, Jizzax, Uzbekistan, November 11, 2024.

9 Dr. K. NandhaKumar presented “An optimization Data analytics on real time application using Machine Learning techniques” at a National Conference organized by VEMU Institute of Technology, Andhra Pradesh, ISBN: 978-93-48512-15-4, February 27–28, 2025.

Book Chapter Publications:

1 Dr. R. Mohanraj, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “The artificial intelligence for NGO,” PP: 92 | 4 pages, ISBN9781003606185, 2025.

2 M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Comparative analysis of ransomware attacks: Tactics, impact, and response,” PP: 122 | 4 pages, ISBN9781003606185, 2025.

3 M. Lavanya, Associate Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “Behavior digital forensic model as a digital forensics techniques for investigating cyber incidents,” PP: 119 | 4 pages, ISBN9781003606185, 2025.

4 S. Savitha, Assistant Professor, Sri Venkateswara College and Technology, has published a book chapter in Digital Transformation and Sustainability of Business, Taylor & Francis Group, CPR Press entitled “A Survey on text classification approaches based on NLP,” PP: 80 | 4 pages, ISBN9781003606185, 2025.

Data will be updated soon!

Data Analytics Using R Programming Laboratory

Objectives

  • Understand R Programming Basics – Learn the fundamentals of R, including syntax, data types, and basic operations.
  • Data Manipulation & Cleaning – Use packages like dplyr and tidyverse to manipulate and clean datasets.
  • Data Visualization – Develop skills in visualizing data using ggplot2, base R, and other libraries.
  • Statistical Analysis – Perform statistical operations such as hypothesis testing, regression, and correlation.
  • Machine Learning Basics – Implement basic machine learning algorithms like linear regression, decision trees, and clustering in R.
  • Time Series Analysis – Analyze time series data using packages like forecast and TSA.
  • Exploratory Data Analysis (EDA) – Learn to summarize and interpret datasets using various statistical techniques.
  • Big Data Handling – Work with large datasets using data.table, sparklyr, and other big data tools in R.
  • Text Mining & NLP – Perform basic text analysis using tm and tidytext packages.
  • Project Work – Apply concepts learned in a real-world dataset to derive insights.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Write and execute R scripts for data analysis.
  • Perform data cleaning, transformation, and wrangling efficiently.
  • Create meaningful visualizations to interpret data.
  • Apply statistical techniques to draw inferences from datasets.
  • Implement predictive modeling using machine learning algorithms.
  • Work with real-world datasets and conduct exploratory data analysis (EDA).
  • Handle time series data and perform forecasting.
  • Utilize R for text analytics and natural language processing.
  • Optimize performance when working with large datasets.
  • Develop a mini-project using R for data analytics applications.

DEEP LEARNING Laboratory

Objectives

  • Understand Neural Networks – Learn the fundamentals of artificial neural networks (ANNs), perceptrons, and activation functions.
  • Implement Deep Learning Models – Develop and train deep learning models using frameworks like TensorFlow and PyTorch.
  • Work with Different Architectures – Explore different deep learning architectures such as CNNs, RNNs, LSTMs, and GANs.
  • Data Preprocessing for Deep Learning – Perform data augmentation, normalization, and handling of large-scale datasets.
  • Model Training and Optimization – Learn techniques for optimizing deep learning models, including loss functions, gradient descent, and backpropagation.
  • Hyperparameter Tuning – Experiment with hyperparameters like learning rate, batch size, and number of layers for better model performance.
  • Image Classification & Object Detection – Implement and evaluate models for computer vision tasks using convolutional neural networks (CNNs).
  • Natural Language Processing (NLP) – Apply deep learning models for text-based tasks such as sentiment analysis, text generation, and machine translation.
  • Reinforcement Learning Basics – Understand and implement reinforcement learning algorithms.
  • Deploy Deep Learning Models – Learn to deploy trained models using APIs, cloud platforms, or embedded systems.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Implement Basic Neural Networks – Design and implement simple feedforward and deep neural networks.
  • Work with Deep Learning Frameworks – Utilize TensorFlow, Keras, and PyTorch for developing deep learning models.
  • Preprocess and Handle Large Datasets – Perform data cleaning, augmentation, and normalization techniques for deep learning applications.
  • Train and Optimize Deep Learning Models – Apply optimization techniques like gradient descent, backpropagation, and batch normalization to improve model performance.
  • Develop Computer Vision Applications – Build and train Convolutional Neural Networks (CNNs) for tasks like image classification and object detection.
  • Apply Deep Learning in NLP – Use Recurrent Neural Networks (RNNs), LSTMs, and Transformers for tasks such as text classification and sentiment analysis.
  • Experiment with Advanced Architectures – Implement and evaluate deep learning architectures like GANs, Autoencoders, and Transfer Learning models.
  • Fine-tune Hyperparameters – Optimize deep learning models using techniques like learning rate tuning, dropout, and early stopping.
  • Deploy Deep Learning Models – Deploy trained models in real-world applications using cloud platforms, APIs, or embedded systems.
  • Work on Real-World Projects – Apply deep learning concepts to solve practical problems in domains like healthcare, finance, and autonomous systems.

C Programming and Data Structures Laboratory

Objectives

  • Understand the Basics of C Programming – Learn fundamental concepts such as data types, operators, control structures, and functions.
  • Develop Problem-Solving Skills – Implement algorithms and logic to solve computational problems using C.
  • Work with Arrays and Pointers – Gain proficiency in handling arrays, pointers, and dynamic memory allocation.
  • Implement String Manipulation Techniques – Perform operations such as searching, sorting, and pattern matching on strings.
  • Explore Data Structures – Implement and analyze fundamental data structures such as stacks, queues, linked lists, trees, and graphs.
  • Understand Recursion and Iteration – Apply recursive and iterative approaches to problem-solving.
  • Perform File Handling Operations – Read from and write to files using C for data processing.
  • Analyze Algorithm Efficiency – Understand time and space complexity for different data structures and algorithms.
  • Implement Sorting and Searching Algorithms – Apply algorithms like Bubble Sort, Quick Sort, Merge Sort, and Binary Search.
  • Develop Real-World Applications – Build small applications using C and data structures to reinforce learning.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Write Efficient C Programs – Develop structured and optimized code using C programming principles.
  • Apply Logical Thinking for Problem-Solving – Use programming constructs effectively to solve real-world problems.
  • Manipulate Arrays and Pointers – Perform efficient data processing using arrays, pointers, and memory management.
  • Implement and Analyze Data Structures – Construct and evaluate different data structures for various applications.
  • Use Recursion Effectively – Solve problems using recursive functions where applicable.
  • Perform File Handling Operations – Read, write, and manipulate data stored in files using C.
  • Optimize Code for Performance – Evaluate and improve the efficiency of algorithms using complexity analysis.
  • Design and Implement Sorting and Searching Algorithms – Apply different sorting and searching techniques based on problem requirements.
  • Develop Small-Scale Projects – Create real-world applications integrating C programming with data structures.
  • Gain Practical Experience for Further Studies – Establish a strong foundation in C and data structures, which is essential for advanced programming and software development.

Problem Solving and Programming Using Python Lab Laboratory

Objectives

  • Understand Python Basics – Learn fundamental concepts such as syntax, variables, data types, and operators.
  • Develop Problem-Solving Skills – Use Python to implement logic-based solutions for computational problems.
  • Work with Control Structures – Utilize conditional statements, loops, and functions for efficient programming.
  • Manipulate Data Structures – Implement lists, tuples, sets, and dictionaries for data storage and processing.
  • Apply Functions and Modules – Write modular programs using functions, lambda expressions, and built-in libraries.
  • Perform File Handling Operations – Read from and write to files for data storage and retrieval.
  • Work with Object-Oriented Programming (OOP) – Implement classes and objects to solve real-world problems.
  • Use Exception Handling – Write robust programs by managing errors and exceptions effectively.
  • Explore Libraries for Data Analysis – Utilize NumPy, Pandas, and Matplotlib for basic data processing and visualization.
  • Develop Small-Scale Applications – Build mini-projects to reinforce programming concepts.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Write and Execute Python Programs – Develop structured and efficient Python code.
  • Implement Logical Solutions to Problems – Apply problem-solving strategies using Python.
  • Use Control Structures Effectively – Implement loops, conditionals, and functions for program flow control.
  • Handle Data Structures Efficiently – Store and manipulate data using lists, tuples, dictionaries, and sets.
  • Develop Modular and Reusable Code – Use functions and modules to improve code reusability and maintainability.
  • Perform File Operations – Read, write, and manipulate files for data processing applications.
  • Apply Object-Oriented Programming Concepts – Design and implement programs using OOP principles.
  • Handle Exceptions Gracefully – Use exception handling mechanisms to write error-free programs.
  • Utilize Python Libraries for Data Analysis – Work with NumPy, Pandas, and Matplotlib for basic data science tasks.
  • Create Real-World Applications – Develop Python-based mini-projects demonstrating problem-solving skills.

Artificial Intelligence and Machine Learning Laboratory

Objectives

  • Understand AI and ML Fundamentals – Learn the core concepts of artificial intelligence and machine learning.
  • Implement Basic AI Algorithms – Develop AI techniques such as search algorithms, game playing, and knowledge representation.
  • Work with Machine Learning Models – Implement supervised and unsupervised learning models using Python libraries like Scikit-Learn and TensorFlow.
  • Perform Data Preprocessing – Clean, transform, and prepare datasets for training ML models.
  • Train and Evaluate ML Models – Use metrics such as accuracy, precision, recall, and F1-score to assess model performance.
  • Apply Feature Engineering Techniques – Optimize datasets by selecting and transforming features for better model performance.
  • Explore Neural Networks and Deep Learning – Understand and implement artificial neural networks (ANNs) and deep learning models using TensorFlow/Keras.
  • Develop AI-Based Solutions – Solve real-world problems using AI techniques like NLP, computer vision, and reinforcement learning.
  • Work with Big Data and Cloud AI Tools – Use cloud platforms and big data tools for scalable AI and ML applications.
  • Deploy ML Models – Learn to deploy trained ML models as web applications or APIs for practical use.

Outcomes

Upon successful completion of this lab, students will be able to:

  • Implement AI Search Algorithms – Develop AI solutions using informed and uninformed search techniques.
  • Build and Evaluate Machine Learning Models – Apply classification, regression, clustering, and dimensionality reduction techniques.
  • Handle and Preprocess Large Datasets – Clean and prepare raw data for effective ML model training.
  • Apply Supervised and Unsupervised Learning – Implement models like decision trees, SVMs, k-means clustering, and principal component analysis (PCA).
  • Develop Neural Networks for Deep Learning – Train and optimize deep learning models using frameworks like TensorFlow and PyTorch.
  • Analyze Model Performance – Use performance evaluation metrics to compare and improve ML models.
  • Implement Natural Language Processing (NLP) – Apply AI for text processing tasks such as sentiment analysis and text classification.
  • Work with Computer Vision Techniques – Implement image classification and object detection using CNNs.
  • Deploy ML Models in Real-World Applications – Host and integrate trained models into web or mobile applications.
  • Build AI and ML-Based Projects – Solve domain-specific problems using AI and ML methodologies.

Syllabus Downloads


B.Tech R23 Artificial Intelligence Detailed Syllabus

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Department Association

INCREDIBLE INTELLIGENCE DEPARTMENT ASSOCIATION TEAM (IIDA)

S.No. Name Designation

1

Mr. L. Karthikeyan

FACULTY ADVISOR

2

Mr. G. Akash

21781A3349

PRESIDENT

3

Mr. J. Venkata Subbaiah

22781A3343

VICE PRESIDENT

4

Mr. C. Sharan Sai

23781A3326

SECRETARY

5

Mr. A. Charan

23781A3102

JOINT SECRETARY

6

Mr. Gokul Sai

23785A3104

STUDENT TREASURER

7

Mr. S. Jamshid

21781A33E2

STUDENT EVENT ORGANIZER

8

Mr. Naveen

22781A33

STUDENT EVENT ORGANIZER

9

Mr. Muneeswar

22781A33

STUDENT EVENT ORGANIZER

10

Mr. V. S. Revanth(CSM)

23781A33G7

STUDENT ORGANIZER

11

Ms. T. Vedhavathi

23781A33G0

STUDENT ORGANIZER

12

Ms. P. Nagathanusha

23781A3139

STUDENT ORGANIZER

13

Ms. K. Deepika

23781A3373

STUDENT ORGANIZER

14

Ms. C. Sneha

22781A3332

STUDENT ORGANIZER

15

Ms. T. Charitha Reddy

22781A33D1

STUDENT ORGANIZER

16

Ms. N. Jaya Lakshmi

22781A3128

STUDENT ORGANIZER

Data will be updated soon!

Data will be updated soon!

Latest Events

Academic year 2024-2025 : Events Organized:

S.No

Title

Name of the Event: Guest Lecture/Workshop/Training Programme

Resource Person

Period and Duration

1

One Week Workshop on “Advanced Deep Learning Techniques with Real Time Applications

Workshop

Mr. Jai Ganesh Suresh. Senior AI Architect Lead, Valeo India Pvt. Ltd, Mr. U. Ravindran, Dr. K. Nandha Kumar, Dr. R. Mohan Raj, and Dr. G. Kavitha from CSE allied branches

23-04-2025 to 28-04-2025

2

Deep Learning Techniques for Image Processing -real Time Application

Expert Lecture

Dr. G. N Vivekananda, Professor, VIT University

9/25/2024

3

Advanced data Structures for Real Time Applications

Skill oriented Program

Mr. A. Rangaraj, Associate Professor, Dept. of MCA

02-09-2024

4

Institutional Benchmarking and quality Improvement

Guest Lecture

Dr. Dharmendra Singh Rajput, professor, VIT University, Vellore

25-01-2025

5

Embedded System using MATLAB

Workshop

Dr. J. Satheesh Kumar, Associate Professor, Dept. of ECE, SVCET

27-2-2025 to 28-2-2025

6

IPR for Engineers and Technologists: Safeguarding Innovative Creations

Expert Lecture

Dr. Ramalingam, Assistant Professor, VIT University, Vellore

5-03-2025
Academic year 2024-2025 : Workshop/Training Programme/FDP/ Attended:

S.No

Name of the Faculty

Name of the Event: Lecture/Workshop/Training Programme/FDP/Conferences

Oranganized by

Period (From date-To Date Duration)

Duration

1

Dr. M. Lavanya

Modern Cyber Security with AI&ML

Sri Venkateswara College of Engineering and Technology

27/03/2025

31/03/2025

2

Dr. G. Kavitha

Modern Cyber Security with AI&ML

Sri Venkateswara College of Engineering and Technology

27/03/2025

31/03/2025

3

Mr K Anjaneyulu

Instructional Design and Delivery Systems

Sri Venkateswara College of Engineering and Technology

12/08/2024

17/08/2024

4

Dr. M. Lavanya

Effective Analysis of Mental Health Using AI and Machine Learning in Social Media Analytics

Sri Venkateswara College of Engineering and Technology

28/03/2025

29/03/2025.

 

5

Dr. G. Kavitha

Food Calaory Estimation and BMI Prediction Using ML

Sri Venkateswara College of Engineering and Technology

28/03/2025

29/03/2025

6

Dr. M. Lavanya

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

7

Dr. K. Nandha Kumar

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

8

Dr. R. Mohanraj

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

9

Dr. G. Kavitha

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

10

Ms. D. Gayathri

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

11

Ms. D. Shruthi

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

12

Ms. T. Princess Raichel

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

13

Mr. U. Ravindran

Advanced Applications of AI in Research & Innovations

Sri Venkateswara College of Engineering and Technology

24/03/2025

28/03/2025

14

Dr. M. Lavanya

GEN AI Tools and Techniques

Sri Venkateswara College of Engineering and Technology

11/11/2024

16/11/2024

15

Ms. D. Gayathri

Artificial Intelligence-Soft Computing

Sri Venkateswara College of Engineering and Technology

9/12/2024

14/12/2024

16

Ms. D. Gayathri

GEN AI Tools and Techniques

Sri Venkateswara College of Engineering and Technology

11/11/2024

16/11/2024.

 

17

Ms. D. Gayathri

FDP on Cloud Architect

Sri Venkateswara College of Engineering and Technology

18/11/2024

22/11/2024

18

Mr. U. Ravindran

OpenCV for Image and Video Processing Applications

Sri Venkateswara College of Engineering and Technology

22/07/2024

26/07/2024

19

Dr. R. Mohanraj

OpenCV for Image and Video Processing Applications

Sri Venkateswara College of Engineering and Technology

22/07/2024

26/07/2024

20

Dr. K. Nandha Kumar

The power of Trio: Communication soft skills and 21st century competencies

Sri Venkateswara College of Engineering and Technology

13/09/2024

 

21

Dr. K. Nandha Kumar

Gen-AI and Prompt Engineering Using Microsoft Co-Pilot

Sri Venkateswara College of Engineering and Technology

16/09/2024

20/09/2024

22

Dr. K. NandhaKumar

International Faculty Development Program on Project Excellence Guide: Digital Marketing

Sri Venkateswara College of Engineering and Technology

21/10/2024

25/10/2024

23

Dr. K. NandhaKumar

AI TOOLS

Sri Venkateswara College of Engineering and Technology

17/02/2025

21/02/2025

24

Dr. G. Kavitha

Instructional Design and Delivery Systems

Sri Venkateswara College of Engineering and Technology

12/08/2024

17/08/2024

25

Dr. R. Mohanraj

Instructional Design and Delivery Systems

Sri Venkateswara College of Engineering and Technology

12.08.2024

17.08.2024

Data will be updated soon!

Contact Details

faculty photo

Dr. M. Lavanya

HOD & ASSOCIATE PROFESSOR

MCA, M.Tech, Ph.D

hodcse@svcetedu.org

Data will be updated soon!

Data will be updated soon!

International Conference On SVCET  ICAECC - 2026  10th & 11th April, 2026  Organized By Department Of CSE & Allied Branches, ECE & EEE

Cybersecurity Fundamentals With CTF-based Learning Organized By The Department Of CSE (IoT) & CSE (Cs) 

"Hands On Training Program On Explore IT Teams: Roles, Responsibilities & Career Path's On 24-01-2026 Organized By Department Of MCA

5 Day National Level FDP On " Modern Cloud Computing Platforms And Service Technologies" From 27-01-2026 To 31-01-2026 Organized By Department Of MCA And ACT Academy

Announcements

International Conference On SVCET  ICAECC - 2026  10th & 11th April, 2026  Organized By Department Of CSE & Allied Branches, ECE & EEE

Cybersecurity Fundamentals With CTF-based Learning Organized By The Department Of CSE (IoT) & CSE (Cs) 

"Hands On Training Program On Explore IT Teams: Roles, Responsibilities & Career Path's On 24-01-2026 Organized By Department Of MCA

5 Day National Level FDP On " Modern Cloud Computing Platforms And Service Technologies" From 27-01-2026 To 31-01-2026 Organized By Department Of MCA And ACT Academy

About Us

Academics

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Contact Details

7729999158 principal@svcetedu.org info@svcetedu.org R.V.S Nagar, Tirupathi Road, Chittoor, Andhra Pradesh - 517127.