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ARTIFICIAL INTELLIGENCE IN
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DI05000261
Code: DI05000261
Credits: 4
Semester 5
Nano
Artificial Intelligence in Automation
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Follow on Instagram for UpdatesArtificial Intelligence in Automation Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence in Automation (subject code DI05000261) as prescribed for Nano · Semester 5 divides the course into 5 units, covering 36 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI05000261)Unit 1 Introduction to AI in Industrial Automation
- 1.1. Overview of AI: Definition and history of AI in manufacturing; Comparison between Conventional Automation vs. AI- based Automation.
- 1.2. Intelligent Agents: Structure of agents in industrial environments (PLC/SCADA integration); Perception-Action cycle in automated systems.
- 1.3. AI Search Algorithms: Path planning for AGVs (Automated Guided Vehicles) and AMRs (Autonomous Mobile Robots); Heuristic search basics for optimization.
Unit 2 Machine Learning for Industrial Data
- 2.1. Data Pre-processing: Handling industrial sensor noise and missing data; Normalization and Feature Extraction from IIoT streams.
- 2.2. Supervised Learning: Linear and Logistic Regression for sensor calibration; Support Vector Machines (SVM) for equipment state classification.
- 2.3. Unsupervised Learning: K-means clustering for anomaly detection in process loops; Principal Component Analysis (PCA) for dimensionality reduction.
Unit 3 Neural Networks and Deep Learning
- 3.1. Artificial Neural Networks (ANN): Biological vs. Artificial Neuron; Multilayer Perceptrons (MLP) and Backpropagation.
- 3.2. Deep Learning for Automation: Introduction to CNN (Convolutional Neural Networks) for visual inspection; Convolution, Pooling, and Padding layers.
- 3.3. Time-Series Forecasting:
- 11. 25 % GUJARAT TECHNOLOGICAL UNIVERSITY Program Name: Diploma Engineering Level: Diploma Branch: Instrumentation and Control / Automation & Robotics Subject Code: DI05000261 Subject Name : Artificial Intelligence in Automation w.e.f. 2026-27 http://syllabus.gtu.ac.in/ Page 3 of 7 RNN(Recurrent Neural Networks) and LSTM (Long Short-Term Memory) for predicting Remaining Useful Life (RUL) of machinery.
Unit 4 Fuzzy Logic and Expert Systems
- 4.1. Fuzzy Logic Fundamentals: Fuzzy sets and Membership functions; Fuzzification and Defuzzification methods.
- 4.2. Industrial Fuzzy Control: Design of Fuzzy PID controllers; Rule-based expert systems for industrial troubleshooting.
- 4.3. Applications: Case studies in temperature and pressure control using fuzzy logic.
Unit 5 AI Implementation and Case Studies
- 5.1. Hardware for AI: Edge AI vs. Cloud AI in factories; Deployment on Nvidia Jetson, Raspberry Pi, or industrial PCs.
- 5.2. Industrial Vision Systems: Automated sorting and defect detection using OpenCV library.
- 5.3. Ethics and Safety: Reliability of AI in safety-critical systems; Human-Robot Collaboration (Cobots).
- 07. 15 % Total 45 Hrs 100% SUGGESTED PRACTICAL EXERCISES: The following practical outcomes (PrOs) that are the sub-components of the COs. Some of the PrOs marked ‘*’ are compulsory, as they are crucial for that particular CO. These PrOs need to be attained at least at the ‘Precision Level’ of Dave’s Taxonomy related to ‘Psychomotor Domain’. Sr. No. Practical Outcomes (PrOs) Approx. Hrs. Required
- 1. Set up Python environment (Anaconda/VS Code) and install ML libraries (Scikit- Learn, TensorFlow, NumPy). 02
- 2. Perform exploratory data analysis (EDA) and visualization on an industrial sensor dataset. 02 GUJARAT TECHNOLOGICAL UNIVERSITY Program Name: Diploma Engineering Level: Diploma Branch: Instrumentation and Control / Automation & Robotics Subject Code: DI05000261 Subject Name : Artificial Intelligence in Automation w.e.f. 2026-27 http://syllabus.gtu.ac.in/ Page 4 of 7 Sr. No. Practical Outcomes (PrOs) Approx. Hrs. Required
- 3. Implement Linear Regression to predict the output of a temperature sensor based on historical calibration data. 04
- 4. Develop a classification model (SVM/Logistic Regression) to detect 'Faulty' vs 'Healthy' motor states. 04
- 5. Apply K-means clustering to group different operating modes of a process plant using sensor data. 02
- 6. Design and simulate a Fuzzy Logic controller for a liquid level system (MATLAB or Python Scikit-Fuzzy). 04
- 7. Build a simple Neural Network (ANN) to predict hourly energy consumption in a factory. 04
- 8. Implement an image classification task to identify defective parts on a conveyor belt using CNN. 04
- 9. Demonstrate real-time object detection or Edge AI deployment for an automation task. 02 Total 28 Hrs Note i. More Practical Exercises can be designed and offered by the respective course teacher to develop the industry relevant skills/outcomes to match the COs. The above table is only a suggestive list. ii. The following are some sample ‘Process’ and ‘Product’ related skills (more may be added/deleted depending on the course) that occur in the above listed Practical Exercises of this course required which are embedded in the COs and ultimately the competency. Sr. No. Sample Performance Indicators for the PrOs Weightage in %
- 1. Preparation of AI development environment and library configuration 20
- 2. Logic development, data cleaning, and Code implementation 30
- 3. Model training, validation, and accuracy analysis 20
- 4. Troubleshooting, optimization, and performance tuning 10
- 5. Interpretation of results and preparation of technical lab report 20 Total 100 GUJARAT TECHNOLOGICAL UNIVERSITY Program Name: Diploma Engineering Level: Diploma Branch: Instrumentation and Control / Automation & Robotics Subject Code: DI05000261 Subject Name : Artificial Intelligence in Automation w.e.f. 2026-27 http://syllabus.gtu.ac.in/ Page 5 of 7 MAJOR EQUIPMENT/ INSTRUMENTS AND SOFTWARE REQUIRED: These major equipment/instruments and Software required to develop PrOs are given below with broad specifications to facilitate procurement of them by the administrators/management of the institutes. This will ensure conduction of practical in all institutions across the state in proper way so that the desired skills are developed in students. Sr. No. Equipment Name with Broad Specifications PrO. No.
- 1. High-performance Workstations: i7, 16GB RAM, GPU preferred All
- 2. Python Environment: NumPy, Pandas, Scikit-Learn, Keras, TensorFlow All
- 3. Simulation Software: MATLAB with Fuzzy/Deep Learning Toolboxes or Open- source Scikit-Fuzzy 6
- 4. Visual Hardware: USB Webcams for industrial inspection tasks 8, 9
- 5. Edge Computing (Optional): Nvidia Jetson Nano or Raspberry Pi for deployment demos 9
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