skilldevelopment.cpe@lpu.co.in

What you'll learn

This course introduces the fundamentals and applications of Soft Computing techniques including Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, Swarm Intelligence, and Nature-Inspired Optimization. The focus is on developing problem-solving and decision-making skills under uncertainty, with hands-on labs and case studies using Python-based implementations. Students will explore both classical methods and modern metaheuristics to solve real-world optimization and prediction problems.

  • This unit introduces the foundations of Artificial Intelligence and Soft Computing, highlighting the difference between hard computing and soft computing. It covers Artificial Neural Networks, Genetic Algorithms, Swarm Intelligence, and Expert Systems
  • Covers the core concepts of fuzzy logic and fuzzy set theory, including fuzzy sets, set-theoretic operations, and fuzzy relations. Students learn how to design Fuzzy Rules, perform fuzzy reasoning, and develop Fuzzy Inference Systems (Mamdani & Sugeno models). The unit also explores Fuzzy Expert Systems to handle uncertainty in real-world decision-making problems.
  • Focuses on advanced neural architectures beyond the perceptron. Students explore Backpropagation Neural Networks (BPNN) for classification, Kohonen networks for clustering, Learning Vector Quantization (LVQ), and Radial Basis Function (RBF) networks.
  • Introduces Genetic Algorithms (GA) as an optimization tool inspired by Darwinian evolution. Students study GA operators such as selection, crossover, and mutation. The unit explains the working of GA, genetic programming, and their application in search, scheduling, and optimization problems.
  • Focuses on nature-inspired collective intelligence. It covers Swarm Intelligence concepts with algorithms such as Ant Colony Optimization (ACO), Swarm Intelligence in Bees, and Cuckoo Search. Students also learn Hybrid Systems, which combine fuzzy logic, ANN, and GA
  • This unit expands on advanced metaheuristics inspired by animals and natural phenomena. Techniques include the Firefly Algorithm, Crow Search Algorithm, Hybrid Wolf-Bat Algorithm, Whale Optimization, Moth-Flame Optimization, and Grasshopper Optimization. These methods are applied to solve high-dimensional and complex optimization problems in engineering, data science, and decision-making.

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Sheveta
Assistant Professor

Sheveta Vashisht is an Assistant Professor in the School of Computer Science and Engineering at Lovely Professional University (LPU), Phagwara. She has over 13 years of teaching and industry experience and is actively engaged in areas such as Artificial Intelligence, Soft Computing, Web Programming, and Fault-Tolerant Systems. Her academic contributions include guiding students in project-based learning, mentoring for skill development courses, and supporting research in emerging computing domains. She has also played a role in academic coordination and curriculum enhancement, while fostering innovation and collaborative learning among students.


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Dr. Avinash Kaur
Professor

Dr. Avinash Kaur