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ANN, Deep Learning Projects for B.E, M.E and Ph.D
We
offer projects in artificial neural networks, differential
protection, genetic algorithm, pattern recognition, resilient
back propagation, unit protection, Artificial neural network
(ANN), transformer, inrush current, transformer faults,
harmonics, SHR, magnetizing inrush current, Protection, internal
faults, transformers, PCA, ANN, RBF, Neural networks,
transformer, fault detection, discrete wavelet transform (DWT),
inrush current
ANN
Project Titles
The ANN applications
for Power systems deals with Simulation methodologies of complex
power systems and transducers, transient behavior of transducers,
signal conditioning and sampling for on line relay applications.
Algorithms for protective relaying, digital protection schemes
for transmission lines, generators and transformers, substation
control, microprocessor based testing of relays. Suggested
projects topics in ANN are listed below.
ANN projects For B.E EEE and M.E Electrical Engg.,
1. Image
Compression based on lifting scheme.
2.
Load Forecasting in power systems using ANN.
3. EHV
fault location using ANN.
4.
Transformer fault identification using ANN.
5.
Pattern identification using ANN.
6.
Transformer Protection using ANN.
Machine
learning and data science projects
( B.E
Computer Science, B.E AI and Machine learning)
Here are some
project ideas focused on machine learning (ML) and artificial
intelligence (AI):
1.
Sentiment Analysis on Social Media
Objective:
Develop a model to analyze and classify sentiments (positive,
negative, neutral) from social media posts or product reviews.
Tools/Techniques:
Natural Language Processing (NLP), LSTM/RNN models, Python
(e.g., NLTK, TensorFlow, Keras).
Outcome:
A sentiment analysis tool that can be applied to social media
monitoring or customer feedback systems.
2. Image
Classification using Convolutional Neural Networks (CNNs)
Objective:
Build a CNN model to classify images into different categories
(e.g., animals, objects, medical images).
Tools/Techniques:
Python (TensorFlow, Keras), data augmentation, transfer learning
(e.g., using pre-trained models like VGG16, ResNet).
Outcome:
An image classification model with high accuracy, potentially
deployed as a web application.
3.
Predictive Maintenance using Time Series Analysis
Objective:
Create a predictive maintenance system that uses historical
sensor data to predict equipment failures.
Tools/Techniques:
Time series analysis, ARIMA models, LSTM, Python (Pandas,
Scikit-learn).
Outcome:
A predictive model that can alert maintenance teams before
machinery fails, saving costs and reducing downtime.
4.
Reinforcement Learning for Game AI
Objective:
Implement a reinforcement learning agent that can learn to play
a game (e.g., chess, tic-tac-toe, or a custom game) and improve
over time.
Tools/Techniques:
Python (OpenAI Gym, TensorFlow, PyTorch), Q-learning, deep
Q-networks (DQN).
Outcome:
A self-learning AI agent capable of playing a game with
increasing proficiency.
5.
Recommendation System
Objective:
Design a recommendation system for products, movies, or music
based on user behavior and preferences.
Tools/Techniques:
Collaborative filtering, content-based filtering, hybrid models,
Python (Scikit-learn, Surprise, TensorFlow).
Outcome:
A recommendation engine that provides personalized suggestions,
which could be integrated into an e-commerce platform.
6. Natural
Language Processing (NLP) for Text Summarization
Objective:
Develop a model that can automatically summarize long pieces of
text into concise summaries.
Tools/Techniques:
Python (NLTK, Hugging Face Transformers), extractive and
abstractive summarization, BERT, GPT models.
Outcome:
A text summarization tool useful for news aggregation, legal
document analysis, or content curation.
7.
Autonomous Driving Simulation
Objective:
Create a simulation environment for an autonomous vehicle using
computer vision and deep learning techniques.
Tools/Techniques:
Python (OpenCV, TensorFlow, CARLA simulator), CNNs,
reinforcement learning.
Outcome:
A simulated environment where an AI agent can learn to navigate
a car autonomously.
8. Anomaly
Detection in Network Traffic
Objective:
Build a machine learning model to detect anomalies in network
traffic that may indicate cybersecurity threats.
Tools/Techniques:
Clustering algorithms (e.g., K-means), autoencoders, Python
(Scikit-learn, TensorFlow), data from network logs.
Outcome:
A real-time anomaly detection system that can alert network
administrators to potential security breaches.
9. Voice
Recognition System
Objective:
Implement a voice recognition system that can transcribe speech
to text or recognize specific commands.
Tools/Techniques:
Python (SpeechRecognition, PyDub), deep learning models (e.g.,
RNNs, CNNs), pre-trained models (e.g., Wav2Vec).
Outcome:
A voice-activated assistant or command recognition system,
potentially integrated with IoT devices.
10.
AI-based Chatbot
Objective:
Develop a conversational AI chatbot capable of understanding and
responding to user queries in natural language.
Tools/Techniques:
Python (NLTK, Rasa, Dialogflow), NLP models, reinforcement
learning.
Outcome:
A chatbot that can handle customer service queries, act as a
virtual assistant, or provide information on specific topics.
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