Long Short-Term Memory (LSTM) projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Long Short-Term Memory (LSTM) projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Long Short-Term Memory (LSTM) Projects
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A Novel Hybrid Deep Learning Architecture for Dynamic Hand Gesture Recognition
The main objective of this project is to improve the recognition of dynamic hand gestures in real-world videos. The study aims to extract meaningful features from gesture videos and classify them accurately. It combines a convolutional neural network with a recurrent neural network to handle spatial and temporal information. The project focuses on six common gestures and seeks to achieve higher accuracy in realistic environments. Another goal is to compare the proposed model’s performance with existing benchmark methods. -
A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection
The project aims to enhance security and privacy in Industrial Internet of Things networks by detecting malicious activities accurately. It focuses on improving traditional machine learning and deep learning methods that struggle with imbalanced and repetitive network data. The approach uses a self-attention-based deep convolutional neural network to monitor network behavior. It also applies data cleaning and feature filtering techniques to reduce redundancy and improve model performance. The system is tested on benchmark datasets and compared with existing models to show its effectiveness. -
Machine Learning Approaches for Power System Parameters Prediction – A Systematic Review
The main objective of this project is to improve prediction accuracy in power system networks. It aims to predict load, voltage, and frequency using machine learning models. The project focuses on using network topology behavior as input instead of isolated bus data. It tests different models like regression, decision tree, and LSTM. The goal is to provide better planning and reliability for dynamic and interconnected power systems.
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