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  Long Short-Term Memory (LSTM) Networks LSTMs are a type of Recurrent Neural Network (RNN) designed to handle sequential data with long-term dependencies. Key Features: Cell State: Preserves information over long periods. Gates: Control information flow (input, output, and forget gates). Hidden State: Temporary memory for short-term information. Related Technologies: Recurrent Neural Networks (RNNs): Basic architecture for sequential data. Gated Recurrent Units (GRUs): Simplified version of LSTMs. Bidirectional RNNs/LSTMs: Process input sequences in both directions. Encoder-Decoder Architecture: Used for sequence-to-sequence tasks. Real-World Applications: Language Translation Speech Recognition Text Generation Time Series Forecasting GRUs are an alternative to LSTMs, designed to be faster and more efficient while still capturing long-term dependencies. Key Differences from LSTMs: Simplified Architecture: Fewer gates (update and reset) and fewer state vectors. Faster Computation: ...

Developing an AI-based automated construction progress tracking system for a solar plant is a complex project that involves various components, including computer vision, data analysis, and integration with existing construction management systems. Here's a step-by-step plan through the development process:         Main features of the application:   Work in progress   Reality capture   Schedule   Budget   Quality check       Steps we need to follow:   1. Define Project Objectives and Scope:      - Clearly define the goals and objectives of your construction progress tracking system.      - Identify the specific metrics and key performance indicators (KPIs) you want to track, such as completion of solar panel installation, infrastructure construction, and overall project progress.   2. Data Collection and Infrastructure Setup:      - Set up the necessary infrastruc...