Long Short-Term Memory (LSTM) is a variant of RNN that is designed to address the vanishing and exploding gradient problem. LSTMs use specialized gating mechanisms to better capture long-term dependencies in data, making them suitable for sequences with long-term dependencies.

In this scenario, minimizing false negatives is critical, as failing to detect fraud has a higher cost. Recall (Option B) focuses on minimizing false negatives, making it the most crucial metric to optimize in this context. While precision is important, the emphasis here is on avoiding false negatives. F1 Score balances precision and recall but may not prioritize minimizing false negatives. Accuracy is not the most relevant metric.

RNNs (Recurrent Neural Networks) have a hidden state that allows them to maintain a form of memory from previous inputs. This hidden state is crucial for processing sequences and time-series data, making them different from feedforward neural networks.

Forward Fill (FFill) is a method in time series data analysis used to fill missing values by taking the value of the nearest previous data point. This is often used when there's a trend in the data and using the average of nearby points makes sense.

In SQL, the "DISTINCT" command is used to eliminate duplicate values and retrieve unique values from a specified column. It helps in data analysis by providing distinct records for further analysis.

For real-time object detection in computer vision, the most suitable technique is "Object Detection." It allows the system to identify and locate specific objects, such as pedestrians, traffic lights, and vehicles, in a given frame or scene. Image classification, semantic segmentation, and OCR serve different purposes.

Power BI is a powerful business intelligence tool that offers real-time data analytics and live dashboard updates. It's designed for organizations that require dynamic and interactive data visualization capabilities, making it a suitable choice for real-time data analytics and live dashboards.

Recurrent Neural Networks (RNNs) are specifically designed to handle sequential data, where the order of inputs matters. They are widely used in tasks like natural language processing, speech recognition, and time series analysis, thanks to their ability to retain and process information from previous inputs in the sequence.

The "Transform" component in ETL (Extract, Transform, Load) is responsible for converting and cleaning data into a format suitable for analysis. It involves data cleansing, aggregation, and manipulation.

The process described is Data Normalization. It involves organizing data into tables and minimizing redundancy to ensure data integrity and prevent anomalies. This is a fundamental concept in database design. Normalization helps maintain data consistency and efficiency.