Long Short-Term Memory (LSTM) is a variant of RNN designed to handle long-term dependencies by maintaining a cell state that can capture information over long sequences. LSTM's ability to store and retrieve information over extended time steps makes it well-suited for tasks involving long-term dependencies in data sequences.

The F1 Score is a metric that balances the trade-off between precision and recall. It is especially useful when dealing with imbalanced datasets or when you want to find a balance between correctly identifying positive cases (precision) and capturing all positive cases (recall). The F1 Score is the harmonic mean of precision and recall. It is a suitable choice for evaluating models when both precision and recall are essential.

Transfer learning allows the startup to use pre-trained deep neural networks (e.g., a pre-trained CNN) as a starting point. This approach significantly reduces training time and computational resources, while still benefiting from the capabilities of deep learning.

In real-time data processing, Apache Spark is commonly used to ingest and process streaming data. Spark provides the capabilities to handle streaming data in real time, making it a popular choice for such applications.

The median is the most resistant measure of central tendency in a skewed distribution. It is less affected by extreme values or outliers since it represents the middle value when the data is arranged in order. The mean, mode, and geometric mean can be heavily influenced by outliers, causing them to be less representative of the data's central location.

Canny Edge Detection is a widely used technique for edge detection in images. It applies multiple filters to detect edges with varying intensities, providing information about the location and strength of edges in the image.

Transfer learning in computer vision involves fine-tuning a pre-trained model to adapt it for a new task. It leverages knowledge from a source task to improve performance on a target task, making it more efficient and effective than training from scratch.

Macro-averaging computes the average metric score for each class, treating all other classes as the "negative" class. It provides an equal weight to each class and is useful when you want to assess the model's overall performance while giving equal importance to each class, regardless of class size. Macro-averaging can be particularly useful in imbalanced multi-class classification problems.

The Box Plot, also known as a box-and-whisker plot, considers the spread of data points around the median and helps identify outliers based on the interquartile range (IQR). Outliers are data points that fall outside the whiskers of the box plot. Z-Score is used for standardization, One-Hot Encoding is used for categorical variables, and K-Means Clustering is a clustering technique and not used for identifying outliers.

In Big Data processing, the first blank should be filled with "Filter," and the second blank with "Join." Filtering and sorting are common operations in data preparation, while aggregations and transformations are typically done using join operations.