Median filtering is commonly used to reduce noise in an image. It replaces each pixel value with the median value in a local neighborhood, making it effective for removing salt-and-pepper noise and preserving the edges and features in the image.

To address the performance deterioration of a deployed Gradient Boosting model, it's crucial to regularly update the model with new data (option D). Data drift is common, and updating the model ensures it adapts to the changing environment. While re-training with additional data (option A) may help, regularly updating the model with new data is more sustainable. Increasing the learning rate (option B) or reducing model complexity (option C) may not be effective in addressing performance deterioration over time.

The technique used to prevent overfitting in neural networks is called "Dropout." During training, dropout randomly removes a fraction of neurons, helping to prevent overreliance on specific neurons and improving generalization.

In time series analysis, a sequence of data points measured at successive points in time is called "time series data." This data structure is used to analyze and forecast trends, patterns, and dependencies over time. It's fundamental in fields like finance, economics, and climate science.

In transfer learning, a model trained on a large dataset is used as a starting point to leverage the knowledge gained in a similar task. By fine-tuning the pretrained model on a related task, you can often achieve better results with less training data and computational resources. This approach is particularly useful when the target task is similar to the source task, as it allows the model to transfer useful feature representations and patterns.

Combining multiple levels of categorical variables into a single level based on frequency or other criteria is known as "category merging" or "level merging." This simplifies the categorical variable, reduces complexity, and can improve the efficiency of certain models.

The algorithm inspired by biological neural networks is the Artificial Neural Network (ANN). ANNs consist of interconnected artificial neurons that attempt to simulate the structure and function of the human brain, making them suitable for various tasks like pattern recognition.

Model serving is the method that allows you to deploy multiple models and route traffic to them based on specific conditions. It plays a critical role in managing different model versions and serving the right model for different use cases.

Predicting the next word in a sentence is a sequential data problem, making it suitable for recurrent neural networks. LSTMs are particularly effective for this task as they can capture long-term dependencies in the data, which is essential for predicting words in a sentence.

Pandas is a popular Python library for data manipulation and cleaning. It provides data structures and functions for working with structured data, making it a valuable tool in data science, which makes option B the correct answer.