## 3 Proposed Methods
The use of a higher number of attention heads can capture more fine-grained relationships within the video frames. Further, the **\"classes\"** parameter represents the number of unique classes or categories that are present in the video classification dataset, which is crucial for determining the output layer's dimensionality and correctly predicting the video's class. The **\"attention\"** layer within the **\"TransformerEncoder0\"** implements the self-attention mechanism with multiple attention heads and enables the model to focus on different parts of the video sequence during training, thus contributing to better learning and representation of complex patterns. For convenient serialization and later recreation of the **\"TransformerEncoder0\"** layer, we implement the **\"Config_Update0\"** method, which returns a dictionary containing the layer's configuration. This allows us to save and load the model architecture along with custom properties such as embed_dim, dense_dim, num_heads, etc. Once the model architecture is established, we compile the model using the **\"Model_Compiled0\"** function. The sparse categorical cross-entropy loss function is chosen for video classification tasks, as it can efficiently handle datasets with multiple classes while avoiding the need for one-hot encoding. The Adam optimizer is used to optimize the model parameters during training. After compiling the model, the practical training process is initiated. The model is trained on labeled video data, and the training progress and performance metrics are logged for analysis. During training, we typically monitor several metrics, including accuracy, loss, and validation. Following training, we evaluate the model on a separate test dataset to assess its generalization performance. This evaluation provides insight into how well the model performs on unseen data and also helps us identify potential overfitting problems. As a final step, we save the trained model along with its weights and architecture, which allows us to reuse the trained model to make predictions on new videos without having to retrain it from scratch. Our proposed video classification workflow utilizing the Transformer model architecture has the following five advantages over traditional approaches: (1) Long-range Dependency Modeling: The Transformer's self-attention mechanism enables the model to capture long-range dependencies in video frames. (2) Parallel Processing: Transformers can process video frames in parallel, which significantly speeds up the computation, particularly for longer video sequences with the capability to efficiently analyze and classify videos in real-time or near real-time scenarios. (3) Positional Embeddings: The inclusion of positional embeddings enables the model to understand the temporal ordering of video frames to better recognize actions and events in videos where the sequence of frames matters. (4) Transfer Learning: By utilizing a pre-trained ResNet50 model as a feature extractor, we benefit from transfer learning by leveraging pre-learned visual representations, which can be advantageous when working with a relatively small video classification dataset. (5) Scalability: Our Transformer architecture has demonstrated scalability that is superior to that of traditional RNNs, thus making it more suitable for processing longer videos with complex temporal dynamics.