## 4 Proposed Framework
* **Bi-LSTM**[49]: Bi-directional long short-term memory is an improved version of the LSTM model (a variant of the recursive DL architecture) that can process data in both forward and backward directions. Bi-LSTM helps map models that allow for sequential dependencies in words and phrases. For our framework, Bi-LSTM assisted in training MIR and NMIR sequences by preserving this information using two independent RNN cells that stored the iterative states for a longer time. Bi-LSTM contained the IT incident input \((X_{t})\) concatenated with hidden state \((h_{t}-1)\), which was further forwarded to three gates (Forget, Input, and Output). The Input gate has an embedded gate known as an Update. The Update gate memorized the past and present sentence sequence. The value from the update gate multiplied the cell state \((C_{t}-1)\), resulting in the hidden state or unit \((h_{t})\). Our Bi-LSTM architecture comprised five layers: one embedding layer with a size of 300, one bi-directional layer with 280 LSTM neurons, two dropout layers, and one classification layer (dense layer). The total number of trainable parameters for the model was 23,346,401. * **CNN**[50]: Convolutional neural network is synonymous with image classification, but recently, it has significantly contributed to NLP. CNN is used to extract high-level feature functions from n-gram. For example, ref. [51] developed a word-embedding matrix layer that memorized the weights during the network training phase. We used our framework's input labels (MIR or NMIR) as tokens to map to the word-embedding matrix. In this mapping, every convolutional filter mapped to each window of the embedding layer. In CNN, matrix reduction reduced the dimension of the matrix to a constant length. We executed the matrix reduction on every possible window. Each reduced matrix was the input for the fully connected layer. In the next layer, we used the activation function to create a single dimension input of features per tensor output; The global max pooling layer with 256 batch sizes and 23,027,144 trainable parameters were used. In the final layer, we applied the Softmax activation function to translate the real-probability values into MIR or NMIR labels. * **GRU**[52]: Gated recurrent unit is an RNN DL variant that retains information for a longer time and improves the computing speed. GRU consists of two neural gates(Update and Reset gates) for updating the previous cell state and discarding the irrelevant state. For this purpose, we provided MIR or NMIR values as input \(X_{t}\), which concatenated with hidden states \(H_{t-1}\) and moved to the Update gate. In the final phase, we used the Sigmoid activation function to maintain an output within the range of (0,1), resulting in an MIR or NMIR value. The architecture comprised 5 layers, containing one embedding layer with a size of 300 vectors, one GRU layer with 140 neurons, and one dense layer with a sigmoid activation function. The model had a total of 23,037,981 trainable parameters. * **BERT**[14]: Bidirectional encoder representations from transformers consists of several encoder transformers within a pool of pre-trained models. BERT follows the bidirectional orientation of learning information from a sequence of words from left to right and right to left. Each encoder encapsulates two sub-layers: a self-attention layer and a feed-forward layer. We have employed a trained BERT architecture contain 12 layers of the encoder, 12 attention heads, 768 hidden sizes, and 110M trainable parameters. It was pre-trained on 800M unlabeled data extracted from BooksCorpus and 2500 M words from Wikipedia, and then, it was transferred to the incident prediction problem. We performed an additional preprocessing step for our dataset using the BERTtokenizer. It tokenized and reformatted the sequence of tokens by adding CLS (a classification token indicating the start of a sequence) and SEP tokens (appended to the end of the sequence).