###### Abstract
Using bi-directional long-short-term-memory neural networks, a social media network monitoring system is proposed for analyzing and detecting traffic accidents [11]. The proposed method retrieves traffic-related information from social media (Facebook and Twitter) using query-based crawling: this process collects sentences related to any traffic events, such as jams, road closures, etc. Subsequently, several pre-processing techniques are carried out, such as steaming, tokenization, POS tagging and segmentation, in order to transform the retrieved data into structured form. Thereafter, the data are automatically labeled as 'traffic' or 'non-traffic', using a latent Dirichlet allocation (LDA) algorithm. Traffic- labeled data are analyzed into three types; positive, negative, and neutral. The output from this stage is a sentence labeled according to whether it is traffic or non-traffic, and with the polarity of that traffic sentence (positive, negative or neutral). Then, using the bag-of-words (BoW) technique, each sentence is transformed into a one-hot encoding representation in order to feed it to the Bi-directional LSTM neural network (Bi-LSTM). After the learning process, the neural networks perform multi-class classification using the softmax layer in order to classify the sentence in terms of location, traffic event and polarity types. The proposed method compares different classical machine learning and advanced deep learning approaches in terms of accuracy, F-score and other criteria. Many initiatives and workshops have been conducted in order to improve and develop the healthcare systems using machine learning, such as [12]. In these workshops several proposed machine algorithms have been used, such as K Nearest-Neighbors, logistic regression, K-means clustering, Random Forest (RF) etc, together with deep learning algorithms such as CNN, RNN, fully connected layer and auto-encoder. These varieties of techniques allow the researchers to deal with several data types, such as medical imaging, history, medical notes, video data, etc. Therefore, different topics and applications are introduced, with significant performance results such as causal inference, in investigations of Covid-19, disease prediction, such as disorders and heart diseases. Using intelligent ensemble deep learning methods, healthcare monitoring is carried out for prediction of heart diseases [13]. Real-time health status monitoring can prevent and predict any heart attacks before occurrence. For disease prediction, the proposed ensemble deep learning approach achieved a brilliant accuracy performance score of 98.5%. The proposed model takes two types of data that are transferred and saved on an online cloud database. The first is the data transferred from the sensors; these sensors have been placed in different places on the body in order to extract more than 10 different types of medical data. The second type is the daily electronic medical records from doctors, which includes various types of data, such as smoking history, family diseases, etc. The features are fused using the feature fusion Framingham Risk factors technique, which executes two tasks at a time, fusing the data together, and then extracting a fused and informative feature from this data. Then different pre-processing techniques are used to transform the data into a structured and well-prepared form, such as normalization, missing values filtering and feature weighting. Subsequently, an ensemble deep learning algorithm starts which learns from the data in order to predict whether a heart disease will occur or the threat is absent. IDS refers to a mechanism capable of identifying or detecting intrusive activities. In a broader view, this encompasses all the processes used in the discovery of unauthorized uses of network devices or computers. This is achieved through software designed specifically to detect unusual or abnormal activities. IDS can be classified according to several surveys and sources in the literature [14; 15; 16; 17] into four types (HIDS, NIDS, WIDS, NBA).