The usage of local languages is being common in social media and news channels. The people share the worthy insights about various topics related to their lives in different languages. A bulk of text in various local languages exists on the Internet that contains invaluable information. The analysis of such type of stuff (local language’s text) will certainly help improve a number of Natural Language Processing (NLP) tasks. The information extracted from local languages can be used to develop various applications to add new milestone in the field of NLP. In this paper, we presented an applied research task, “multiclass sentence classification for Urdu language text at sentence level existing on the social networks, i.e., Twitter, Facebook, and news channels by using N-grams features.” Our dataset consists of more than 1,00000 instances of twelve (12) different types of topics. A famous machine learning classifier Random Forest is used to classify the sentences. It showed 80.15%, 76.88%, and 64.41% accuracy for unigram, bigram, and trigram features, respectively.

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by using machine learning classifiers. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. The title, its length, and the last four words of a sentence are used as features to classify the events. The Term Frequency-Inverse Document Frequency (tf-idf) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. Random Forest (RF) and K-Nearest Neighbor (KNN) are among the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively. The novelty lies in the fact that the features aforementioned are not applied, up to the best of our knowledge, in the event extraction of the text written in the Urdu language.

In today’s world, higher security deployments are needed, as the expansion of the transportation system has accelerated with time. Road traffic disasters have become a widespread problem in recent years. With the tremendous increase in traffic accidents, the fatality rate among people is quickly expanding. Whenever a mishap occurs on the roadways, it becomes a devastating situation for the victims. As an output result of the proposed model in this article, critical notification regarding the scene of an accident and car number was successfully sent to the pre-programmed number after determining the accident scene. The relevant contacts that were configured and added to the system successfully received an emergency message, providing the exact geographic coordinates of the accident scene. Following the receipt of the message, an audio call with a recorded voice was made to the pre-defined number. Moreover, Global Positioning System (GPS) was used to get the coordinates from the satellite. For this purpose, Global System for Mobile Communications (GSM) was utilized to attain the (GPS) coordinates in the event of an accident. Following on, the current location of an automobile through (GPS) was transmitted to certain contact details that were pre-programmed within the application. The system also reported the severity of the accident, as well as whether a vehicle collided with another vehicle or a disaster occurred to the vehicle itself.