Vehicular Ad-Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS), enabling communication between vehicles and roadside infrastructure. This paper proposes an Adaptive Context-Aware VANET Routing (ACAVR) protocol designed to handle the challenges of high mobility, dynamic topology, and variable vehicle density in urban environments. The proposed protocol integrates context-aware routing, dynamic clustering, and geographic forwarding to enhance performance under diverse traffic conditions. Simulation results demonstrate that ACAVR achieves higher throughput, improved packet delivery ratio, lower end-to-end delay, and reduced routing overhead compared to existing routing schemes. The proposed ACAVR outperforms benchmark protocols such as DyTE, RGoV, and CAEL, improving PDR by 12–18%, reducing delay by 10–15%, and increasing throughput by 15–22%.

The increasing Internet of Things (IoT) device integration in smart home environments has increased the options available for intelligent energy management. In the context of smart homes, this paper provides a detailed analysis on the use of IoT data for energy consumption trend prediction and anomaly detection. We propose a novel approach that combines the advantages of the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for accurate consumption forecasting. Real-world data from a smart home setting is utilised to evaluate the proposed models. Results will therefore show that our approach performs best in optimally utilizing resources, minimizing waste, and improving energy consumption. The current study contributes to the development of energy-efficient smart houses through providing a reliable method for consumption forecasting and anomaly detection. Results indicate that the LSTM model outperformed ARIMA in prediction accuracy, achieving a lower Mean Absolute Error (MAE) of 0.110 compared to ARIMA's 0.176. Furthermore, the LSTM model demonstrated superior performance in anomaly detection, with higher precision and recall scores.

Twitter is a famous social media platform, which supports short posts limited to 280 characters. Users tweet about many topics like movie reviews, customer service, meals they just ate, and awareness posts. Tweets carrying information about some crime scenes are crime tweets. Crime tweets are crucial and informative and separate classification is required. Identification and classification of crime tweets is a challenging task and has been the researcher’s latest interest. The researchers used different approaches to identify and classify crime tweets. This research has used an ensemble approach for the identification and classification of crime tweets. Tweepy and Twint libraries were used to collect datasets from Twitter. Both libraries use contrasting methods for extracting tweets from Twitter. This research has applied many ensemble approaches for the identification and classification of crime tweets. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) Classifier assigned with the weights of 1,2,1,1 and 1 respectively ensemble together by a soft weighted Voting classifier along with term frequency – inverse document frequency (TF-IDF) vectorizer gives the best performance with an accuracy of 96.2% on the testing dataset.

In recent years, Twitter has caught the attention of many researchers because of the fact that it is growing very rapidly in terms of number of users and also all the data present as tweets on twitter is public in nature while other social media networks such as Facebook, data is not completely public as users can restrict their post to only users present in their friend list. In this research study, aspect based sentiment analysis (ABSA) was done on the data acquired from social media related to the major cellular network companies of Pakistan (Telenor Pakistan, Mobilink Jazz, Zong, Warid and Ufone). For this research, we have specifically selected all tweets which are not only in English and Roman Urdu but also mixture of above two languages. We have employed natural language processing (NLP) techniques for pre-processing the dataset and machine learning (ML) techniques to detect the sentiments present in the data. The results are interesting and informative specially for policy makers of cellular companies. These companies can utilize this information to increase the performance of their services. In comparison with the state of the art algorithms, the performance of bagging algorithm with this framework on the acquired dataset has produced F Score of 92.25, which is very encouraging outcome of this research work.

The Coronavirus was first reported in China in the city of Wuhan in December 2019, after a couple of months, it was widespread around the world. The whole world was in a state of lockdown. This hazardous disease affects the normal daily life of every individual and the tourism industry, especially the airline business was at a greater loss. Considering the airline business, this study contains data on commercial flights from 2019 to 2020. The conducted research analyzed the rise and fall of different flights in the lockdown period. The research is based on the variants of Long Short-Term Memory (LSTM) such as standard Recurrent Neural Network (RNN) and stack LSTM. The comparative research shows that the prediction of the stack LSTM model is better than the standard RNN keeping view of taking a considerable amount of time to train.