Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called “Assured Clear Distance Ahead” (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. Experimental results show that Safe Driving Distance and Safe Driving Speed depend on several parameters such as weight of the vehicle, tires status, length of the vehicle, speed of the vehicle, type of road (snowy asphalt, wet asphalt, or dry asphalt or icy road) and the weather condition (clear or foggy). The study found that the technique is effective in calculating Safe Driving Distance, thereby resulting in forward collision avoidance by connected vehicles and maximizing road utilization by dynamically enforcing the minimum required safe separating gap as a function of the current values of the affecting parameters, including the speed of the surrounding vehicles, the road condition, and the weather condition.

Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.

The leading cause of death among people around the world is cardiovascular disease (CVD). In order to prevent patients from other damages, precise diagnostics of CVD on time is a crucial factor. Researcher workers are inspired to apply machine learning (ML) for accurate and quick diagnosis of CVD. ML algorithm extracts patterns and hidden relationships in the medical dataset for detecting or predicting disease development. But the prediction of CVD is a challenging task. The increasing size of healthcare dataset has made it a complex task for practitioners to make disease predictions and understand the feature relations. And so, selection of crucial features in a dataset plays a key role in optimizing the performance of ML algorithm. This study develops an Automated Cardiovascular Disease Diagnosis using Honey Badger Optimization with Modified Deep Learning (ACVD-HBOMDL) Model. The major aim of the ACVD-HBOMDL technique lies in the classification of CVD using feature selection (FS) and hyperparameter tuning strategies. Initially, the ACVD-HBOMDL technique applies min-max scaler to preprocess the medical data. To elect an optimal subset of features, the HBO algorithm is used in this work. For CVD classification, deep learning modified neural network (DLMNN) classifier is used and its hyperparameters can be optimally chosen by Bayesian optimization. The experimental results of the ACVD-HBOMDL technique can be tested on benchmark medical dataset and the obtained results demonstrate the significant outcomes of the ACVD-HBOMDL technique over other existing techniques.

The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data.

The rising adoption of Consumer Internet of Things (CIoT) devices brings heightened concerns about security vulnerabilities and privacy breaches. Given their connection to daily routine and data collection abilities, it is vital to protect individual data against unauthorized entry and possible leaks proactively. Data protection’s maximum communication and storage charge will gradually challenge the typical ecology of integrated over-the-cloud learning and handling for IoT environments. Federated learning (FL) is developed as the most reasonable method for this challenge. In FL, training data-driven machine learning (ML) approaches is a form of association among several users with no need for data carried to a fundamental point; therefore, improving communication and storage charge offers a great degree of consumer-level secrecy. FL is an advanced ML that performs appropriately by inherently prioritizing confidentiality, circumventing requirements for centralized data collection, and supporting the defence. FL improves security in IoT by enabling decentralized model training without centralizing sensitive data, preserving privacy, and reducing communication costs. Integrating FL into attack detection models ensures zero-trust security, minimizing vulnerabilities and enhancing protection against cyber threats while keeping data on-device. This study develops an enhanced artificial gorilla troop optimizer with an FL-based attack detection and classification (EGTO-FLADC) approach on the CIoT environment. The EGTO-FLADC approach aims to improve security in the CIoT environment via FL and the attack detection process. Primarily, the EGTO-FLADC technique utilizes the FL model for collaborative training of combined models with many devices with no data-sharing process. In addition, the EGTO-FLADC technique uses a min-max scalar for data normalization. For attack detection, the EGTO-FLADC technique employs the temporal convolution networks with a gated recurrent unit (TCN-GRU) model and its hyperparameters are chosen by the EGTO model. The EGTO technique involves the integration of the conventional GTO model with the marine predator’s algorithm (MPA) model. The performance evaluation of the EGTO-FLADC approach is examined under the EdgeIIoTset dataset. The experimental validation of the EGTO-FLADC approach portrayed a superior accuracy value of 97.11% over existing models.

In the recent past, the COVID-19 epidemic has impeded global economic progress and, by extension, all of society. This type of pandemic has spread rapidly, posing a threat to human lives and the economy. Because of the growing scale of COVID-19 cases, employing artificial intelligence for future prediction purposes during this pandemic is crucial. Consequently, the major objective of this research paper is to compare various deep learning forecasting algorithms, including auto-regressive integrated moving average, long short-term memory, and conventional neural network techniques to forecast how COVID-19 would spread in Saudi Arabia in terms of the number of people infected, the number of deaths, and the number of recovered cases. Three different time horizons were used for COVID-19 predictions: short-term forecasting, medium-term forecasting, and long-term forecasting. Data pre-processing and feature extraction steps were performed as an integral part of the analysis work. Six performance measures were applied for comparing the efficacy of the developed models. LSTM and CNN algorithms have shown superior predictive precision with errors of less than 5% measured on available real data sets. The best model to predict the confirmed death cases is LSTM, which has better RMSE and $R^2$ values. Still, CNN has a similar comparative performance to LSTM. LSTM unexpectedly performed badly when predicting the recovered cases, with RMSE and $R^2$ values of 641.3 and 0.313, respectively. This work helps decisionmakers and health authorities reasonably evaluate the status of the pandemic in the country and act accordingly.