One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

COVID-19, a deadly disease that originated in Wuhan, China, has resulted in a global outbreak. Patients infected with the causative virus SARS-CoV-2 are placed in quarantine, so the virus does not spread. The medical community has not discovered any vaccine that can be immediately used on patients infected with SARS-CoV-2. The only method discovered so far to protect people from this virus is keeping a distance from other people, wearing masks and gloves, as well as regularly washing and sanitizing hands. Government and law enforcement agencies are involved in banning the movement of people in different cities, to control the spread and monitor people following the guidelines of the CDC. But it is not possible for the government to monitor all places, such as shopping malls, hospitals, government offices, and banks, and guide people to follow the safety guidelines. In this paper, a novel technique is developed that can guide people to protect themselves from someone who has high exposure to the virus or has symptoms of COVID-19, such as having fever and coughing. Different deep Convolutional Neural Networks (CNN) models are implemented to test the proposed technique. The proposed intelligent monitoring system can be used as a complementary tool to be installed at different places and automatically monitor people adopting the safety guidelines. With these precautionary measurements, humans will be able to win this fight against COVID-19.

This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases. To ensure the model’s robustness and generalizability beyond the training dataset, it was further tested on a set of unseen images sourced from Google Images. This additional testing aimed to assess the model’s effectiveness in real-world scenarios, where it might encounter new data. The evaluation results were auspicious, demonstrating the model’s capability to classify plant conditions, such as diseases, with high accuracy. Moreover, the use of YOLOv8 offers significant improvements in speed and precision, making it suitable for real-time plant disease monitoring applications. The findings highlight the potential of this methodology for broader agricultural applications, including early disease detection and prevention.

With the development of wireless technology, two basic wireless network models that are commonly used, known as infrastructure and wireless ad hoc networks (WANETs), have been developed. In the literature, it has been observed that channel contention is one of the main reasons for packet drop in WANETs. To handle this problem, this paper presents a routing protocol named CCBR (Channel Contention Based Routing). CCBR tries to determine a least contended path between the endpoints to increase packet delivery ratio and to reduce packet delay and normalized routing overhead. Moreover, throughout the active data section, each intermediate node computes its channel contention value. If an intermediate node detects an increase in channel contention, it notifies the source node. Then the source node determines another least contended route for transmission. The advantages of CCBR are verified in our NS2‐based performance study, and the results show that CCBR outperforms ad hoc on‐demand distance vector (AODV) in terms of packet delivery ratio, end‐to‐end delay, and routing overhead by 4% to 9%.

Information is exploding on the web at exponential pace, and online movie review over the web is a substantial source of information for online users. However, users write millions of movie reviews on regular basis, and it is not possible for users to condense the reviews. Classification and summarization of reviews is a difficult task in computational linguistics. Hence, an automatic method is demanded to summarize the vast amount of movie reviews, and this method will permit the users to speedily distinguish between positive and negative features of a movie. This work has proposed a classification and summarization method for movie reviews. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents and represent the review documents as a vector. Next, the Na¨ıve Bayes algorithm is employed to categorize the movie reviews (signified as a feature vector) into negative and positive reviews. For the task of movie review summarization, word2vec model is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are employed to compute the salience score of all review sentences in clusters. Finally, the best-ranked review sentences are picked based on top salience scores to form a summary of movie reviews. Empirical results indicate that the suggested machine learning approach performed better than benchmark summarization approaches.