Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail.

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models.

Human action recognition from video has several potential to apply in different real-life applications, but the most cases in this field suffer from the variation in viewpoint. Most of published methods in this area are considered the performance of each single camera, therefore the change in the viewpoints significantly decrease the recognition rate. In this paper, multiple views are considered together and a method has proposed to recognize human action depicted in multi-view image sequences. In the first step, the border of the human body's silhouette is extracted and distance signal is calculated. In the next step, the wavelet transform is applied to extract coefficients of single-view features, and then the extracted features are combined to compose multi-view features. Finally a hierarchical classifier using support vector machine and Naïve Bayes classifiers is implemented to classify the actions. The average of overall action recognition accuracy for 12 actions using 5 different angles of views on the IXMAS dataset is 88.22. The results of experiments on the popular multi-view dataset have shown the proposed method achieves high and state-of-the-art success rates. In other word, combination of single-view extracted features from the wavelet approximation coefficients and composing the multi-view features can be used as the multi-view features. Further, the hierarchical classifier can be applied to recognize actions in multi-view human action recognition area.

Background: The provision of facilities to achieve better living conditions for patients with cystic fibrosis (CF) is of particular importance. Smoking is one of the known factors that aggravate CF. However, the effects of exposure to secondhand smoke (consumed by parents) if not consumed by the individual him/herself are not very evident. Objectives: This study aimed to evaluate the quality of life in patients with CF based on a history of smoking in parents. Methods: This cross-sectional analytical study was conducted on 100 patients with CF referred to the National Research Institute of Tuberculosis and Lung Diseases in Tehran, Iran, who were admitted in 2019 - 2021, using convenience sampling. After measuring height and weight, body mass index (BMI) was calculated, and the relationship between these factors (including a family history of smoking, pulmonary function tests, and BMI) with the quality of life of these patients was assessed. The data collection tool was a questionnaire. Data analysis was performed using SPSS software (version 20). The independent t-test and Pearson correlation test were used in this study, with a significance level of 0.05. Results: There was no statistically significant difference in the quality of life of the patients in the two groups whose parents were smokers and nonsmokers; however, physical and social functions and disease severity were lower in the smoking family group than in the other group (P > 0.05). In this study, there was no significant relationship between the quality of life of patients with CF in physical, emotional, and social functions and disease severity with a family history of smoking (P > 0.05); nevertheless, the quality of life was lower in patients whose fathers were smokers. In the present study, there was no significant relationship between patients’ quality of life in the smoking family group with the number of daily consumption of tobacco (cigarettes) in those around and the duration of family smoking (year). Conclusions: The findings of this study showed that smoking reduces the quality of life of patients with CF. Therefore, the need for the attention of parents and relatives of patients with CF to increase patients’ quality of life is seriously felt. There was no statistically significant relationship between smoking in patients with CF and reduced quality of life in this study; however, it is suggested to perform this study on a larger sample and in different cities in patients with CF and compare the results.