With the expansion of communication in today’s world and the possibility of creating interactions between people through communication networks regardless of the distance dimension, the issue of creating security for the data and information exchanged has received much attention from researchers. Various methods have been proposed for this purpose; one of the most important methods is intrusion detection systems to quickly detect intrusions into the network and inform the manager or responsible people to carry out an operational set to reduce the amount of damage caused by these intruders. The main challenge of the proposed intrusion detection systems is the number of erroneous warning messages generated and the low percentage of accurate detection of intrusions in them. In this research, the Suricata IDS/IPS is deployed along with the NN model for the metaheuristic’s manual detection of malicious traffic in the targeted network. For the metaheuristic-based feature selection, the neural network, and the anomaly-based detection, the fuzzy logic is used in this research paper. The latest stable version of Kali Linux 2020.3 is used as an attacking system for web applications and different types of operating systems. The proposed method has achieved 96.111% accuracy for detecting network intrusion.

The energy of sensor nodes in wireless sensor networks is limited, which is one of the most important challenges due to the lack of a fixed power supply. Because data transmission consumes the most energy of nodes, a node that transmits more packets runs out of energy faster than the others. When the energy of a node comes to the end of a network, the process of network operation may be disrupted. In this case, critical information in the network with the desired quality may not reach the hole and eventually the base stations. Therefore, considering the dynamic topology and distributed nature of wireless sensor networks, designing energy-efficient routing protocols is the main challenge. In this paper, an energy-aware routing protocol based on a multiobjective particle swarm optimization algorithm is presented. In the proposed particle swarm optimization algorithm method, the proportionality function for selecting the optimal threaded node is set based on the goals related to service quality including residual energy, link quality, end-to-end delay, and delivery rate. The simulation results show that the proposed method consumes less energy and has a longer lifespan compared with the state-of-the-art methods due to balancing the goals related to service quality criteria.

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder (DBP-DAE) which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern (LBP) into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network (DSRCN) which extracts high (deep) and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s.

Cardiovascular disease is one of the most common diseases in the modern world, which, if diagnosed early, can greatly reduce the damage to the patient. Diagnosis of heart disease requires great care, and in some cases, the process can be disrupted by human error. Machine learning methods, especially data mining, have gained international acceptance in almost all aspects of life, especially the prediction of heart disease. On the other hand, datasets related to heart patients have many biological features that most of these features do not have a direct impact on diagnosis. By removing redundant features from the dataset, in addition to reducing computational complexity, the accuracy of heart patients’ predictions can also be increased. This paper presents a density-based unsupervised approach to the diagnosis of abnormalities in heart patients. In this method, the basic features in the dataset are first selected based on the filter-based feature selection approach. Then, the DBSCAN clustering method with adaptive parameters has used to increase the clustering accuracy of healthy instances and to determine abnormal instances as cardiac patients. Partition clustering methods suffer from the selection of the number of clusters and the initial central points and are very sensitive to noise. The DBSCAN method solves these problems by creating density-based clusters, but the selection of the neighborhood radius threshold and the number of connected points in the neighborhood remains unresolved. In the proposed method, these two parameters are selected adaptively to achieve the highest accuracy for the diagnosis and prediction of heart patients. The results of the experiments show that the accuracy of the proposed method for predicting heart patients is approximately 95%, which has improved in comparison with previous methods.

Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods.