Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning

One of the diseases that attacks the lungs is pneumonia. Pneumonia is inflammation and fluid in the lungs making it difficult to breathe. This disease is diagnosed using X-Ray. Against the darker background of the lungs, infected tissue shows denser areas, which causes them to appear as white spots called infiltrates. In the image processing approach, pneumonia-infected X-rays can be detected using machine learning as well as deep learning. The convolutional neural network model is able to recognize images well and focus on points that are invisible to the human eye. Previous research using a convolutional neural network model with 10 convolution layers and 6 convolution layers has not achieved optimal accuracy. The aim of this research is to develop a convolutional neural network with a simpler architecture, namely two convolution layers and three convolution layers to solve the same problem, as well as examining the combination of various hyperparameter sizes and regularization techniques. We need to know which convolutional neural network architecture is better. As a result, the convolutional neural network classification model can recognize chest x-rays infected with pneumonia very well. The best classification model obtained an average accuracy of 89.743% with a three-layer convolution architecture, batch size 32, L2 regularization 0.0001, and dropout 0.2. The precision reached 94.091%, recall 86.456%, f1-score 89.601%, specificity 85.491, and error rate 10.257%. Based on the results obtained, convolutional neural network models have the potential to diagnose pneumonia and other diseases

GTRF: A Game Theory Approach for Regulating Node Behavior in Real-Time Wireless Sensor Networks

The selfish behaviors of nodes (or selfish nodes) cause packet loss, network congestion or even void regions in real-time wireless sensor networks, which greatly decrease the network performance. Previous methods have focused on detecting selfish nodes or avoiding selfish behavior, but little attention has been paid to regulating selfish behavior. In this paper, a Game Theory-based Real-time & Fault-tolerant (GTRF) routing protocol is proposed. GTRF is composed of two stages. In the first stage, a game theory model named VA is developed to regulate nodes’ behaviors and meanwhile balance energy cost. In the second stage, a jumping transmission method is adopted, which ensures that real-time packets can be successfully delivered to the sink before a specific deadline. We prove that GTRF theoretically meets real-time requirements with low energy cost. Finally, extensive simulations are conducted to demonstrate the performance of our scheme. Simulation results show that GTRF not only balances the energy cost of the network, but also prolongs network lifetime

Intrusion Detection into Cloud-Fog-Based IoT Networks Using Game Theory

The Internet of Things is an emerging technology that integrates the Internet and physical smart objects. This technology currently is used in many areas of human life, including education, agriculture, medicine, military and industrial processes, and trade. Integrating real-world objects with the Internet can pose security threats to many of our day-to-day activities. Intrusion detection systems (IDS) can be used in this technology as one of the security methods. In intrusion detection systems, early and correct detection (with high accuracy) of intrusions is considered very important. In this research, game theory is used to develop the performance of intrusion detection systems. In the proposed method, the attacker infiltration mode and the behavior of the intrusion detection system as a two-player and nonparticipatory dynamic game are completely analyzed and Nash equilibrium solution is used to create specific subgames. During the simulation performed using MATLAB software, various parameters were examined using the definitions of game theory and Nash equilibrium to extract the parameters that had the most accurate detection results. The results obtained from the simulation of the proposed method showed that the use of intrusion detection systems in the Internet of Things based on cloud-fog can be very effective in identifying attacks with the least amount of errors in this network

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

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

An Improved Energy-Aware Routing Protocol Using Multiobjective Particular Swarm Optimization Algorithm

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

D-PFA: A Discrete Metaheuristic Method for Solving Traveling Salesman Problem Using Pathfinder Algorithm

The Traveling Salesman Problem (TSP) which is a theoretical computer science and operations research problem, has several applications even in its purest formulation, such as the manufacture of microchips, planning, and logistics. There are many methods proposed in the literature to solve TSP with gains and losses. We propose a discrete metaheuristic method called D-PFA to solve this problem more efficiently. Initially, the Pathfinder Algorithm (PFA) was presented to handle issues involving continuous optimization, where it worked effectively. In recent years, there have been various published variants of PFA, and it has been frequently employed to address engineering challenges. In this study, the original PFA algorithm is broken into four sub-algorithms and every sub-algorithm is discretized and coupled to form a new algorithm. The proposed algorithm has a high degree of flexibility, a quick response time, strong exploration and exploitation. To validate the significant advantages of the proposed D-PFA, 34 different instances with different sizes are used in simulation results. The proposed method was also compared with 12 State-of-the-Art algorithms. Results indicate that the suggested approach is more competitive and resilient in solving TSP than other algorithms in different aspects. A conclusion and an outlook on future studies and applications are given at the end of the paper