fuzzy logic

This research has been done with the aim of providing a routing method in Internet of Things networks based on fuzzy logic and clustering for the use of smart transportation. Today, transportation is one of the basic issues and major problems in cities and this issue bigger cities such as Tehran have become an important concern, hence transportation systems have been created and expanded. But rapid population growth, migration to cities, increasing car ownership and the use of personal vehicles are the challenges that urban transportation systems are facing. Meanwhile, passenger transportation systems can play a significant role in improving the city's traffic situation. Cluster-based routing protocols are one of the most important ways to reduce energy consumption in wireless sensor networks. In this thesis, a new clustering protocol using bee colony optimization algorithm is presented and a new method to reduce energy consumption and. Lifetime improvement in wireless sensor network routing includes three stages of clustering sensor nodes into a number of clusters using fuzzy clustering algorithm, selecting optimal cluster heads and data transmission. The results of implementing the proposed method and comparing its performance with other methods the former showed the proper performance of this method in this field

An automatic guidance system in drones can be divided into two parts: path design and follow the path. In these systems, due to the time limitations and the uncertainty in the conditions prevailing on the battlefield, it is very important to use the expertise of the military commanders and emulate their behavior in these systems, due to the time constraints and the uncertainty in the conditions prevailing on the battlefield, it is very important to use the expertise of military commanders and simulate their behavior in the design and route tracking processes. Therefore, according to the characteristics of fuzzy inference systems, by using them, the commanders' expertise can be applied in autonomous drones. In this article, in order to model the UAVs offensive routing, a Mamdani fuzzy inference system with five inputs and one output is used to determine the edges weight. At each stage of decision-making process of choosing the optimal route, the need to perform complex mathematical calculations can make drone routing algorithms useless in real-world conditions. Therefore, in order to reduce the dependence of the routing and target tracking system on mathematical calculations and to use the advantages of anytime algorithms to produce the optimal answer, genetic algorithm and non-dominant sorting genetic algorithm-II have been used as the method of solving the model. The simulation results show that the combination of genetic algorithm and fuzzy inference system has a very favorable efficiency in performing the routing process in real world conditions and can meet the operational needs of commanders in this field.

In most Internet of Things (IoT) applications, network nodes are limited in terms of energy source. Therefore, the need for innovative methods to eliminate energy loss which shortens the life of networks is fully felt in such networks. One of the optimization techniques of energy consumption on the Internet of things is efficient energy routing that the required energy can be reduced by choosing an optimal path. In this paper, an informed or efficient energy approach is proposed for routing on the Internet of Things in which focus is on the sleep-wake schedule of nodes; therefore, a new optimization algorithm called chaos fuzzy grasshopper optimization algorithm was used. In chaos fuzzy grasshopper algorithm, the initial population of grasshoppers is generated by Lorenz chaos theory and the input and output parameters of the algorithm are adjusted by fuzzy approach. To evaluate the efficiency of the proposed method, three criteria of evaluation of remaining energy, network life and coverage rate were used. Investigating the findings in two different scenarios (efficiency over time and efficiency per number of different nodes) showed that the proposed method always is better than the base methods in all scenarios and for all performance evaluation criteria. So that in the study of the death of 30% of nodes which indicates the life of the network, results showed that the proposed method of the paper (FLGOA) has 9% better efficiency than FGOA, 12% better than GOA and 16% better than GSO. Also, the findings about the remaining energy of the network showed that the proposed method has 16% better efficiency than FGOA method, 21% better than GOA and 22% better than GSO. Finally, studies in the coverage rate evaluation criterion showed that the proposed method has 12% coverage rate better than FGOA method, 15% better than GOA and 16% better than GSO.

Today, due to high costs, it is not possible to perform a comprehensive and complete test on all parts of thesoftware. But if the fault-prone parts are identified before the test, the main focus of the test can be placedon these parts, which leads to cost savings. Identifying fault-prone components is the main purpose ofsoftware fault prediction. A predictive model receives software modules along with their features as inputand predicts which ones are prone to fault. Machine learning techniques are commonly used to constructthese models, the performance of which is highly dependent on the training dataset. Training datasetsusually have many software features, some of which are irrelevant or redundant, and the removal of thesefeatures is done using feature selection methods. In this research, a new method for wrapper-based featureselection is proposed that uses memetic algorithm, random forest technique and a new criterion based onfuzzy inference system. The results show that the proposed fuzzy evaluation criterion has a betterperformance than the existing criteria and improves the performance of feature selection. The final purposeof this research is to achieve a robust model for predicting high performance software faults and thecomparison results show that the proposed model has higher performance than other models.

Nowadays, computer networks are being widely used in the world. Due to the widespread use of the internet, computer systems are prone to information theft and this has led to the emergence of intrusion detection systems (IDS). Thus, network security has become an essential subject in computer science responding to the increase of sensitive information. The current research has used fuzzy C-means (FCM) and Whale optimization algorithm (WOA) to propose an unsupervised machine learning intrusion identification system and has tested it with the KDD Cup 99 standard intrusion detection dataset. In this method, fuzzy C-means has been applied in order to distinguish intrusive activities from normal activities and Whale optimization algorithm has been used to achieve optimal separations among these activities. In order to help FCM, the WOA has been applied to start with suitable cluster centers rather than randomly initialized centers. Experimental results on KDD Cup 99 dataset showed that the proposed method offers higher detection accuracy and a lower false alarm rate compared to other similar algorithms. Therefore, the findings of the present study would be effective in solving complex problems related to IDS.

The selection of an appropriate cover to embedding a secret message is one of the main issues in the steganography. This leads to increasing the performance and decreasing the error from the point of view of passive defence. In this paper, preparation of a cover image with the suitable security level is described. For this porpose, some important features such as contrast, energy and the use of fuzzy logic with a selection threshold for Harris corners have been considered. Firstly, some images with different contrast were obtained but a fixed threshold for Harris corners from each cover image which extracting the features is done using Gray Levels Co-Occurrence Matrix (GLCM). Then, the security of images was graded using fuzzy logic. The images with different security levels can be stored in the different bank of images. According to the simulation which is done, selected images based on the proposed method, have an appropriate SSIM and PSNR in the steganography.

Trust brings a novel means to improve the security and enable interoperability of current heterogeneous independent cloud platforms. In fact, trust is the most complex relationship among entities, because it is extremely subjective, context-dependant, non-symmetric, uncertain, partially transitive, and difficult to evaluate and establish. In the cloud environment, however, entities potentiallyinitiate transactions with each other without having had prior contacts. However, there is no special trust evaluation model for a cloud computing environment. This paper, presents a trust model based on fuzzy logic and fuzzy mathematics in cloud computing environment according to success and failure interaction between cloud entities based on the properties and semantics of trust. To compute trust in cloud systems, an algorithm based on proposed model is given and also, we designed the proposed moded with petri net. Simulation results show that the proposed model has some identification and containment capability in synergies cheating, malicious entities identification, promotes interaction between entities, and improves the performance of the entire cloud environment.

One of the most important factors in determining the necessity of passive defense consideration in civil project is the amount of probability of military attacks for that project. Therefore، in the risk analysis of passive defense studies، it is very important to determine between two or more civil projects which one have more probability to be attacked by military. This probability can be equal to the parameter that is known in different risk analysis methods as the probability of occurrence (O). This parameter is calculated typically quantitatively or qualitatively by making predictable criteria and by weighting them in different projects. But regardless of what analysis method has been used in determining the probability and that analysis method is based on how many of criteria، always some of the criteria remain as unpredictable issues. This paper offers a new method، based on fuzzy logic that have directly related to level of knowledge of attacker’s force (X) and can be used for calculation of probability increase of occurrence due to unpredictable criteria () in the different projects. By adding it to the value of probability of occurrence due to predictable criteria (O)، the amount of modified occurrence probability () can be calculated in the range that unpredictable criteria has great importance (very low probability range).