The directional sensor networks (DSNs) are mainly focused to prolong the network lifetime and to optimize the energy consumption of sensors. The number of sensors deployed in an environment is much higher than those required for providing the coverage; therefore, the energy-aware methods are needed to select the sensors. Coverage is considered a major problem in DSNs and is a criterion for quality of service (QOS).In this regard, the sensor scheduling method has been discussed by researchers to prolong the sensor lifetime in a network. The present paper proposes an NSGAII-based algorithm to solve the sensors 'scheduling. This paper aimed at finding a practical solution in solving the multi-objective problems by using the multi-objective evolutionary algorithm method. There are two parameters presented for evaluating the solutions, including the number of sensors, the target coverage. To confirm the high performance of the proposed algorithm, it was compared with the recently presented algorithm. According to the simulation findings, the algorithm had better results in the comparison parameters. Manuscript profile

The Directional Sensor Networks (DSNs) have recently drawn considerable attention with respect to their extensive applications in various situations. In this regard, covering a set of targets in a specific region while maximizing network lifetime is considered as a major problem related to the DSN, which is resulted from limitation in sensing angle and battery power of directional sensors. The problem gets more challenging when the targets have different coverage quality requirements. In the present study, this problem is referred to as Priority-based Target Coverage (PTC) that has been proved to be an NP-complete problem. In this regard, a genetic-based algorithm along with a repair operator is developed, which is able to select a proper subset of directional sensors for providing the coverage quality requirements for all targets. In order to evaluate the performance of the proposed algorithm, several experiments were performed and the results were compared to those of another algorithms already introduced to literature. Manuscript profile

The production of fake news were practiced even before the advent of the internet. However, with the development of the internet and traditional media giving way to social media, the growing and unstoppable process of making and spreading this kind of news have become a widespread concern. Fake news by disrupting the proper flow of information and deluding public opinion, potentially causes serious problems in society. Therefore, it is necessary to detect such news, which is associated with some challenges. These challenges may be related to various issues such as datasets, events, or audiences. Lack of sufficient information about news samples, or an imbalance are the main problems in some of these datasets, which will be addressed in this paper. In the proposed model, firstly the key features in relevant datasets will be extracted to increase information about news samples. After that, using the K-nearest neighbors, a genetic, and TomekLink algorithms as the cleaning techniques, as well as designing a Generative Adversarial network, as a technique for generating synthetic data, three novel methods in the area of hybrid resampling will be presented to balance these datasets. The presented methods cause a significant increase in the performance of the deep learning algorithms to detect fake news. Manuscript profile

Software Reliability (SR) is a key non-operational feature measured when evaluating software quality. To enhance this feature, it is important to detect failures and mitigate them in the testing phase. SR can be increased by identifying and removing this failure from the defect data. The existing literature consists of many models/methods applicable to measuring SR, including the SR Growth Models (SRGMs). Generally, SRGMs are in two main types: parametric and non-parametric. As these models are diverse, when applying to a certain problem, the particular requirements and conditions of that problem should be taken into account. The current paper explains the fundamental concepts of reliability, then reviews the Parametric SR Growth Models (PSRGMs) and evaluates various approaches already proposed in this domain. In addition, this study investigates the SRGMs compatibility by means of a novel Parallel Real-valued Genetic Algorithm (PRGA)-based method. The results achieved under a variety of conditions for each model showed the extent of compatibility with GA. Manuscript profile