EER-AL: AN ENERGY EFFICIENT ROUTING PROTOCOL BASED ON AUTOMATED LEARNING METHOD

The issue of energy in a wireless sensor network is one of the most important challenges for these networks. This issue is also being considered today in the new IoT topic. This paper studies the ability of the learning automata model to solve the problem in the sensor networks. Because they have capabilities such as low computational load, ability to use in distributed environments, and inaccurate information, require the least feedback from the environment, etc. One of the solutions to energy optimization is to provide routing protocols. In the routing area, a routing protocol based on learning automata has been proposed in which the network lifetime criterion is considered. The simulation results and the comparison of the proposed protocol with other protocols indicate that this protocol has better performance in the energy conversation and network lifetime

AR-RBFS: Aware-Routing Protocol Based on Recursive Best-First Search Algorithm for Wireless Sensor Networks

Energy issue is one of the most important problems in wireless sensor networks. They consist of low-power sensor nodes and a few base station nodes. They must be adaptive and efficient in data transmission to sink in various areas. This paper proposes an aware-routing protocol based on clustering and recursive search approaches. The paper focuses on the energy efficiency issue with various measures such as prolonging network lifetime along with reducing energy consumption in the sensor nodes and increasing the system reliability. Our proposed protocol consists of two phases. In the first phase (network development phase), the sensors are placed into virtual layers. The second phase (data transmission) is related to routes discovery and data transferring so it is based on virtual-based Classic-RBFS algorithm in the lake of energy problem environments but, in the nonchargeable environments, all nodes in each layer can be modeled as a random graph and then begin to be managed by the duty cycle method. Additionally, the protocol uses new topology control, data aggregation, and sleep/wake-up schemas for energy saving in the network. The simulation results show that the proposed protocol is optimal in the network lifetime and packet delivery parameters according to the present protocols

Detecting SQL Injection Attacks by Binary Gray Wolf Optimizer and Machine Learning Algorithms

SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application’s database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset’s smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features

TARGET TRACKING BASED ON BASE STATION NODE USING PREDICTION METHOD AND CLUSTER STRUCTURE IN WIRELESS SENSOR NETWORKS

One of the most important and major challenging issues of wireless sensor networks is the tracking of mobile targets. The network continuously reports the spatial information of moving objects during specified periods to the base station. In this paper, by introducing new a protocol with two versions, of which, one of them is based on dynamic clustering with a focus on the base station, and the other is based on a predictive system for increasing the tracking accuracy of the objects movement and decreasing the energy consumption as well. In this paper, the task of clustering involves in determining the cluster heads, the number of cluster members, the selection of cluster members, and managing the activation of the nodes that is done by the base station. On the other hand, given that the base station is outside the field of wireless sensor networks and is connected to an unlimited power source. The second version of the proposed protocol is based on a predictive algorithm that it was inspired from the first proposed version in the role of the base station node by a prediction method. In this paper, three heuristic models are introduced to select the speed and direction in prediction models. They are instant, average and exponential-average models. These models can track the relevant targets more accurately and reduce the number of missing targets. The simulations are done in different scenarios in a custom developed tool. The results of simulation show a good performance of them in the network lifetime and target tracking applications

Chaotic Sand Cat Swarm Optimization

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results

A METHOD FOR FORECASTING WEATHER CONDITION BY USING ARTIFICIAL NEURAL NETWORK ALGORITHM

This article presents a method to forecast and make decision on weather condition. In most of the cities around the world, people try to decide on leisure activities on their spare time but weather condition would not be suitable for them. By this fact, we suggest a solution to solve this problem with ANN. Therefore, users of our proposed method can organize their daily life in accordance with weather condition. Artificial Neural Network (ANN) is one of the popular research subjects in computer science, thus, this paper aims to familiarize the reader with ANN. In our proposed method, at first, people can organize weather condition, and then the program suggest whether the time is suitable for them or not on chosen hour of day. In ANN, we discuss about neuron that have relation with performance. Mean Square Error (MSE) is the key issue for the performance of our method. At the end, the simulation results show that relation between Neuron and MSE is applicable for daily usage

Effect of dietary vitamin E on Eimeria tenella-induced oxidative stress in broiler chickens

An experiment was carried out to investigate the impact of high doses of dietary vitamin E on antioxidant  status in broiler chickens (Ross 308) experimentally infected with Eimeria tenella. One day old chicks were assigned to five groups (25 each) and given basal diet (A and B) or basal diet supplemented with 100, 316 or 562 mg/kg of vitamin E (C to E), respectively. On the 21st day, all chicks except those in group A were  inoculated with E. tenella and monitored for any change in blood vitamin E, malondialdehyde (MDA) and  superoxide dismutase (SOD). Plasma vitamin E decreased by infection, but increased with dietary vitamin E (p<0.05). A significant rise of plasma and erythrocyte MDA was observed in infected birds (p<0.05), however, the chicks fed diet with 316 mg/kg added vitamin E had a lower MDA compared to infected controls (p<0.001). The erythrocyte SOD was not affected by infection (p>0.05), but it was significantly higher in group D than in groups B and E (p<0.05). In conclusion, addition of dietary vitamin E at 316 mg/kg can afford antioxidant protection to chickens infected with E. tenella, but at higher doses it may aggravate the unbalanced  oxidant/antioxidant status.Key words: Eimeria tenella, oxidative stress, broiler chickens, vitamin E, malondialdehyde, superoxide dismutase

The Best Approximation of Generalized Fuzzy Numbers Based on Scaled Metric

The ongoing study has been vehemently allocated to propound an ameliorated α-weighted generalized approximation of an arbitrary fuzzy number. This method sets out to lessen the distance between the original fuzzy set and its approximation. In an effort to elaborate the study, formulas are designed for computing the ameliorated approximation by using a multitude of examples. The numerical samples will be exemplified to illuminate the improvement of the nearest triangular approximation (Abbasbandy et al., Triangular approximation of fuzzy numbers using α-weighted valuations, Soft Computing, 2019). A variety of features of the ameliorated approximation are then proved. © 2022 Tofigh Allahviranloo et al

Existence of a Unique Solution and the Hyers–Ulam-H-Fox Stability of the Conformable Fractional Differential Equation by Matrix-Valued Fuzzy Controllers

In this paper, we consider a conformable fractional diferential equation with a constant coefcient and obtain an approximation for this equation using the Radu–Mihet method, which is derived from the alternative fxed- point theorem. Considering the matrix-valued fuzzy k-normed spaces and matrix-valued fuzzy H-Fox function as a control function, we investigate the existence of a unique solution and Hyers–Ulam-H-Fox stability for this equation. Finally, by providing numerical examples, we show the application of the obtained results

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software