The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the

Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mo

In this study, the optimum conditions for COD removal from petroleum refinery wastewater by using a combined electrocoagulation- electro-oxidation system were attained by Taguchi method. An orthogonal array experimental design (L18) which is of four controllable parameters including NaCl concentration, C.D. (current density), PH, and time (time of electrolysis) was employed. Chemical oxygen demand (COD) removal percentage was considered as the quality characteristics to be enhanced. Also, the value of turbidity and TDS (total dissolved solid) were estimated. The optimum levels of the studied parameters were determined precisely by implementing S/N analysis and analysis of variance (ANOVA). The optimum conditions were found to be NaCl = 2.5

     The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time.   Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle  to involve four types of binary code books (i.e. Pour when , Flat when  , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding procedure, with very small distortion per block, by designing s

The paper aims is to solve the problem of choosing the appropriate project from several service projects for the Iraqi Martyrs Foundation or arrange them according to the preference within the targeted criteria. this is done by using Multi-Criteria Decision Method (MCDM), which is the method of Multi-Objective Optimization by Ratios Analysis (MOORA) to measure the composite score of performance that each alternative gets and the maximum benefit accruing to the beneficiary and according to the criteria and weights that are calculated by the Analytic Hierarchy Process (AHP). The most important findings of the research and relying on expert opinion are to choose the second project as the best alternative and make an arrangement acco

A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

The use of deep learning.