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A Hybrid Meta-Heuristic Approach for Test Case Prioritization and Optimization
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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 capabilities of considering the imperatives such as code coverage, fault finding rate and execution time from search algorithms in our hybrid approach to refine test cases considerations repetitively. The strategy accomplished this by putting experiments on a large-scale project of industrial software developed. The hybrid meta-heuristic technique ends up being better than the routine techniques. It helps in higher code coverage, which, in turn, enables to detect crucial defects at an early stage and also to allocate the testing resources in a better way. In particular, the best APFD value was 0.9321, which was achieved in 6 generations with 4.879 seconds the value to which the computer was run. Besides these, , the approach resulted in the mean value of APFD as 0.9247 and 0.9302 seconds which took from 10.509 seconds to 30.372 seconds. The carried out experiment proves the feasibility of this approach in implementing complex systems and consistently detecting the changes, enabling it to adapt to rapidly changing systems. In the end, this research provides us with a new hybrid meta-heuristic way of test case prioritization and optimization, which, in turn, helps to tackle the obstacles caused by large-scale test cases and constantly changing systems.

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Publication Date
Mon Jul 31 2023
Journal Name
Iraqi Geological Journal
Building of a Static Model for Jeribe Formation in Jambour Oil Field
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The Jeribe Formation, the Jambour oil field, is the major carbonate reservoir from the tertiary reservoirs of the Jambour field in northern Iraq, including faults. Engineers have difficulty organizing carbonate reserves since they are commonly tight and heterogeneous. This research presents a geological model of the Jeribe reservoir based on its facies and reservoir characterization data (Permeability, Porosity, Water Saturation, and Net to Gross). This research studied four wells. The geological model was constructed with the Petrel 2020.3 software. The structural maps were developed using a structural contour map of the top of the Jeribe Formation. A pillar grid model with horizons and layering was designed for each zone. Followin

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Publication Date
Fri Apr 30 2021
Journal Name
Iraqi Journal Of Science
A Genetic Algorithm for Task Allocation Problem in the Internet of Things
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In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonic

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Publication Date
Mon Oct 30 2023
Journal Name
Traitement Du Signal
A Comprehensive Review on Machine Learning Approaches for Enhancing Human Speech Recognition
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Publication Date
Fri Apr 30 2021
Journal Name
Iraqi Journal Of Science
A Genetic Algorithm for Task Allocation Problem in the Internet of Things
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In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonical de

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Publication Date
Mon Apr 02 2018
Journal Name
Al-nahrain Journal For Engineering Sciences (njes)
Output Feedback Adaptive Sliding Mode Control Design for a Plate Heat Exchanger
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The heat exchanger is a device used to transfer heat energy between two fluids, hot and cold. In this work, an output feedback adaptive sliding mode controller is designed to control the temperature of the outlet cold water for plate heat exchanger. The measurement of the outlet cold temperature is the only information required. Hence, a sliding mode differentiator was designed to estimate the time derivative of outlet hot water temperature, which it is needed for constructing a sliding variable. The discontinuous gain value of the sliding mode controller is adapted according to a certain adaptation law. Two constraints which imposed on the volumetric flow rate of outlet cold (control input) were considered within the rules of the proposed

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Publication Date
Sat Dec 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
About Semi-parametric Methodology for Fuzzy Quantile Regression Model Estimation: A Review
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In this paper, previous studies about Fuzzy regression had been presented. The fuzzy regression is a generalization of the traditional regression model that formulates a fuzzy environment's relationship to independent and dependent variables. All this can be introduced by non-parametric model, as well as a semi-parametric model. Moreover, results obtained from the previous studies and their conclusions were put forward in this context. So, we suggest a novel method of estimation via new weights instead of the old weights and introduce

Paper Type: Review article.

another suggestion based on artificial neural networks.

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Publication Date
Sun Oct 10 2021
Journal Name
Journal Of Physics
A novel kite cross hexagonal search algorithm for fast block motion estimation
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The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to resul

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Engineering Science And Technology
A proposed dual size design for energy minimization in sub-threshold circuits
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Sub-threshold operation has received a lot of attention in limited performance applications.However, energy optimization of sub-threshold circuits should be performed with the concern of the performance limitation of such circuit. In this paper, a dual size design is proposed for energy minimization of sub-threshold CMOS circuits. The optimal downsizing factor is determined and assigned for some gates on the off-critical paths to minimize the energy at the maximum allowable performance. This assignment is performed using the proposed slack based genetic algorithm which is a heuristic-mixed evolutionary algorithm. Some gates are heuristically assigned to the original and the downsized design based on their slack time determined by static tim

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Publication Date
Wed Jun 28 2023
Journal Name
The Iraqi Journal Of Veterinary Medicine
Haemoglobin Epsilon as a Biomarker for the Molecular Detection of Canine ‎Lymphoma
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Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

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Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
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