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Optimal Dimensions of Small Hydraulic Structure Cutoffs Using Coupled Genetic Algorithm and ANN Model
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A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil was considered homogeneous anisotropic. For each case, the length of protection (L) and the volume of the superstructure (V) required to satisfy the factors of safety mentioned above were calculated. These data were used to obtain an artificial neural network model for estimating (L) and (V) for a given length of upstream cutoff (S1), length of downstream cutoff (S2), head difference (H), length of floor (B), depth of impervious layer (D) and degree of anisotropy (kx/ky).
A MatLAB code was written to perform a genetic algorithm optimization modeling using the obtained ANN model .The obtained optimum solution for some selected cases were compared with the Geo-studio modeling to find the length of protection required in the downstream side and volume required for superstructure. Values estimated were found comparable to the obtained values from the Genetic Algorithm model.

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Publication Date
Sun Jan 01 2012
Journal Name
International Journal Of Cyber-security And Digital Forensics (ijcsdf)
Genetic Algorithm Approach for Risk Reduction of Information Security
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Nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will lose plenty of competitive advantages as well. The core point of information security (InfoSecu) is risk management. There are a great deal of research works and standards in security risk management (ISRM) including NIST 800-30 and ISO/IEC 27005. However, only few works of research focus on InfoSecu risk reduction, while the standards explain general principles and guidelines. They do not provide any implementation details regarding ISRM; as such reducing the InfoSecu risks in uncertain environments is painstaking. Thus, this paper applied a genetic algorithm (GA) for InfoSecu risk reduction in uncertainty. Finally, the ef

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest
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Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Or

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Publication Date
Sun Jan 01 2023
Journal Name
Physical Mesomechanics Of Condensed Matter: Physical Principles Of Multiscale Structure Formation And The Mechanisms Of Nonlinear Behavior: Meso2022
Optimal control strategy applied to diabetes model
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Publication Date
Sat Jul 31 2021
Journal Name
Iraqi Journal Of Science
A Decision Tree-Aware Genetic Algorithm for Botnet Detection
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     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Engineering Business Management
Using ANN in emergency reconstruction projects post disaster
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The purpose of this study is to avoid delays and cost changes that occur in emergency reconstruction projects especially in post disaster circumstances. This study is aimed to identify the factors that affect the real construction period and the real cost of a project against the estimated period of construction and the estimated cost of the project. The case study is related to the construction projects in Iraq. Thirty projects in different areas of construction in Iraq were selected as a sample for this study. Project participants from the projects authorities provided data about the projects through a data collection distributed survey made by the authors. Mathematical data analysis was used to construct a model to predict change

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
A Parallel Adaptive Genetic Algorithm for Job Shop Scheduling Problem
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Publication Date
Fri Apr 01 2011
Journal Name
Al-mustansiriyah Journal Of Science
A Genetic Algorithm Based Approach For Generating Unit Maintenance Scheduling
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Publication Date
Sun Dec 07 2008
Journal Name
Baghdad Science Journal
Optimal Color Model for Information Hidingin Color Images
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In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.

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Publication Date
Sat May 28 2022
Journal Name
Abstract And Applied Analysis
Discretization Fractional-Order Biological Model with Optimal Harvesting
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In this paper, a discretization of a three-dimensional fractional-order prey-predator model has been investigated with Holling type III functional response. All its fixed points are determined; also, their local stability is investigated. We extend the discretized system to an optimal control problem to get the optimal harvesting amount. For this, the discrete-time Pontryagin’s maximum principle is used. Finally, numerical simulation results are given to confirm the theoretical outputs as well as to solve the optimality problem.

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Publication Date
Tue Apr 18 2023
Journal Name
Bmc Plant Biology
Unravelling the genetic diversity and population structure of common walnut in the Iranian Plateau
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Abstract<sec> <title>Background

Common walnut (Juglans regia L.) has a long cultivation history, given its highly valuable wood and rich nutritious nuts. The Iranian Plateau has been considered as one of the last glaciation refugia and a centre of origin and domestication for the common walnut. However, a prerequisite to conserve or utilize the genetic resources of J. regia in the plateau is a comprehensive evaluation of the genetic diversity that is conspicuously lacking. In this regard, we used 31 polymorphic simple sequence repeat (SSR) markers to delineate the genetic variation and population stru

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