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Utilizing an Artificial Neural Network Model to Predict Bearing Capacity of Stone Columns
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ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous related literature including parameters handling the behavior of stone column and governing its bearing capacity. They included some parameters that were not considered previously; the undrained cohesion of soil, angle of internal friction and modulus of elasticity of fill material, area replacement ratio, and length to diameter ratio. The new model was generated using Neural Network Toolbox in MATLAB, all the five key parameters were treated as input data while the bearing capacity as the output data wanted to be predicted. A single hidden layer of twenty artificial neurons has been adopted in the generation of the model. The results and the regression analysis showed a high potential of using neural artificial network method in predicting the ultimate bearing capacity of soil strengthened with stone column. Thus, the study contributes in producing a reliable outcome as an alternative to using findings of costly and time consuming field or experimental tests

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Publication Date
Fri Jan 01 2021
Journal Name
Latin American Journal Of Solids And Structures
Structural Behavior of Reinforced Hybrid Concrete Columns under Biaxial Loading
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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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Publication Date
Thu Mar 31 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Permeability Prediction in One of Iraqi Carbonate Reservoir Using Hydraulic Flow Units and Neural Networks
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Permeability determination in Carbonate reservoir is a complex problem, due to their capability to be tight and heterogeneous, also core samples are usually only available for few wells therefore predicting permeability with low cost and reliable accuracy is an important issue, for this reason permeability predictive models become very desirable.

   This paper will try to develop the permeability predictive model for one of  Iraqi carbonate reservoir from core and well log data using the principle of Hydraulic Flow Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good parameter to determine (HFUs).

   Histogram analysis, probability analysis and Log-Log plot of Reservoir Qua

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Publication Date
Sat Feb 01 2020
Journal Name
Journal Of Economics And Administrative Sciences
Effect of applying the CAMEL model to profitability of banks )An applied study on a number of Iraqi banks for the period 2010-2016(
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The evaluation of banks plays an important role in maintaining the interests of customers with the bank as well as providing continuous supervision and control by the Central Bank. The Central Bank of Iraq conducted an assessment of the Iraqi banks through the implementation of the CAMEL model during a certain period. This evaluation did not continue. The research provides continuity to the Central Bank's assessment and as a step to continue the evaluation process for all banks through the use of the CAMEL model. ROA and ROE by using the regression model for four Iraqi banks registered in the Iraqi market for securities during the period 2010-2016. The results showed that the capital and profitability indicators have a significan

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
A Word Cloud Model based on Hate Speech in an Online Social Media Environment
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Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acq

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Publication Date
Sun May 27 2018
Journal Name
Journal Of Advanced Transportation
Accident Management System Based on Vehicular Network for an Intelligent Transportation System in Urban Environments
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As cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficie

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Scopus (49)
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Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Harnessing Technological Innovation and Artificial Intelligence in Iraqi Commercial Banks to Achieve Sustainability
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Publication Date
Tue Feb 28 2017
Journal Name
Journal Of Engineering
Estimation of Lifting Capacity for Selected Wells in Rumaila Field
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          This paper deals with studying the effect of hole inclination angle on computing slip velocity and consequently its effect on lifting capacity. The study concentrates on selected vertical wells in Rumaila field, Southern Iraq. Different methods were used to calculate lifting capacity. Lifting capacity is the most important factor for successful drilling and which reflex on preventing hole problems and reduces drilling costs. Many factors affect computing lifting capacity, so hence the effect of hole inclination angle on lifting capacity will be shown in this study. A statistical approach was used to study the lifting capacity values which deal with the effect of hole

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Publication Date
Sat Apr 01 2017
Journal Name
Iracst – International Journal Of Computer Networks And Wireless Communications (ijcnwc)
Optimizing Network Security Considerations with Transition to IPv6 in University of Baghdad, A Prototype
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It is believed that Organizations around the world should be prepared for the transition to IPv6 and make sure they have the " know how" to be able to succeed in choosing the right migration to start time. This paper focuses on the transition to IPv6 mechanisms. Also, this paper proposes and tests a deployment of IPv6 prototype within the intranet of the University of Baghdad (BUniv) using virtualization software. Also, it deals with security issues, improvements and extensions of IPv6 network using firewalls, Virtual Private Network ( VPN), Access list ( ACLs). Finally, the performance of the obtainable intrusion detection model is assessed and compared with three approaches.

Publication Date
Wed Apr 01 2026
Journal Name
كلية العلوم السياسية / جامعة الموصل / مؤتمرها العلمي الدولي الخامس والسنوي
The state's Orientation towards using sustainable electrical energy with the contribution of Artificial Intelligence (AI) / Geothermal energy As a model / contributes to strengthening the building of a future the state's strength "A study in political geography"
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المستخلص: هناك مشكلة تعاني منها الدولة وهي الأعتماد على الطاقات التقليدية الملوثة Abstract: The country suffers from a problem which is the reliance on traditional polluting and non-renewable energies and the failure to discover and exploit sustainable sources such as geothermal energy. The research hypothesis is whether the modern state’s orientation towards exploiting a modern energy source such as geothermal energy contributes in some way to achieving another source of energy and benefiting from it to enhance the building of a new type of state power from buried energy sources? And does artificial intelligence technology contribute to solving a specific problem by exp

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