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FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
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In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, with a
good degree of accuracy reaching 97.26, 95.92 and 86.43% respectively. These ANN models could be used as a support for workers in operating the filters in water treatment plants and to improve water treatment process. With the use of ANN, water systems will get more efficient, so reducing operation cost and improving the quality of the water produced.

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Publication Date
Wed Jan 01 2020
Journal Name
Iraqi Geological Journal
Reservoir modeling for mishrif formation in Nasiriyah oilfield
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Scopus (5)
Scopus
Publication Date
Mon Sep 23 2019
Journal Name
Baghdad Science Journal
A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network
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Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm

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Publication Date
Mon Apr 01 2013
Journal Name
International Journal Of Electrical, Electronics And Telecommunication Engineering
Performance Analysis of xPON Network for Different Queuing Models
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Passive optical network (PON) is a point to multipoint, bidirectional, high rate optical network for data communication. Different standards of PONs are being implemented, first of all PON was ATM PON (APON) which evolved in Broadband PON (BPON). The two major types are Ethernet PON (EPON) and Gigabit passive optical network (GPON). PON with these different standards is called xPON. To have an efficient performance for the last two standards of PON, some important issues will considered. In our work we will integrate a network with different queuing models such M/M/1 and M/M/m model. After analyzing IPACT as a DBA scheme for this integrated network, we modulate cycle time, traffic load, throughput, utilization and overall delay

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Publication Date
Fri Sep 12 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Evaluation of shear bond strength of artificial teeth to heat cure acrylic and high impact heat cure acrylic using autoclave processing method
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Background: Debonding and fracture of artificial teeth from denture bases are common clinical problem, bonding of artificial teeth to heat cure acrylic and high impact heat cure acrylic denture base materials with autoclave processing method is not well known. The aim of this study was to evaluate the effect of autoclave processing method on shear bond of artificial teeth to heat cure denture base material and high impact heat cure denture base material. Materials and methods: Heat polymerized (Vertex) and high impact acrylic (Vertex) acrylic resins were used. Teeth were processed to each of the denture base materials after the application of different surface treatments. The sample (which consist of artificial tooth attached to the dentur

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Publication Date
Thu Jan 30 2020
Journal Name
Journal Of Engineering
Design and Analysis WIMAX Network Based on Coverage Planning
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In this paper, wireless network is planned; the network is predicated on the IEEE 802.16e standardization by WIMAX. The targets of this paper are coverage maximizing, service and low operational fees. WIMAX is planning through three approaches. In approach one; the WIMAX network coverage is major for extension of cell coverage, the best sites (with Band Width (BW) of 5MHz, 20MHZ per sector and four sectors per each cell). In approach two, Interference analysis in CNIR mode. In approach three of the planning, Quality of Services (QoS) is tested and evaluated. ATDI ICS software (Interference Cancellation System) using to perform styling. it shows results in planning area covered 90.49% of the Baghdad City and used 1000 mob

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Publication Date
Tue Dec 31 2024
Journal Name
Frontiers In Health Informatics
The Implementation Of Artificial Intelligence In Education: Systematic Analysis
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Background: The rapid evolution of Artificial Intelligence (AI) has significantly influenced Education, demonstrating substantial potential to transform traditional teaching and learning methods. AI reshapes teacher-student interactions and the relationship with knowledge. Objective: To analyze the potential benefits, ethical challenges, and limitations of AI in Education based on recent scientific literature, emphasizing the balance between technology and human interaction. Methods: A documentary research approach with a descriptive focus was employed, following the PRISMA protocol for systematic reviews. The search strategy involved analyzing evidence from 18 scientific articles published within the last six years. Results:AI o

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Publication Date
Sun Oct 15 2023
Journal Name
Journal Of Yarmouk
Artificial Intelligence Techniques for Colon Cancer Detection: A Review
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Publication Date
Fri Dec 15 2023
Journal Name
Al-academy
The role of artificial intelligence in revolutionizing the clothing and textile industry
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 The integration of AI technologies is revolutionizing various aspects of the apparel and textile industry, from design and manufacturing to customer experience and sustainability. Through the use of artificial intelligence algorithms, workers in the apparel and textile industry can take advantage of a wealth of opportunities for innovation, efficiency and creativity.
The research aims to display the enormous potential of artificial intelligence in the clothing and textile industry through published articles related to the title of the research using the Google Scholar search engine. The research contributes to the development of the cultural thought of researchers, designers, merchants and the consumer with the importance of integ

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Publication Date
Sat Jul 28 2018
Journal Name
Journal Of Engineering
Effectiveness of Meso-Scale Approach in Modeling of Plain Concrete Beam
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The main aim of this research paper is investigating the effectiveness and validity of Meso-Scale Approach (MSA) as a modern technique for the modeling of plain concrete beams. Simply supported plain concrete beam was subjected to two-point loading to detect the response in flexural. Experimentally, a concrete mix was designed and prepared to produce three similar standard concrete prisms for flexural testing. The coarse aggregate used in this mix was crushed aggregate. Numerical Finite Element Analysis (FEA) was conducted on the same concrete beam using the meso-scale modeling. The numerical model was constructed to be a bi-phasic material consisting of cement mortar and coarse aggregate. The interface between the two c

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq
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It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

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