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Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial voids ratio. Multi-layer perceptron training by the backpropagation algorithm was used in creating the network. It was found that both models can predict shear strength parameters for gypseous soils with good reliability. Sensitivity analysis of the first model indicated that dry unit weight and plasticity index have the most significant effect on the predicted cohesion. While in the second model, the results indicated that the gypsum content and plasticity index have the most significant effect on the predicted angle of internal friction.

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Publication Date
Sat Feb 15 2025
Journal Name
Iraqi Journal Of Pharmaceutical Sciences
Academic Staff Perspectives on the Impact of Artificial Intelligence on Pharmaceutical Sciences Research and Writing: A Qualitative Study.
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Artificial intelligence (AI) offers significant benefits to biomedical research and academic writing. Nevertheless, using AI-powered writing aid tools has prompted worries about excessive dependence on these tools and their possible influence on writing proficiency. The current study aimed to explore the academic staff’s perspectives on the impact of AI on academic writing. This qualitative study incorporated in-person interviews with academic faculty members. The interviews were conducted in a semi-structured manner, using a predetermined interview guide consisting of open-ended questions. The interviews were done in person with the participants from May to November 2023. The data was analyzed using thematic analysis. Ten academics aged

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Intelligent Systems And Applications In Engineering
Artificial Intelligence Based Statistical Process Control for Monitoring and Quality Control of Water Resources: A Complete Digital Solution
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Publication Date
Mon Nov 03 2025
Journal Name
Lecture Notes In Networks And Systems
The Role of Social Media Sites Supported by Artificial Intelligence Tools in Spreading and Promoting the Civilizations Dialogues
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Mixing this strategy with a qualitative research design and an idea known as AI-supported journalism, the paper is going to approach the requirements of how AI technologies may transform journalism content and culture in a way beyond what one anticipates; therefore, enabling more of it to reach an audience. The current research used descriptive research design to investigate the potential applications of the AI tools that mediate civilizational conversation and a structured questionnaire to media professionals. AI-driven journalism can promote peaceful cohabitation and mutual respect and thus act as a bridge between cultures, the research said. The piece even goes on to mention the need for media establishments and civil soc

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Publication Date
Wed Jan 01 2020
Journal Name
Dental Research Journal
Impact of a mixture of nanofiller and intrinsic pigment on tear strength and hardness of two types of maxillofacial silicone elastomers
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Background: The ideal maxillofacial prosthesis should have fine and thin boundaries that bindwith the surrounding facial structures and possess high tear strength.This study aims to determinethe best percentages of nanofiller (TiO2) and intrinsic pigment (silicone functional intrinsic) thatcould be mixed in as additives to improve the tear strength of Cosmesil M511 andVST50F siliconeelastomers with the least effect on their hardness.Materials and Methods: In this in vitro experimental study, a total of 80 samples, 40 for eachelastomer, were fabricated. Each elastomer sample was split into two equal groups to test for tearstrength and Shore A hardness. Each group consisted of 20 samples, including 10 control sampleswithout additives and 10 e

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Publication Date
Mon Apr 11 2011
Journal Name
Icgst
Employing Neural Network and Naive Bayesian Classifier in Mining Data for Car Evaluation
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Publication Date
Wed Mar 31 2021
Journal Name
Electronics
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model
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Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

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Publication Date
Sun Oct 01 2023
Journal Name
Nanomedicine Research Journal
The effect of ZrO2 NPs addition on denture adaptation and diametral compressive strength of 3D printed denture base resin
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Objective(s): The world of dentistry is constantly evolving, and with the advent of 3D printing technology, the possibilities are endless. However, little is known about the effects of adding ZrO2 NPs to the denture base resin of 3D additive manufacturing technique.Aim of this study is to evaluate the behavior of resin which is used to 3D printing of denture base with the addition of ZrO2 NPs on denture adaptation property and diametral compression strength.Methods: 60 samples were printed, 30 disks for diametral compressive test and 30 denture base for denture adaptation test. Three groups per test (n=10). The control group for each test included unreinforced 3Dprinted denture base resin, and the other groups were  reinforced with (2&

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Publication Date
Fri Jan 07 2022
Journal Name
Iraqi Journal Of Laser
Influence of Fractional CO2 Laser Irradiation on Temperature Elevation and Bonding Strength of Resin Cement to the Zirconia Ceramic
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Abstract: Objectives: To investigate the effect of temperature elevation on the bonding strength of resin cement to the zirconia ceramic using fractional CO2 laser. Background: Fractional CO2 laser is an effective surface treatment of zirconia ceramic, as it increases the bonding strength of zirconia to resin cement. Methods: Thirty sintered zirconia discs (10 mm diameter, 2 mm thickness) were prepared and divided to three groups (N=10) and five diffident pulse durations were used in each group (0.1, 0.5, 1, 5 and 10 ms). Group A was treated with 10 W power setting, group B with 20 W and group C with 30 W. During laser irradiation, temperature elevation measurement was recorded for each specimen. Luting cement was bonded to the treated z

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Publication Date
Wed Sep 01 2010
Journal Name
Materials & Design
Influence of glass addition and sintering temperature on the structure, mechanical properties and dielectric strength of high-voltage insulators
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Publication Date
Fri Jun 11 2021
Journal Name
Journal Of Baghdad College Of Dentistry
The influence of different root canal irrigants on the push-out bond strength of AH plus and Bioceramic sealers
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Background: The bond strength of endodontic sealers with dentin is a very important property for maintaining the integrity and seal of the root canal filling. The aim of this study was to evaluate and compare the effect of various irrigants (QMix, 17% EDTA and 2.5% NaOCl) on the push-out bond strength of AH plus and Bioceramic sealers. Materials and methods: Forty eight freshly extracted maxillary first molars human teeth with striaght palatal root were used in the study. The collected samples were randomly divided into three groups of equal sample size (n=16), according to the final irrigation regimen as follows: Group (1): QMix 2 in 1, Group (2): 17% ethylenediaminetetraacetic acid, Group (3): 2.5% sodium hypochloride. All samples were

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