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DYNAMIC MODELING FOR DISCRETE SURVIVAL DATA BY USING ARTIFICIAL NEURAL NETWORKS AND ITERATIVELY WEIGHTED KALMAN FILTER SMOOTHING WITH COMPARISON
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Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms represented by Iteratively Weighted Kalman Filter Smoothing (IWKFS) algorithm and in combination with the Expectation Maximization (EM) algorithm. Average Mean Square Error (AMSE) and Cross Entropy Error (CEE) were used as comparison’s criteria. The methods and procedures were applied to data generated by simulation using a different combination of sample sizes and the number of intervals.

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Publication Date
Mon May 04 2020
Journal Name
Environmental Engineering Research
Experimental and modeling study of water defluoridation using waste granular brick in a continuous up-flow fixed bed
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Contamination of surface and groundwater with excessive concentrations of fluoride is of significant health hazard. Adsorption of fluoride onto waste materials of no economic value could be a potential approach for the treatment of fluoride-bearing water. This experimental and modeling study was devoted to investigate for the first the fluoride removal using unmodified waste granular brick (WGB) in a fixed bed running in continuous mode. Characterization of WGB was carried out by FT-IR, SEM, and EDX analysis. The batch mode experiments showed that they were affected by several parameters including contact time, initial pH, and sorbent dosage. The best values of these parameters that provided maximum removal percent (82%) with the in

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Publication Date
Tue Aug 19 2025
Journal Name
Scientific Reports
Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study
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Publication Date
Sat Jun 01 2019
Journal Name
Collegian
Medication adherence and predictive factors in patients with cardiovascular disease: A comparison study between Australia and Iraq
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Publication Date
Thu Jun 25 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn: 1683 - 3597 , E-issn : 2521 - 3512)
Comparison Between Conventional and Supersaturable Self-nanoemulsion Loaded with Nebivolol: Preparation and In-vitro/Ex-vivo Evaluation
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Nebivolol (NBH) is a third-generation B1-blocker with high selectivity and vasodilation activity. Nevertheless, nebivolol exhibits low oral bioavailability, which may adversely affect its efficacy. Recently, supersaturable self-nanoemulsion (Su-SNE) is an advanced SNE approach that can address low bioavailability The study aims to prepare nebivolol-loaded Su-SNE by reduction the amount of the prepared conventional SNE to half. Besides, an appropriate polymer type and concentration to prevent NBH precipitation upon oral administration have investigated.. A conventional self-nanoemulsion (formula A) was prepared by dissolving NBH in 500 mg vehicle mixture of imwitor®988: cremophor-EL: propylene glycol. Then, eight Su-SNE formulas wit

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Publication Date
Thu Jun 25 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Comparison Between Conventional and Supersaturable Self-nanoemulsion Loaded with Nebivolol: Preparation and In-vitro/Ex-vivo Evaluation
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Nebivolol (NBH) is a third-generation B1-blocker with high selectivity and vasodilation activity. Nevertheless, nebivolol exhibits low oral bioavailability, which may adversely affect its efficacy. Recently, supersaturable self-nanoemulsion (Su-SNE) is an advanced SNE approach that can address low bioavailability The study aims to prepare nebivolol-loaded Su-SNE by reduction the amount of the prepared conventional SNE to half. Besides, an appropriate polymer type and concentration to prevent NBH precipitation upon oral administration have investigated.. A conventional self-nanoemulsion (formula A) was prepared by dissolving NBH in 500 mg vehicle mixture of imwitor®988: cremophor-EL: propylene glycol. Then, eight Su-SNE formul

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Publication Date
Tue Oct 01 2019
Journal Name
2019 12th International Conference On Developments In Esystems Engineering (dese)
Roadway Deterioration Prediction Using Markov Chain Modeling (Wasit Governorate/ Iraq as a Case Study)
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Publication Date
Thu Dec 15 2022
Journal Name
Engineering, Technology & Applied Science Research
Numerical Modeling of a Pile Group Subjected to Seismic Loading Using the Hypoplasticity Model
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Various simple and complicated models have been utilized to simulate the stress-strain behavior of the soil. These models are used in Finite Element Modeling (FEM) for geotechnical engineering applications and analysis of dynamic soil-structure interaction problems. These models either can't adequately describe some features, such as the strain-softening of dense sand, or they require several parameters that are difficult to gather by conventional laboratory testing. Furthermore, soils are not completely linearly elastic and perfectly plastic for the whole range of loads. Soil behavior is quite difficult to comprehend and exhibits a variety of behaviors under various circumstances. As a result, a more realistic constitutive model is

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Publication Date
Sat Aug 03 2024
Journal Name
Proceedings Of Ninth International Congress On Information And Communication Technology
Offline Signature Verification Based on Neural Network
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

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Publication Date
Fri Jul 17 2026
Journal Name
Journal Of Revenue And Pricing Management
Economic sustainability in state-owned extractive industries: a dynamic pricing framework for the Iraqi mining sector
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This study provides an empirical and strategic contribution to the revenue management literature by investigating the transition from static cost-plus pricing to a dynamic, elasticity-based framework within state-owned extractive industries (SOEs). While dynamic pricing is extensively studied in flexible, data-rich private markets, its application in structurally rigid SOEs within transitional economies remains a critical research gap. Drawing on primary fiscal and production data from the 2024 fiscal year of the General Company for Mining Industries (SCMI), we developed a mathematical model to determine optimal pricing ( ) based on marginal cost principles and demand elasticity. The findings demonstrate that replacing rigid accounting rout

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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Engineering
A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot
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In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal

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