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Inference for Generalized Inverted Exponential Distribution UnderProgressive Type-I Interval Censored Data
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This article discusses the estimation methods for parameters of a generalized inverted exponential distribution with different estimation methods by using Progressive type-I interval censored data. In addition to conventional maximum likelihood estimation, the mid-point method, probability plot method and method of moments are suggested for parameter estimation. To get maximum likelihood estimates, we utilize the Newton-Raphson, expectation -maximization and stochastic expectation-maximization methods. Furthermore, the approximate confidence intervals for the parameters are obtained via the inverse of the observed information matrix. The Monte Carlo simulations are used to introduce numerical comparisons of the proposed estimators. In addition, we use the percentile bootstrapping technique that is used to calculate confidence intervals. The proposed methodology in a real-life using the survival times of guinea pigs inoculated with different doses of tubercle bacilli data are considered to offer the applicability of the suggested methods.

 

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Using Genetic Algorithm to Estimate the Parameters of the Gumbel Distribution Function by Simulation
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In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

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Publication Date
Sat Feb 26 2022
Journal Name
Iraqi Journal Of Science
Effects of Lipid Peroxidation, Thyroid Hormones, and Some Vitamins in Type 2 Diabetic Patients
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   One of the most common forms of diabetes is Type-2 that occurs due to the failure of cells in recognizing and responding to insulin if not accurately treated. The aim of this work is to evaluate the relations of thyroid hormones, vitamins, and lipid peroxidation with the glycemic index in patients experiencing Type-2 diabetes. Some tests of biochemical parameters and vitamins were conducted on 35 patients experiencing Diabetes Mellitus (DM) and 35 healthy subjects. The results indicated the increase in the levels of MDA (3.86 ± 0.97 µmol/L), HbA1c (8.27 ± 1.66 %), FBS (198.34 ± 32.41 mg/dl) and TSH (5.67 ±0.34 mIU/L) in the blood of diabetic subjects in comparison to the controls at a P value lower than 0.05. These incr

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Publication Date
Wed Jan 01 2014
Journal Name
Proceedings Of The Aintec 2014 On Asian Internet Engineering Conference - Aintec '14
LTE Peak Data Rate Estimation Using Modified alpha-Shannon Capacity Formula
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Publication Date
Fri Jun 01 2018
Journal Name
International Journal Of Computer Science Trends And Technology
Secure Video Data Deduplication in the Cloud Storage Using Compressive Sensing
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Cloud storage provides scalable and low cost resources featuring economies of scale based on cross-user architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. The most important cloud service is data storage. In order to protect the privacy of data owner, data are stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for data storage. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. This paper proposes a combined compressive sensing and video deduplication to maximize

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Publication Date
Fri Dec 15 2023
Journal Name
Al-academy
Aesthetics Contents of Data Visualization as an Input to its humanization
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The aesthetic contents of data visualization is one of the contemporary areas through which data scientists and designers have been able to link data to humans, and even after reaching successful attempts to model data visualization, it wasn't clear how that reveals how it contributed to choosing the aesthetic content as an input to humanize these models, so the goal of the current research is to use The analytical descriptive approach aims to identify the aesthetic contents in data visualization, which the researchers interpreted through pragmatic philosophy and Kantian philosophy, and analyze a sample of data visualization models to reveal the aesthetic entrances in them to explain how to humanize them. The two researchers reached seve

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Publication Date
Tue Mar 01 2022
Journal Name
Asian Journal Of Applied Sciences
Comparison between Expert Systems, Machine Learning, and Big Data: An Overview
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Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

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Publication Date
Tue Oct 01 2019
Journal Name
Journal Of Engineering
Characterization Performance of Monocrystalline Silicon Photovoltaic Module Using Experimentally Measured Data
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Solar photovoltaic (PV) system has emerged as one of the most promising technology to generate clean energy. In this work, the performance of monocrystalline silicon photovoltaic module is studied through observing the effect of necessary parameters: solar irradiation and ambient temperature. The single diode model with series resistors is selected to find the characterization of current-voltage (I-V) and power-voltage (P-V) curves by determining the values of five parameters ( ). This model shows a high accuracy in modeling the solar PV module under various weather conditions. The modeling is simulated via using MATLAB/Simulink software. The performance of the selected solar PV module is tested experimentally for differ

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Publication Date
Wed Jun 26 2019
Journal Name
Iraqi Journal Of Science
Modeling and Forecasting Periodic Time Series data with Fourier Autoregressive Model
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Most frequently used models for modeling and forecasting periodic climatic time series do not have the capability of handling periodic variability that characterizes it. In this paper, the Fourier Autoregressive model with abilities to analyze periodic variability is implemented. From the results, FAR(1), FAR(2) and FAR(2) models were chosen based on Periodic Autocorrelation function (PeACF) and Periodic Partial Autocorrelation function (PePACF). The coefficients of the tentative model were estimated using a Discrete Fourier transform estimation method. FAR(1) models were chosen as the optimal model based on the smallest values of Periodic Akaike (PAIC) and Bayesian Information criteria (PBIC). The residual of the fitted models was diagn

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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,

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Publication Date
Tue Aug 29 2023
Journal Name
Geomatics And Environmental Engineering
Challenges and Issues in Spatial Data Infrastructure (SDI) Development in Iraq
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This paper addresses the nature of Spatial Data Infrastructure (SDI), considered as one of the most important concepts to ensure effective functioning in a modern society. It comprises a set of continually developing methods and procedures providing the geospatial base supporting a country’s governmental, environmental, economic, and social activities. In general, the SDI framework consists of the integration of various elements including standards, policies, networks, data, and end users and application areas. The transformation of previously paper-based map data into a digital format, the emergence of GIS, and the Internet and a host of online applications (e.g., environmental impact analysis, navigation, applications of VGI dat

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