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Compression-based Data Reduction Technique for IoT Sensor Networks
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Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature.

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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
A comparison between the logistic regression model and Linear Discriminant analysis using Principal Component unemployment data for the province of Baghdad
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     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

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Publication Date
Fri Jan 01 2021
Journal Name
Aip Conference Proceedings
Integration between hydrochemical and physical data with geographic information systems (GIS) for selecting the best locations groundwater wells in Baghdad city
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Publication Date
Thu Apr 01 2021
Journal Name
Complexity
Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem
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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

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Publication Date
Fri Jun 30 2023
Journal Name
Iraqi Geological Journal
Integrated Core and Log Data to Determine the Reservoir Flow Unit and Rock Facies for Mishrif Formation in South Eastern Iraq
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This work represents study the rock facies and flow unit classification for the Mishrif carbonate reservoir in Buzurgan oil Field, which located n the south eastern Iraq, using wire line logs, core samples and petrophysical data (log porosity and core permeability). Hydraulic flow units were identified using flow zone indicator approach and assessed within each rock type to reach better understanding of the controlling role of pore types and geometry in reservoir quality variations. Additionally, distribution of sedimentary facies and Rock Fabric Number along with porosity and permeability was analyzed in three wells (BU-1, BU-2, and BU-3). The interactive Petrophysics - IP software is used to assess the rock fabric number, flow zon

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Publication Date
Fri Sep 17 2021
Journal Name
Journal Of Petroleum Exploration And Production Technology
Characterization of flow units, rock and pore types for Mishrif Reservoir in West Qurna oilfield, Southern Iraq by using lithofacies data
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Abstract<p>This study has been accomplished by testing three different models to determine rocks type, pore throat radius, and flow units for Mishrif Formation in West Qurna oilfield in Southern Iraq based on Mishrif full diameter cores from 20 wells. The three models that were used in this study were Lucia rocks type classification, Winland plot was utilized to determine the pore throat radius depending on the mercury injection test (r35), and (FZI) concepts to identify flow units which enabled us to recognize the differences between Mishrif units in these three categories. The study of pore characteristics is very significant in reservoir evaluation. It controls the storage mechanism and reservoir fluid prope</p> ... Show More
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Publication Date
Fri Dec 30 2011
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Improved Method to Correlate and Predict Isothermal VLE Data of Binary Mixtures
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Accurate predictive tools for VLE calculation are always needed. A new method is introduced for VLE calculation which is very simple to apply with very good results compared with previously used methods. It does not need any physical property except each binary system need tow constants only. Also, this method can be applied to calculate VLE data for any binary system at any polarity or from any group family. But the system binary should not confirm an azeotrope. This new method is expanding in application to cover a range of temperature. This expansion does not need anything except the application of the new proposed form with the system of two constants. This method with its development is applied to 56 binary mixtures with 1120 equili

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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction
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Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
State-of-the-Art in Data Integrity and Privacy-Preserving in Cloud Computing
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Cloud computing (CC) is a fast-growing technology that offers computers, networking, and storage services that can be accessed and used over the internet. Cloud services save users money because they are pay-per-use, and they save time because they are on-demand and elastic, a unique aspect of cloud computing. However, several security issues must be addressed before users store data in the cloud. Because the user will have no direct control over the data that has been outsourced to the cloud, particularly personal and sensitive data (health, finance, military, etc.), and will not know where the data is stored, the user must ensure that the cloud stores and maintains the outsourced data appropriately. The study's primary goals are to mak

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Publication Date
Tue Jan 29 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Effect of Cost Reduction and Price Policy in Earning Customer Satisfaction: Study in Public Company liquid Battery Industry
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That any industrial company that must think about marketing mechanisms and who are the beneficiaries and the pursuit of gain customer satisfaction with access to competitive advantages. Most industrialized our companies from a variety of problems experienced both in the field of production, marketing, or keep up with new developments or commitment to quality standards, which caused the reluctance of customers to buy local products for various reasons, and especially in light of unplanned economic policy and exposure to market to imported products flooding the market with various goods and services that do not can the domestic industry rival, which has competitive advantages and meet business requirements, but others arrived to stop or as

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Publication Date
Sat May 07 2022
Journal Name
American Journal Of Economics And Business Management
The Integrating Effect Resource Consumption Accounting (RCA) and Enterprise Resource Planning (ERP) On Cost Reduction and Quality Improvement
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The research aims to examine the integration effect among resource consumption accounting (RCA) system and the enterprise resource planning (ERP) on both costs reduction and quality improvement. The study questioner form distributed to two different respondents as the unit of analysis. The research reached various conclusions most important of which is the integration relationship can help solve the special difficulties in managing the economic unit data. Moreover, the integration provides a clear picture of the causal relationships between resources, resource quantities, and associated costs

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