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Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation
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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide su the optimal split value.

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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Engineering
Evaluation of the Accuracy of Digital Elevation Model Produced from Different Open Source Data
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This study aims to estimate the accuracy of digital elevation models (DEM) which are created with exploitation of open source Google Earth data and comparing with the widely available DEM datasets, Shuttle Radar Topography Mission (SRTM), version 3, and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), version 2. The GPS technique is used in this study to produce digital elevation raster with a high level of accuracy, as reference raster, compared to the DEM datasets. Baghdad University, Al Jadriya campus, is selected as a study area. Besides, 151 reference points were created within the study area to evaluate the results based on the values of RMS.Furthermore, th

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Publication Date
Thu Apr 25 2019
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
STUDY OF THE SYNERGISTIC EFFECT OF BACTERIOCIN PRODUCED FROM Leuconostoc mesenteroides ssp. cremoris With Antibiotics Against Multi Resistance Diarrheal Bacteria: STUDY OF THE SYNERGISTIC EFFECT OF BACTERIOCIN PRODUCED FROM Leuconostoc mesenteroides ssp. cremoris With Antibiotics Against Multi Resistance Diarrheal Bacteria
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The study included the collection of samples of raw cow milk to isolate Leuconostoc bacteria, samples were sub cultured on De-Man Rogosa Sharpe-Vancomycin medium, the pure colonies were selected and subjected to the cultural and microscopically tests, according to that 25 cocci bacterial isolates were obtained, then isolates were subjected to biochemical tests. Result of tests showed that 12 isolates belong to the genus Leuconostoc out of 25 cocci bacterial isolates, Vitek2 system was used as a supplementary step. Results of final identification showed that 3 sub species were obtained included Leuconostoc mesenteroides ssp. cremoris 9 out of 12 isolates, while it was 2 isolates of Leuconostoc mesenteroides ssp. mesenteroides and one isol

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Publication Date
Thu Aug 01 2024
Journal Name
The American Journal Of Management And Economics Innovations
THE ROLE OF ECONOMIC DATA ANALYSIS IN MANAGING MEDIUM AND SMALL COMPANIES TO MAKE STRATEGIC DECISIONS AND IMPROVE PERFORMANCE: AN ANALYTICAL STUDY
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Economic analysis plays a pivotal role in managerial decision-making processes. This analysis is predicated on deeply understanding economic forces and market factors influencing corporate strategies and decisions. This paper delves into the role of economic data analysis in managing small and medium-sized enterprises (SMEs) to make strategic decisions and enhance performance. The study underscores the significance of this approach and its impact on corporate outcomes. The research analyzes annual reports from three companies: Al-Mahfaza for Mobile and Internet Financial Payment and Settlement Services Company Limited, Al-Arab for Electronic Payment Company, and Iraq Electronic Gateway for Financial Services Company. The paper concl

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Publication Date
Tue Jan 06 2004
Journal Name
Iraqi Journal Of Science
Cluster and factor analyses R and Q mode technique of geochemical and petrological data of the Shalair Metamorphic Rock Group, Shalair Valley area, Northeastern Iraq
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Publication Date
Sun Mar 15 2020
Journal Name
Journal Of The College Of Education For Women
Data-Driven Approach for Teaching Arabic as a Foreign Language: Eygpt
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Corpus linguistics is a methodology in studying language through corpus-based research. It differs from a traditional approach in studying a language (prescriptive approach) in its insistence on the systematic study of authentic examples of language in use (descriptive approach).A “corpus” is a large body of machine-readable structurally collected naturally occurring linguistic data, either written texts or a transcription of recorded speech, which can be used as a starting-point of linguistic description or as a means of verifying hypotheses about a language.  In the past decade, interest has grown tremendously in the use of language corpora for language education. The ways in which corpora have been employed in language pedago

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Publication Date
Sun Dec 09 2018
Journal Name
Baghdad Science Journal
Optimal UAV Deployment for Data Collection in Deadline-based IoT Applications
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The deployment of UAVs is one of the key challenges in UAV-based communications while using UAVs for IoT applications. In this article, a new scheme for energy efficient data collection with a deadline time for the Internet of things (IoT) using the Unmanned Aerial Vehicles (UAV) is presented. We provided a new data collection method, which was set to collect IoT node data by providing an efficient deployment and mobility of multiple UAV, used to collect data from ground internet of things devices in a given deadline time. In the proposed method, data collection was done with minimum energy consumption of IoTs as well as UAVs. In order to find an optimal solution to this problem, we will first provide a mixed integer linear programming m

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Publication Date
Mon Dec 25 2023
Journal Name
Ieee Access
ITor-SDN: Intelligent Tor Networks-Based SDN for Data Forwarding Management
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Tor (The Onion Routing) network was designed to enable users to browse the Internet anonymously. It is known for its anonymity and privacy security feature against many agents who desire to observe the area of users or chase users’ browsing conventions. This anonymity stems from the encryption and decryption of Tor traffic. That is, the client’s traffic should be subject to encryption and decryption before the sending and receiving process, which leads to delay and even interruption in data flow. The exchange of cryptographic keys between network devices plays a pivotal and critical role in facilitating secure communication and ensuring the integrity of cryptographic procedures. This essential process is time-consuming, which causes del

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Publication Date
Tue Mar 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
The suggested threshold to reduce data noise for a factorial experiment
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In this research, a factorial experiment (4*4) was studied, applied in a completely random block design, with a size of observations, where the design of experiments is used to study the effect of transactions on experimental units and thus obtain data representing experiment observations that The difference in the application of these transactions under different environmental and experimental conditions It causes noise that affects the observation value and thus an increase in the mean square error of the experiment, and to reduce this noise, multiple wavelet reduction was used as a filter for the observations by suggesting an improved threshold that takes into account the different transformation levels based on the logarithm of the b

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Publication Date
Fri Apr 26 2019
Journal Name
Journal Of Contemporary Medical Sciences
Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques
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Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using

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Publication Date
Sat Mar 01 2008
Journal Name
Iraqi Journal Of Physics
Comparison between Different Data Image Compression Techniques Applied on SAR Images
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In this paper, image compression technique is presented based on the Zonal transform method. The DCT, Walsh, and Hadamard transform techniques are also implements. These different transforms are applied on SAR images using Different block size. The effects of implementing these different transforms are investigated. The main shortcoming associated with this radar imagery system is the presence of the speckle noise, which affected the compression results.

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