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Twitter Location-Based Data: Evaluating the Methods of Data Collection Provided by Twitter Api
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Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based data provided by Twitter API, Twitter places and Geocode parameters. We studied these methods to determine their accuracy and their suitability for research. The study concludes that the places method is the more accurate, but it excludes a lot of the data, while the geocode method provides us with more data, but special attention needs to be paid to outliers. Copyright © Research Institute for Intelligent Computer Systems, 2018. All rights reserved.

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Publication Date
Mon Oct 01 2018
Journal Name
Data In Brief
Chemical and structural data of (1, 2, 3-triazol-4-yl) pyridine-containing coordination compounds
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The data presented in this paper are related to the research article entitled “Novel dichloro(bis{2-[1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal(II) coordination compounds of seven transition metals (Mn, Fe, Co, Ni, Cu, Zn and Cd)” (Conradie et al., 2018) [1]. This paper presents characterization and structural data of the 2-(1-(4-methyl-phenyl)-1H-1,2,3-triazol-1-yl)pyridine ligand (L2 ) (Tawfiq et al., 2014) [2] as well as seven dichloro(bis{2- [1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal (II) coordination compounds, [M(L2 )2Cl2], all containing the same ligand but coordinated to different metal ions. The data illustrate the shift in IR, UV/VIS, and NMR (for diamagnetic complexes) peaks wh

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Publication Date
Sat Jul 01 2023
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The role of big data analytics in measuring and analyzing the quality costs of economic units : applied research in the Iraqi company for seed production
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                The research aims to explain the role of huge data analyzes in measuring quality costs in the Iraqi company for the production of seed, and the research problem was diagnosed with the weakness of the approved method to measure quality costs, and the weak traditional systems of data analyzes, the researcher in the theoretical aspect relied on collecting sources and previous studies, as well as Adoption of the applied analytical approach in the practical aspect, as a set of financial analyzes were applied within the measurement of quality costs and a statement of the role of data analyzes in the practical side, the research concluded to a set of conc

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Publication Date
Tue Jan 01 2019
Journal Name
Baghdad Science Journal
Hazard Rate Estimation Using Varying Kernel Function for Censored Data Type I Article Sidebar
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n this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used: local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the types of the kernel boundary func

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Publication Date
Wed Sep 23 2020
Journal Name
Artificial Intelligence Research
Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Geological Journal
Inverting Gravity Data to Density and Velocity Models for Selected Area in Southwestern Iraq
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The gravity method is a measurement of relatively noticeable variations in the Earth’s gravitational field caused by lateral variations in rock's density. In the current research, a new technique is applied on the previous Bouguer map of gravity surveys (conducted from 1940–1950) of the last century, by selecting certain areas in the South-Western desert of Iraqi-territory within the provinces' administrative boundary of Najaf and Anbar. Depending on the theory of gravity inversion where gravity values could be reflected to density-contrast variations with the depths; so, gravity data inversion can be utilized to calculate the models of density and velocity from four selected depth-slices 9.63 Km, 1.1 Km, 0.682 Km and 0.407 Km.

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Publication Date
Sun Jun 05 2016
Journal Name
Baghdad Science Journal
Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set
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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

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Publication Date
Sat Dec 01 2007
Journal Name
Journal Of Economics And Administrative Sciences
دور تنقيب البيانات Data Mining في زيادة أداء المنظمة (( دراسة تحليلية في المصرف الصناعي ))
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تمهيد

غالبا ما يكون تعامل المنظمات المالية والمصرفية مع الزبائن بشكل أساسي مما يتطلب منها جمع كميات هائلة من البيانات عن هؤلاء الزبائن هذا بالإضافة الى ما يرد اليها يوميا من بيانات يجعلها أمام أكداس كبيرة من البيانات تحتاج الى جهود جبارة تحسن التعامل معها والاستفادة منها بما يخدم المنظمة.

ان التعامل اليدوي مع مثل هذه البيانات دون استخدام تقنيات حديثة يبعد المنظمة عن التط

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology
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Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and p

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Publication Date
Fri Oct 31 2025
Journal Name
Mathematical Modelling Of Engineering Problems
Heterogeneous Traffic Management in SDN-Enabled Data Center Network Using Machine Learning-SPIKE Model
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Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
A study on predicting crime rates through machine learning and data mining using text
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Abstract<p>Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based o</p> ... Show More
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