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Mining Deviations in Document Writing Style through Vector Dissimilarity
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     Doubts arise about the originality of a document when noticing a change in its writing style. This evidence to plagiarism has made the intrinsic approach for detecting plagiarism uncover the plagiarized passages through the analysis of the writing style for the suspicious document where a reference corpus to compare with is absent.      The proposed work aims at discovering the deviations in document writing style through applying several steps: Firstly, the entire document is segmented into disjointed segments wherein each corresponds to a paragraph in the original document. For the entire document and for each segment, center vectors comprising average  weight of their word  are constructed. Second, the degree of closeness is calculated through applying Cosine similarity to measure for each segment, the deviation of its center vector from the center vector of the entire document. Additionally, word n-gram length will be investigated to show its effect on the proposed system performance wherein, center vectors are computed considering word n-grams for different values of n (n= 1, 2, and 3). Performance evaluation of the proposed method was accomplished through the use of Precision, Recall, F-measure, Granularity, and Plagdet as evaluation measures. Moreover, PAN-PC-09 and PAN-PC-11 were used for detecting intrinsic plagiarism as evaluation corpora. It is shown that the proposed approach has achieved results that are comparable to the state-of-the-art methods. Positive impact was observed through discovering deviations in document writing style by computing weight vectors dissimilarity rather than calculating the difference between the word n-grams that exist in segments and their corresponding word n-grams in the suspicious document. Furthermore, when considering the length of word n-gram, better results were recorded for system performance when word bi-grams was used compared to word uni-grams and word tri-grams.

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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Publication Date
Tue Dec 07 2021
Journal Name
2021 14th International Conference On Developments In Esystems Engineering (dese)
Content Based Image Retrieval Based on Feature Fusion and Support Vector Machine
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine
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Publication Date
Sun Oct 07 2012
Journal Name
Journal Of Educational And Psychological Researches
Bullying and its relation to parental treatment style
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The current research aims to know the relationship between bullying and parental treatment. (200) pupils were selected randomly from the fifth and sixth grades of primary schools.

Two instruments were used. The first was to measure bullying and it included 19 items. To measure parental treatment, the researchers adopted (Aletaby 2001) scale.

Statistical analysis showed that correlation between bullying , wiggle and Firm treatment style was positive Statistically significant .Bulling was correlated negatively with (neglect, careless, and Authoritarian treatment style.

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Publication Date
Tue Jan 03 2023
Journal Name
College Of Islamic Sciences
Formulas The command and its style in Surat Al-Ma’idah is a rhetorical fundamentalist study.: The command and its style in Surat Al-Ma’idah is a rhetorical fundamentalist study
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Research Summary :

      Praise be to God, Lord of the Worlds, and prayers and peace be upon the Master of the Messengers, his family and all his companions, then after:

     This is a brief research that contained its two rudders the command and its Style in Surat Al-Ma’idah a fundamental rhetorical study, and the study clarified the meaning of imperative in both;  the Arabic  language and in the terminology of the fundamentalists and rhetoricians in a concise manner, and then indicated the imperative of the command , the true meaning, and the meanings interpretated as an  imperative form. I have mentioned some verses of Surat Al-Ma’idah, so what I have quot

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Publication Date
Sun Apr 06 2014
Journal Name
Journal Of Economics And Administrative Sciences
Modeling Absolute Deviations Method by using Numerical Methods to measure the dispersion of the proposal for error
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Is in this research review of the way minimum absolute deviations values ​​based on linear programming method to estimate the parameters of simple linear regression model and give an overview of this model. We were modeling method deviations of the absolute values ​​proposed using a scale of dispersion and composition of a simple linear regression model based on the proposed measure. Object of the work is to find the capabilities of not affected by abnormal values by using numerical method and at the lowest possible recurrence.

 

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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
Sun Dec 01 2019
Journal Name
Applied Soft Computing
A new evolutionary multi-objective community mining algorithm for signed networks
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Publication Date
Tue Mar 01 2016
Journal Name
Al-academy
Employing the elements of film construction in embodying the style cinematically: عامر الحديثي
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The researcher concluded that it consists of several types of structures, where the intellectual structure related to the choice of the idea, its treatment, its effect, the choice of the topic, the way ideas are addressed and presented in the film, and then the dramatic structure of the film's characters, the formulation of its events, its dramatic composition, its temporal arrangement, the identification of the parties to the conflict and their management, then these are formulated. Visions and ideas through the visual and audio signal systems, which work to highlight the stylistic features in the character of the film director.

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Publication Date
Tue Sep 01 2009
Journal Name
Journal Of Economics And Administrative Sciences
Determine the optimal decision in the process of production planning usingDynamic programming style
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The operation of production planning is a difficult operation and it's required High effect and large time especially it is dynamic activity which it's basic variables change in continuous with the time, for this reason it needs using one of the operation research manner (Dynamic programming) which has a force in the decision making process in the planning and control on the production and its direct affect on the cost of production operation and control on the inventory.

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