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An Improved Outlier Detection Model for Detecting Intrinsic Plagiarism

     In the task of detecting intrinsic plagiarism, the cases where reference corpus is absent are to be dealt with. This task is entirely based on inconsistencies within a given document. Detection of internal plagiarism has been considered as a classification problem. It can be estimated through taking into consideration self-based information from a given document.

The core contribution of the work proposed in this paper is associated with the document representation. Wherein, the document, also, the disjoint segments generated from it, have been represented as weight vectors demonstrating their main content. Where, for each element in these vectors, its average weight has been considered instead of its frequency.

The proposed work has been evaluated in terms of Precision, Recall, F-measure, Granularity, and Plagdet. It is shown that the attained results are comparable to the ones attained by the best state-of-the-art methods. Where, through applying the proposed method to PAN-PC-09 and PAN-PC-11 for the detection of intrinsic plagiarism, a Recall scores of 0.4503 and 0.4303 have been recorded, even though further improvement for Precision (0.3308 and 0.2806) and Granularity (1.1765 and 1.1111) needs to be made. Concerning f-measure, the proposed approach has recorded 0.3814 and 0.3397. In terms of the total performance of a plagiarism detection approach, Plagdet, the proposed method has recorded 0.3399 and 0.3151.

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Sequential feature selection for heart disease detection using random forest

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential f

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Publication Date
Wed May 01 2024
Journal Name
Scientific Visualization
Shadow Detection and Elimination for Robot and Machine Vision Applications

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

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Publication Date
Thu Dec 01 2022
Journal Name
Neuroscience Informatics
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Publication Date
Fri Sep 01 2023
Journal Name
Asian Pacific Journal Of Cancer Prevention
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Publication Date
Tue Dec 01 2020
Journal Name
Iraqi Journal Of Physics
Modifications to Accelerate the Iterative Algorithm for the Single Diode Model of PV Model

This paper discussed the solution of an equivalent circuit of solar cell, where a single diode model is presented. The nonlinear equation of this model has suggested and analyzed an iterative algorithm, which work well for this equation with a suitable initial value for the iterative. The convergence of the proposed method is discussed. It is established that the algorithm has convergence of order six. The proposed algorithm is achieved with a various values of load resistance. Equation by means of equivalent circuit of a solar cell so all the determinations is achieved using Matlab in ambient temperature. The obtained results of this new method are given and the absolute errors is demonstrated.

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Publication Date
Tue Jan 18 2022
Journal Name
Iraqi Journal Of Science
Improved Weighted 0-1 Knapsack Method (WKM) to Optimize Resource Allocation

In this paper an improved weighted 0-1 knapsack method (WKM) is proposed to optimize the resource allocation process when the sum of items' weight exceeds the knapsack total capacity .The improved method depends on a modified weight for each item to ensure the allocation of the required resources for all the involved items. The results of the improved WKM are compared to the traditional 0-1 Knapsack Problem (KP). The proposed method dominates on the other one in term of the total optimal solution value of the knapsack .

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Publication Date
Tue Sep 01 2020
Journal Name
Acta Polytechnica
COMPLEXITY REDUCTION OF CYCLOSTATIONARY SENSING TECHNIQUE USING IMPROVED HYBRID SENSING METHOD

In cognitive radio system, the spectrum sensing has a major challenge in needing a sensing method, which has a high detection capability with reduced complexity. In this paper, a low-cost hybrid spectrum sensing method with an optimized detection performance based on energy and cyclostationary detectors is proposed. The method is designed such that at high signal-to-noise ratio SNR values, energy detector is used alone to perform the detection. At low SNR values, cyclostationary detector with reduced complexity may be employed to support the accurate detection. The complexity reduction is done in two ways: through reducing the number of sensing samples used in the autocorrelation process in the time domain and through using the Slid

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Publication Date
Fri Jun 30 2023
Journal Name
International Journal Of Intelligent Engineering And Systems
DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

DeepFake is a concern for celebrities and everyone because it is simple to create. DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches. On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer. Moreover, the detection of video manipulation depends entirely on its detection through images. Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc. Also, the accuracy of their counterfeit detectio

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Publication Date
Tue Apr 18 2023
Journal Name
Corporate Reputation Review
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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Educational And Psychological Researches
An Instructional Design According to the constructivism Model for Chemistry Subject and Its Effect on the Life Skill by the Second Intermediate Female Students

The research aims to identify the effect of teaching-learning design based on the constructivism theory on the life skills of middle stage students. The sample consisted of (60) students from one of the middle schools in Baghdad's governorate. The experimental group and control group consisted of 30 students for each group, the research tool was the life skills scale composed of (78) items. The scale proved its validity and reliability, which was found to be (0.85). The results showed a statistically significant difference in life skills between the two groups of research in favor of the experimental group, which studied the educational design according to models of constructivism theory compared to the usual method of teaching.

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