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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
Mon Feb 27 2023
Journal Name
Applied Sciences
Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

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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

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 Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
Evaluation of a fire safety risk prediction model for an existing building
Abstract<p>Fire is one of the most critical risks devastating to human life and property. Therefore, humans make different efforts to deal with fire hazards. Many techniques have been developed to assess fire safety risks. One of these methods is to predict the outbreak of a fire in buildings, and although it is hard to predict when a fire will start, it is critical to do so to safeguard human life and property. This research deals with evaluating the safety risks of the existing building in the city of Samawah/Iraq and determining the appropriateness of these buildings in terms of safety from fire hazards. Twelve parameters are certified based on the National Fire Protection Association (NFPA20</p> ... Show More
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Publication Date
Wed Jul 01 2020
Journal Name
Journal Of Engineering
Bat Algorithm Based an Adaptive PID Controller Design for Buck Converter Model

The aim of this paper is to design a PID controller based on an on-line tuning bat optimization algorithm for the step-down DC/DC buck converter system which is used in the battery operation of the mobile applications. In this paper, the bat optimization algorithm has been utilized to obtain the optimal parameters of the PID controller as a simple and fast on-line tuning technique to get the best control action for the system. The simulation results using (Matlab Package) show the robustness and the effectiveness of the proposed control system in terms of obtaining a suitable voltage control action as a smooth and unsaturated state of the buck converter input voltage of ( ) volt that will stabilize the buck converter sys

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Publication Date
Tue Sep 09 2014
Journal Name
Iosr Journal Of Mathematics (iosr-jm)
Publication Date
Wed Mar 01 2023
Journal Name
Journal Of Engineering
A An Authentication and Access Control Model for Healthcare based Cloud Services

Electronic Health Record (EHR) systems are used as an efficient and effective method of exchanging patients’ health information with doctors and other key stakeholders in the health sector to obtain improved patient treatment decisions and diagnoses. As a result, questions regarding the security of sensitive user data are highlighted. To encourage people to move their sensitive health records to cloud networks, a secure authentication and access control mechanism that protects users’ data should be established. Furthermore, authentication and access control schemes are essential in the protection of health data, as numerous responsibilities exist to ensure security and privacy in a network. So, the main goal of our s

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Publication Date
Mon Oct 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
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Publication Date
Sat Aug 01 2015
Journal Name
Modern Applied Science
A New Method for Detecting Cerebral Tissues Abnormality in Magnetic Resonance Images

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

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Publication Date
Thu Aug 03 2023
Journal Name
Journal Of Legal Sciences
Authorities and Procedures for Detecting Financial Violations : A Study in French Legislation

France attached great importance to public funds Article 15 of the Declaration on Human and Citizen's Rights (DDHC) stipulates that society has the right to hold any public official accountable for his or her administration. For that reason, the French legislature has established a body specialized in the control of public funds, which it calls the Court of Accounting. It has established the Court of Budget and Financial Discipline to assist it. The courts are run by judges who cannot be dismissed. The courts are also given jurisdiction, administrative jurisdiction and the role and purpose for which they have been assigned

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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning

       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific

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