Plagiarism is becoming more of a problem in academics. It’s made worse by the ease with which a wide range of resources can be found on the internet, as well as the ease with which they can be copied and pasted. It is academic theft since the perpetrator has ”taken” and presented the work of others as his or her own. Manual detection of plagiarism by a human being is difficult, imprecise, and time-consuming because it is difficult for anyone to compare their work to current data. Plagiarism is a big problem in higher education, and it can happen on any topic. Plagiarism detection has been studied in many scientific articles, and methods for recognition have been created utilizing the Plagiarism analysis, Authorship identification, and Near-duplicate detection (PAN) Dataset 2009- 2011. Verbatim plagiarism, according to the researchers, plagiarism is simply copying and pasting. They then moved on to smart plagiarism, which is more challenging to spot since it might include text change, taking ideas from other academics, and translation into a more difficult-to-manage language. Other studies have found that plagiarism can obscure the scientific content of publications by swapping words, removing or adding material, or reordering or changing the original articles. This article discusses the comparative study of plagiarism detection techniques.
An idiom is a group of words whose meaning put together is different from the meaning of
individual words. English is a rich language when it comes to idioms, they represent variety. For
foreign learners, idioms are problematic because even if they know the meaning of individual
words that compose an idiom the meaning of it might be something completely different.
The present study investigates Iraqi third year college students’ recognition of idioms. To
achieve this, the researchers have conducted a test which comprises three questions. Certain
conclusions are reached here along with some suggestions and recommendations.
Researchers are interested in the issue of children abuse and they look for its cause in the past and present. Their interest is limited to identifying penal liability which is caused by children abuse away from focusing on civil liability. So, the study is going to clarify the parents' responsibility for children abuse rather than civil liability of the medic in case he wouldn’t notify the authorities about the case according to the American law rules and the attitude of Iraqi law rather than some judicial application of civil cases that were exposed to American judiciary concerning children abuse.
The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an
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The cuneiform images need many processes in order to know their contents
and by using image enhancement to clarify the objects (symbols) founded in the
image. The Vector used for classifying the symbol called symbol structural vector
(SSV) it which is build from the information wedges in the symbol.
The experimental tests show insome numbersand various relevancy including
various drawings in online method. The results are high accuracy in this research,
and methods and algorithms programmed using a visual basic 6.0. In this research
more than one method was applied to extract information from the digital images
of cuneiform tablets, in order to identify most of signs of Sumerian cuneiform.
Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
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Malicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly unrecognized attack attempt and raising an early alarm to inform the system about this suspicious intrusion attempt. This paper proposed a hybrid IDS for detection intrusion, especially malware, with considering network packet and host features. The hybrid IDS designed using Data Mining (DM) classification methods that for its ability to detect new, previously unseen intrusions accurately and automatically. It uses both anomaly and misuse dete
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