Encapsulation of semantic description with syntactic components for the Arabic language

Al-Janabi, Adel; Kareem, Ehsan Ali; Sagheer, Radhwan Hussein Abdulzhraa Al

doi:10.11591/ijeecs.v22.i2.pp961-967

Cited by 3 publications

(5 citation statements)

References 28 publications

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“…According to Tsybenko's n, mappings from the class of neural network models will approximate continuous models arbitrarily well [18]. Thus, the composite optimized function (2) allows us to obtain a model that, on the one hand, has generalizing properties that the language model ( 3) is responsible for, on the other hand, effectively separates similar and dissimilar phrases from the training sample (4). The hyperparameter α is responsible for the contribution of each of the optimized terms to this function.…”

Section: The Model Of the Vector Representation Of The Phrasementioning

confidence: 99%

“…To optimize the model of the vector representation of text fragments, the AdaDelta algorithm was used with the parameters 𝜀 = 10 −6 , 𝜇 = 0.94 regularization 𝜆 2 = 10 −6 . For the final loss function (2), the following values of hyperparameters were set: 𝛿 = 0.3; 𝛼 = 0.1 . The classifier thresholds (6) were selected based on the cross-validation procedure: 𝑡1 = 0.6; 𝑡2 = 0.5.…”

Section: Optimization Of the Parameters Of The Considered Modelsmentioning

confidence: 99%

“…The problem of incorrect text borrowings is relevant for the field of education and scientific research [1]. According to the materials of the study by Al-Janabi et al [2] conducted in 2019, more than 1,500 dissertations on historical sciences defended in Iraq after 2,000 contain significant borrowings from other dissertations. For the task of detecting borrowings within one language, industrial tools show high search completeness [1], whose work is based on the representation of documents in the form of a set of overlapping word-by-word ngrams (shingles) [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting translation borrowings in huge text collections using various methods

Al-Janabi

Al-Zubaidi

Merzah

2023

IJEECS

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The purpose of this work is to investigate the problem of detecting transportable borrowings and text reuse. The article proposes a monolingual solution to this problem: translating the suspicious material into language collections for additional monolingual analysis. One of the major requirements for the suggested technique is robustness against machine learning ambiguities. The next step in the document analysis is split into two parts. The authors begin by retrieving documents-candidates that are similarity to other types of text recurrence. The paper proposes retrieving texts utilizing word clusters formed using distributional semantic for robustness. In the second stage, the authors use deep learning neural networks to compare the suspected document to candidates utilizing phrase embedding. The experimentation is carried out for the language pair “English-Arabic” on both articles and synthetic data.

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Section: The Model Of the Vector Representation Of The Phrasementioning

confidence: 99%

Section: Optimization Of the Parameters Of The Considered Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting translation borrowings in huge text collections using various methods

Al-Janabi

Al-Zubaidi

Merzah

2023

IJEECS

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“…Machine learning [1]- [4] is the most exciting science today in the research community, which is characterised by its ability to design and develop algorithms that allow machines to learn [5], [6]. It is a subfield of artificial intelligence where the learning process consists of automatically extracting rules and patterns from a data file [7], [8]. Machine learning is closely related to fields such as data mining, statistics, pattern recognition, other things [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Null-values imputation using different modification random forest algorithm

Mijwil

Abdulqader²,

Ali³

et al. 2023

IJ-AI

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Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the same row of datasets, respectively.

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“…Many published papers noted that most of the social media applications, including IM, violate user privacy and unsafe to a different type of vulnerabilities. The authors listed the weaknesses, which include passwords revealing and store private information on the application server [21], [22]. However, the majority of the articles and researches that studied the security of IM applications have only evaluated their safety according to experiment and lab setups [23].…”

Section: Introductionmentioning

confidence: 99%

Security approach for instant messaging applications: viber as a case study

Kadhim

Al-Janabi

Alhilali

et al. 2022

IJEECS

Self Cite

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A variety of internet-based applications are widely used in our animation activities because they provide free and useful services. These applications, such as Instant Messaging, can be run via the web, mobile, or computer-based devices. Therefore, the security and privacy of user data over these apps have been concerned in recent years because of sensitive and confidential information considerations. Consequently, many instant messaging applications, like Viber, have various security and privacy issues that need to be understood and resolved. Viber users reached 800 million, and they increased dramatically due to the efficient services that this app provides. Hence, a loophole in an application’s design may allow illegal access to the app and gain confidential and sensitive data. In this article, we proposed a security approach for Viber to safeguard user confidential data and sensitive information. The proposed approach involves two theoretical solutions: Short message service (SMS) authentication code and the physical hardware number to prevent illegal access to user data. Several scenarios are adopted to assess the proposed approach and achieve security and privacy for the user information.

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Encapsulation of semantic description with syntactic components for the Arabic language

Cited by 3 publications

References 28 publications

Detecting translation borrowings in huge text collections using various methods

Detecting translation borrowings in huge text collections using various methods

Null-values imputation using different modification random forest algorithm

Security approach for instant messaging applications: viber as a case study

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