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Short Text Semantic Similarity Measurement Approach Based on Semantic Network
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Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that represents the relationship between the compared texts and extracts the degree of similarity between them. Representing a text as a semantic network is the best knowledge representation that comes close to the human mind's understanding of the texts, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. The network representation is a visual representation of knowledge objects, their qualities, and their relationships. WordNet lexical database has been used as a knowledge-based source while the GloVe pre-trained word embedding vectors have been used as a corpus-based source. The proposed method was tested using three different datasets, DSCS, SICK, and MOHLER datasets. A good result has been obtained in terms of RMSE and MAE.

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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory
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Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)

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Publication Date
Thu Jan 11 2024
Journal Name
Научный Форум
Structural and semantic features of military metaphors in the modern Russian press
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Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
The Islamic Terms in Moufdi Zakaria’s Poems : A Lexicographic and Semantic Study
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This study seeks to examine the language context of the prominent Revolution’s poet, Moufdi Zakaria, in his anthology “ The Algerian Odyssey " )إلياذة الجزائر( in which he makes use of the Coranic terms explicitly and implicitly. Besides, it seeks to explore the content of his terms from a lexicographic and semantic point of view in their Islamic and human dimensions

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Publication Date
Mon Sep 09 2024
Journal Name
مجلة قسم الدراسات اللغوية و الترجمية التابعة لبيت الحكمة
- Structural and semantic features of predicative adverbs IN THE RUSSIAN SYNTAX SYSTEM
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Publication Date
Tue Jun 15 2021
Journal Name
Al-academy
Employing semantic coding to build meaning in action films: ابراهيم خلف جاسم
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Action films employ many artistic and literary elements that contribute greatly to building the general meaning of the film and push the wheel of the film forward. The element of mystery and suspense is used as two basic elements in action films. The cinematic language in action films depends on global coding, which is not models as it might be. It is based on logic, rather as units that aspire to morphology and not their homogeneity as the physical sense, but as the logical harmony of interpretive authority and enlightenment and in action films as a field of communication and a field in its origin in which the signifier contrasts with the perceptions of the meaning and in it takes a certain number of units preventing each other and thro

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Publication Date
Fri Nov 17 2023
Journal Name
Nasaq
A Semantic Study of Thematic Progression in Selected American and British Movies
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ZG Abdulrazaq, MR Younus, Nasaq, 2023

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Current Researches On Educational Studies
Study of Swear Words in Selected Literary Works: A Syntactic-Semantic Analysis
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BN RASHİD, 2022

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Publication Date
Thu Dec 01 2022
Journal Name
مجلة األستاذ للعلوم اإلنسانية واالجتماعية
THE ANALYSIS OF SEMANTIC MACRO STRUCTURE IN WHO SPEECHES DURING CORONA VIRUS
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Publication Date
Fri Sep 23 2022
Journal Name
Specialusis Ugdymas
Text Cryptography based on Arabic Words Characters Number
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Cryptography is a method used to mask text based on any encryption method, and the authorized user only can decrypt and read this message. An intruder tried to attack in many manners to access the communication channel, like impersonating, non-repudiation, denial of services, modification of data, threatening confidentiality and breaking availability of services. The high electronic communications between people need to ensure that transactions remain confidential. Cryptography methods give the best solution to this problem. This paper proposed a new cryptography method based on Arabic words; this method is done based on two steps. Where the first step is binary encoding generation used t

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

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