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Arabic Keywords Extraction using Conventional Neural Network

    Keywords provide the reader with a summary of the contents of the document and play a significant role in information retrieval systems, especially in search engine optimization and bibliographic databases. Furthermore keywords help to classify the document into the related topic. Keywords extraction included manual extracting depends on the content of the document or article and the judgment of its author. Manual extracting of keywords is costly, consumes effort and time, and error probability. In this research an automatic Arabic keywords extraction model based on deep learning algorithms is proposed. The model consists of three main steps: preprocessing, feature extraction and classification to classify the document tokens into keyword or not, Conventional Neural Networks (CNN) is used as a classifier.

Two types of dataset are building in this research to test the proposed model, the first dataset form Arab Journal for Scientific Publishing (AJSP), the other dataset from Jordan Journal of Social Sciences (JJSS). The experiment results indicate promising results in the field of Arabic keyword extraction; the average accuracy of Conventional Neural Networks is found 0.97 with average precision 0.92.

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Publication Date
Sun Jan 10 2016
Journal Name
British Journal Of Applied Science & Technology
Illumination - Invariant Facial Components Extraction Using Adaptive Contrast Enhancement Methods

The process of accurate localization of the basic components of human faces (i.e., eyebrows, eyes, nose, mouth, etc.) from images is an important step in face processing techniques like face tracking, facial expression recognition or face recognition. However, it is a challenging task due to the variations in scale, orientation, pose, facial expressions, partial occlusions and lighting conditions. In the current paper, a scheme includes the method of three-hierarchal stages for facial components extraction is presented; it works regardless of illumination variance. Adaptive linear contrast enhancement methods like gamma correction and contrast stretching are used to simulate the variance in light condition among images. As testing material

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Liquid-Liquid Extraction of Metal Ions Using Aqueous Biphasic Systems

An investigation was conducted for the study of extraction of metal ions using aqueous biphasic systems. The extraction of iron, zinc and copper from aqueous sulphate media at different kinds of extractants SCN− , Cl- and I- , different values of pH of the feed solution, phase ratio, concentration of metals, concentration of extractant, concentration of polymer, and concentration of salt was investigated. Atomic absorption spectrophotometer was used to measure the concentration of iron, zinc and copper in the aqueous phase throughout the experiments. The results of the extraction experiments showed the use of SCN− as extractant, pH=2.5, phase ratio=1.5, concentration of metals 1g/l, concentration of extractant 0.06 %, concentration o

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Publication Date
Fri Jan 01 2021
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Stamps extraction using local adaptive k- means and ISODATA algorithms

<span>One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cluster from stamp cluster. Thirdly, a region growing algorithm is applied to segment the image and then choosing the connected regi

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Publication Date
Tue Nov 01 2022
Journal Name
2022 International Conference On Data Science And Intelligent Computing (icdsic)
An improved Bi-LSTM performance using Dt-WE for implicit aspect extraction

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

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Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Design &amp; Nature And Ecodynamics
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Publication Date
Thu Feb 07 2019
Journal Name
Iraqi Journal Of Laser
Gingival Enlargement Management using Diode Laser 940 nm and Conventional Scalpel Technique (A Comparative Study)

Diode lasers are becoming popular in periodontal surgery due to their highly absorption by pigments such as melanin and hemoglobin their weak absorption by water and hydroxyapatite makes them safe to be used around dental hard tissues. Objective: The aim of the present study was to evaluate the efficiency of diode laser in performing gingivectomy in comparison to conventional scalpel technique in patients with chronic inflammatory enlargement. Materials and methods: Thirty patients were selected for this study. All of them required surgical treatment of gingival enlargements and were randomly divided into two groups: Control group (treated by scalpel and include sixteen patients) and study group (treated with diode laser 940nm and includ

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Publication Date
Sat Dec 11 2021
Journal Name
Engineering, Technology &amp; Applied Science Research
Evaluation of Rutting in Conventional and Rubberized Asphalt Mixes Using Numerical Modeling Under Repeated Loads

This research aimed to predict the permanent deformation (rutting) in conventional and rubberized asphalt mixes under repeated load conditions using the Finite Element Method (FEM). A three-dimensional (3D) model was developed to simulate the Wheel Track Testing (WTT) loading. The study was conducted using the Abaqus/Standard finite element software. The pavement slab was simulated using a nonlinear creep (time-hardening) model at 40°C. The responses of the viscoplastic model under the influence of the trapezoidal amplitude of moving wheel loadings were determined for different speeds and numbers of cycles. The results indicated that a wheel speed increase from 0.5Km/h to 1.0Km/h decreased the rut depth by about 22% and 24% in conv

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)

    Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA usin

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Publication Date
Sat Apr 30 2022
Journal Name
Iraqi Journal Of Science
Biometric Identification System Based on Contactless Palm-Vein Using Residual Attention Network

Palm vein recognition technology is a one of the most effective biometric technologies for personal identification. Palm acquisition techniques are either contact-based or contactless-based. The contactless-based palm vein system is considered more accurate and efficient when used in modern applications, but it may suffer from problems like pose variations and the delay in the matching process. This paper proposes a contactless-based identification system for palm vein that involves two main steps; First, the central region of the palm is cropped using fast extract region of interest algorithm, then the features are extracted and classified using altered structure of Residual Attention Network, which is a developed version of convolution

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Publication Date
Wed Sep 30 2015
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Extraction of Essential Oils from Citrus By-Products Using Microwave Steam Distillation

The main objectives of this research is to extract essential oil from: orange ( citrus sinensis), lemon( citrus limon) and mandarin( citrus reticulata) peels by two methods: steam distillation (SD) and microwave assisted steam distillation (MASD), study the effect of extraction conditions (weight of the sample, extraction time, and microwave power, citrus peel type) on oil yield and compare the results of the two methods, the resulting essential oil was analyzed by Gas Chromatography (GC).

   Essential oils are highly concentrated substances used for their flavor and therapeutic or odoriferous properties, in a wide selection of products such as foods, medicines and cosmetics. Extracti

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