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bsj-2405
Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor
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There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming. In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process. The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description.

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Publication Date
Tue Dec 07 2021
Journal Name
2021 14th International Conference On Developments In Esystems Engineering (dese)
Content Based Image Retrieval Based on Feature Fusion and Support Vector Machine
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Publication Date
Mon Aug 01 2016
Journal Name
2016 38th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control
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Publication Date
Fri Jan 01 2021
Journal Name
Computers, Materials & Continua
A New Hybrid Feature Selection Method Using T-test and Fitness Function
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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Feature - Based Approach to Automatic Fixturing System Planning For Uniform Polyhedra Workpiece
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This paper demonstrates the design of an algorithm to represent the design stages of fixturing system that serve in increasing the flexibility and automation of fixturing system planning for uniform polyhedral part. This system requires building a manufacturing feature recognition algorithm to present or describe inputs such as (configuration of workpiece) and built database system to represents (production plan and fixturing system exiting) to this algorithm. Also knowledge – base system was building or developed to find the best fixturing analysis (workpiece setup, constraints of workpiece and arrangement the contact on this workpiece) to workpiece.

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Publication Date
Mon Dec 01 2014
Journal Name
2014 Ieee Student Conference On Research And Development
Feature extraction for co-occurrence-based cosine similarity score of text documents
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Publication Date
Tue Apr 01 2025
Journal Name
Mesopotamian Journal Of Cybersecurity
The Impact of Feature Importance on Spoofing Attack Detection in IoT Environment
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The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Mon Sep 15 2025
Journal Name
Journal Of Physical Education
A Safe Environment Scale from Professional Basketball Players and Coaches’ Point Of View
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The aim of the research was to prepare the fields and items for safe environment scale form professional basketball players and coaches’ point of view. The researchers used the descriptive method on professional athletes of players and coaches from (11) clubs; for the pilot study (21) athletes were selected and for the building sample (103) athletes, and standardization sample (101) athletes from the participants in the Iraqi league (2018-2019). The scale of the safe environment was concluded with (6) fields and (52) items. The researchers came up with the safe environment scale from the point of view of professional basketball players and coaches. Finally the researchers recommended paying attention to providing safe environment for athl

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Publication Date
Thu Feb 24 2022
Journal Name
Journal Of Educational And Psychological Researches
The Psychometric Properties of the Arabic Version of Nomophobia Scale among Omani Youth
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The current research aimed to investigate the psychometric characteristics of the Arabic version of the Nomophobia scale for the Omani youth. The scale was administered to a random sample of students from public and private universities and colleges in Oman. The research sample consisted of 2507 students, of whom 868 males and 1639 females. The validity of the measure was first checked by presenting the scale to a group of experts in this field. Then the exploratory and confirmatory factor analysis was carried out. The exploratory factor analysis revealed the existence of three main factors: the fear of connectivity loss, the fear of communication loss with others, and the fear of network outages. These factors accounted for 65.6% of the

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Publication Date
Sat Jun 01 2013
Journal Name
International Journal Of Astronomy And Astrophysics
Distances Scale Determination for a Planetary Nebula in Galagtic Bulge
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ABSTRACT Planetary Nebulae (PN) distances represent the fundamental parameter for the determination the physical properties of the central star of PN. In this paper the distances scale to Planetary Nebulae in the Galactic bulge were calculated re- lated to previous distances scales. The proposed distance scale was done by recalibrated the previous distance scale technique CKS/D82. This scale limited for nearby PN (D ≤ 3.5 kpc), so the surface fluxes less than other distance scales. With these criteria the results showed that the proposed distance scale is more accurate than other scales related to the observations for adopted sample of PN distances, also the limit of ionized radius (Rio) for all both optically thick and optically thin in

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