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A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven classifiers. A hybrid supervised learning system that takes advantage of rich intermediate features extracted from deep learning compared to traditional feature extraction to boost classification accuracy and parameters is suggested. They provide the same set of characteristics to discover and verify which classifier yields the best classification with our new proposed approach of “hybrid learning.” To achieve this, the performance of classifiers was assessed depending on a genuine dataset that was taken by our camera system. The simulation results show that the support vector machine (SVM) has a mean square error of 0.011, a total accuracy ratio of 98.80%, and an F1 score of 0.99. Moreover, the results show that the LR classifier has a mean square error of 0.035 and a total ratio of 96.42%, and an F1 score of 0.96 comes in the second place. The ANN classifier has a mean square error of 0.047 and a total ratio of 95.23%, and an F1 score of 0.94 comes in the third place. Furthermore, RF, WKNN, DT, and NB with a mean square error and an F1 score advance to the next stage with accuracy ratios of 91.66%, 90.47%, 79.76%, and 75%, respectively. As a result, the main contribution is the enhancement of the classification performance parameters with images of varying brightness and clarity using the proposed hybrid learning approach.

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Publication Date
Sat Nov 27 2021
Journal Name
International Conference On Fibre-reinforced Polymer (frp) Composites In Civil Engineering
Hybrid Anchors in Reinforced Concrete Slabs Strengthened with FRP Sheets
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Reinforced concrete (RC) slabs strengthened with carbon fibre reinforced polymer (CFRP) and subjected to flexural actions may experience many types of failure, including FRP debonding, FRP rupture and concrete crushing. Of these different types of failure modes, FRP debonding stands out as the most predominant type of failure because of its dependence on the relatively weak bond interface between the soffit of the RC member and the FRP sheet attached to it. Many anchorage systems have been developed to enhance the performance of strengthened systems, one of which is the hybrid anchor, which combines the effects of patch anchors and spike anchors. Hybrid anchors have shown significant enhancement when used with RC members subjected to shear

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

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Publication Date
Sat Aug 02 2025
Journal Name
Engineering, Technology & Applied Science Research
A New Method for Face-Based Recognition Using a Fuzzy Face Deep Model
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Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

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Publication Date
Fri Jul 12 2024
Journal Name
International Journal Of Advances In Scientific Research And Engineering (ijasre), Issn:2454-8006, Doi: 10.31695/ijasre
A Proposed Blockchain-Based Digital-Identity Management using ERC 725/735
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In modern technology, the ownership of electronic data is the key to securing their privacy and identity from any trace or interference. Therefore, a new identity management system called Digital Identity Management, implemented throughout recent years, acts as a holder of the identity data to maintain the holder’s privacy and prevent identity theft. Therefore, an overwhelming number of users have two major problems, users who own data and third-party applications will handle it, and users who have no ownership of their data. Maintaining these identities will be a challenge these days. This paper proposes a system that solves the problem using blockchain technology for Digital Identity Management systems. Blockchain is a powerful techniqu

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Publication Date
Tue Aug 01 2023
Journal Name
Journal Of Engineering
Developing a Proposed System to Organize the Investment Opportunities in Iraq
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The Iraqi government seeks to overcome the financial crisis by investing and privatizing some projects to achieve sustainable growth. Most of the investment projects in Iraq suffer from many constraints that greatly impact the success of these projects. A survey of the opinions of a group of experts was conducted to identify the most important constraints facing the investment process in Iraq. Then the experts' answers were arranged in a closed questionnaire and distributed to the research sample for which the statistical analysis was conducted. Through it, the most important (17) factors that had the greatest impact on the failure of investment projects in Iraq were reached. One of the main constraints was

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Publication Date
Sun Dec 31 2023
Journal Name
College Of Islamic Sciences
The Approach of Discretion in Contemporary Jurisprudential Issues, with Imam Al-Qaradawi The Jurisprudence of Minorities as a Model
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Research Objectives: The research aims to highlight the approach of Imam Al-Qaradawi in contemporary jurisprudence in the recent issues of the jurisprudence of minorities, and mentioning the foundations of jurisprudence of minorities, along with some of the practical applications of Imam Al-Qaradawi.

Study Methodology: The researcher applied the inductive, analytical and comparative approach by tracking the scientific material related to the subject of the study from the books of Al-Qaradawi in the first place, then by comparing the legal provisions with what had been stated in the four schools of jurisprudence.

Findings: The interest and need of Muslim minorities in non-

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Publication Date
Fri Mar 20 2020
Journal Name
Remote Sensing
Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics
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The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, rec

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Publication Date
Sat Feb 25 2017
Journal Name
International Journal On Advanced Science, Engineering And Information Technology
A Novel DNA Sequence Approach for Network Intrusion Detection System Based on Cryptography Encoding Method
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A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh

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Publication Date
Sat Oct 01 2022
Journal Name
The Egyptian Journal Of Hospital Medicine
Some Clinical Features of Trichomoniasis Associated with Pelvic Organs Tenderness in Sample of Iraqi women
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Publication Date
Wed Dec 13 2017
Journal Name
Al-khwarizmi Engineering Journal
Design of a Kinematic Neural Controller for Mobile Robots based on Enhanced Hybrid Firefly-Artificial Bee Colony Algorithm
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The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then  proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is impleme

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