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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
Fri Nov 11 2011
Journal Name
Thesis
Performance Analysis of a New Compact Magneto-Rheological Proportional Control Valve for Hydraulic Actuation Using FEM and Experimental Approach
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One of the main parts in hydraulic system is directional control valve, which is needed in order to operate hydraulic actuator. Practically, a conventional directional control valve has complex construction and moving parts, such as spool. Alternatively, a proposed Magneto-rheological (MR) directional control valve can offer a better solution without any moving parts by means of MR fluid. MR fluid consists of stable suspension of micro-sized magnetic particles dispersed in carrier medium like hydrocarbon oil. The main objectives of this present research are to design a MR directional control valve using MR fluid, to analyse its magnetic circuit using FEMM software, and to study and simulate the performance of this valve. In this research, a

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Publication Date
Sun Dec 01 2002
Journal Name
Iraqi Journal Of Physics
New DCT-Based Image Hiding Technique
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A new technique for embedding image data into another BMP image data is presented. The image data to be embedded is referred to as signature image, while the image into which the signature image is embedded is referred as host image. The host and the signature images are first partitioned into 8x8 blocks, discrete cosine transformed “DCT”, only significant coefficients are retained, the retained coefficients then inserted in the transformed block in a forward and backward zigzag scan direction. The result then inversely transformed and presented as a BMP image file. The peak signal-to-noise ratio (PSNR) is exploited to evaluate the objective visual quality of the host image compared with the original image.

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Publication Date
Sun Dec 09 2018
Journal Name
Baghdad Science Journal
New Spectrophotometric Estimation and Cloud Point Extraction of Cefdinir
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A sensitive spectrophotometric method was developed for the estimation of cefdinir (CFD), a cephalosporin species. This study involves two methods, and the first method includes the preparing of azo dye by the reaction of CFD diazonium salt with 4-Tert-Butylphenol (4-TBP) and 2-Naphthol (2-NPT) in alkaline medium, which shows colored dyes measured at λmax 490 and 535 nm, respectively. Beer's law was obeyed along the concentration range of (3-100) μg.ml-1. The limits of detection were 0.246, 0.447 μg.ml-1 and molar absorptivities were 0.6129×104, 0.3361×104 L.mol-1cm-1 for (CFD-4-TBP) and (CFD-2-NPT), respectively. The second method includes preconcentration for cefdinir dyes by using cloud point extraction in the presence of Triton

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers
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Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Keratoconus Severity Detection From Elevation, Topography and Pachymetry Raw Data Using a Machine Learning Approach
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Publication Date
Tue Jan 30 2024
Journal Name
International Journal Of Engineering Pedagogy (ijep)
E-learning in the Cloud Computing Environment: Features, Architecture, Challenges and Solutions
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The need to constantly and consistently improve the quality and quantity of the educational system is essential. E-learning has emerged from the rapid cycle of change and the expansion of new technologies. Advances in information technology have increased network bandwidth, data access speed, and reduced data storage costs. In recent years, the implementation of cloud computing in educational settings has garnered the interest of major companies, leading to substantial investments in this area. Cloud computing improves engineering education by providing an environment that can be accessed from anywhere and allowing access to educational resources on demand. Cloud computing is a term used to describe the provision of hosting services

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Publication Date
Sun Mar 04 2012
Journal Name
Baghdad Science Journal
A proposed Mathematical Expression for Computer Design of Electrostatic Mirror
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A computational investigation has been carried out on the design and properties of the electrostatic mirror. In this research, we suggest a mathematical expression to represent the axial potential of an electrostatic mirror. The electron beam path under zero magnification condition had been investigated as mirror trajectory with the aid of fourth – order – Runge – Kutta method. The spherical and chromatic aberration coefficients of mirror has computed and normalized in terms of the focal length. The choice of the mirror depends on the operational requirements, i.e. each optical element in optical system has suffer from the chromatic aberration, for this case, it is use to operate the mirror in optical system at various values

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Publication Date
Fri Nov 01 2019
Journal Name
International Journal Of Computer Science And Mobile Computing
Adaptive Color Image Compression of Hybrid Coding and Inter Differentiation Based Techniques
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Publication Date
Fri Aug 13 2021
Journal Name
Neural Computing And Applications
Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Multiwavelet and Estimation by Interpolation Analysis Based Hybrid Color Image Compression
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Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. The major topic of this paper, is improve the compresses of still images by Multiwavelet based on estimation the high Multiwavelet coefficients in high frequencies sub band  by interpolation instead of sending all Multiwavelet coefficients. When comparing the proposed approach with other compression methods Good result obtained

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