Proposed Handwriting Arabic Words classification Based On Discrete Wavelet Transform and Support Vector Machine

doi:10.24996/ijs.2017.58.2c.19

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…To minimize the number of sample images required from each person, the system utilizes the data augmentation process on the input image [31]. Augmentation of data also helps avoid overfitting by adding more complex data to the model.…”

Section: Deep Learning Methodologymentioning

confidence: 99%

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Ahmed

Kadhem

2021

Int. J. Interact. Mob. Technol.

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— Chest imaging diagnostics is crucial in the medical area due to many serious lung diseases like cancers and nodules and particularly with the current pandemic of Covid-19. Machine learning approaches yield prominent results toward the task of diagnosis. Recently, deep learning methods are utilized and recommended by many studies in this domain. The research aims to critically examine the newest lung disease detection procedures using deep learning algorithms that use X-ray and CT scan datasets. Here, the most recent studies in this area (2015-2021) have been reviewed and summarized to provide an overview of the most appropriate methods that should be used or developed in future works, what limitations should be considered, and at what level these techniques help physicians in identifying the disease with better accuracy. The lack of various standard datasets, the huge training set, the high dimensionality of data, and the independence of features have been the main limitations based on the literature. However, different architectures of deep learning are used by many researchers but, Convolutional Neural Networks (CNN) are still state-of-art techniques in dealing with image datasets.

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Section: Deep Learning Methodologymentioning

confidence: 99%

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Ahmed

Kadhem

2021

Int. J. Interact. Mob. Technol.

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“…Hassan and Alawi [13] designed holistic offline HATRS based on Discrete Wavelet Transform (DWT) and SVM with Gaussian kernel. Their model was developed based on four levels of the DWT via segmentation of the wavelet space into 16x16 segments.…”

Section: Related Workmentioning

confidence: 99%

“…Several kernel functions can transform the non-linear divisible problem into a linear separable problem by projecting data into clarified feature space [4]. Thereafter, the SVM can determine the hyperplane with the best separation [13]. In this paper, multi-class SVM classifiers were employed for the text images classification using the following SVM kernel functions [19]: Artificial Neural Network (ANN): Artificial Neural Network (ANN) is considered as a part of a computing system that is designed to mimic the way of analyzing and processing information by the human brain.…”

Section: Text Classificationmentioning

confidence: 99%

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi

Al-Saqqar

Alhusban

2020

Int. J. Interact. Mob. Technol.

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In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.

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“…Gene selecting is the method for selecting a lesser portion from the genes from a wider gene collection that solely contains important genes. A machine learning algorithm is used in this study to predict AD using gene expression data [4]. Support vector machines (SVMs) were originally proposed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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Alzheimer's Disease (AD), which is left untreated, is the primary cause of neurodegenerative dementia, which primarily affects those over 65. Memory and thought processes are gradually disrupted by the irreversible brain disruption and, eventually, the capacity to fulfil simple tasks. Detecting AD early prevents its progression and diminishes its signs. The study aimed to build a classification model that might predict the early stages of Alzheimer's disease using accurate early-stage gene expression data from blood obtained from a clinical Alzheimer's dataset. The datasets used in this work were gathered from the Gene Expression Omnibus (GEO) and are GSE63060 and GSE63061.It has the right rows for (569 samples) and columns for (16382 genes). The suggested GWO gene selection method aims to identify the ideal feature subset for medical data. A model for predicting early Alzheimer's disease is proposed based on the modified Grey Wolf Optimizer and Support Vector Machine (GWO-SVM). These methods will help reduce the number of trivial and redundant genes in the original datasets. We attained an accuracy of 82%-88% using only the Support Vector Machine method. When we utilized several evolutionary algorithms to implement gene selection, we observed an increase in accuracy of 6%-11%, with modified Grey Wolf optimization with Crossover (CGWO) called the proposed algorithm (CGWO-SVM) achieving the highest accuracy of (97.41%) with the Alzheimer's disease dataset in comparison to the other competitive schemes in the existing literature. Thus, the proposed system is suitable for picking more accurate classification and interesting genes to increase classification accuracy while decreasing gene dimensions.

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Proposed Handwriting Arabic Words classification Based On Discrete Wavelet Transform and Support Vector Machine

Cited by 4 publications

References 4 publications

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

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