ORCID Profile
0000-0003-4173-2323
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Publisher: Wiley
Date: 07-03-2019
DOI: 10.1002/CPE.5214
Abstract: Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi‐orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered in idually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy.
Publisher: Springer Science and Business Media LLC
Date: 23-06-2020
Publisher: Springer Science and Business Media LLC
Date: 26-02-2019
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 13-12-2018
Publisher: Springer Science and Business Media LLC
Date: 16-12-2016
Publisher: IEEE
Date: 12-2011
Publisher: Elsevier BV
Date: 02-2018
Publisher: IEEE
Date: 09-2016
Publisher: Hindawi Limited
Date: 2011
DOI: 10.1155/2011/136034
Abstract: The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise.
Publisher: Research Square Platform LLC
Date: 21-12-2022
DOI: 10.21203/RS.3.RS-2364360/V1
Abstract: SCADA (supervisory control and data acquisition) system is an advanced control system used in industrial and service sectors such as oil and gas, water treatment, nuclear energy, and electrical power generation to control and monitor the manufacturing or servicing process and maintain the process within predetermined values in order to induce desirable outcomes. The SCADA architecture employs several protocols, including Modbus/RTU, Modbus TCP/IP, DNP3 and IEC60870. This paper will concentrate on the most widely used protocol in SCADA systems, Modbus TCP/IP, due to its widespread use, simplicity, and robustness. SCADA systems are vulnerable to cyber-attacks because Modbus/TCP lacks security measures for access control, authentication, and confidentiality. The paper contains valuable contribution by introducing an authentication technique to check the validity of messages in which the authorized users only pass this check, thereby adding a security measure to the SCADA system in terms of cyber-attacks.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 18-04-2023
DOI: 10.3390/AGRICULTURE13040889
Abstract: Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.
Publisher: IEEE
Date: 06-2019
Publisher: MDPI AG
Date: 19-09-2022
DOI: 10.3390/ELECTRONICS11182964
Abstract: Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.
Publisher: IEEE
Date: 10-2018
Publisher: Bentham Science Publishers Ltd.
Date: 03-2022
DOI: 10.2174/2666255813999200904114023
Abstract: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.
Publisher: MDPI AG
Date: 20-02-2023
DOI: 10.3390/FI15020085
Abstract: Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for in iduals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for in iduals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for in iduals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%.
Publisher: IEEE
Date: 11-2016
Publisher: Elsevier BV
Date: 06-2018
Publisher: Emerald
Date: 04-2019
Abstract: This paper aims to propose a new efficient semantic recommender method for Arabic content. Three semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature. Experiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time. Although many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.
Publisher: IEEE
Date: 05-2011
Publisher: IEEE
Date: 02-2011
Publisher: IEEE
Date: 05-2011
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 2017
Publisher: IEEE
Date: 12-2013
Publisher: Springer Science and Business Media LLC
Date: 21-02-2019
Publisher: Springer Science and Business Media LLC
Date: 26-11-2021
Publisher: IEEE
Date: 02-2011
Publisher: Springer Science and Business Media LLC
Date: 19-04-2022
Publisher: Springer Science and Business Media LLC
Date: 02-11-2021
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for shadi alzubi.