Search

Your search keyword '"Sallam, Amer A."' showing total 44 results
Start Over Author "Sallam, Amer A."
44 results on '"Sallam, Amer A."'

4. Enhancing Emotion Detection in Textual Data: A Comparative Analysis of Machine Learning Models and Feature Extraction Techniques.

Author

Saif, Wedad Q. A., Alshammari, Majid Khalaf, Mohammed, Badiea Abdulkarem, and Sallam, Amer A.

Subjects
MACHINE learning, FACIAL expression & emotions (Psychology), EXTRACTION techniques, DIGITAL technology, LINGUISTICS
Abstract

The digital age has resulted in a massive increase in the amount of available textual data, including articles, comments, texts, and updates on social networks. The value of analyzing such a large volume of data extends to many other industries and applications, as it provides important insights into the perspectives of customers, strategic decision-making, and market demands. Detecting emotions in texts faces challenges due to linguistic patterns and cultural nuances. This study proposes a system capable of accurately identifying emotions expressed in text using a variety of machine learning models, including logistic regression, extra randomized tree, voting, SGD, and LinearSVC. It also employs different feature extraction techniques, such as TF-IDF, Bag-of-Words, and N-grams, comparing their performance in these models. An evaluation was carried out using two English emotion datasets, namely ISEAR and AIT-2018, using F1 score, accuracy, recall, and precision. The findings demonstrate the ability and effectiveness of the system to detect emotions conveyed within texts. The LinearSVC model with N-grams achieved the highest accuracy of 88.63% on the ISEAR dataset, while the extra randomized tree classifier with N-grams achieved 89.14% accuracy on the AIT-2018 dataset. Furthermore, the SGD model with TF-IDF achieved 88.18% and 84.54% accuracy on the ISEAR and the AIT-2018 datasets, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

5. A Racial Recognition Method Based on Facial Color and Texture for Improving Demographic Classification

Author

Sallam, Amer A., Kabir, Muhammad Nomani, Shamhan, Athmar N. M., Nasser, Heba K., Wang, Jing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Md Zain, Zainah, editor, Ahmad, Hamzah, editor, Pebrianti, Dwi, editor, Mustafa, Mahfuzah, editor, Abdullah, Nor Rul Hasma, editor, Samad, Rosdiyana, editor, and Mat Noh, Maziyah, editor

Published
2021
Full Text
View/download PDF

11. Epilepsy Detection from EEG Signals Using Artificial Neural Network

Author

Sallam, Amer A., Kabir, Muhammad Nomani, Ahmed, Abdulghani Ali, Farhan, Khalid, Tarek, Ethar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Zelinka, Ivan, editor, and Weber, Gerhard-Wilhelm, editor

Published
2019
Full Text
View/download PDF

29. Iris recognition system using deep learning techniques

Author

Sallam, Amer A., Amery, Hadeel Al, and Saeed, Ahmed Y.A.

Abstract

Deep learning has been used and demonstrated intensively as a vital technique in data mining that can accurately and effectively evaluate enormous amounts of data for various applications. Iris recognition is one of those applications that necessitate complex algorithms for analysing and perfectly detecting the hidden patterns among its data in order to effectively distinguish one person from another. In this paper, an iris recognition system based on various deep learning techniques has been proposed. Through many experiments that were conducted on CASIA-V1 and ATVS datasets, the proposed system based on the Xception model was able to achieve significant results with 99.9% accuracy on CASIA-V1 dataset.

Published
2023
Full Text
View/download PDF

44. Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Author

Mohammed, Badiea Abdulkarem, Senan, Ebrahim Mohammed, Al-Mekhlafi, Zeyad Ghaleb, Rassem, Taha H., Makbol, Nasrin M., Alanazi, Adwan Alownie, Almurayziq, Tariq S., Ghaleb, Fuad A., Sallam, Amer A., Mohammed, Badiea Abdulkarem, Senan, Ebrahim Mohammed, Al-Mekhlafi, Zeyad Ghaleb, Rassem, Taha H., Makbol, Nasrin M., Alanazi, Adwan Alownie, Almurayziq, Tariq S., Ghaleb, Fuad A., and Sallam, Amer A.

Abstract

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%

Catalog

Books, media, physical & digital resources
See catalog results