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Your search keyword '"Al-Dabagh, Mustafa Zuhaer Nayef"' showing total 15 results
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15 results on '"Al-Dabagh, Mustafa Zuhaer Nayef"'

1. Enhancing Face Recognition for Security Systems: An Approach Using Gabor Wavelet, t-SNE, and SVM

Author

Al-Dabagh Mustafa Zuhaer Nayef, Hussein Hussein Ibrahim, Raheem Salar Ameen, Ahmed Muhammed Imran, and Othman Nashwan Adnan

Subjects
Information technology, T58.5-58.64
Abstract

Facial recognition is crucial for safety and security, especially for identifying people. This paper applies facial recognition to a database of facial images by analyzing the images and subsequently assigning a set of unique features to each one. The process of extracting features from the input image is accomplished using the gabor wavelet transform. t-SNE (tdistributed Stochastic Neighbor Embedding) select and reduce the dimension of features, thus specifying various aspects within the input image. These features are then used in a classification step, where a multiclass Support Vector Machine (SVM) is employed to categorize the face. Three popular databases (Yale, ORL and JAFFE) were the sources of the images used to evaluate the effectiveness of the proposed technique. The results show the system’s high accuracy in identifying facial images. Specifically, our method achieved a 97.78% accuracy rate on the Yale, 97.50 % in the ORL databases and 100 % in the JAFFE databases, outperforming traditional methods by 2%. These results approved the system’s accuracy in recognizing facial images.

Published
2024
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2. Face Recognition Using PCA Implemented on Raspberry Pi

Author

Mohammed, Ibrahim Majid, Al-Dabagh, Mustafa Zuhaer Nayef, Ahmad, Muhammad Imran, Isa, Mohd Nazrin Md, 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
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3. DCT Image Compression Implemented on Raspberry Pi to Compress Image Captured by CMOS Image Sensor

Author

Mohsin, Ibrahim Saad, Ahmad, Muhammad Imran, Salman, Saad M., Al-Dabagh, Mustafa Zuhaer Nayef, Isa, Mohd Nazrin Md, Ahmad, Raja Abdullah Raja, 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
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6. Privacy Preserving Data Mining Using Random Decision Tree Over Partition Data: Survey

Author

Othman Nashwan Adnan and Al-Dabagh Mustafa Zuhaer Nayef

Subjects
Information technology, T58.5-58.64
Abstract

The development of data mining with data protection and data utility can manage distributed data efficiently. This paper revisits the concepts and techniques of privacy-preserving Random Decision Tree (RDT). In existing systems, cryptography-based techniques are effective at managing distributed information. Privacy-preserving RDT handles distributed information efficiently. Privacy-preserving RDT gives better precision data mining while preserving information and reducing the calculation time. This paper deals with this headway in privacy-preserving data mining technology utilizing emphasized approach of RDT. RDT gives preferable productivity and information privacy than cryptographic technique. Various data mining tasks utilize RDT, like classification, relapse, ranking, and different classifications. Privacy-preserving RDT utilizes both randomization and the cryptographic method, giving information privacy for some decision tree-based learning tasks; this is an effective technique for data mining with privacy-preserving distributed information. Thus, in horizontal partitioning of the dataset, parties gather information for various entities but have data for all attributes. On the other hand, various associations may gather different data about a similar set of people. Thus, in vertically partitioned data, all parties gather data for the same collection of items. In all of these cases, both horizontal and vertical partitioning of datasets is somewhat inaccurate.

Published
2022
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