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387 results on '"Alzubi, Jafar"'

3. Hybrid Optimization-Based Support Vector Machine for Detecting the Network Attacks in IoT

Author

Alzubi, Jafar A., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fortino, Giancarlo, editor, Kumar, Akshi, editor, Swaroop, Abhishek, editor, and Shukla, Pancham, editor

Published
2025
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4. A Deep Dive into Brain-Computer Interface

Author

Alzubi, Jafar A., Subhra, Snahil, Mishra, Sushruta, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fortino, Giancarlo, editor, Kumar, Akshi, editor, Swaroop, Abhishek, editor, and Shukla, Pancham, editor

Published
2024
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5. A Deep Neural Network for Image Classification Using Mixed Analog and Digital Infrastructure

Author

Kala, R., Punitha, M. Poomani Alias, Banupriya, P. G., Veerasamy, B., Bharathi, B., Alzubi, Jafar Ahmad Abed, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jain, Shruti, editor, Marriwala, Nikhil, editor, Singh, Pushpendra, editor, Tripathi, C.C., editor, and Kumar, Dinesh, editor

Published
2024
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16. Left Atrial Appendage Closure: What Do We Know?

Author

Babapoor-Farrokhran, Savalan, Alzubi, Jafar, Port, Zachary, Kaul, Risheek, Rasekhi, Roozbeh Tarighati, Farrokhran, Ali Babapour, Sooknanan, Naveen, Wiener, Philip C., Khraisha, Ola, Frishman, William H., Mainigi, Sumeet K., and Aronow, Wilbert S.

Published
2023
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32. Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization.

Author

Ahmad, Ahmad Yahiya Ahmad Bani, Alzubi, Jafar, James, Sophers, Nyangaresi, Vincent Omollo, Kutralakani, Chanthirasekaran, and Krishnan, Anguraju

Subjects
MACHINE learning, CONVOLUTIONAL neural networks, HUMAN behavior, HEURISTIC algorithms, FEATURE extraction, DEEP learning, HUMAN activity recognition
Abstract

In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is developed using multiple types of data and an advanced deep-learning approach. Firstly, the required signals and sensor data are accumulated from the standard databases. From these signals, the wave features are retrieved. Then the extracted wave features and sensor data are given as the input to recognize the human activity. An Adaptive Hybrid Deep Attentive Network (AHDAN) is developed by incorporating a "1D Convolutional Neural Network (1DCNN)" with a "Gated Recurrent Unit (GRU)" for the human activity recognition process. Additionally, the Enhanced Archerfish Hunting Optimizer (EAHO) is suggested to fine-tune the network parameters for enhancing the recognition process. An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model. The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36, 95.25 for recall, 95.48 for specificity, and 95.47 for precision, respectively. The result proved that the developed model is effective in recognizing human action by taking less time. Additionally, it reduces the computation complexity and overfitting issue through using an optimization approach. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Packet Error Rate Performance of IEEE802.11g under Bluetooth Interface

Author

Abukharis, Salim, Alzubi, Jafar A., Alzubi, Omar A., Alamri, Saeed, and O'Farrell, Tim

Subjects
Computer Science - Networking and Internet Architecture
Abstract

This study approaches the issues concerning the coexistence of the IEEE 802.11g wireless networks and the adhoc Bluetooth networks that operate within the same 2.4 GHz band. The performance of 802.11g is evaluated by simulating the PER (Packet Error Rate) parameter and coverage area. The simulation experiments are based on the worst case scenario presumption, which entails the transmission of HV1 packet which HV1 link requires transmission on 100% of the Bluetooth (BT) time slots using the maximum hop rate of 1600 hops per sec. The paper suggests a practical approach to mitigate the interference using symbol erasures technique through the assessment of the PER parameter at various Eb/No values. The results show that the symbol erasures can effectively mitigate the interference and enhance the performance of the 802.11g., Comment: 5 pages, 2 figures, Journal paper

Published
2016

39. Key Economic and Environmental Perspectives on Sustainability in the ICT Sector

Author

Hamdoun, Hassan, Alzubi, Jafar A., Alzubi, Omar A., and Mangeni, Solomon

Subjects
Computer Science - Computers and Society
Abstract

Telecommunication networks have become as critical to the 21st century development as were railways, roads and canals, to the 19th Century developments and is now seen as enabler to a more sustained business, environment and society as a whole. Still fascinating has been and is the exponential rate of growth in this industry. This is one sector where the next revolution is always just around the corner whether known or unknown. The telecoms industry is categorized by high rates of innovation in a rapidly changing technological environment. This in turn is associated with an immense range of sustainability concerns and challenges for the Telecoms service providers, the service users and the whole industry and its far reaching influence on other industries. This paper discusses three key aspects of such challenges namely; the question of sustainable power/energy supply for the industry when the change is resulting in increasing energy and operational cost, the exploitation of technologies advancement for sustainability and their business and environmental benefits., Comment: 5 pages, 4 figures, Journal paper

Published
2016
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40. Near Capacity Irregular Turbo Code

Author

Sholiyi, Abiodun, Alzubi, Jafar A., Alzubi, Omar A., Almomani, Omar, and O'Farrell, Tim

Subjects
Computer Science - Information Theory
Abstract

The purpose of this study is to construct a near capacity Irregular Turbo Code and to evaluate its performance over Gaussian channel. The methodology used to evaluate and measure the performance of the new design is by simulating the system by developing a software platform using Matlab. The simulation carried out by implementing the system over different modulation schemes, different frame sizes, and different code rates in order to achieve a fair comparison between irregular turbo code and regular Turbo Code. The simulation results showed that the Irregular Turbo Code with 64QAM modulation is capable of achieving a coding gain of 1.29 dB over its corresponding Turbo Code for a low Bit Error Rate 10 to 5, when used in a Gaussian noisy channel. Also, the Irregular Turbo Code designed in this paper, utilizes a single recursive Convolution encoder with short frame size of 5012 bits. The new designed codec can play an important role in many commercial applications such as Third-Generation wireless phones, Digital Video Broadcasting systems, or Wireless Metropolitan Area Networks, etc., Comment: 10 pages, 11 figures, 3 tables, journal paper, Indian Journal of Science and Technology, Vol 8(23), September 2015

Published
2016
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