The Adjacency Matrix of The Compatible Action Graph for Finite Cyclic Groups of p-Power Order

Shahoodh, Mohammed Khalid

doi:10.25130/tjps.v26i1.109

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…However, the advent of advanced neural networks, commonly referred to as deep learning (DL), has obviated the need for feature engineering [55][56][57]. DL, ML, and NNs are frequently intertwined in discussions about artificial intelligence, leading to misconceptions.…”

Section: Deep Learningmentioning

confidence: 99%

FEDDBN-IDS: Federated Deep Belief Network-based Wireless Network Intrusion Detection System

et al. 2023

Preprint

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Past two decades, Wi-Fi innovation has developed so generally that most contemporary gadgets are compact and use Wi-Fi to access the web. Because no physical boundary separates a wireless network from a wired network, Wi-Fi network security is seriously questioned, and current security measures are helpless to an extensive variety of attacks. The objective of this research was to evaluate a novel approach called federated learning as a potential solution to address privacy concerns and high costs associated with data collection in the field of recognizing network attacks. The study introduces FEDDBN-IDS, a ground-breaking intrusion detection system (IDS) that utilizes deep belief networks (DBNs) within a federated deep learning (FDL) framework to detect and identify cyber threats specifically in Wi-Fi networks. A DBN with stacked restricted Boltzmann machines (RBM) was trained on each device to learn the low-dimensional features from unlabelled private and local data. A central server later combines these models into a global model (FL) using federated learning. Subsequently, the central server adds fully connected SoftMax layers to the overall model to create a supervised neural network and train the model using freely available labeled AWID datasets. The results obtained from our experiments on the AWID intrusion detection dataset indicate that our federated strategy achieves a high level of classification accuracy, ranging from 88% to 98%.

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Section: Deep Learningmentioning

confidence: 99%

FEDDBN-IDS: Federated Deep Belief Network-based Wireless Network Intrusion Detection System

et al. 2023

Preprint

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Balancing Usability and Security Innovative Approaches to Privacy in Digital Environments

Baswaraju,

Yadav,

Nijhawan

et al. 2024

2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)

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E-Health System Characteristics, Medical Performance, and Healthcare Quality at Jordan’s Health Centers

Alazzam

Khatib

Mohammad

et al. 2021

Journal of Healthcare Engineering

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This research explores how e-health systems’ features (information quality, quality of the system, usability perceived, and perceived usefulness) contribute to improving medical personnel performance in medical centers, patient care, and physician-patient interactions in Jordan. The objective is to evaluate a single integrated model consisting of the technology acceptance model. This study used the logical research method and approach. A collection of data from 212 medical personnel working in 19 healthcare facilities throughout Jordan were gathered. To analyze the data collected and test the hypotheses of the research, a partially square/structural equation modeling method has been employed. The study found that the health information system (HIS) information quality has a direct and indirect beneficial effect on the performance of the staff, beneficial effects on patient care alone, and only favorable, indirect effects on the doctor-patient relationship. On the contrary, system quality was shown to influence directly and indirectly and to have a direct and indirect beneficial effect both on the connection between doctors and patients. Remember that the HIS has accessibility, speed, and mistake detection and avoids error issues. These shortcomings are suggested to be rectified in conjunction with improved user perception towards easy usage and utilization of the system.

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The Adjacency Matrix of The Compatible Action Graph for Finite Cyclic Groups of p-Power Order

Abstract: Let G and H be two finites -groups, then is the non-abelian tensor product of G and H. In this paper, the compatible action graph for has been considered when and for the two finite -groups by determining the adjacency matrix for and studied some of its properties.

Cited by 6 publications

References 4 publications

FEDDBN-IDS: Federated Deep Belief Network-based Wireless Network Intrusion Detection System

FEDDBN-IDS: Federated Deep Belief Network-based Wireless Network Intrusion Detection System

Balancing Usability and Security Innovative Approaches to Privacy in Digital Environments

E-Health System Characteristics, Medical Performance, and Healthcare Quality at Jordan’s Health Centers

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