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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Turab, Nidal
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Progress in self-powered medical devices for breathing recording Abu Owida, Hamza; Turab, Nidal; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.5253

Abstract

Wearable and implantable medical technologies are increasingly being used for the diagnosis, treatment, and prevention of illnesses and other health concerns. One's respiration can be monitored using any number of different biosensors and tracking devices. Self-powered sensors, for example, have a reduced total cost, are easy to prepare, have a high degree of design-ability, and are available in a number of different forms when compared to other types of sensors. The mechanical energy stored in the respiratory system could be converted into electrical energy by using airflow to operate self-powered sensors. Self-recharging sensors and systems are now in development to make home health monitoring and diagnosis more practical. There has not been a lot of study devoted to the models of respiratory sickness or the output signals that connect with them. Thus, investigating the character of their bond is not only difficult but also crucial. This article examined the theory behind self-powered breathing sensors and systems, as well as their output characteristics, detection indices, and other cutting-edge developments. To help communicate knowledge to other academics working in this field and interested in this topic, we also explored the challenges and potential benefits of autonomous sensors.
Advancement in self-powered implantable medical systems Abu Owida, Hamza; Al-Nabulsi, Jamal; Turab, Nidal; Al-Ayyad, Muhammad; Al Hawamdeh, Nour; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.5881

Abstract

Many different elements of patient monitoring and treatment can be supported by implantable devices, which have proven to be extremely reliable and efficient in the medical profession. Medical professionals can use the data they collect to better diagnose and treat patients as a result. The devices’ power sources, on the other hand, are battery-based, which introduces a slew of issues. As part of this review, we explore the use of harvesters in implanted devices and evaluate various materials and procedures and look at how new and improved circuits can enable the harvesters to sustain medical devices.
Challenges in applying DeepInsight for cyber threat detection AL-Essa, Malik; Qatawneh, Mohammad; Turab, Nidal; Alsarhan, Yazeed
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9649

Abstract

As the world suffers from intrusions and malware extensively nowadays, intru-sion detection systems (IDS) play a critical role in protecting cyberspace from attacks. However, attacks become more complex every day, leading to the neces-sity of developing new techniques that can protect our digital infrastructure from cyber-attacks. Deep learning (DL) is one of the techniques that are investigated to fight against cyber-attacks. However, due to the nature of traffic data, most of the techniques focus on the deep neural network (DNN) as the performance of the DNN dependsonthetraining data. In this paper, we investigate the effective-ness of using convolutional neural networks (CNN) to detect malware apps and network intrusions. The cybersecurity datasets are converted from tabular data into images using the DeepInsight technique. Experiments are conducted using two datasets, NSL-KDD and CICMaldroid20 datasets. The proposed method demonstrates that converting cybersecurity datasets from tabular data into im-ages may decrease the model’s accuracy. Furthermore, this approach introduces additional challenges in the detection of network intrusions and malware. More-over, the added architectural complexity may cause a dilution or distortion of feature representations, making it harder for the model to preserve the original semantic meaning of critical features.
Co-Authors Abdul Jawwad, Abdul Kareem Abu Owida, Hamza Abuowaida, Suhaila Abushattal, Abdelrahman Al Hawamdeh, Nour Al Nabulsi, Jamal Al-Ayyad, Muhammad AL-Essa, Malik Al-Mahadin, Ghayth Al-Nabulsi, Jamal Al-Nabulsi, Jamal I. AlMahadin, Ghayth Alsarhan, Yazeed Alshdaifat, Nawaf Izneid, Basem Abu Owida, Hamza Abu Qatawneh, Mohammad