Bigradient neural network-based quantum particle swarm optimization for blind source separation

Salman, Hussein; Al-Qurabat, Ali Kadhum M.; Finjan, Abd Alnasir Riyadh

doi:10.11591/ijai.v10.i2.pp355-364

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…In contrast, the qualities are classified as predictors or "non-class attributes." SVM, decision tree, random forest, and eXtreme Gradient Boosting are just a few of the classifiers that have found widespread usage in seizure detection [ 51 ].…”

Section: Methodology For Identifying Epileptic Seizuresmentioning

confidence: 99%

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

Al-hajjar

Al-Qurabat

2023

J Supercomput

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The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today’s world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people’s lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.

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Section: Methodology For Identifying Epileptic Seizuresmentioning

confidence: 99%

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

Al-hajjar

Al-Qurabat

2023

J Supercomput

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“…Additional data packets are not transmitted to the sink because of the energy hole [11]. This issue causes the network to split, which makes the network as a whole non-functional and reduces network lifetime and quality of services [12], [13].…”

Section: A Data Gathering Using a Static Sinkmentioning

confidence: 99%

An Overview of Using Mobile Sink Strategies to Provide Sustainable Energy in Wireless Sensor Networks

Majid Lateef,

Kadhum M. Al-Qurabat

2024

IJCDS

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Wireless sensor networks (WSNs) can effectively address the issue with the static sink's sink-hole or hot spot brought on by multi-hop routing by data collecting using mobile sinks (MS). The optimal path's design, however, is a famous NP-hard issue. Data transmission from source nodes to the base station that is both successful and efficient while reducing energy consumption and data loss is what determines the architecture's overall performance. In WSNs, data collection is done via mobile sinks or static sinks (SS). he MS data collection methods are more effective than sink-based approaches because they can gather sensor node data efficiently. Nevertheless, the MS-based data collection methods have a number of shortcomings and restrictions such as energy usage, complexity, cost implications, and scalability issues. Designing a trajectory is therefore an NP-hard task. In this work, we suggest a survey that utilizes path optimization techniques such as swarm intelligence, ant colony optimization (ACO), machine learning and artificial intelligence. We also have an overview of different approaches for using SS and MS-based techniques to collect data from a sensor network, as well as different kinds of data collection using MS and some of the difficulties it encounters. Lastly, we offer a level-based categorization of the various trajectory techniques that were employed to gather the data. We divided schemes into three categories at the first level: static, dynamic, and Hybrid.

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“…The size of the deployment area increases both the quantity and expense of anchor point deployment. Furthermore, using anchor sensors in emergency situations is inappropriate because the anchor points need to be set up ahead of time [6], [7].…”

Section: Introduction and Overviewmentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.

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Bigradient neural network-based quantum particle swarm optimization for blind source separation

Cited by 7 publications

References 20 publications

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

An Overview of Using Mobile Sink Strategies to Provide Sustainable Energy in Wireless Sensor Networks

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

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