Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed, Mohammed Imran Basheer; Zaghdoud, Rim Ali; Al-Abdulqader, Mohammed; Kurdi, Mohammed; Altamimi, Rayan; Alshammari, Abdullah; Noaman, Abdulaziz; Ahmed, Mohammed Salih; Alshamrani, Rahaf; Alkharraa, Mariam; Rahman, Atta-ur; Krishnasamy, Gomathi

doi:10.18280/mmep.100110

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…In that research, we suggested an electronic data exchange (EDI) framework for smart healthcare in the context of smart cities supported by blockchain technology. To enable secure and effective EHR maintenance and exchange among multiple entities in the smart healthcare ecosystem, the proposed platform makes use of the benefits of blockchain technology [56][57][58][59][60]. Four essential parts made up the proposed framework: (1) Personal servers are local storage devices that store and encrypt EHR and communicate with cloud servers.…”

Section: Discussionmentioning

confidence: 99%

Blockchain Empowered Interoperable Framework for Smart Healthcare

Rahman,

Almomen,

Albahrani

et al. 2024

MMEP

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In the past, healthcare industry used paper-based systems to manage and store medical records. However, these systems are vulnerable to data breaches, loss, and errors. To overcome these issues, a research study has been conducted to create a safe and efficient Electronic Data Interchange (EDI) system for healthcare using blockchain technology. The study utilized various tools and methods including Python as the programming language to implement the blockchain environment, the pyQT5 library for graphical user interface (GUI), and the MySQL database management system as a repository for Electronic Health Records (EHR) with DBeaver, a cross-platform tool for data management. The research work involves the development of a blockchain-based smart contract for the storage, exchange, and retrieval of EHR. Additionally, a Python application based on pyQT5 is created to provide users with a friendly GUI. The proposed blockchain-based healthcare system provides a secure and efficient platform for storing and managing EHR as well as enabling secure EDI among healthcare stakeholders like practices, doctors, labs, and pharmacies. Furthermore, the system is scalable and user-friendly, and includes various features like patient visits, history, practices, doctors, and appointment scheduling. Blockchain technology ensures EHR integrity, secure EDI, and confidentiality, while the user-friendly interface enhances the user experience compared to the existing EDI standards like health level 7 (HL7).

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Section: Discussionmentioning

confidence: 99%

Blockchain Empowered Interoperable Framework for Smart Healthcare

Rahman,

Almomen,

Albahrani

et al. 2024

MMEP

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“…On the other side, the inconsistency of these signals due to varying physiological and psychological conditions points out the reliability problem of EEG signals under different motor/mental tasks. Besides, the large data size of EEG signals is another problem [93] for cognitive systems. Therefore, a Cube-Code methodology is proposed to overcome these problems in this paper.…”

Section: Discussionmentioning

confidence: 99%

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

Esener,

Kılınç,

Urazel

et al. 2023

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Advancements in EEG biometric technologies have been hindered by two persistent challenges: the management of large data sizes and the unreliability of data resulting from various measurement environments. Addressing these challenges, this study introduces a novel methodology termed 'Cube-Code' for cognitive biometric authentication. As a preliminary step, Automatic Artifact Removal (AAR) leveraging wavelet Independent Component Analysis (wICA) is applied to EEG signals. This step transforms the signals into independent sub-components, effectively eliminating the effects of muscle movements and eye blinking. Subsequently, unique 3-Dimensional (3-D) Cube-Codes are generated, each representing an individual subject in the database. Each Cube-Code is constructed by stacking the alpha, beta, and theta sub-band partitions, obtained from each channel during each task, back-to-back. This forms a third-order tensor. The stacking of these three subbands within a Cube-Code not only prevents a dimension increase through concatenation but also permits the direct utilization of non-stationary data, bypassing the need for fiducial component detection. Higher-Order Singular Value Decomposition (HOSVD) is then applied to perform a subspace analysis on each Cube-Code, an approach supported by previous literature concerning its effectiveness on 3-D tensors. Upon completion of the decomposition process, a flattening operation is executed to extract lower-dimensional, taskindependent feature matrices for each subject. These feature matrices are then employed in five distinct deep learning architectures. The Cube-Code methodology was tested on EEG signals, composed of different tasks, from the PhysioNet EEG Motor Movement/Imagery (EEGMMI) dataset. The results demonstrate an authentication accuracy rate of approximately 98%. In conclusion, the novel Cube-Code methodology provides highly accurate results for subject recognition, delivering a new level of reliability in EEG-based biometric authentication.

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“…The Euclidean distance is the unit of measurement used by the K-Means method to determine the distance between an object and the centroid. The process goes on until there is no net change left [43][44][45][46]. Figure 6 presents the clustering plot using a simple K-Means algorithm.…”

Section: Simple K-meansmentioning

confidence: 99%

Predicting Global Energy Consumption Through Data Mining Techniques

Rahman,

Abahussin,

Alghamdi

et al. 2024

IJDNE

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With the explosion of the global population and technological progress, the electricity demand has skyrocketed. To ensure a consistent flow of power, it's essential to accurately predict energy usage ahead of time. Failure to do so could lead to potential outages and disrupt our daily lives. This research reviews previous research in the field of using data mining techniques to analyze electricity consumption data, optimize energy performance of buildings, and predict energy consumption in various industries. The study also aims to uncover patterns, correlations, and rules in electricity consumption worldwide using data mining techniques. The analysis is performed using various data mining techniques, such as simple K-Means and Expectation Maximization (EM). This selection is based on their prominent applications for similar problems in literature. The simple K-Means and EM algorithms showed successful outcomes on the dataset, which is evident in the clustering plots. Further, the performance of the Hierarchical Clustering algorithm was not up to the desired standard. This is probably due to the nature of the available dataset. These outcomes of the analysis will provide a valuable resource for decision-makers and stakeholders in the energy sector, as it will provide a deeper understanding of electricity consumption patterns and trends. This could lead to a sustainable future.

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Ensemble Machine Learning Based Identification of Adult Epilepsy

Cited by 5 publications

References 13 publications

Blockchain Empowered Interoperable Framework for Smart Healthcare

Blockchain Empowered Interoperable Framework for Smart Healthcare

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

Predicting Global Energy Consumption Through Data Mining Techniques

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