Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study

Ibrahim, N. S.; Gabr, Dalia G.; Rahman, Atta; Musleh, Dhiaa; AlKhulaifi, Dania; Alkharraa, Mariam

doi:10.3390/bdcc7030128

Cited by 10 publications

(10 citation statements)

References 38 publications

(42 reference statements)

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“…Additionally, blockchain can preserve patient data privacy and confidentiality while ensuring the authenticity, integrity, and immutability of EHRs. Additionally, blockchain can help EHR systems become more scalable and interoperable and lower the costs and dangers related to centralized data administration [51][52][53][54][55].…”

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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“…This section provides the statistical analysis of the dataset which will eventually be helpful during the modelling, designing and analysis phase [33][34][35][36][37]. That mainly includes:…”

Section: Discussionmentioning

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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“…It can be safely forecasted that balancing the dataset prior to training the model may further improve the results in terms of average accuracy of the model. Moreover, it is greatly emphasized to use the concept of transfer learning and pretrained models to further fine-tune the proposal and make it robust against potentially diverse datasets investigated in the literature [49,50].…”

Section: Discussionmentioning

confidence: 99%

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

Ahmed,

Rahman,

AlGhamdi

et al. 2023

Diagnostics

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Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

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Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study

Cited by 10 publications

References 38 publications

Blockchain Empowered Interoperable Framework for Smart Healthcare

Blockchain Empowered Interoperable Framework for Smart Healthcare

Predicting Global Energy Consumption Through Data Mining Techniques

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

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