A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

Al-Azani, Sadam; Sait, Sadiq M.; Al-Utaibi, Khaled A.

doi:10.1109/access.2022.3146008

Cited by 5 publications

(2 citation statements)

References 127 publications

(202 reference statements)

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“…Therefore, the bitmap index will not be evaluated later for the performance evaluation. Performance studies applied to autonomous data warehouses can be found in [43][44][45].…”

Section: Figure 8 Architecture Of Bitmap Indexmentioning

confidence: 99%

The Complexity of the Data Retrieval Process Using the Proposed Index Extension

Kvet

Papán²

2022

IEEE Access

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Data retrieval, access, and tuple identification are inevitable in database processing, ensuring performance. The entities and relationships form a relational database. Data themselves are not specifically formatted, requiring sequential data block scanning if the particular index is unavailable. This paper summarizes existing indexing principles focusing on the B+tree, which forms the default structure for data access based on the index key. Such design is reliable and prone to an increase in the amount of data. However, it cannot manage undefined values properly, whereas they cannot be mathematically compared. Secondly, migrated rows can be present due to the size demand extension after the update operation. Thirdly, the index is always balanced, resulting in additional demands of the transaction. All these factors are covered by the proposed paper, discussing the limitations, opportunities, and own solutions to improve the performance. Several architectures are discussed, maintained, and computationally studied, focusing on the size demands, processing time, and costs. By using proposed techniques, significant improvements can be reached. A pointer list is introduced for migrated row management to reference the index set from the data block and store reference path or using a data reflector. When dealing with transaction support, index management and rebalancing are shifted to the separate autonomous transaction. Thanks to that, the main transaction can be approved sooner with no reliability issues. Finally, the proposed paper introduces the NULL value management structure in the instance memory for the index node reference.

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“…Therefore, the bitmap index will not be evaluated later for the performance evaluation. Performance studies applied to autonomous data warehouses can be found in [43][44][45].…”

Section: Figure 8 Architecture Of Bitmap Indexmentioning

confidence: 99%

The Complexity of the Data Retrieval Process Using the Proposed Index Extension

Kvet

Papán²

2022

IEEE Access

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“…The self-organizing map (SOM) technique is an effective tool for visualizing multidimensional data. A low-dimensional display translates complicated, nonlinear statistical correlations among highdimensional data into simple geometric relationships [71]. The traditional approximation theory is the cornerstone of radial basis function networks (RBFN).…”

Section: Introductionmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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Machine Learning Approaches for Kids’ E-learning Monitoring

Al-kaaf

2023

Studies in Computational Intelligence

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A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

Cited by 5 publications

References 127 publications

The Complexity of the Data Retrieval Process Using the Proposed Index Extension

The Complexity of the Data Retrieval Process Using the Proposed Index Extension

Machine learning applications for COVID-19 outbreak management

Machine Learning Approaches for Kids’ E-learning Monitoring

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