Usage of Classification Algorithm for Extracting Knowledge in Cholesterol Report towards Non-communicable Disease Analysis

Asor, Jonardo R.; Catedrilla, Gene Marck B.; Lerios, Jefferson L.

doi:10.12720/jait.11.4.265-270

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The inclusion of features such as geographical coordinates, timestamps, fare details, and trip distances is strategic, aligning seamlessly with the overarching goal of exploring dimensionality reduction techniques in practical scenarios. The "TLC" dataset's inherent complexity mirrors and embodies the challenges prevalent in various big data applications [8,9].…”

Section: Rationalementioning

confidence: 99%

Harmonizing Dimensionality: Unveiling the Prowess of Variational Auto-Encoder in Spark for Big Data Processing

Jawad,

Al-Bakry

2024

RIA

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In the dynamic realm of big data processing, conquering the challenges imposed by highdimensional datasets is imperative. This paper introduces a groundbreaking advancement in dimensionality reduction, employing Variational Auto-Encoder (VAE) within the Spark distributed framework. The deliberate selection of the "TLC" dataset, representative of New York City taxi trips with inherent high dimensionality, highlights the practicality of our approach. Our research showcases the virtuoso performance of VAE, achieving an impressive 95.12% reduction ratio and 89.26% accuracy. This highlights VAE's ability to elegantly distill essential information while discarding superfluous dimensions, achieving a harmonious balance between reduction and accuracy. Furthermore, building on the demonstrated superiority of Spark over Hadoop in prior successes, our adoption of VAE aligns with the overarching goal of enhancing big data processing. Spark's consistent advantage as a distributed framework reaffirms its reliability in handling diverse machine learning algorithms. This paper not only contributes to the advancement of machine learning in big data processing but also underscores the adaptability, versatility, and consistent performance of our approach across various methodologies and frameworks. The success of VAE in reducing dimensionality, coupled with Spark's inherent advantages, positions this research as a valuable contribution to the exploration of advanced techniques in distributed big data processing.

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Section: Rationalementioning

confidence: 99%

Harmonizing Dimensionality: Unveiling the Prowess of Variational Auto-Encoder in Spark for Big Data Processing

Jawad,

Al-Bakry

2024

RIA

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“…The fire incidents data has numerous attributes in the given raw data which are not needed in developing the project. In assuring that a quality model will be developed, attributes with the same meaning was removed [19], [20]. Since date occurred and incident report are the same, one of these attributes was removed and the other one was sustained to remove data noises.…”

Section: Pre-processing Of Datamentioning

confidence: 99%

“…The basic approach is embodied in one of the core principles of decision analysis, that of the decomposition by which large, complex problems can be better understood by breaking them down or "decomposing" them into smaller more manageable problems that can be resolved or defined in some detail [15]. Today, machine learning algorithms are commonly used to identify trends not only in crime, education, health and business but also in fire incidents [16]- [18]. The main objective of this paper is to recognized pattern and identify trends of fire incidents in Laguna, Philippines using machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fire incidents visualization and pattern recognition using machine learning algorithms

Asor

Lerios

Sapin

et al. 2021

IJEECS

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A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.

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The Method of Selecting the Evaluation Model for the State of Children's Growth Based on Machine Learning

Chen

Wang

et al. 2021

2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD)

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Usage of Classification Algorithm for Extracting Knowledge in Cholesterol Report towards Non-communicable Disease Analysis

Cited by 4 publications

References 8 publications

Harmonizing Dimensionality: Unveiling the Prowess of Variational Auto-Encoder in Spark for Big Data Processing

Harmonizing Dimensionality: Unveiling the Prowess of Variational Auto-Encoder in Spark for Big Data Processing

Fire incidents visualization and pattern recognition using machine learning algorithms

The Method of Selecting the Evaluation Model for the State of Children's Growth Based on Machine Learning

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