A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments

Ahn, Seokjin; Couture, Stefan V.; Cuzzocrea, Alfredo; Dam, Kevin; Grasso, G.; Leung, Carson K.; McCormick, Kaleigh L.; Wodi, Bryan H.

doi:10.1109/fuzz-ieee.2019.8858791

Cited by 80 publications

(12 citation statements)

References 28 publications

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“… Ahn et al (2019) introduced fuzzy logic dependent on ML tools for enhancing business big data business analytics in complex AI environments. Specifically, they have provided a suitable and extended the traditional C4.5 algorithm by incorporating fuzzy logic for business processes.…”

Section: Literature Review On Complex Disease and Applicationsmentioning

confidence: 99%

“…Computational innovation may play a role in constructing and validating the theoretical model and designing new intervention protocols for AD. Ahn et al (2019) introduced fuzzy logic dependent on ML tools for enhancing business big data business analytics in complex AI environments. Specifically, they have provided a suitable and extended the traditional C4.5 algorithm by incorporating fuzzy logic for business processes.…”

Section: Different Applicationsmentioning

confidence: 99%

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Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

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Background and Objectives This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain’s genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer’s disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. Methods We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. Results It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. Conclusions Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

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Section: Literature Review On Complex Disease and Applicationsmentioning

confidence: 99%

Section: Different Applicationsmentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

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“…Since then, MVL and Fuzzy logic have been viable alternatives to traditional systems. Nowadays, Fuzzy logic is widely used in natural language processing and many other artificial intelligence applications [1,2]. Fuzzy sets are also broadly used to describe imprecise or vague data [1].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive survey of ternary full adders: Statistics, corrections, and assessments

Nemati

Kashani

Mirzaee

2023

IET Circuits, Devices & Syst

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The history of ternary adders goes back to more than 6 decades ago. Since then, a multitude of ternary full adders (TFAs) have been presented in the literature. This article conducts a review of TFAs so that one can be familiar with the utilised design methodologies and their prevalence. Moreover, despite numerous TFAs, almost none of them are in their simplest form. A large number of transistors could have been eliminated by considering a partial TFA instead of a complete one. According to our investigation, only 28.6% of the previous designs are partial TFAs. Also, they could have been simplified even further by assuming a partial TFA with an output carry voltage of 0 V or V DD . This way, in a single-V DD design, voltage division inside the Carry generator part would have been eliminated and less power dissipated. As far as we have searched, there are only three partial TFAs with this favourable condition in the literature. Additionally, most of the simulation setups in the previous articles are not realistic enough. Therefore, the simulation results reported in these papers are neither comparable nor entirely valid. Therefore, the authors got motivated to conduct a survey, elaborate on this issue, and enhance some of the previous designs. Among 84 papers, 10 different TFAs (from 11 papers) are selected, simplified, and simulated in this article. Simulation results by HSPICE and 32 nm carbon nanotube FET technology reveal that the simplified partial TFAs outperform their original versions in terms of delay, power, and transistor count. K E Y W O R D Sadders, carbon nanotube field effect transistors, digital arithmetic, multivalued logic, multivalued logic circuits, ternary logicThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…Useful information and valuable knowledge is usually embedded in these big data. This calls for data science [23], which aims to discover knowledge from these big data via data mining algorithms [24][25][26], machine learning tools [27][28][29], online analytical processing (OLAP) techniques [30][31][32], mathematical and statistical models [33,34], data analytics, and visual analytics. The discovered knowledge is useful.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and OLAP on Big COVID-19 Data

Leung

Chen

Hoi

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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In the current technological era, huge amounts of big data are generated and collected from a wide variety of rich data sources. These big data can be of different levels of veracity in the sense that some of them are precise while some others are imprecise and uncertain. Embedded in these big data are useful information and valuable knowledge to be discovered. An example of these big data is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data-via data science techniques such as machine learning, data mining, and online analytical processing (OLAP)-helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a machine learning and big data analytic tool for processing and analyzing COVID-19 epidemiological data. Specifically, the tool makes good use of taxonomy and OLAP to generalize some specific attributes into some generalized attributes for effective big data analytics. Instead of ignoring unknown or unstated values of some attributes, the tool provides users with flexibility of including or excluding these values, depending on their preference and applications. Moreover, the tool discovers frequent patterns and their related patterns, which help reveal some useful knowledge such as absolute and relative frequency of the patterns. Furthermore, the tool learns from the patterns discovered from historical data and predicts useful information such as clinical outcomes for future data. As such, the tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for machine learning and analytics of big epidemiological data, it would be applicable to machine learning and analytics of big data in many other real-life applications and services.

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A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments

Cited by 80 publications

References 28 publications

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Comprehensive survey of ternary full adders: Statistics, corrections, and assessments

Machine Learning and OLAP on Big COVID-19 Data

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