JT2FISA Java Type-2 Fuzzy Inference Systems Class Library for Building Object-Oriented Intelligent Applications

Castañón–Puga, Manuel; Castro, Juan R.; Flores-Parra, Josué Miguel; Gaxiola–Pacheco, Carelia; Martínez-Méndez, Luis-Guillermo; Palafox, Luis E.

doi:10.1007/978-3-642-45111-9_18

Cited by 8 publications

(11 citation statements)

References 15 publications

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“…The main purpose of this library is data mining large data sets to set up automatically a Type 2 Inference System applying the paradigm of object-oriented programming relying on JT2FIS [4] class library. JT2FISClustering is a structure in packets containing a collection of classes.…”

Section: Jt2fisclustering Data Mining Extension For Jt2fismentioning

confidence: 99%

“…We described the JT2FIS architecture, design, implementation, and Java programmers examples in detail in [4].…”

Section: Introductionmentioning

confidence: 99%

“…JT2FIS 1 is a class library developed for Java applications [4]. The main purpose of JT2FIS is to deploy a library to build interval Type-2 fuzzy inference system (FIS) with an object-oriented programming language for Java developers.…”

Section: Introductionmentioning

confidence: 99%

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Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Castañón–Puga¹,

Flores-Parra²,

Castro³

et al. 2015

Procedia Computer Science

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This paper introduces JT2FISClustering, a data mining extension for JT2FIS. JT2FIS is a Java class library for building intelligent applications. This extension is used to extract information from a data set and transform it into an Interval Type-2 Fuzzy Inference System in Java applications. Mamdani and Takagi-Sugeno Fuzzy Inference Systems can be generated using fuzzy c-means or subtractive data mining methods. We compare the outputs and performance of Matlab R versus Java in order to validate the proposed extension.

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Section: Jt2fisclustering Data Mining Extension For Jt2fismentioning

confidence: 99%

“…We described the JT2FIS architecture, design, implementation, and Java programmers examples in detail in [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Castañón–Puga¹,

Flores-Parra²,

Castro³

et al. 2015

Procedia Computer Science

Self Cite

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“…In addition, if we add tools such as JT2FIS [34] to facilitate these interactions, it should be possible to deal with the uncertainty presented by the inputs (interaction level, distance), task conflicts, and interruptions that occur when a user is involved in multiple interactions simultaneously.…”

Section: Modelling Interaction On Hcimentioning

confidence: 99%

“…Some utility programs have specific modules to facilitate the accomplishment of this task and provide the necessary tools to conduct effective fuzzification; these include the utility JT2FIS [34], which is a Java class library for an interval Type-2 fuzzy inference system, used to build intelligent object-oriented applications. It also provides an effective fuzzification method and tools.…”

Section: Fuzzy Perceptions In the Museum Elementsmentioning

confidence: 99%

Modelling the Interruption on HCI Using BDI Agents with the Fuzzy Perceptions Approach: An Interactive Museum Case Study in Mexico

et al. 2017

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Abstract:Technological advancements have revolutionized the proliferation and availability of information to users, which has created more complex and intensive interactions between users and systems. The learning process of users is essential in the construction of new knowledge when pursuing improvements in user experience. In this paper, the interruption factor is considered in relation to interaction quality due to human-computer interaction (HCI) being seen to affect the learning process. We present the results obtained from 500 users in an interactive museum in Tijuana, Mexico as a case study. We model the HCI of an interactive exhibition using belief-desire-intention (BDI) agents; we adapted the BDI architecture using the Type-2 fuzzy inference system to add perceptual human-like capabilities to agents, in order to describe the interaction and interruption factor on user experience. The resulting model allows us to describe content adaptation through the creation of a personalized interaction environment. We conclude that managing interruptions can enhance the HCI, producing a positive learning process that influences user experience. A better interaction may be achieved if we offer the right kind of content, taking the interruptions experienced into consideration.

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A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

Pandya

Pourabdollah

Lotfi

2022

Int. J. Fuzzy Syst.

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Traditionally, fuzzy logic systems are linked to specific hardware or software systems. Observations reveal that dispersed and distributed designs of intelligent systems are gaining attraction. Due to the possible complexities of fuzzy logic computations, distributed architectures have the potential to add value to the development of fuzzy systems. However, the absence of best practices and standard methodologies may prevent widespread adoption. By broadening the IEEE-1855 (2016) standard in terms of system definition and data exchange, this research offers a standard solution for building a Service-Oriented Architecture (SOA) as a novel method of implementing fuzzy logic systems by means of a cloud-based collecting, processing, and examining data over the web. A comparison between the performances of a stand-alone hardware-dependent solution and a cloud-based solution (known as fuzzy-as-a-service) is performed. The analysis is also carried out on two different cloud service providers and software libraries (Amazon Web Services using JFML as a java-based library and Azure Web Services using Simpful as a python-based library). The analysis and evaluation are performed on a human fall detection scenario involving wearable sensors. The proposed algorithm can identify between fall and non-fall events. However, the results show that the processing time taken per 10,000 samples using smartwatch and mobile was 2220 s and 101 s for a cloud-based non-fuzzy machine learning system, 1111 s and 45 s for a cloud-based fuzzy system with AWS and JFML, and 1250 s and 97 s for a cloud-based fuzzy system with Microsoft Azure and Simpful libraries. It has been observed that a smartwatch with a fuzzy stand-alone crashed after processing 5000 samples and a mobile phone requires 179.42 s to process 10,000 samples.

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JT2FISA Java Type-2 Fuzzy Inference Systems Class Library for Building Object-Oriented Intelligent Applications

Cited by 8 publications

References 15 publications

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Modelling the Interruption on HCI Using BDI Agents with the Fuzzy Perceptions Approach: An Interactive Museum Case Study in Mexico

A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

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