An integrated fuzzy logic and web-based framework for active protocol support

2008 ◽  
Vol 77 (4) ◽  
pp. 256-271 ◽  
Author(s):  
Elisabetta Binaghi ◽  
Ignazio Gallo ◽  
Cristina Ghiselli ◽  
Luca Levrini ◽  
Katia Biondi
Keyword(s):  
Fuzzy Logic ◽  
Web Based

scholarly journals ANALISA PERBANDINGAN LOGIC FUZZY METODE TSUKAMOTO, SUGENO, DAN MAMDANI ( STUDI KASUS : PREDIKSI JUMLAH PENDAFTAR MAHASISWA BARU FAKULTAS SAINS DAN TEKNOLOGI UIN SUNAN GUNUNG DJATI BANDUNG)

2018 ◽  
Vol 10 (1) ◽  
pp. 9-16
Author(s):  
Mohamad Irfan ◽  
Laras Purwati Ayuningtias ◽  
Jumadi Jumadi
Keyword(s):  
Fuzzy Logic ◽  
Web Based ◽  
Fuzzy Logic Algorithm ◽  
Average Value ◽  
Fuzzy Methods ◽  
New Students ◽  
The University ◽  
Selection Stage ◽  
New Student ◽  
Academic Year

ABSTRAK Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung salah satu institusi perguruan tinggi yang memiliki kualitas yang bagus dan memiliki potensi yang dapat menyerap mahasiswa baru berdasarkan berlimpahnya data awal yang diperoleh dari tahun ajaran 2013/2014 sampai dengan 2016/2017, dengan tahapan seleksi penerimaan yang banyak bahkan mahasiswa baru yang terserap beberapa tahun terakhir mengalami peningkatan dan penurunan.Dalam penelitian dilakukan analisa perbandingan algoritma fuzzy logic metode Tsukamoto, Sugeno dan Mamdani untuk memprediksi jumlah pendaftar untuk tahun kedepan dilihat dari jumlah mahasiswa yang lulus dan registrasi dari tahun sebelumnya dan dalam membandingkan perhitungannya menggunakan nilai rata-rata dari hasil yang diperoleh pada ketiga metode fuzzy tersebut dengan aplikasi berbasis web.Hasil dari penelitian yang telah dihitung bahwa metode fuzzy Mamdani mempunyai tingkat error yang lebih kecil sebesar 19,76% dibandingkan dengan metode Tsukamoto sebesar 39,03% dan Sugeno sebesar 86,41% pada prediksi jumlah pendaftar mahasiswa baru.   ABSTRACT State Islamic University (UIN) Sunan Gunung Djati Bandung one of the university institutions that have good quality and has the potential to absorb new students based on the abundance of preliminary data obtained from the academic year 2013/2014 until 2016/2017, with the selection selection stage Which many new students even absorbed in the last few years have increased and decreased. In the research, comparative analysis of fuzzy logic algorithm of Tsukamoto, Sugeno and Mamdani method is used to predict the number of applicants for the next year seen from the number of students who graduated and registration from the previous year and in comparing the calculation uses the average value of the results obtained in the three fuzzy methods with web-based applications. The result of the research has been calculated that the fuzzy Mamdani method has a smaller error rate of 19.76% compared to the tsukamo method To equal to 39.03% and Sugeno equal to 86.41% in predicted number of new student enrollment How to Cite : Irfan, M. Ayuningtias, L.P. Jumadi, J. (2017). ANALISA PERBANDINGAN LOGIC FUZZY METODE TSUKAMOTO, SUGENO, DAN MAMDANI (STUDI KASUS : PREDIKSI JUMLAH PENDAFTAR MAHASISWA BARU FAKULTAS SAINS DAN TEKNOLOGI UIN SUNAN GUNUNG DJATI BANDUNG). Jurnal Teknik Informatika, 10(1), 9-16.doi:10.15408/jti.v10i1.6810Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i1.6810

scholarly journals Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Robert A. Sowah ◽  
Kwaku Apeadu ◽  
Francis Gatsi ◽  
Kwame O. Ampadu ◽  
Baffour S. Mensah
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Convolutional Neural Networks ◽  
Fire Detection ◽  
Web Based ◽  
Notification System ◽  
Multisensor Data Fusion ◽  
Standard Fire ◽  
Area Of Interest ◽  
Detection Technology

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

Features Influencing Researchers’ Selection of Reference Management Software

2016 ◽  
Vol 15 (03) ◽  
pp. 1650032 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Ibrahim ◽  
Shamila Sohaei ◽  
Hossein Ahmadi ◽  
Alireza Almaee
Keyword(s):  
Fuzzy Logic ◽  
Management Program ◽  
Next Generation ◽  
Web Based ◽  
Management Software ◽  
Bibliographic Citation ◽  
Different Types ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Selection Of

Reference management software (RMS) is the most important aspect that is essential for all levels of researchers. They are established as research tools to help scholars in organising their work, improving workflows, and ultimately saving time. Choosing an appropriate RMS for managing records and utilising the bibliographic citation has been a challenge among researchers. They always seek for the features of an appropriate RMS prior to making an investment to buy the software. In this paper, a fuzzy logic approach is adopted for assessing the features of RMS from the researchers’ perspectives. Accordingly, a web-based survey was conducted and data collected from the researchers who had experience with different types of RMS. Then, we analyse the effects of RMS features on researcher perception in selecting an appropriate reference management program and find the importance level of those features. This study provides a toolset for RMS developers to identify the importance level of RMS features and accordingly consider these important features in developing the next generation of citation management software.

Fuzzy Posture Input for Virtual-Hand Models

2000 ◽  
Vol 9 (5) ◽  
pp. 473-485
Author(s):  
Hanqiu Sun ◽  
Kwok-hang Tsang
Keyword(s):  
Fuzzy Logic ◽  
Recognition Rate ◽  
Recognition System ◽  
Noise Distribution ◽  
Imprecise Data ◽  
Web Based ◽  
Virtual Hand ◽  
Indexing Structure ◽  
Fuzzy Logic Method ◽  
Standard Derivation

Virtual-hand input realizes the natural and dexterous functionality in direct humancomputer interaction. The recognition of virtual-hand models is difficult due to two reasons: the complexity of hand structure and the lack of accurate measure, which may be caused by either mechanical noise or human factors. This paper presents a novel fuzzy-logic recognition system that can effectively deal with imprecise hand data. The system consists of three components: the classifier, identifier, and posture database. Fuzzy-logic processing is applied in both the classifier (to build class-indexing structure in the posture database) and in the identifier (to find the most likely match from the database in real-time VR applications). The posture database provides a GUI interface for the user to browse the posture images and interactively update the database by adding and deleting the sample postures, as well as adjusting the certainty threshold in recognition. Our experiments show that the fuzzy-logic method can keep nearly constant performance in recognition using tens of microseconds, even as the size of posture database increases. The recognition rate declines only slightly when the standard derivation of the noise distribution (Gaussian distribution) in the input parameters is below 15 deg. The bounded value in uniform distribution to achieve accurate recognition is below 20. The results show that the fuzzy-logic processing can improve the tolerance of noise or imprecise data in an efficient way. A free-hand modeler based on fuzzy-logic processing has been developed for creating virtual objects and Web-based 3-D VRML worlds.

scholarly journals Decision Support System For Computer Recommendations Using Web-Based Fuzzy Tahani Logic Method

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Farrell Ega Santoso ◽  
Ika Ratna Indra Astutik
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Research Method ◽  
Web Based ◽  
Computer Monitors ◽  
Logic Method

Nowadays people are familiar with computers and many use computers to support their needs, but many people do not understand the specifications of computers that suit their needs, so a web-based application is made that provides recommendations for computer specifications using fuzzy logic. This research method uses observation to obtain information about computer components. The results of this study indicate the recommendations for computer components, namely computer, monitors, keyboard, and mouse according to the user's wishes. The conclusion of this study, this website makes it easier for users to choose computer components.

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