Computational Fuzzy Inference Logic for Effectively Analyzing Customer Survey

Sahunthala, S.; Geetha, Angelina; Parthiban, Latha

doi:10.1109/i-smac49090.2020.9243516

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Fuzzy output is obtained through the execution of some fuzzy membership functions. There are several methods that can be used in the defuzzification process, namely (1) Weighted Average Method (58)(59)(60), a method that calculates the average value by assigning a certain weight or weight to each element in the data set aimed at reflecting the relative importance or contribution of each element to the final result, (2) Mean-Max Membership (61)(62)(63), a method that combines several overlapping membership rules by taking the maximum value of each set membership at a point, then calculate the average of those maximum values, (3) Centroid (Center of Gravity) Method (64,65), a method that calculates the center point (centroid) of a membership set using the weighted principle based on membership level. The membership value of each point on the set is multiplied by the position of that point, then added and divided by the total membership value, (4) Height Method (Max-Membership Principle) (66,67), a method that takes the maximum value of the membership level in a membership set as a representation of the membership value of the entire set, (5) Center of Sums (68-70), a method that calculates the center of input values by adding input points and dividing them by the total number of inputs, (6) First (or Last) of Maxima (66,71,72), a method that selects the first (or last) point at which the membership level reaches the maximum value of a membership set as a representation of the overall membership value of that set, and (7) Center of Largest Area (73,74), a method that calculates the center of the area of a membership set by taking the midpoint at the interval with the largest set area.…”

Section: Defuzzificationmentioning

confidence: 99%

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Furizal,

Sunardi,

Yudhana

et al. 2024

IJE

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Energy consumption is a crucial aspect in the effort to optimize the utilization of resources and reduce energy wastage. Focusing on energy efficiency can result in operational cost savings, a reduction in greenhouse gas emissions, and support for environmental sustainability for future generations. Therefore, it is important to consider energy efficiency in daily life, especially in the use of electricity for electronic devices. This research aims to compare the energy efficiency of two different approaches to Air Conditioner (AC) usage: the manual method and the fuzzy logic method. The manual method involves eight tests with direct power measurements over a 30-minute period at various AC temperature settings, namely 18°C, 20°C, 23°C, 24°C, 25°C, 26°C, 27°C, and 30°C. On the other hand, the fuzzy logic method involves six tests allowing for dynamic temperature adjustments based on room conditions. The research findings indicate that the fuzzy logic method achieves lower average power consumption, except at 30°C, where the manual method is slightly more efficient (a difference of 140,745 watts). This difference is primarily attributed to the "cooling and fan" mode used at lower temperatures in the manual method, resulting in higher power consumption. Furthermore, this research reveals the potential of the fuzzy logic in optimizing AC power usage based on real-time conditions, achieving approximately a 41.96% energy savings. The primary contribution of this study is to provide practical insights into how the fuzzy logic method can significantly reduce AC energy consumption, support energy efficiency efforts, and contribute to environmental sustainability.

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Section: Defuzzificationmentioning

confidence: 99%

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Furizal,

Sunardi,

Yudhana

et al. 2024

IJE

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“…More than once, it has undergone real-time application [14], [15]. The traditional machine learning approach for classification and clustering is explained in [16], [17]. The efficient classification based on without features is discussed in [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Study and innovation of effective classification of XML documents using an advanced deep learning approach

Sanmugam

Geetha²,

Parthiban³

2023

IJEECS

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<span lang="EN-US">In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.</span>

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An Improved Fuzzy Clustering Method for Analyzing Clustering Quality in XML Web Data

Sahunthala

Geetha

Parthiban

2022

2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)

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Computational Fuzzy Inference Logic for Effectively Analyzing Customer Survey

Cited by 3 publications

References 17 publications

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Study and innovation of effective classification of XML documents using an advanced deep learning approach

An Improved Fuzzy Clustering Method for Analyzing Clustering Quality in XML Web Data

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