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10.1002/spe.2853

The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products.

Senti‐eSystem: A sentiment‐based eSystem‐using hybridized fuzzy and deep neural network for measuring customer satisfaction

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10.1109/ACCESS.2020.3049049

A maiden hybridized fuzzy-Proportional Integral (FPI)-Linear Active Disturbance Rejection Control (LADRC) is proposed and successfully implemented for IFR.

A Novel Hybridized Fuzzy PI-LADRC Based Improved Frequency Regulation for Restructured Power System Integrating Renewable Energy and Electric Vehicles

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10.1002/spe.2952

Those accepted papers address the semantic eSystems theme from different perspectives including, for instance, efficient semantic–visual indexing model for large-scale image retrieval in cloud environment, trilateration-based indoor localization engineering technique for visible light communication system, graph-based system to enable efficient transformation of enterprise infrastructures, recognizing physical activities having complex interclass variations using semantic data of smartphone, spatiotemporal-based sentiment analysis on tweets for risk assessment of an event using the deep-learning approach, sentiment-based eSystem using hybridized fuzzy and deep neural network for measuring customer satisfaction, data fusion analysis for emotion recognition with thermal image and Internet of Thing devices, unified framework to manage cybersecurity and safety in manufacturing industry, graph-based convolutional neural network stock price prediction with leading indicators, author classification using transfer learning and predicting stars in coauthor networks, and DNA signal analysis tool: intelligent noise suppression window filter.

Semantic eSystems: Engineering methods, techniques, and tools

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10.35940/ijrte.d7684.118419

This article proposes an Hybridized Fuzzy Clustering (HFC), which groups adjacent nodes with comparable sets of idle channels and optimally forming power-efficient clusters based on three fuzzy energy parameters, proximity to the base station, and the level of the node to determine the possibility of each node being a cluster head.

Hybridized Fuzzy Based Clustering For Wireless Sensor Networks Based On Cognitive Internet Of Things

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10.1080/03772063.2018.1464972

ABSTRACT This paper presents the hybridized fuzzy-neural predictive intelligent (HFNPI) based approach for predicting FinFET model.

A Hybridized Fuzzy-Neural Predictive Intelligent (HFNPI) Modelling Approach-based Underlap FinFET Model

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Hybridized Fuzzy - The proposed system, namely Senti‐eSystem, aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. [1] A maiden hybridized fuzzy-Proportional Integral (FPI)-Linear Active Disturbance Rejection Control (LADRC) is proposed and successfully implemented for IFR. [2] Those accepted papers address the semantic eSystems theme from different perspectives including, for instance, efficient semantic–visual indexing model for large-scale image retrieval in cloud environment, trilateration-based indoor localization engineering technique for visible light communication system, graph-based system to enable efficient transformation of enterprise infrastructures, recognizing physical activities having complex interclass variations using semantic data of smartphone, spatiotemporal-based sentiment analysis on tweets for risk assessment of an event using the deep-learning approach, sentiment-based eSystem using hybridized fuzzy and deep neural network for measuring customer satisfaction, data fusion analysis for emotion recognition with thermal image and Internet of Thing devices, unified framework to manage cybersecurity and safety in manufacturing industry, graph-based convolutional neural network stock price prediction with leading indicators, author classification using transfer learning and predicting stars in coauthor networks, and DNA signal analysis tool: intelligent noise suppression window filter. [3] This article proposes an Hybridized Fuzzy Clustering (HFC), which groups adjacent nodes with comparable sets of idle channels and optimally forming power-efficient clusters based on three fuzzy energy parameters, proximity to the base station, and the level of the node to determine the possibility of each node being a cluster head. [4] ABSTRACT This paper presents the hybridized fuzzy-neural predictive intelligent (HFNPI) based approach for predicting FinFET model. [5]