Hybrid fuzzy logic with SVM based prediction analysis model to predict innovation performance of 3C Industry

2021 ◽  
pp. 1-8
Author(s):  
Chiu-Lan Chang ◽  
Ming Fang
Keyword(s):  
Fuzzy Logic ◽  
Statistical Technique ◽  
Innovation Performance ◽  
Support Vector ◽  
Top Management ◽  
Management Innovation ◽  
Analysis Model ◽  
Financial Flexibility ◽  
Prediction Analysis ◽  
Econometric Approach

This paper explores the applications of support vector machines (SVM) technique and panel data econometric approach in innovation performance. We proposed Hybrid Fuzzy Logic with SVM based prediction analysis model to predict Innovation Performance of 3C Industry and then we construct the top management innovation awareness by text mining analysis, which differs from traditional methods, and then discusses the mediation function of financial flexibility in the relationship between innovation awareness and enterprise performance. Then, we use the financial econometric method panel regression and apply the SVM, a statistical technique that has gained special popularity in the field of AI to test the relation between innovation performance and innovation awareness of top management in 3C industry. The findings show that there exists a significant position relationship between innovation awareness and innovation performance, and that the mediation function of financial flexibility does work. With the SVM approach, the innovation performance can be predicted well by top management innovation awareness.

scholarly journals Voting Based Classification Method for Diabetes Prediction

2019 ◽  
Vol 8 (2S6) ◽  
pp. 913-918
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Research Work ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Prediction Analysis ◽  
Tree Classifier ◽  
Diabetes Prediction

This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.

scholarly journals Feature Extraction in Six Blocks to Detect and Recognize English Numbers

2021 ◽  
pp. 3790-3803
Author(s):  
Heba Kh. Abbas ◽  
Haidar J. Mohamad
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Suggested Method ◽  
Support Vector ◽  
Horizontal Line ◽  
Svm Algorithm ◽  
Fuzzy Logic Method ◽  
Logic Method

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

scholarly journals Factors impacting on adoption of international education standards: an empirical study using factor analysis

2021 ◽  
Vol 5 (1) ◽  
pp. 25-44
Author(s):  
Adel BOULDJENIB
Keyword(s):  
International Education ◽  
Original Data ◽  
Economic Governance ◽  
Analysis Model ◽  
Accounting Profession ◽  
Education Standards ◽  
Factors Affecting ◽  
Econometric Approach ◽  
Political Economic ◽  
Ordinal Logistic Regression Model

The aim of this study is to determine the factors affecting International Education Standards (IESs) adoption, by using an econometric approach based on a sample of 64 Countries. To do that, data about legal, political, economic and cultural environment of each country was summarized using factorial analysis model. This model extracts four common factors from original data that’s likely to affect IESs adoption, those factors are legal, political and economic governance, opening of accounting profession to the outside world, initiative degree of the society, and accepting change and differences. The study concludes, using an ordinal logistic regression model (logit model), that legal, political and economic governance, opening of accounting profession to the outside world, initiative degree of the society has a significant effect on IESs adoption, while accepting change and differences have no effect.

scholarly journals Design and implementation of an intelligent vision and sorting system

2009 ◽  
Author(s):  
◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Support Vector Machines ◽  
Robot Control ◽  
Support Vector ◽  
Design And Implementation ◽  
The Neural Network ◽  
Vector Machines ◽  
Control Computer ◽  
Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

scholarly journals PENGARUH KARAKTERISTIK TIM MANAJEMEN PUNCAK TERHADAP MANAJEMEN LABA DENGAN KUALITAS AUDIT SEBAGAI VARIABEL PEMODERASI

2020 ◽  
Vol 3 (2) ◽  
pp. 133-152
Author(s):  
Achmad Zwageri
Keyword(s):  
Regression Analysis ◽  
Earnings Management ◽  
Hypothesis Testing ◽  
Research Method ◽  
Audit Quality ◽  
Top Management Team ◽  
Negative Influence ◽  
Management Team ◽  
Top Management ◽  
Analysis Model

The purpose of this study is to analyze; the influence of the characteristics of the top management team on earnings management with audit quality as a moderating variable. The research sample was selected using a purposive method. The research method used is hypothesis testing, and the analysis model of this study uses a moderation regression analysis (MRA). The results showed that the characteristics of top management namely knowledge and tenure had a negative influence on earnings management, and on audit quality as a moderating factor not proven to strengthen its influence on earnings management.

Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 32-62 ◽  
Author(s):  
Elshrif Ibrahim Elmurngi ◽  
Abdelouahed Gherbi
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Statistical Technique ◽  
Machine Learning Techniques ◽  
Reputation Systems ◽  
Analysis Model ◽  
Learning Techniques ◽  
Reputation Score ◽  
Reporting Systems ◽  
Online Reputation

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.

scholarly journals A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 1009 ◽  
Author(s):  
Rahman Azis Prasojo ◽  
Harry Gumilang ◽  
Suwarno ◽  
Nur Ulfa Maulidevi ◽  
Bambang Anggoro Soedjarno
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Local Population ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
New Approach ◽  
Dissolved Gas Analysis ◽  
Novel Approach

In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach.

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