scholarly journals Optimized Hybrid Soft Computing Model for Weather Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 9793-9798
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Weather Forecasting ◽  
Statistical Prediction ◽  
Energy Prediction ◽  
Advantages And Disadvantages ◽  
Computing Model ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Weather Parameters

Soft computing techniques have become very popular now-a-days as these techniques have replaced the traditional and statistical prediction mechanisms in weather forecasting, stock market prediction, crop prediction, solar energy prediction, and predictions in physics and chemistry etc. Each model has its advantages and disadvantages. Hybrid soft computing model is the mechanism of designing the models by exploiting the advantages of two or more models and suppressing their disadvantages. If the advantages of two or more number of models will be taken together in the new proposed model, then the accuracy in the prediction will be enhanced with decrease in error rate. This paper intends to design a hybrid model by taking the advantages of J48 Decision Tree and Fuzzy Logic andit is used to predict the weather parameters in Delhi with better accuracy.

scholarly journals Soft Computing Techniques for Weather Change Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 793-800
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Research Work ◽  
Absolute Error ◽  
Support Vector ◽  
Squared Error ◽  
Proposed Model ◽  
Soft Computing Techniques

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

Soft case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 267-269 ◽  
Author(s):  
WILLIAM CHEETHAM ◽  
SIMON SHIU ◽  
ROSINA O. WEBER
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Network Theory ◽  
Probabilistic Reasoning ◽  
Case Based Reasoning ◽  
Case Representation ◽  
Soft Computing Techniques ◽  
Neural Network Theory ◽  
Case Based

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

scholarly journals A multi-objective evolutionary algorithm-based soft computing model for educational data mining

2017 ◽  
Vol 12 (1) ◽  
pp. 106-123
Author(s):  
Choo Jun Tan ◽  
Ting Yee Lim ◽  
Chin Wei Bong ◽  
Teik Kooi Liew
Keyword(s):  
Student Achievement ◽  
Evolutionary Algorithm ◽  
Soft Computing ◽  
Real World ◽  
Classification Accuracy ◽  
Content Type ◽  
Computing Model ◽  
Model Based ◽  
Proposed Model

Purpose The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide. Findings The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

Deep Learning Based Sentiment Classification on User-Generated Big Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1047-1056
Author(s):  
Akshi Kumar ◽  
Arunima Jaiswal
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Soft Computing ◽  
Sentiment Classification ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Computing Technique ◽  
Proposed Model ◽  
Soft Computing Techniques

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.

A Fuzzy Based Approach for Empirical Analysis of Unstructured Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4375-4379
Author(s):  
Mausumi Goswami ◽  
B. S. Purkayastha
Keyword(s):  
Fuzzy Logic ◽  
Empirical Analysis ◽  
Soft Computing ◽  
Engineering Problem ◽  
Data Uncertainty ◽  
Natural Languages ◽  
Text Data ◽  
Random Uncertainty ◽  
Soft Computing Techniques ◽  
Set Membership

Computational intelligence and soft computing has many promising technologies such as Text Mining. Document Classification using soft computing techniques like fuzzy logic helps to find a more practical solution due to ambiguity and uncertainty present in the text data. Uncertainty and information may be reflected as the part and parcel of any industrial or engineering problem to be solved. Information refers to the facts required to solve it and uncertainty refers to the non-random lack of certainty (‘non-random uncertainty’), ambiguity, haziness in the system. It is very important to ponder on the nature of uncertainty involved in a problem. Father of fuzzy logic, Lofti Zadeh (1965) suggested that decision-making using set membership is the key when it is required to deal with uncertainty. Fuzzy clustering helps to identify patterns which are difficult to be discovered using crisp clustering. Natural languages contain non-random uncertainty. To deal with non-random uncertainty or different degrees of truth or partial truth Fuzzy logic may be used. This work focuses on fuzzy logic based approaches being utilized for identification of coherent patterns. Empirical Analysis are conducted to realize and evaluate the effect of the methodology proposed.

Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques

2015 ◽  
Vol 32 (3) ◽  
pp. 270-290 ◽  
Author(s):  
Jaganathan Gokulachandran ◽  
K. Mohandas
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Tool Life ◽  
Cutting Tool ◽  
Support Vector ◽  
Taguchi Approach ◽  
Content Type ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Life Values

Purpose – The accurate assessment of tool life of any given tool is a great significance in any manufacturing industry. The purpose of this paper is to predict the life of a cutting tool, in order to help decision making of the next scheduled replacement of tool and improve productivity. Design/methodology/approach – This paper reports the use of two soft computing techniques, namely, neuro-fuzzy logic and support vector regression (SVR) techniques for the assessment of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Findings – The analysis is carried out using the two soft computing techniques. Tool life values are predicted using aforesaid techniques and these values are compared. Practical implications – The proposed approaches are relatively simple and can be implemented easily by using software like MATLAB and Weka. Originality/value – The proposed methodology compares neuro – fuzzy logic and SVR techniques.

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