scholarly journals Comparative analysis of FCM and HCM algorithm on Iris data set

2010 ◽  
Vol 5 (2) ◽  
pp. 33-37 ◽  
Author(s):  
Pawan Kumar ◽  
Deepika Sirohi
Author(s):  
Luigi Leonardo Palese

In 2019, an outbreak occurred which resulted in a global pandemic. The causative agent of this serious global health threat was a coronavirus similar to the agent of SARS, referred to as SARS-CoV-2. In this work an analysis of the available structures of the SARS-CoV-2 main protease has been performed. From a data set of crystallographic structures the dynamics of the protease has been obtained. Furthermore, a comparative analysis of the structures of SARS-CoV-2 with those of the main protease of the coronavirus responsible of SARS (SARS-CoV) was carried out. The results of these studies suggest that, although main proteases of SARS-CoV and SARS-CoV-2 are similar at the backbone level, some plasticity at the substrate binding site can be observed. The consequences of these structural aspects on the search for effective inhibitors of these enzymes are discussed, with a focus on already known compounds. The results obtained show that compounds containing an oxirane ring could be considered as inhibitors of the main protease of SARS-CoV-2.


Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.


2021 ◽  
pp. 1-11
Author(s):  
Velichka Traneva ◽  
Stoyan Tranev

Analysis of variance (ANOVA) is an important method in data analysis, which was developed by Fisher. There are situations when there is impreciseness in data In order to analyze such data, the aim of this paper is to introduce for the first time an intuitionistic fuzzy two-factor ANOVA (2-D IFANOVA) without replication as an extension of the classical ANOVA and the one-way IFANOVA for a case where the data are intuitionistic fuzzy rather than real numbers. The proposed approach employs the apparatus of intuitionistic fuzzy sets (IFSs) and index matrices (IMs). The paper also analyzes a unique set of data on daily ticket sales for a year in a multiplex of Cinema City Bulgaria, part of Cineworld PLC Group, applying the two-factor ANOVA and the proposed 2-D IFANOVA to study the influence of “ season ” and “ ticket price ” factors. A comparative analysis of the results, obtained after the application of ANOVA and 2-D IFANOVA over the real data set, is also presented.


2011 ◽  
Vol 44 (1) ◽  
pp. 14271-14276 ◽  
Author(s):  
H. Chang ◽  
A. Astolfi
Keyword(s):  
Data Set ◽  

Paleobiology ◽  
2011 ◽  
Vol 37 (4) ◽  
pp. 696-709 ◽  
Author(s):  
Kenny J. Travouillon ◽  
Gilles Escarguel ◽  
Serge Legendre ◽  
Michael Archer ◽  
Suzanne J. Hand

Minimum Sample Richness (MSR) is defined as the smallest number of taxa that must be recorded in a sample to achieve a given level of inter-assemblage classification accuracy. MSR is calculated from known or estimated richness and taxonomic similarity. Here we test MSR for strengths and weaknesses by using 167 published mammalian local faunas from the Paleogene and early Neogene of the Quercy and Limagne area (Massif Central, southwestern France), and then apply MSR to 84 Oligo-Miocene faunas from Riversleigh, northwestern Queensland, Australia. In many cases, MSR is able to detect the assemblages in the data set that are potentially too incomplete to be used in a similarity-based comparative taxonomic analysis. The results show that the use of MSR significantly improves the quality of the clustering of fossil assemblages. We conclude that this method can screen sample assemblages that are not representative of their underlying original living communities. Ultimately, it can be used to identify which assemblages require further sampling before being included in a comparative analysis.


2016 ◽  
Vol 26 (3) ◽  
pp. 395-427 ◽  
Author(s):  
Sebastian Porębski ◽  
Ewa Straszecka

Abstract The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.


2007 ◽  
Vol 29 (10) ◽  
pp. 1869-1870 ◽  
Author(s):  
P. Jonathon Phillips ◽  
Kevin W. Bowyer ◽  
Patrick J. Flynn
Keyword(s):  
Data Set ◽  

Author(s):  
Melinda Melinda ◽  
Patar Sianturi ◽  
Agus Santoso Tamsir

Author(s):  
Peter Grabusts

This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set.


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