scholarly journals EMPLOYABILITY OF SELECTED DATAMINING ALGORITHMS IN THE EARLY PREDICTION OF CORONARY DISEASES

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Prachi Juneja
Keyword(s):  
Data Mining ◽  
Coronary Disease ◽  
Social Protection ◽  
Support Vector ◽  
Data Sets ◽  
Essential Information ◽  
Human System ◽  
Coronary Diseases ◽  
Content Mining ◽  
Human Management

The objective of our work is to take apart unique data mining methods and procedures in the healthcare system that can use an assumption for coronary disease structure and their impact investigation. A coronary disease prediction model, which executes the data mining method, can help the therapeutic experts perceive the coronary sickness status subject to the patient's clinical data. Data mining description techniques for the great fundamental initiative in human system are specifically Decision trees, Naive Bayes, Neural Networks and Support Vector Machines. Hybridizing or merging any of these calculations makes decisions snappier and assigned dynamically. Information mining is a notable new improvement for extracting hypermetropic and critical information from enormous data sets to build significant and novel encounters. Using impelled data mining systems to extract essential information has been considered a fanatic method to improve human management organization's quality and precision while trimming down the social protection cost and execution time. Using this technique can expect the early detection of coronary disease. Using more information properties, for instance, could develop controllable and natural danger factors, progressively detailed results. Can also broaden this strategy. It can use an extensive part of data properties. Other data mining strategies use for forecasts, such as clustering, time series plan, and association rules. The unstructured data open in the human system industry information base can mine using content mining.

scholarly journals Data Mining Techniques for Identification and Classification of Various Diseases in Plants

2019 ◽  
Vol 9 (2S) ◽  
pp. 676-680
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Crop Productivity ◽  
Vital Role ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Mining Techniques

Data mining is currently being used in various applications; In research community it plays a vital role. This paper specify about data mining techniques for the preprocessing and classification of various disease in plants. Since various plants has different diseases based on that each of them has different data sets and different objectives for knowledge discovery. Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity. This paper provides various classification techniques Such as K-Nearest Neighbors, Support Vector Machine, Principle component Analysis, Neural Network. Thus among various techniques neural network is effective for disease detection in plants.

Data mining in manufacturing: Significance analysis of process parameters

2008 ◽  
Vol 222 (11) ◽  
pp. 1503-1516 ◽  
Author(s):  
M Perzyk ◽  
R Biernacki ◽  
J Kozlowski
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Process Parameters ◽  
Regression Models ◽  
Simulated Data ◽  
Real Data ◽  
Support Vector ◽  
Data Sets ◽  
Interaction Factors

Determination of the most significant manufacturing process parameters using collected past data can be very helpful in solving important industrial problems, such as the detection of root causes of deteriorating product quality, the selection of the most efficient parameters to control the process, and the prediction of breakdowns of machines, equipment, etc. A methodology of determination of relative significances of process variables and possible interactions between them, based on interrogations of generalized regression models, is proposed and tested. The performance of several types of data mining tool, such as artificial neural networks, support vector machines, regression trees, classification trees, and a naïve Bayesian classifier, is compared. Also, some simple non-parametric statistical methods, based on an analysis of variance (ANOVA) and contingency tables, are evaluated for comparison purposes. The tests were performed using simulated data sets, with assumed hidden relationships, as well as on real data collected in the foundry industry. It was found that the performance of significance and interaction factors obtained from regression models, and, in particular, neural networks, is satisfactory, while the other methods appeared to be less accurate and/or less reliable.

scholarly journals SupportNet: a novel incremental learning framework through deep learning and support data

2018 ◽  
Author(s):  
Yu Li ◽  
Zhongxiao Li ◽  
Lizhong Ding ◽  
Yuhui Hu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Incremental Learning ◽  
Learning Model ◽  
Biological Data ◽  
Classification Model ◽  
Support Vector ◽  
Data Sets ◽  
Essential Information ◽  
New Information ◽  
Deep Learning Model

ABSTRACTMotivationIn most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems.ResultsHere we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data.AvailabilityOur program is accessible at: https://github.com/lykaust15/SupportNet.

scholarly journals Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2019 ◽  
pp. 131-136
Author(s):  
Umar Sidiq ◽  
Syed Mutahar Aaqib ◽  
Rafi Ahmad Khan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Research Work ◽  
Support Vector ◽  
Data Sets ◽  
Data Mining Technique ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Techniques ◽  
Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

scholarly journals A Novel Weighted Ensemble Method to Overcome the Impact of Under-fitting and Over-fitting on the Classification Accuracy of the Imbalanced Data Sets

2021 ◽  
pp. 483-496
Author(s):  
Ghulam Fatima ◽  
Sana Saeed
Keyword(s):  
Data Mining ◽  
Imbalanced Data ◽  
Ensemble Method ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Imbalanced Data Sets ◽  
Sampling Procedures ◽  
The Impact

In the data mining communal, imbalanced class dispersal data sets have established mounting consideration. The evolving field of data mining and information discovery seeks to establish precise and effective computational tools for the investigation of such data sets to excerpt innovative facts from statistics. Sampling methods re-balance the imbalanced data sets consequently improve the enactment of classifiers. For the classification of the imbalanced data sets, over-fitting and under-fitting are the two striking problems. In this study, a novel weighted ensemble method is anticipated to diminish the influence of over-fitting and under-fitting while classifying these kinds of data sets. Forty imbalanced data sets with varying imbalance ratios are engaged to conduct a comparative study. The enactment of the projected method is compared with four customary classifiers including decision tree(DT), k-nearest neighbor (KNN), support vector machines (SVM), and neural network (NN). This evaluation is completed with two over-sampling procedures, an adaptive synthetic sampling approach (ADASYN), and a synthetic minority over-sampling (SMOTE) technique. The projected scheme remained efficacious in diminishing the impact of over-fitting and under-fitting on the classification of these data sets.

scholarly journals Multi-choice wavelet thresholding based binary classification method

Methodology ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 127-146 ◽  
Author(s):  
Seung Hyun Baek ◽  
Alberto Garcia-Diaz ◽  
Yuanshun Dai
Keyword(s):  
Data Mining ◽  
Binary Classification ◽  
High Dimensional ◽  
Support Vector ◽  
Published Data ◽  
Data Sets ◽  
Wavelet Thresholding ◽  
Reliability Of Results ◽  
Vector Machines ◽  
Cognition Model

Data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize on reliability of results and computational efficiency are required for the analysis of high-dimensional data. Optimization principles can play a significant role in the rationalization and validation of specialized data mining procedures. This paper presents a novel methodology which is Multi-Choice Wavelet Thresholding (MCWT) based three-step methodology consists of three processes: perception (dimension reduction), decision (feature ranking), and cognition (model selection). In these steps three concepts known as wavelet thresholding, support vector machines for classification and information complexity are integrated to evaluate learning models. Three published data sets are used to illustrate the proposed methodology. Additionally, performance comparisons with recent and widely applied methods are shown.

scholarly journals Effectiveness of Support Vector Machines in Medical Data mining

2015 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
Author(s):  
Padmavathi Janardhanan ◽  
Heena L. ◽  
Fathima Sabika
Keyword(s):  
Data Mining ◽  
Medical Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Medical Data Mining ◽  
Rbf Network ◽  
Vector Machines ◽  
Hidden Knowledge ◽  
Using Data

The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. One of the positive aspects is to discover the important patterns. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. In this case, data mining prepares the ability of research and discovery that may not have been evident. This paper analyzes the effectiveness of SVM, the most popular classification techniques in classifying medical datasets. This paper analyses the performance of the Naïve Bayes classifier, RBF network and SVM Classifier. The performance of predictive model is analysed with different medical datasets in predicting diseases is recorded and compared. The datasets were of binary class and each dataset had different number of attributes. The datasets include heart datasets, cancer and diabetes datasets. It is observed that SVM classifier produces better percentage of accuracy in classification. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust and effective classifier for medical data sets.

A Preliminary Framework to Fight Tax Evasion in the Home Renovation Market

2021 ◽  
pp. 304-325
Author(s):  
Cataldo Zuccaro ◽  
Michel Plaisent ◽  
Prosper Bernard
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Predictive Modeling ◽  
Tax Evasion ◽  
Predictive Models ◽  
Support Vector ◽  
Government Agencies ◽  
Data Sets ◽  
Network Support ◽  
Vector Machines

This chapter presents a preliminary framework to tackle tax evasion in the field of residential renovation. This industry plays a major role in economic development and employment growth. Tax evasion and fraud are extremely difficult to combat in the industry since it is characterized by a large number of stakeholders (manufacturers, retailers, tradesmen, and households) generating complex transactional dynamics that often defy attempts to deploy transactional analytics to detect anomalies, fraud, and tax evasion. This chapter proposes a framework to apply transactional analytics and data mining to develop standard measures and predictive models to detect fraud and tax evasion. Combining big data sets, cross-referencing, and predictive modeling (i.e., anomaly detection, artificial neural network support vector machines, Bayesian network, and association rules) can assist government agencies to combat highly stealth tax evasion and fraud in the residential renovation.

Soft Computing for XML Data Mining

2011 ◽  
pp. 1806-1809
Author(s):  
K. G. Srinivasa ◽  
K. R. Venugopal ◽  
L. M. Patnaik
Keyword(s):  
Data Mining ◽  
Soft Computing ◽  
Information Exchange ◽  
Large Data ◽  
Heterogeneous Data ◽  
Approximate Reasoning ◽  
Support Vector ◽  
Data Sets ◽  
Document Collections ◽  
Extensible Markup

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic Algorithms (GA), Artificial Neural Networks, Fuzzy Logic, Rough Sets and Support Vector Machines (SVM) when used in combination was found to be effective. Therefore, soft computing algorithms are used to accomplish data mining across different applications (Mitra S, Pal S K & Mitra P, 2002; Alex A Freitas, 2002). Extensible Markup Language (XML) is emerging as a de facto standard for information exchange among various applications of World Wide Web due to XML’s inherent data self-describing capacity and flexibility of organizing data. In XML representation, the semantics are associated with the contents of the document by making use of self describing tags which can be defined by the users. Hence XML can be used as a medium for interoperability over the Internet. With these advantages, the amount of data that is being published on the Web in the form of XML is growing enormously and many naïve users find the need to search over large XML document collections (Gang Gou & Rada Chirkova, 2007; Luk R et al., 2000).

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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