Improvement of Association Algorithm and Its Application in Audit Data Mining

2021 ◽  
Author(s):  
Haitao Lu ◽  
C. B. Sivaparthipan ◽  
A. Antonidoss
Keyword(s):  
Data Mining ◽  
Performance Ratio ◽  
Data Set ◽  
Audit Risk ◽  
Audit Data ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Accuracy Ratio ◽  
Efficient Data ◽  
Association Algorithm

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

scholarly journals A Machine Learning Approach to Fitting Prescription for Hearing Aids

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 736
Author(s):  
Mondol ◽  
Lee
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Real Life ◽  
Training Data ◽  
Prescription Database ◽  
Data Set ◽  
Promising Tool ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Two Phases

A successful Hearing-Aid Fitting (HAF) is more than just selecting an appropriate HearingAid (HA) device for a patient with Hearing Loss (HL). The initial fitting is given by the prescriptionbased on user’s hearing loss; however, it is often necessary for the audiologist to readjust someparameters to satisfy the user demands. Therefore, in this paper, we concentrated on a new applicationof Neural Network (NN) combined with a Transfer Learning (TL) strategy to develop a fittingalgorithm with the prescription database for hearing loss and readjusted gain to minimize the gapbetween fitting satisfaction. As prior information, we generated the data set from two popularhearing-aid fitting software, then fed the training data to our proposed model, and verified theperformance of the architecture. Pondering real life circumstances, where numerous fitting recordsmay not always be accessible, we first investigated the number of minimum fitting records requiredfor possible sufficient training. After that, we evaluated the performance of the proposed algorithmin two phases: (a) NN with refined hyper parameter showed enhanced performance in compareto state-of-the-art DNN approach, and (b) the TL approach boosted the performance of the NNalgorithm in a broad way. Altogether, our model provides a pragmatic and promising tool for HAF.

scholarly journals Intrusion detection using clustering

2010 ◽  
pp. 248-255
Author(s):  
Kusum Kumari Bharti ◽  
Sanyam Shukla ◽  
Sweta Jain
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Hybrid Model ◽  
Clustering Algorithm ◽  
Security Threats ◽  
Network Environment ◽  
Data Set ◽  
Proposed Model ◽  
The One ◽  
Increasing Trends

In increasing trends of network environment every one gets connected to the system. So there is need of securing information, because there are lots of security threats are present in network environment. A number of techniques are available for intrusion detection. Data mining is the one of the efficient techniques available for intrusion detection. Data mining techniques may be supervised or unsuprevised.Various Author have applied various clustering algorithm for intrusion detection, but all of these are suffers form class dominance, force assignment and No Class problem. This paper proposes a hybrid model to overcome these problems. The performance of proposed model is evaluated over KDD Cup 1999 data set.

scholarly journals Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ge Zhou
Keyword(s):  
Data Mining ◽  
Mobile Internet ◽  
Distributed Data ◽  
Cloud Platform ◽  
Data Set ◽  
Internet Applications ◽  
Internet Service ◽  
Clustering Quality ◽  
Efficient Data ◽  
Application Aware

Because the static cloud platform cannot satisfy the diversity of mobile Internet service and inefficient data mining problems, we presented a reliable and efficient data mining cloud platform construction scheme based on the mobile Internet service opportunistic driving and application perception. In this scheme, first of all data selection mechanism was established based on mobile Internet service opportunistic drive. Secondly, through the cloud platform different cloud and channel aware, nonlinear mapping from the service to a data set of proposed perceptual model is applied. Finally, on the basis of the driving characteristics and extraction of perceptual features, the cloud platform would be constructed through the service opportunities of mobile Internet applications, which could provide robust and efficient data mining services. The experimental results show that the proposed mechanism, compared to the cloud platform based on distributed data mining, has obvious advantages in system running time, memory usage, and data clustering required time, as well as average clustering quality.

Prediction of gestational diabetes diagnosis using SVM and J48 classifier model

2018 ◽  
Vol 7 (2.21) ◽  
pp. 323 ◽  
Author(s):  
S Saradha ◽  
P Sujatha
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Gestational Diabetes ◽  
Missing Values ◽  
Automated System ◽  
Diabetes Diagnosis ◽  
Data Set ◽  
Proposed Model

Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may  occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common   predictive data mining tasks is classification. It classifies the data and builds a model based on the test data values and attributes to produce the new classified data. For detecting GDM and also its risk factors, two classifier models namely modified SVM and modified J48 classifier models are proposed. The data set were collected from various hospitals and clinical labs and preprocessed with discretize filter using weka tool. Missing values are replaced by the suitable values. The final preprocessed data applied in the proposed classifier Model.  The output of the proposed model is compared with all the other existing methodologies. Since the proposed model modified J48 classifier model produces more accuracy and low error rate against other existing classifier models. 

scholarly journals A Hybrid Clustering Data Mining Technique (HCDMT) for Predicting SLE

2019 ◽  
Vol 8 (12) ◽  
pp. 3927-3930
Keyword(s):  
Data Mining ◽  
Lupus Erythematosus ◽  
Complex Disease ◽  
Data Mining Technique ◽  
Data Set ◽  
Hybrid Clustering ◽  
Mining Technique ◽  
Machine Learning Approach ◽  
Clustering Data ◽  
High Level

SLE is an auto immune and complex disease. Predicting Systemic Lupus Erythematosus (SLE) is significantly challenging due to its high level of heterogeneity in symptoms. There is a limitation on the tools used for predicting SLE accurately. This paper proposes a machine learning approach to predict the disease from SLE data set and classify patients in whom the disease is active. The data purified and selected for classification improves the accuracy of the proposed method called HCDMT (Hybrid Clustering Data Mining Technique), an amalgamation of CART and k-Means, was evaluated on SLE data. It was found to predict above 95% of SLE cases

scholarly journals A Hybrid Clustering Data Mining Technique (HCDMT) for Predicting SLE

2019 ◽  
Vol 8 (11S2) ◽  
pp. 512-515
Keyword(s):  
Data Mining ◽  
Lupus Erythematosus ◽  
Complex Disease ◽  
Data Mining Technique ◽  
Data Set ◽  
Hybrid Clustering ◽  
Mining Technique ◽  
Machine Learning Approach ◽  
Clustering Data ◽  
High Level

SLE is an auto immune and complex disease. Predicting Systemic Lupus Erythematosus (SLE) is significantly challenging due to its high level of heterogeneity in symptoms. There is a limitation on the tools used for predicting SLE accurately. This paper proposes a machine learning approach to predict the disease from SLE data set and classify patients in whom the disease is active. The data purified and selected for classification improves the accuracy of the proposed method called HCDMT (Hybrid Clustering Data Mining Technique), an amalgamation of CART and k-Means, was evaluated on SLE data. It was found to predict above 95% of SLE cases.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Research on the strategy of intelligent analysis to improve sports person psychological experience in the era of artificial intelligence

2021 ◽  
pp. 1-11
Author(s):  
Lei Wu ◽  
Juan Wang ◽  
Long Jin ◽  
P. Hemalatha ◽  
R Premalatha
Keyword(s):  
Artificial Intelligence ◽  
Vital Role ◽  
Performance Ratio ◽  
Psychological Experience ◽  
The Public ◽  
Usable Knowledge ◽  
Intelligent Analysis ◽  
Accuracy Ratio ◽  
Depression Level ◽  
Predictive Research

Artificial intelligence (AI) is an excellent potential technology that is evolving day-to-day and a critical avenue for exploration in the world of computer science & engineering. Owing to the vast volume of data and the eventual need to turn this data into usable knowledge and realistic solutions, artificial intelligence approaches and methods have gained substantial prominence in the knowledge economy and community world in general. AI revolutionizes and raises athletics to an entirely different level. Although it is clear that analytics and predictive research have long played a vital role in sports, AI has a massive effect on how games are played, structured, and engaged by the public. Apart from these, AI helps to analyze the mental stability of the athletes. This research proposes the Artificial Intelligence assisted Effective Monitoring System (AIEMS) for the specific intelligent analysis of sports people’s psychological experience. The comparative analysis suggests the best AI strategies for analyzing mental stability using different criteria and resource factors. It is observed that the growth in the present incarnation indicates a promising future concerning AI use in elite athletes. The study ends with the predictive efficiency of particular AI approaches and procedures for further predictive analysis focused on retrospective methods. The experimental results show that the proposed AIEMS model enhances the athlete performance ratio of 98.8%, emotion state prediction of 95.7%, accuracy ratio of 97.3%, perception level of 98.1%, and reduces the anxiety and depression level of 15.4% compared to other existing models.

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