scholarly journals A New Compound Lifetime Distribution: Model, Characterization, Estimation and Application

2018 ◽  
Vol 14 (2) ◽  
pp. 45-57
Author(s):  
Adil Rashid ◽  
Tariq Rashid Jan ◽  
Akhtar Hussain Bhat ◽  
Z. Ahmad
Keyword(s):  
Power Series ◽  
Probability Distributions ◽  
Distribution Model ◽  
Data Sets ◽  
Lifetime Data ◽  
Data Set ◽  
Power Series Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Random Events

Abstract There are diverse lifetime models available to the researchers to predict the uncertain behavior of random events but at times they fail to provide adequate fit for some complex and new data sets. New probability distributions are emerging as lifetime models to meet this ever growing demand of modeling complex real world phenomena from different sciences with better efficiency. Here, in this manuscript we shall compose Ailamujia distribution with that of power series distribution. This newly developed distribution called Ailamujia power series distribution reduces to four new special lifetime models on simple specific function parametric setting. Apart from this some important mathematical properties in the form of propositions will also be discussed. Furthermore, characterization and some statistical properties that include mgf, moments, and parameter estimation have also been discussed. Finally, the potency of newly proposed model has been analyzed statistically and graphically and it has been established from the statistical analysis that newly proposed model offers a better fit when it comes to model some lifetime data set.

scholarly journals Complementary Kumaraswamy Weibull Power Series Distribution: Some Properties and Application

2020 ◽  
pp. 361-398
Author(s):  
Innocent Boyle Eraikhuemen ◽  
Julian Ibezimako Mbegbu ◽  
Friday Ewere
Keyword(s):  
Power Series ◽  
Likelihood Estimation ◽  
Real Data ◽  
Model Parameters ◽  
Data Set ◽  
Power Series Distribution ◽  
Mathematical Properties ◽  
Method Of Maximum Likelihood ◽  
The Family ◽  
Distance Problem

In this paper, we propose Complementary Kumaraswamy Weibull Power Series (CKWPS) Distributions. The method is obtained by compounding the Kumaraswamy-G distribution and Power Series distribution on a latent complementary distance problem base. The mathematical properties of the proposed class of distribution are studied. The method of Maximum Likelihood Estimation is used for obtaining the estimates of the model parameters. A member of the family is investigated in detail. Finally an application of the proposed class is illustrated using a real data set.

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.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

scholarly journals A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5262
Author(s):  
Meizhu Li ◽  
Shaoguang Huang ◽  
Jasper De Bock ◽  
Gert de Cooman ◽  
Aleksandra Pižurica
Keyword(s):  
Hyperspectral Image ◽  
Probability Distributions ◽  
Imprecise Probabilities ◽  
Data Sets ◽  
Classifier Selection ◽  
Training Samples ◽  
Proposed Model ◽  
Label Information ◽  
Dynamic Classifier Selection ◽  
The Individual

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

scholarly journals The Adjusted Log-logistic Generalized Exponential Distribution with Application to Lifetime Data

2017 ◽  
Vol 5 (4) ◽  
pp. 1
Author(s):  
I. E. Okorie ◽  
A. C. Akpanta ◽  
J. Ohakwe ◽  
D. C. Chikezie ◽  
C. U. Onyemachi ◽  
...  
Keyword(s):  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Data Sets ◽  
Lifetime Data ◽  
Generalized Exponential Distribution ◽  
Data Set ◽  
Method Of Maximum Likelihood ◽  
The One ◽  
Special Case

This paper introduces a new generator of probability distribution-the adjusted log-logistic generalized (ALLoG) distribution and a new extension of the standard one parameter exponential distribution called the adjusted log-logistic generalized exponential (ALLoGExp) distribution. The ALLoGExp distribution is a special case of the ALLoG distribution and we have provided some of its statistical and reliability properties. Notably, the failure rate could be monotonically decreasing, increasing or upside-down bathtub shaped depending on the value of the parameters $\delta$ and $\theta$. The method of maximum likelihood estimation was proposed to estimate the model parameters. The importance and flexibility of he ALLoGExp distribution was demonstrated with a real and uncensored lifetime data set and its fit was compared with five other exponential related distributions. The results obtained from the model fittings shows that the ALLoGExp distribution provides a reasonably better fit than the one based on the other fitted distributions. The ALLoGExp distribution is therefore ecommended for effective modelling of lifetime data sets.

Managing return flow of end-of-life products for product recovery operations

2017 ◽  
Vol 7 (1) ◽  
pp. 169-177
Author(s):  
Seval Ene ◽  
Nursel Öztürk
Keyword(s):  
End Of Life ◽  
Reverse Logistics ◽  
Product Recovery ◽  
Forecasting Model ◽  
Return Flow ◽  
Data Sets ◽  
Product Returns ◽  
Data Set ◽  
Product Return ◽  
Proposed Model

Increased consciousness on environment and sustainability, leads companies to apply environmentally friendly strategies such as product recovery and product return management. These strategies are generally applied in reverse logistics concept. Implementing reverse logistics successfully becomes complicated for companies due to uncertain parameters of the system like quantity, quality and timing of returns. A forecasting methodology is required to overcome these uncertainties and manage product returns. Accurate forecasting of product return flows provides insights to managers of reverse logistics. This paper proposes a forecasting model based on grey modelling for managing end-of-life products’ return flow. Grey models are capable for handling data sets characterized by uncertainty and small sized. The proposed model is applied to data set of a specific end-of-life product. Attained results show that the proposed forecasting model can be successfully used as a forecasting tool for product returns and a supportive guidance can be provided for future planning. Keywords: End-of-life products, grey modelling, product return flow, product recovery; 

Working framework of semantic interoperability for CRIS with heterogeneous data sources

2017 ◽  
Vol 73 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Amed Leiva-Mederos ◽  
Jose A. Senso ◽  
Yusniel Hidalgo-Delgado ◽  
Pedro Hipola
Keyword(s):  
Heterogeneous Data ◽  
Semantic Interoperability ◽  
Data Sets ◽  
Research Information ◽  
Data Set ◽  
Content Type ◽  
Proposed Model ◽  
Semantic Alignment ◽  
Technical Specifications ◽  
Open Archives

Purpose Information from Current Research Information Systems (CRIS) is stored in different formats, in platforms that are not compatible, or even in independent networks. It would be helpful to have a well-defined methodology to allow for management data processing from a single site, so as to take advantage of the capacity to link disperse data found in different systems, platforms, sources and/or formats. Based on functionalities and materials of the VLIR project, the purpose of this paper is to present a model that provides for interoperability by means of semantic alignment techniques and metadata crosswalks, and facilitates the fusion of information stored in diverse sources. Design/methodology/approach After reviewing the state of the art regarding the diverse mechanisms for achieving semantic interoperability, the paper analyzes the following: the specific coverage of the data sets (type of data, thematic coverage and geographic coverage); the technical specifications needed to retrieve and analyze a distribution of the data set (format, protocol, etc.); the conditions of re-utilization (copyright and licenses); and the “dimensions” included in the data set as well as the semantics of these dimensions (the syntax and the taxonomies of reference). The semantic interoperability framework here presented implements semantic alignment and metadata crosswalk to convert information from three different systems (ABCD, Moodle and DSpace) to integrate all the databases in a single RDF file. Findings The paper also includes an evaluation based on the comparison – by means of calculations of recall and precision – of the proposed model and identical consultations made on Open Archives Initiative and SQL, in order to estimate its efficiency. The results have been satisfactory enough, due to the fact that the semantic interoperability facilitates the exact retrieval of information. Originality/value The proposed model enhances management of the syntactic and semantic interoperability of the CRIS system designed. In a real setting of use it achieves very positive results.

scholarly journals The Weighted Power Lindley Distribution with Applications on the Life Time Data

2020 ◽  
pp. 225-237
Author(s):  
Aafaq A. Rather ◽  
Gamze Özel
Keyword(s):  
Information Matrix ◽  
Life Time ◽  
Real Data ◽  
Data Sets ◽  
Lifetime Data ◽  
Time Data ◽  
Data Set ◽  
Lindley Distribution ◽  
Power Lindley Distribution ◽  
New Distribution

In this paper, we have proposed a new version of power lindley distribution known as weighted power lindley distribution. The different structural properties of the newly model have been studied. The maximum likelihood estimators of the parameters and the Fishers information matrix have been discussed. It also provides more flexibility to analyze complex real data sets.  An application of the model to a real data set is analyzed using the new distribution, which shows that the weighted power Lindley distribution can be used quite effectively in analyzing real lifetime data.

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