scholarly journals A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID-19 Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wael S. Abu El Azm ◽  
Ehab M. Almetwally ◽  
Sundus Naji AL-Aziz ◽  
Abd Al-Aziz H. El-Bagoury ◽  
Randa Alharbi ◽  
...  
Keyword(s):  
Interval Estimation ◽  
Structural Features ◽  
Data Sets ◽  
Unknown Parameters ◽  
Real World Data ◽  
Simulation Research ◽  
New Model ◽  
Lomax Distribution ◽  
Type Ii Censored Data ◽  
Percentile Bootstrap

A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.

scholarly journals Parametric Rough Sets with Application to Granular Association Rule Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xu He ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Recommender Systems ◽  
Association Rule ◽  
Rough Sets ◽  
Relational Databases ◽  
Data Sets ◽  
Rule Mining ◽  
Real World Data ◽  
New Model ◽  
New Type ◽  
Two Universes

Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable.

scholarly journals Extended Poisson-Pareto Type II Distribution: Theoretical and Computational Aspects

2019 ◽  
pp. 713-730
Author(s):  
Rania Hassan Abd El Khaleq
Keyword(s):  
Poisson Model ◽  
Real Data ◽  
Continuous Model ◽  
Data Sets ◽  
Type Ii ◽  
Unknown Parameters ◽  
New Model ◽  
Method Of Maximum Likelihood ◽  
Computational Aspects ◽  
Better Than

We introduce a new continuous model with strong physical motivations and wide applications upon compounding the diecreate zero truncated Poisson model and a new continuous model called the Burr X Pareto type II distribution. Some of its mathematical and statistical properties are derived as well as four applications to real data sets are provided with detailes to illustrate the wide importance of the new model. We conclude that the new model is better than other nine competitive models via the four applications. Method of maximum likelihood is used to estimate the unknown parameters of the new model. The new model provide adequate Öts as compared to other related models in the four applications.

Interval Estimation in Lifetime Distributions Using Progressively Type II Censored Data

2021 ◽  
Author(s):  
Ayman Baklizi
Keyword(s):  
Closed Form ◽  
Confidence Intervals ◽  
Censored Data ◽  
Interval Estimation ◽  
Real Data ◽  
Data Sets ◽  
Type Ii ◽  
Lifetime Distributions ◽  
Special Cases ◽  
Type Ii Censored Data

In this paper, we developed a method for constructing confidence intervals for the parameters of lifetime distributions based on progressively type II censored data. The method produces closed form expressions for the bounds of the confidence intervals for several special cases of parameters and lifetime distributions. Closed form approximations are derived for the intervals for the parameters of the location or scale families of distributions. The method is illustrated with several examples and analyses of real data sets are included to illustrate the application of the method.

scholarly journals A Generalization of Lomax Distribution with Properties, Copula and Real Data Applications

2020 ◽  
pp. 697-711
Author(s):  
Hanaa Elgohari ◽  
Haitham Yousof
Keyword(s):  
Real Data ◽  
Data Sets ◽  
Finite Sample ◽  
New Model ◽  
Clayton Copula ◽  
Lomax Distribution

A new generalization of Lomax distribution is derived and studied. Some of its useful properties are derived. A simple clayton copula is used to generate many bivariate and multivariate type models. We performed graphical simulations to assess the finite sample behavior of the estimations. The new model is employed in modelling three real data sets.

scholarly journals The Truncated Power Lomax Distribution: Properties and Applications

2018 ◽  
Vol 16 (9) ◽  
pp. 655-668
Author(s):  
Sirinapa ARYUYUEN ◽  
Winai BODHISUWAN
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Hazard Function ◽  
Likelihood Estimation ◽  
Real Data ◽  
The Other ◽  
Data Sets ◽  
Truncated Distribution ◽  
Unknown Parameters ◽  
Lomax Distribution

A new truncated distribution, called the truncated power Lomax (TPL) distribution, is proposed. This is a truncated version of the power Lomax distribution. The TPL distribution has increasing and decreasing shapes of the hazard function. Some statistical properties, such as moments, survival, hazard, and quantile functions, are discussed. The maximum likelihood estimation (MLE) is constructed for estimating the unknown parameters of the TPL distribution. Moreover, the distribution has been fitted with real data sets to illustrate the usefulness of the proposed distribution. From the results of the example applications, the TPL distribution provides a consistently better fit than the other distributions, i.e., power Lomax and Lomax.

scholarly journals On Progressive Type-II Censored Samples from Alpha Power Exponential Distribution

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Mukhtar M. Salah
Keyword(s):  
Exponential Distribution ◽  
Interval Estimation ◽  
Location Parameter ◽  
Alpha Power ◽  
Type Ii ◽  
Unknown Parameters ◽  
Progressive Type ◽  
Type Ii Censored Data ◽  
Best Linear Unbiased Estimators ◽  
Two Parameter

In this paper the two-parameter α -power exponential distribution is studied. We study the two-parameter α -power exponential μ , λ distribution with the location parameter μ > 0 and scale parameter λ > 0 under progressive Type-II censored data with fixed shape parameter α . We estimate the maximum likelihood estimators of these unknown parameters numerically since it cannot be solved analytically. We use the approximate best linear unbiased estimators μ ∗ and λ ∗ , as an initial guesses to obtain the MLEs μ ^ and λ ^ . We estimate the interval estimation of these unknowns’ parameters. Monte Carlo simulations are performed and data examples have been provided for illustration and comparison.

scholarly journals A New Extension of the Lomax Distribution with Properties and Applications to Failure Times Data

2019 ◽  
pp. 461-479
Author(s):  
Mohamed Abo Raya
Keyword(s):  
Estimation Method ◽  
Likelihood Estimation ◽  
Good Alternative ◽  
Model Parameters ◽  
Data Sets ◽  
New Model ◽  
Lomax Distribution ◽  
Failure Times ◽  
Lifetime Model ◽  
Better Than

A new lifetime model is introduced and studied. The major justi…cation for the practicality of the new model is based on the wider use of the Lomax model. We are also motivated to introduce the new model since the density of the new distribution exhibits various important shapes such as the unimodal, right skewed and left skewed. The new model can be viewed as a mixture of the exponentiated Lomax distribution. It can also be considered as a suitablemodel for testing the symmetric, left skewed, right skewed and unimodal data. The maximum likelihood estimation method is used to estimate the model parameters. We prove empirically the importance and ‡exibility of the new model in modeling two types of aircraft windshield lifetime data sets. The proposed lifetime model is much better than gamma Lomax, beta Lomax, exponentiated Lomax and Lomax models so the exponentiated Lomax, model is a good alternative to these models in modeling aircraft windshield data.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

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