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MATHEMATICS (651 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 3)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 2)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 8)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 11)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 1)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 1)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Mathematical Physics     Open Access   (Followers: 5)
Advances in Mathematics     Full-text available via subscription   (Followers: 10)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 5)
Advances in Pure Mathematics     Open Access   (Followers: 4)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 7)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 3)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Full-text available via subscription   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Biostatistics     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 7)
American Journal of Operations Research     Open Access   (Followers: 5)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 7)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access   (Followers: 1)
Analysis     Hybrid Journal   (Followers: 2)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 3)
Analysis Mathematica     Full-text available via subscription  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales UMCS, Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 9)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription  
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annuaire du Collège de France     Open Access   (Followers: 5)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 1)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access  
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 2)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 19)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Journal of Current Engineering & Maths     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 2)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access  
Baltic International Yearbook of Cognition, Logic and Communication     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
BoEM - Boletim online de Educação Matemática     Open Access  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 1)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 21)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 7)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 1)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 18)
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Full-text available via subscription   (Followers: 2)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 1)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Concrete Operators     Open Access   (Followers: 4)
Confluentes Mathematici     Hybrid Journal  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 14)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 11)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 29)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 2)
Discrete Mathematics     Hybrid Journal   (Followers: 7)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 3)
Energy for Sustainable Development     Hybrid Journal   (Followers: 9)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Ensino da Matemática em Debate     Open Access  
Entropy     Open Access   (Followers: 5)
ESAIM: Control Optimisation and Calculus of Variations     Full-text available via subscription   (Followers: 1)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 4)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 2)
Experimental Mathematics     Hybrid Journal   (Followers: 4)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Fasciculi Mathematici     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 4)
Fixed Point Theory and Applications     Open Access   (Followers: 1)
Formalized Mathematics     Open Access   (Followers: 2)

        1 2 3 4 | Last

Journal Cover Annals of Data Science
  [9 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2198-5804 - ISSN (Online) 2198-5812
   Published by Springer-Verlag Homepage  [2353 journals]
  • Big Data and Causality
    • Authors: Hossein Hassani; Xu Huang; Mansi Ghodsi
      Abstract: Abstract Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory.
      PubDate: 2017-08-01
      DOI: 10.1007/s40745-017-0122-3
  • Parallel String Matching with Linear Array, Butterfly and Divide and
           Conquer Models
    • Authors: S. Viswanadha Raju; K. K. V. V. S. Reddy; Chinta Someswara Rao
      Abstract: Abstract String Matching is a technique of searching a pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text mining, pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.
      PubDate: 2017-07-29
      DOI: 10.1007/s40745-017-0124-1
  • A Feature Selection Method Based on Ranked Vector Scores of Features for
    • Authors: Firuz Kamalov; Fadi Thabtah
      Abstract: Abstract One of the major aspects of any classification process is selecting the relevant set of features to be used in a classification algorithm. This initial step in data analysis is called the feature selection process. Disposing of the irrelevant features from the dataset will reduce the complexity of the classification task and will increase the robustness of the decision rules when applied on the test set. This paper proposes a new filtering method that combines and normalizes the scores of three major feature selection methods: information gain, chi-squared statistic and inter-correlation. Our method utilizes the strengths of each of the aforementioned methods to maximum advantage while avoiding their drawbacks—especially the disparity of the results produced by these methods. Our filtering method stabilizes each variable score and gives it the true rank among the input data’s available variables. Hence it maximizes the stability in the variables’ scores without losing the overall accuracy of the predictive model. A number of experiments on different datasets from various domains have shown that features chosen by the proposed method are highly predictive when compared with features selected by other existing filtering methods. The evaluation of the filtering phase was conducted via thorough experimentations using a number of predictive classification algorithms in addition to statistical analysis of the filtering methods’ scores.
      PubDate: 2017-07-29
      DOI: 10.1007/s40745-017-0116-1
  • Modelling Under-Five Mortality among Hospitalized Pneumonia Patients in
           Hawassa City, Ethiopia: A Cross-Classified Multilevel Analysis
    • Authors: Tariku Tessema
      Abstract: Abstract Community acquired pneumonia refers to pneumonia acquired outside of hospitals or extended health facilities and it is a leading infectious disease. This study aims to model mortality of hospitalized under-5 year child pneumonia patients and investigate potential risk factors associated with child mortality due to pneumonia. The study was a retrospective study on 305 sampled under-five hospitalized patients of community acquired pneumonia. A cross-classified multilevel logistic regression was employed with resident and hospital classified at the second level. Bayesian estimation method was applied in which the posterior distribution was simulated via Markov Chain Monte Carlo. The variability attributable to hospital was found to be larger than variability attributable to residence. The odds of dying from the community acquired pneumonia was higher among patients who were; diagnosed in spring season, complicated with malaria, AGE and AFI, in a neonatal age group, diagnosed late (more than a week). The risk of mortality was also found high for lower nurse: patient and physician: patients’ ratios.
      PubDate: 2017-07-28
      DOI: 10.1007/s40745-017-0121-4
  • Face Recognition and Human Tracking Using GMM, HOG and SVM in Surveillance
    • Authors: Harihara Santosh Dadi; Gopala Krishna Mohan Pillutla; Madhavi Latha Makkena
      Abstract: Abstract Tracking of human and recognition in public places using surveillance cameras is the topic of research in the area computer vision. Recognition of human and then tracking completes the video surveillance system. A novel algorithm for face recognition and human tracking is presented in this article. Human is tracked using Gaussian mixture model. To track the human in specific, template of GMM is divided into four regions which are placed one above the other and tracked simultaneously. For recognizing the human, the histogram of oriented gradients features of the face region are given to the support vector machine classifier. Three experiments are conducted in taking the training faces. Every \(10{\mathrm{th}}\) frame, every \(5{\mathrm{th}}\) frame and every \(3{\mathrm{rd}}\) frame of the first 100 frames are considered. The other frames in the video are considered for testing using SVM classifier. Three datasets namely AITAM1 (simple), AITAM2 (moderate) and AITAM3 (complex) are used in this work. The experimental results show that as the complexity of dataset increases the performance metrics are getting decreased. The more the number of training faces in preparing a classifier, the better is the face recognition rate. This is experimented for all types of datasets. The Performance results show that the combination of the tracking algorithm and the face recognition algorithm not only tracks the person but also recognizes the person. This unique property of both tracking and recognition makes it best suit for video surveillance applications.
      PubDate: 2017-07-25
      DOI: 10.1007/s40745-017-0123-2
  • On a Weibull-Inverse Exponential Distribution
    • Authors: Chandrakant; M. K. Rastogi; Y. M. Tripathi
      Abstract: Abstract In this paper we study various reliability properties of a Weibull inverse exponential distribution. The maximum likelihood and Bayes estimates of unknown parameters and reliability characteristics are obtained. Bayes estimates are obtained with respect to the squared error loss function under proper and improper prior situations. We use the Lindley method and the Metropolis–Hastings algorithm to compute the Bayes estimates. Interval estimation is also considered. Asymptotic and highest posterior density intervals of unknown parameters are constructed in this respect. We perform a numerical study to compare the performance of all methods and obtain comments based on this study. We also analyze two real data sets for illustration purposes. Finally a conclusion is presented.
      PubDate: 2017-07-24
      DOI: 10.1007/s40745-017-0125-0
  • Approximate Shortest Distance Computing Using k-Medoids Clustering
    • Authors: Sakshi Agarwal; Shikha Mehta
      Abstract: Abstract Shortest distance query is widely used aspect in large scale networks. Numerous approaches are present in the literature to approximate the distance between two query nodes. Most popular distance approximation approach is landmark embedding scheme. In this technique selection of optimal landmarks is a NP-hard problem. Various heuristics available to locate optimal landmarks include random, degree, closeness centrality, betweenness and eccentricity etc. In this paper, we propose to employ k-medoids clustering based approach to improve distance estimation accuracy over local landmark embedding techniques. In particular, it is observed that global selection of the seed landmarks causes’ large relative error, which is further reduced using local landmark embedding. The efficacy of the proposed approach is analyzed with respect to conventional graph embedding techniques on six large-scale networks. Results express that the proposed landmark selection scheme reduces the shortest distance estimation error considerably. Proposed technique is able to reduce the approximation error of shortest distance by upto 29% with respect to the other graph embedding technique.
      PubDate: 2017-07-22
      DOI: 10.1007/s40745-017-0119-y
  • Hardware Implementation of Bone Fracture Detector Using Fuzzy Method Along
           with Local Normalization Technique
    • Authors: Abdullah-Al Nahid; Tariq M. Khan; Yinan Kong
      Abstract: Abstract Bone fracture detection from the digital image segmentation is a well-known image processing application which is frequently used to process biomedical images. Hardware realization of different image processing algorithm specially utilizing Field Programmable Gate Array (FPGA) has been gained a great interest among the researchers. FPGA has many significant features like spatial and temporal parallelism that best suits for real-time implementation of image processing. To gain the benefit from these characteristics of a FPGA, a new method for bone fracture detection is proposed and its performance is validated through real-time implementation. Simulation results show that the proposed method give superior performance than the existing method.
      PubDate: 2017-07-21
      DOI: 10.1007/s40745-017-0118-z
  • Evaluation and Comparison of Estimators in the Gompertz Distribution
    • Authors: Sanku Dey; Tanmay Kayal; Yogesh Mani Tripathi
      Abstract: Abstract This article addresses the different methods of estimation of the probability density function and the cumulative distribution function for the Gompertz distribution. Following estimation methods are considered: maximum likelihood estimators, uniformly minimum variance unbiased estimators, least squares estimators, weighted least square estimators, percentile estimators, maximum product of spacings estimators, Cramér–von-Mises estimators, Anderson–Darling estimators. Monte Carlo simulations are performed to compare the behavior of the proposed methods of estimation for different sample sizes. Finally, one real data set and one simulated data set are analyzed for illustrative purposes.
      PubDate: 2017-07-21
      DOI: 10.1007/s40745-017-0126-z
  • $$\alpha $$ α Logarithmic Transformed Family of Distributions with
    • Authors: Sanku Dey; Mazen Nassar; Devendra Kumar
      Abstract: Abstract In this paper, a new three-parameter distribution, called \(\alpha \) logarithmic transformed generalized exponential distribution ( \(\alpha LTGE\) ) is proposed. Various properties of the proposed distribution, including explicit expressions for the moments, quantiles, moment generating function, mean deviation about the mean and median, mean residual life, Bonferroni curve, Lorenz curve, Gini index, Rényi entropy, stochastic ordering and order statistics are derived. It appears to be a distribution capable of allowing monotonically increasing, decreasing, bathtub and upside-down bathtub shaped hazard rates depending on its parameters. The maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms, and they have to be obtained by solving non-linear equations only. The asymptotic confidence intervals for the parameters are also obtained based on asymptotic variance covariance matrix. Finally, two empirical applications of the new model to real data are presented for illustrative purposes.
      PubDate: 2017-07-21
      DOI: 10.1007/s40745-017-0115-2
  • Reliability Estimation in Load-Sharing System Model with Application to
           Real Data
    • Authors: Pramendra Singh Pundir; Puneet Kumar Gupta
      Abstract: Abstract This study deals with the reliability analysis of a multi-component load sharing system where failure of any component within the system induces higher failure rate on the remaining surviving components. It is assumed that each component failure time follows Chen distribution. In classical set up, the maximum likelihood estimates of the load sharing parameters, system reliability and hazard rate along with their standard errors are computed. Since maximum likelihood estimates are not in closed form, so asymptotic confidence intervals and two bootstrap confidence intervals for the unknown parameters have also been constructed. Further, by assuming both informative and non-informative prior for the unknown parameters, Bayes estimates along with their posterior standard errors and HPD intervals of the parameters are obtained. Thereafter, a simulation study elicitates the theoretical developments. A real data analysis, at the end, eshtablishes the applicability of the proposed theory.
      PubDate: 2017-07-20
      DOI: 10.1007/s40745-017-0120-5
  • Using Maximum Subarrays for Approximate String Matching
    • Authors: Ramazan S. Aygun
      Abstract: Abstract In this paper, we evaluate maximum subarrays for approximate string matching and alignment. The global alignment score as well as local sub-alignments are indicators of good alignment. After showing how maximum sub-arrays could be used for string matching, we provide several ways of using maximum subarrays: long, short, loose, strict, and top-k. While long version extends the local sub-alignments, the short method avoids extensions that would not increase the alignment score. The loose method tries to achieve high global score whereas the strict method converts the output of loose alignment by minimizing the unnecessary gaps. The top-k method is used to find out top-k sub-alignments. The results are compared with two global and local dynamic programming methods that use gap penalties in addition to one of the state-of-art methods. In our experiments, using maximum subarrays generated good overall as well as local sub-alignments without requiring gap penalties.
      PubDate: 2017-07-19
      DOI: 10.1007/s40745-017-0117-0
  • Comparisons of Methods of Estimation for the NH Distribution
    • Authors: Sanku Dey; Chunfang Zhang; A. Asgharzadeh; M. Ghorbannezhad
      Abstract: Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes.
      PubDate: 2017-07-17
      DOI: 10.1007/s40745-017-0114-3
  • Classification in Non-linear Survival Models Using Cox Regression and
           Decision Tree
    • Authors: Reza Mokarram; Mehdi Emadi
      Abstract: Abstract Classification is the most important issues that have gained much attention in various fields such as health and medicine. Especially in survival models, classification represents a main objective and it is also one of the main purposes in data mining. Among data mining methods used for classification, implementation of the decision tree due to its simplicity and understandable and accurate results, has gained much attention and popularity. In this paper, first we generate the observations by using Monte-Carlo simulation from hazard model with the three degrees of complexity in different levels of censorship 0 to 70%. Then the accuracy of classification in the Cox and the decision tree models is compared for the number of samples 1000, 5000 and 10,000 by area under the ROC curve(AUC) and the ROC-test.
      PubDate: 2017-07-12
      DOI: 10.1007/s40745-017-0105-4
  • Flexible Heavy Tailed Distributions for Big Data
    • Authors: Yuanyuan Zhang; Saralees Nadarajah
      Abstract: Abstract The Pareto type I distribution (also known as the power law distribution and Zipf’s law) appears to be the main distribution used to model heavy tailed phenomena in the big data literature. The Pareto type I distribution being one of the oldest heavy tailed distributions is not very flexible. Here, we show flexibility of four other heavy tailed distributions for modeling four big data sets in social networks. The Pareto type I distribution is shown not to provide the best or even an adequate fit for any of the data sets.
      PubDate: 2017-06-10
      DOI: 10.1007/s40745-017-0113-4
  • Intelligence Quotient and Intelligence Grade of Artificial Intelligence
    • Authors: Feng Liu; Yong Shi; Ying Liu
      Abstract: Abstract Although artificial intelligence (AI) is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat,this study proposes a “standard intelligence model” that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture; testing and ranking the intelligence quotient (IQ) of naturally and artificially intelligent systems, including humans, Google, Microsoft’s Bing, Baidu, and Siri; and finally, the dividing of artificially intelligent systems into seven grades from robots to Google Brain. Based on this, we conclude that Google’s AlphaGo belongs to the third grade.
      PubDate: 2017-05-16
      DOI: 10.1007/s40745-017-0109-0
  • Internet of Things, Real-Time Decision Making, and Artificial Intelligence
    • Authors: James M. Tien
      Abstract: Abstract In several earlier papers, the author defined and detailed the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use. Adding another layer of physical sensors could then enhance its smartness and intelligence, especially if it were to be connected with other servgoods—thus, constituting an Internet of Things (IoT) or servgoods. More importantly, real-time decision making is central to the Internet of Things; it is about decision informatics and embraces the advanced technologies of sensing (i.e., Big Data), processing (i.e., real-time analytics), reacting (i.e., real-time decision-making), and learning (i.e., deep learning). Indeed, real-time decision making (RTDM) is becoming an integral aspect of IoT and artificial intelligence (AI), including its improving abilities at voice and video recognition, speech and predictive synthesis, and language and social-media understanding. These three key and mutually supportive technologies—IoT, RTDM, and AI—are considered herein, including their progress to date.
      PubDate: 2017-05-16
      DOI: 10.1007/s40745-017-0112-5
  • Gravity Sliding Algorithm for Linear Programming
    • Authors: Pei-Zhuang Wang; Ho-Chung Lui; Hai-Tao Liu; Si-Cong Guo
      Abstract: Abstract An algorithm named Gravity Sliding is presented in the paper, which emulates the gravity sliding motion in a feasible region D constrained by a group of hyper planes in \(R^{m}\) . At each stage point P of the sliding path, we need to calculate the projection of gravity vector g on constraint planes: if a constraint plane blocks the way at P, then it may change the direction of sliding path. The core technique is the synthetical treatment for multiple blocking planes, which is a basic problem of structural adjustment in practice; while the whole path provides the solution of a linear programming. Existing LP algorithms have no intuitive vision to emulate gravity sliding, therefore, their paths are not able to avoid circling and roving, and they could not provide a best direction at each step for structural adjustment. The first author presented the algorithm Cone Cutting (Wang in Inf Technol Decis Mak 10(1):65–82, 2011), which provides an intuitive explanation for Simplex pivoting. And then the algorithm Gradient Falling (Wang in Ann Data Sci 1(1):41–71, 2014. doi:10.1007/s40745-014-0005-9) was presented, which emulates the gradient motion on the feasible region. This paper is an improvement of gradient falling algorithm: in place of the description focusing on the null subspace of norm vectors, we focus the description on the expanding subspace of the very vectors in this paper. It makes the projection calculation easier and faster. We guess that the sliding path realized by the algorithm is the optimal path and the number of stage points of the path is limited by a polynomial function of the dimension number and the number of constraint planes.
      PubDate: 2017-04-05
      DOI: 10.1007/s40745-017-0108-1
  • Influencing Factors of Online P2P Lending Success Rate in China
    • Authors: Zhuopei Yang; Yanmei Zhang; Hengyue Jia
      Abstract: Abstract The low success rate of lending is the main drawback of development of online P2P lending platforms in China. Based on the theory of social capital, this study analysed the influence factors of success rate of P2P lending platform in China, using social network method and multiple linear regression model. Soft information, such as bidding record, has been creatively employed to study the corresponding topics. Data used in this study comes from the largest online P2P lending platform in China. The results show that: compared with other influence factors, the bidding record has a more significant effect on the success rate, and the users depend more on the social capital; the bidding records reduce the asymmetry of information, and help increasing the success rate of lending and decreasing the cost of online P2P lending.
      PubDate: 2017-03-10
      DOI: 10.1007/s40745-017-0103-6
  • Efficient Estimation of the PDF and the CDF of the Frechet Distribution
    • Authors: F. Maleki; E. Deiri
      Abstract: Abstract In this paper, we consider the estimation of the PDF and the CDF of the Frechet distribution. In this regard, following estimators are considered: uniformly minimum variance unbiased estimator, maximum likelihood estimator, percentile estimator, least squares estimator and weighted least squares estimator. To do so, analytical expressions are derived for the bias and the mean squared error. As the result of simulation studies and real data applications indicate, the ML estimator performs better than the others.
      PubDate: 2017-02-27
      DOI: 10.1007/s40745-017-0100-9
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