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Research ArticleOpen accessPhrase in the titleRisks Ratios of Shrinkage Estimators for the Multivariate Normal MeanAbdenour Hamdaoui, Nadia MezouarWe study the estimation of the mean θ of a multivariate Gaussian random variable X∼Np(θ,σ2Ip) in ℜp, σ2 is unknown and estimated by the chi-square variable S2∼σ2χn2. In this work we are interested in studying bounds and limits of risk ratios of shrinkage estimators to the maximum likelihood estimator X, when n and p tend to infinity. We recall that … …Journal of Mathematics and Statistics, Vol. 13, Issue 2 · 3 Apr 2017 · 7,113 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleInterleukin-10, Mean Arterial Blood Pressure and Insulin Resistance in Normal PregnancyAbdolreza Sotoodeh Jahromi, Mohammad Reza Farjam, Mohammad Hassan Davami et al.Problem statement: The purpose of this study was to investigate whether serum of Interleukin-10 (IL-10) Concentration change during normal pregnancy and, if so, to relate these changes corresponding alterations in insulin resistance and blood pressure. Approach: This cross sectional study was carried out on 86 healthy pregnant women including 26, 23 and 37 individuals in the 1st, 2nd and 3rd … …OnLine Journal of Biological Sciences, Vol. 11, Issue 3 · 2 Dec 2011 · 7,816 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSpeech Enhancement Using Minimum Mean-Square Error Amplitude Estimators Under Normal and Generalized Gamma DistributionChabane Boubakir, Daoud BerkaniProblem statement: In this study, DFT-based speech enhancement via Minimum Mean-Square Error (MMSE) amplitude estimators was considered. Approach: Several variants of the basic approach (MMSE-STSA) have been proposed over the years to address certain shortcomings, chiefly the quality of the remnant noise and its trade-off with speech distortion. In this study, we presented a comparative study between the MMLSA and … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 7,112 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComputational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data PointsT. Velmurugan, T. SanthanamProblem statement: Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. Clustering is an unsupervised learning technique. The main advantage of clustering … …Journal of Computer Science, Vol. 6, Issue 3 · 31 Mar 2010 · 19,451 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModeling Positive Time Series Data: A Neglected Aspect in Time Series CoursesMaman A. Djauhari, Lee Siaw Li, Rohayu Mohd SallehSomething has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first whether the data represent a Geometric Brownian Motion (GBM) process. If it is affirmative, unlike … …American Journal of Applied Sciences, Vol. 13, Issue 7 · 1 Jul 2016 · 7,536 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultiple Linear Regression to Predict Electrical Energy Consumption Based on Meteorological Data: Application to Some Sites Supplied by the CEB in TogoApaloo Bara Komla Kpomonè et al.The prediction model developed in this article is based on the use of meteorological variables to estimate the consumption of electrical energy at the substations of the Electric Community of Benin. The objective is to predict this consumption in order to adapt production to it. The posts (Lomé Aflao, Légbasito, and Lomé port) are the targets that were used in … …American Journal of Applied Sciences, Vol. 21, Issue 1 · 10 Jun 2024 · 5,790 accessesView PDFCite
Research ArticleOpen accessIn the abstractFlock Optimization Algorithm-Based Deep Learning Model for Diabetic Disease Detection ImprovementDivager Balasubramaniyan, Nor Azura Husin, Norwati Mustapha et al.Worldwide, 422 million people suffer from diabetic disease, and 1.5 million die yearly. Diabetes is a threat to people who still fail to cure or maintain it, so it is challenging to predict this disease accurately. The existing systems face data over-fitting issues, convergence problems, non-converging optimization complex predictions, and latent and predominant feature extraction. These issues affect the system's … …Journal of Computer Science, Vol. 20, Issue 2 · 8 Jan 2024 · 5,216 accessesView PDFCite
Research ArticleOpen accessIn the abstractHematological and Biochemical Analysis of Asymptomatic Fascioliasis of Bali Cattle (Bos Javanicus)Maskur, Adji Santoso Drajat, Sulaiman Ngongu Depamede et al.The research was to examine the hematological characteristic of fascioliasis in Bali cattle in year-round irrigated rice fields in tropical areas. Fifty cattle raised in rice field areas were examined by hematology, coproscopy, physiology, and liver function tests. The results showed that 50 cattle had heartbeat, respiratory and rectal temperature in normal standard, but, 41 (82%) out of 50 cattle … …American Journal of Animal and Veterinary Sciences, Vol. 17, Issue 3 · 4 Aug 2022 · 6,207 accessesView PDFCite
Research ArticleOpen accessIn the abstractHematological Biomarkers in Nellore Cows of Different Ages from the Bolivian TropicsAtsuko Ikeda, Pablo Roberto MariniThe objective of the work was to evaluate if the multiparous adult cows presented any difference in the values of hematological biomarkers compared to young primiparous Nellore cows from the Bolivian tropics. During the month of February 2021, a study was carried out with 14 multiparous adult cows and 13 young prim parous cows. The cows used were: Adult cows … …American Journal of Agricultural and Biological Sciences, Vol. 17, Issue 1 · 4 Feb 2022 · 6,901 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Misuse and Anomaly Intrusion Detection System Using Hybrid Supervised and Unsupervised Data Mining ApproachesHoma Molavi, Mohammad KhanbabaeiIntrusion Detection Systems (IDSs) play a crucial role in monitoring computer network security. Data mining and machine learning techniques facilitate the identification of intrusion patterns within large volumes of data. Hybrid data mining models have become increasingly popular in IDSs due to their enhanced effectiveness. This study presents a novel hybrid IDS that combines misuse and anomaly detection by integrating … …Journal of Computer Science, Vol. 22, Issue 6 · 6 Jul 2026 · 1,011 accessesView PDFCite