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25 results for “Component Selection”
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Research ArticleOpen accessIn the abstractUltimate Prediction of Stock Market Price MovementRebwar M. Nabi, Soran Ab. M. Saeed, Habibolah Bin Harron, Hamido FujitaInvestment in the stock market is currently very popular due to its economic gain. Numerous researchers’ and academicians’ work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature … …Journal of Computer Science, Vol. 15, Issue 12 · 25 Dec 2019 · 8,545 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Artificial Neural Network Model for Option PricingHanningtone Meshack Simiyu, Anthony Gichuhi Waititu, Jane Aduda AkinyiIn the absence of a well-defined input selection technique associated with the pure ANN models, Option pricing using pure ANN models while relaxing the assumption of constant volatility remains a challenge. The conservative drill espoused has been to make allowances for a large number of input lags with the confidence that the ability of ANN to integrate suppleness and redundancy … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 8 Aug 2019 · 7,410 accessesView PDFCite
Research ArticleOpen accessIn the abstractDimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data ClassificationAdiwijaya, Untari N. Wisesty, E. Lisnawati et al.Cancer is one of the most deadly diseases in the world. The International Agency for Research on Cancer (IARC) noted 14.1 million new cancer cases and 8.2 million deaths from cancer in 2012. In the last few years, DNA microarray technology has increasingly been used to analyze and diagnose cancer. Analysis of gene expression data in the form of microarray … …Journal of Computer Science, Vol. 14, Issue 11 · 15 Nov 2018 · 9,930 accessesView PDFCite
EditorialOpen accessIn the abstractImpact of Quantitative Pharmacology on Drug DevelopmentAna Ruiz-Garcia, Kourosh ParivarThesuccess of drug development rests over two well defined pillars, a goodunderstanding of the drug’s clinical pharmacology and the appropriate targetpopulation for whom the clinical use is intended. The label for human prescription drugsrequires a good understanding of the pharmacological effects (pharmacodynamicsor PD) and the mechanism of action of the drug as well as detailed informationof the drug’s Pharmacokinetics (PK): … …American Journal of Pharmacology and Toxicology, Vol. 9, Issue 4 · 24 Apr 2015 · 7,158 accessesView PDFCite
Research ArticleOpen accessIn the abstractSMOOTHING SPLINE IN SEMIPARAMETRIC ADDITIVE REGRESSION MODEL WITH BAYESIAN APPROACHRita Diana, I. Nyoman Budiantara, Purhadi, Satwiko DarmestoSemiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 12 Jun 2013 · 7,252 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing P300 Component by Spectral Power Ratio Principal Components for a Single Trial Brain-Computer InterfaceS. Andrews, Ramaswamy Palaniappan, Andrew Teoh, Loo chu KiongHere we present a novel approach to detect P300 wave in single trial Visual Event Related Potential (VERP) signals using improved principal component analysis to enable a faster brain-computer interface (BCI) design. In the process, the principal components (PCs) are selected using novel methods, namely spectral power ratio (SPR) and sandwich spectral power ratio (SSPR). We set out to assess … …American Journal of Applied Sciences, Vol. 5, Issue 6 · 30 Jun 2008 · 7,881 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart Orthopaedic Implants: Applications in Total Knee ArthroplastyMatthew K. Dion, John Drazan, Khaled Abdoun et al.Total knee arthroplasty is a common orthopaedic procedure conducted in the United States with approximately 700,000 surgeries performed annually. A common complication following total knee arthroplasty is anterior knee pain which affects tens to hundreds of thousands of people each year. The exact mechanism that leads to anterior knee pain remains unknown, but improper component selection may cause pathologic loading … …American Journal of Engineering and Applied Sciences, Vol. 9, Issue 4 · 23 Dec 2016 · 9,069 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid of Fuzzy Clustering Neural Network Over NSL Dataset for Intrusion Detection SystemDahlia Asyiqin Ahmad Zainaddin, Zurina Mohd HanapiIntrusion Detection System (IDS) is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies … …Journal of Computer Science, Vol. 9, Issue 3 · 4 May 2013 · 6,574 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram SignalPaulchamy Balaiah, IlavennilaProblem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram) from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time), time domain biological signals, which are not reproducible. It is supposed to contain … …American Journal of Applied Sciences, Vol. 9, Issue 10 · 15 Aug 2012 · 7,087 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Improved Face Recognition Technique Based on Modular LPCA ApproachMathu Soothana S. Kumar, Retna Swami, Muneeswaran KaruppiahProblem statement: A face identification algorithm based on modular localized variation by Eigen Subspace technique, also called modular localized principal component analysis, is presented in this study. Approach: The face imagery was partitioned into smaller sub-divisions from a predefined neighborhood and they were ultimately fused to acquire many sets of features. Since a few of the normal facial features of … …Journal of Computer Science, Vol. 7, Issue 12 · 22 Oct 2011 · 6,950 accessesView PDFCite