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Research ArticleOpen accessIn the abstractA Reformed K-Nearest Neighbors Algorithm for Big Data SetsVo Ngoc Phu, Vo Thi Ngoc TranA Data Mining Has Already Had Many Algorithms Which A K-Nearest Neighbors Algorithm, K-NN, Is A Famous Algorithm For Researchers. K-NN Is Very Effective On Small Data Sets, However It Takes A Lot Of Time To Run On Big Datasets. Today, Data Sets Often Have Millions Of Data Records, Hence, It Is Difficult To Implement K-NN On Big Data. In … …Journal of Computer Science, Vol. 14, Issue 9 · 8 Mar 2018 · 7,477 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementing a Hybrid VMD–BiTCN–BiGRU Architecture With Intelligent Optimization for Agricultural Commodity Price Prognostication in Gujarat MarketsJignesh Hirapara, Ratnesh Kumar Namdeo, Ankit Kumar Dubey et al.In the agricultural sector, the prices of pulses require adequate forecasting to plan farming activities, store and market, and guide policy-making. However, the pulse price series is highly volatile, seasonal, nonstationary, and nonlinear, and thus conventional linear time series models are not necessarily very effective. In the suggested framework, the raw price data series is decomposed using Variational Mode Decomposition … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Aug 2026 · 1,042 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultivariate Analysis of Gestational Diabetes Risk Factors in a North African Population: A Call for Targeted Maternal Health InterventionsHafssa Abdellaoui, Nadia Ouzennou, Adnane Hakem, Samia RkhaGestational Diabetes (GD) is a multifactorial condition influenced by demographic, anthropometric and obstetric determinants. Identifying predictive factors is essential to anticipate the disorder, guide prevention strategies and reduce maternal and neonatal complications. To assess the risk factors associated with GDM in pregnant women in the Marrakech-Safi region, in order to fill a gap in national data. A retrospective cross-sectional study … …OnLine Journal of Biological Sciences, Vol. 26, Issue 2 · 10 May 2026 · 1,536 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Extended Semi-Parametric Accelerated Failure Time Cure Model for Partial Cure Information KnownYu Wu, Yong Lin, Shou-En Lu et al.Cure model is a useful model for analyzing failure time data when there is evidence of long-term survivors. In traditional cure models, it is assumed that the cured or uncured status in the censored set cannot be distinguished. However, in many occasions, data of some diagnostic procedures, with some sensitivity and specificity, may have provided partial information about the cured … …Current Research in Biostatistics, Vol. 8, Issue 1 · 11 Dec 2018 · 7,622 accessesView PDFCite
Research ArticleOpen accessIn the abstractKnowledge Based Bayesian Network Construction Algorithm for Medical Data Fusion to Enhance Services and DiagnosisAhmed SamehTraditional Bayesian networks' algorithms are treating the network construction process as an isolated and autonomous data-driven trial-and-error process and completely ignoring the domain knowledge. In this work we are proposing a new 'Semantically Aware Ontology-Based Bayesian Network construction algorithm' that is knowledge centered instead of data centered. The objective of the new algorithm is to empower patients through improving their … …Journal of Computer Science, Vol. 15, Issue 5 · 28 May 2019 · 8,043 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerformance Evaluation of Apache Spark Vs MPI: A Practical Case Study on Twitter Sentiment AnalysisDeepa S Kumar, M Abdul RahmanThe advent of various processing frameworks which happens under big data technologies is due to tremendous dataset size and its complexity. The speed of execution was much higher with High Performance computing frameworks rather than big data processing frameworks. As majority of the jobs under big data are mostly data intensive rather than computation intensive, the High Performance Computing paradigms … …Journal of Computer Science, Vol. 13, Issue 12 · 23 Dec 2017 · 8,074 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnglish Sentiment Classification using Only the Sentiment Lexicons with a JOHNSON Coefficient in a Parallel Network EnvironmentVo Ngoc Phu, Vo Thi Ngoc TranSentiment classification is significant in everyday life, such as in political activities, commodity production and commercial activities. In this survey, we have proposed a new model for Big Data sentiment classification. We use many sentiment lexicons of our basis English Sentiment Dictionary (bESD) to classify 5,000,000 documents including 2,500,000 positive and 2,500,000 negative of our testing data set in English. … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 1 · 22 Dec 2017 · 7,331 accessesView PDFCite
Research ArticleOpen accessIn the abstractProcessing Large Volume of Biometric Data in the Hadoop Single Cluster Node EnvironmentJayakumar Vaithiyashankar, Shohel Sayeed, Anang Muhammad AminIn big data evolution, the analysis of large scale data and scrutinizing the required vital information becomes very demanding task. The emerging cloud platform promises and gives hope in handling the enormous volume of data. Hence, a new kind of methodology is required to tap the full potential of leveraging the big data analytics over the biometric data. In this … …American Journal of Applied Sciences, Vol. 14, Issue 12 · 27 Oct 2017 · 8,528 accessesView PDFCite
Research ArticleOpen accessIn the abstractOn Improving Antivirus Scanning Engines: Memory On-Access ScannerMohammed I. Al-Saleh, Rasha K. Al-HuthaifiThe Antivirus (AV) products are utilized by home user's community to attain protection. To some extent, the AV meets users' expectations by detecting previously known malware samples. In this study, we question the set of events which should trigger the AV to scan data. Scanning every single piece of data as it moves from one location into another could be … …Journal of Computer Science, Vol. 13, Issue 8 · 26 Jul 2017 · 8,494 accessesView PDFCite
Research ArticleOpen accessIn the abstractOn the Mean Estimation using Stratified Double Median Ranked Set SamplingMahmoud Ibrahim Syam, Amer Ibrahim Al-Omari, Kamarulzaman IbrahimStratified Double Median Ranked Set Sampling (SDMRSS) method is suggested for estimating the population mean. The SDMRSS is compared with the Simple Random Sampling (SRS), Stratified Simple Random Sampling (SSRS) and Stratified Ranked Set Sampling (SRSS) methods. It is shown that SDMRSS estimator is an unbiased of the population mean and is more efficient than the SRS, SSRS and SRSS … …American Journal of Applied Sciences, Vol. 14, Issue 4 · 12 Apr 2017 · 7,579 accessesView PDFCite