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285 articles for “Datasets”
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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 · 8,549 accessesView PDFCite
Research ArticleOpen accessIn the abstractExtended Trigger Terms for Extracting Adverse Drug Reactions in Social Media TextsRami Naim Mohammad Yousef, Sabrina Tiun, Nazlia OmarAdverse Drug Reaction (ADR) is a disorder caused by taking medications. Studies have addressed extracting ADRs from social networks where users express their opinion regarding a specific medication. Extracting entities mainly depends on specific terms called trigger terms that may occur before or after ADRs. However, these terms should be extended, especially when examining multiple representation of N-gram. This study … …Journal of Computer Science, Vol. 15, Issue 6 · 29 Jun 2019 · 8,058 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplying Neural Network-Based Approach to Sickle Cell Disease-Related Pain ClassificationZacchaeus Omogbadegun, Israel Ogundele, Olufunke OladipupoSickle Cell Disease (SCD), an inherited Red Blood Cell (RBC) disorder, is characterized by anaemia, end-organ damage, unpredictable episodes of pain and early mortality. SCD affects 25% of people living in Central and West Africa causing life threatening “silent” strokes and lifelong damage. Nigeria accounts for 50% of SCD births worldwide (estimated 150,000 of 300,000 babies born with Symptomatic Sickle … …Journal of Computer Science, Vol. 15, Issue 6 · 28 Jun 2019 · 7,683 accessesView PDFCite
Research ArticleOpen accessIn the abstractONCObc-ST: An Improved Clinical Reasoning Algorithm Based on Select and Test (ST) Algorithm for Diagnosing Breast CancerOlaide Nathaniel Oyelade, Sunday Adeyemi AdewuyiThe need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain of (medicine as example) application of such algorithms. Most reasoning algorithms for medical diagnosis are either limited by their techniques or accuracy and efficiency. Even the Select and Test (ST) algorithm which is considered a more approximate reasoning algorithm is also limited by its approach … …Current Research in Bioinformatics, Vol. 8, Issue 1 · 15 Apr 2019 · 7,168 accessesView PDFCite
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
Review ArticleOpen accessIn the abstractHealthcare Driven by Big Data AnalyticsCheryl Ann Alexander, Lidong WangText messages and social network posts are often included in big data and are frequently invaluable sources of health data. When machine learning or data mining is used, it is important to perform an automatic process for integrating all available and related health data. Artificial Neural Network (ANN) is one of the machine learning methods flexible in using algorithms to … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 3 · 28 Sep 2018 · 7,403 accessesView PDFCite
Research ArticleOpen accessIn the abstractThe Use of Band Selection in Weighted Linear Prediction for Hyperspectral Image ClassificationIbtissam Banit'ouagua, Mounir Ait Kerroum, Ahmed HammouchThe advancement of imaging procedures has made hyperspectral sensors fit for acquiring spectral data in many restricted and bordering bands, which brings about a high relationship between's neighboring bands and high information excess. It is important to decrease these bands previously advance analysis utilizing land cover classification and target location. Going for the classification undertaking, this paper displays another weighted … …Journal of Computer Science, Vol. 14, Issue 8 · 4 Sep 2018 · 8,266 accessesView PDFCite
Research ArticleOpen accessIn the abstractArabic Sentiment Classification using MLP Network Hybrid with Naive Bayes AlgorithmMohammad Subhi Al-Batah, Shakir Mrayyen, Malek AlzaqebahSentiment analysis has recently become one of the growing areas of research related to text mining and natural language processing. Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. Most of the current studies related to this topic focus mainly on English … …Journal of Computer Science, Vol. 14, Issue 8 · 7 Aug 2018 · 9,279 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Scalable Hybrid CNN-LSTM and SDN Based Architecture for Real-Time DDoS Defence in IoT and Cloud NetworksSujit Sutradhar, Joy Lal Sarkar, Rajesh Kumar VermaDistributed Denial-of-Service (DDoS) is one of the most significant threats to the security of IoT, cloud, and Software-Defined Network (SDN) environments because it can aggravate network unavailability and disrupt the continuity of services. This study presents a hybrid CNN-LSTM-based DDoS detection and mitigation system for real-time DDoS detection in the context of heterogeneous network environments, which is integrated with SDN … …Journal of Computer Science, Vol. 22, Issue 10 · 19 Oct 2026 · 535 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Machine Learning Approach to Predictive Analytics in Precision Agriculture for Sustainable Crop OptimizationJignesh Kariya, Pankaj MudholkarDriven by data-enabled technologies, precision agriculture is evolving into an innovative way to increase agricultural productivity and sustainability. Conventional agricultural practices face several challenges, such as poor soil quality, inappropriate crop selection, improper resource utilization, and unpredictable climate conditions. To address these limitations, this paper proposes a hybrid machine learning framework for sustainable crop optimization and predictive analysis in precision … …Journal of Computer Science, Vol. 22, Issue 10 · 10 Oct 2026 · 879 accessesView PDFCite