hidden patterns
Summarize and Discover Hidden Patterns for Decision Making using Big Data Analytics
... Abstract—In this project used to Actionable intelligence It means much more than simply finding or summarizing data; it stresses the discovery of hidden (unknown and hard to find)patterns that can be used ...
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Title: Discovering hidden patterns in Turkish construction projects delays related to project characteristic
... Since the delay in construction projects has an important place, it causes great economic and time losses. Therefore, many researches have been conducted to investigate delays in construction projects. As a difference, ...
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Clustering and Hiding Sensitive Data for Social Network Dataset
... Data Mining is used for analyzing data from various perspectives that converts the previously unknown information into potentially useful data from huge databases. It is the major step in KDD (Knowledge Discovery in ...
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Big Data Analytics for Professionals, Data milling for Laypeople
... 10. “The need to process and analyze such massive datasets has introduced a new form of data analytics called Big Data Analytics. Big Data analytics involves analyzing large amounts of data of a variety of types to ...
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SeDaTiVe:SDN Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber Physical Systems
... Deep learning is used to manage the flow control of traffic in VCPS as follows. Initially, the data is gathered to train the deep learning model from the vehicles present in the network. Then, the network controller ...
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DATA MINING: APPLICATIONS AND USAGE IN HEALTH CARE
... the hidden patterns in the data sets of the medical ...These patterns can be utilized for clinical diagnosis for widely distributed in raw medical data which is heterogeneous in nature and ...and ...
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1. Data mining classification of teaching evaluation using weka
... In general, teacher evaluation refers to the formal process used to review and rate teacher’s performance and effectiveness in the classroom. Ideally, the findings from these evaluations are used to provide feedback to ...
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Performance Analysis of Hybrid approach of Clustering Algorithms
... Clustering is a way that classifies the raw data reasonably and searches the hidden patterns that may exist in datasets. It is a process of grouping data objects into disjoint clusters so that data in the ...
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Enhancement of educational system using data mining techniques
... including, patterns, associations, trees, changes, trends, anomalies and significant structures from large or complex data sets that are not ...the hidden patterns, associations, classification and ...
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BIG DATA ANALYTICS ARCHITECTURE
... Abstract: - The term Big Data describe new architecture, new algorithms and new techniques to capture data from various sources like social sites, sensed data, web forms, emails, climate information etc. This captured ...
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AN OPTIMISTIC ANALYSIS OF BIG DATA BY USING HDFS
... Big Data study allow to a large variety of use cases reach across multiple industries. Numerous data today is not natively in systematic format. Data analysis, retrieval, organization and modeling are the essential ...
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Enhance prediction of autism spectrum disorder using adaptive Bayesian classifier
... Classification plays a major role in the medical field to predict diseases. The prediction analyzes the relation between the expected information and the available information. It’s the duty of the classifier to make a ...
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A STUDY ON DATA MINING TECHNIQUES
... of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data ...for hidden ...
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Privacy Preserving Random Decision Trees over Randomly Partitioned Dataset
... Data mining is a practice of examining large databases in order to look for hidden patterns in a group of data that can be used to predict future behaviour. True data mining techniques doesn’t just change ...
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Character Eyes: Seeing Language through Character Level Taggers
... We train a set of LSTM tagging models, follow- ing the setup of Ling et al. (2015). A word rep- resentation trained from a character-level LSTM submodule is fed into a word-level bidirectional LSTM, with each word’s ...
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Integration of agent-based and GIS-based modelling for geosimulation of human-elephant Interactions in the Bunda district, Tanzania
... emergent patterns are subject to deterministic-centralised mindset because people usually underestimate the role of randomness in creating emergence patterns and therefore they ignore randomness as the ...
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Representation of Linguistic Form and Function in Recurrent Neural Networks
... As our case study we picked the I MAGINET model introduced by Chrupała, K´ad´ar, and Alishahi (2015). It is a multi-task, multi-modal architecture consisting of two gated- recurrent unit (GRU) (Cho et al. 2014; Chung et ...
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Ten quick tips for machine learning in computational biology
... A machine learning algorithm is a computational method based upon statistics, imple- mented in software, able to discover hidden non-obvious patterns in a dataset, and moreover to make reliable statistical ...
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Identification of Concurrent Control Chart Patterns in Time Series
... mixture patterns are then served as the independent sources of the mixture ...The hidden basic patterns of the mixture patterns could be discovered in these independent components ...
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Bayesian Hidden Topic Markov Models
... the Hidden Topic Markov Model (HTMM) proposed by Gruber, Rosen-Zvi, and Weiss (2007) from a purely frequentist framework into a fully Bayesian ...the hidden Markov model embedded in the HTMM was elucidated ...
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