Search Results - (( developing pervasive computing algorithm ) OR ( java applications tree algorithm ))
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Making location-aware computing working accurately in smart spaces
Published 2011“…In smart environment, making a location-aware personal computing working accurately is a way of getting close to the pervasive computing vision. …”
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Book Chapter -
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Graph processing hardware accelerator for shortest path algorithms in nanometer very large-scale integration interconnect routing
Published 2007“…Graphs are pervasive data structures in computer science, and algorithms working with them are fundamental to the field. …”
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Thesis -
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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A hardware implementation of Rivest-Shamir-Adleman co-processor for resource constrained embedded systems
Published 2006“…The concern with security problems has been rapidly increasing as computers and Internet services become a more pervasive part of our daily life. …”
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Thesis -
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
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Conference or Workshop Item -
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LINGUISTIC FUZZY MODELING IN LASER MACHINING QUALITY EVALUATION
Published 2007“…The aim of this scientific research is to design knowledge based linguistic rules, algorithm, architecture & learning ability and further develop fuzzy model for laser machining kerf edge quality prediction. …”
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Conference or Workshop Item -
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Fuzzy expert system modeling of laser processing
Published 2009“…This paper discusses a specific approach of surface roughness predictive modelling by Fuzzy Logic - a unique way of computational solution. The objective of the paper is: to design knowledge based rules, algorithm, architecture & learning ability to develop fuzzy surface roughness predictive model for laser machining; to develop fuzzy predictive model using design parameters which were critically analyzed; to make a comparative study of the observed and modelled surface roughness output of 5mm Mn-Mo pressure vessel plate. …”
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Article -
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AI powered asthma prediction towards treatment formulation: an android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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RSSI Fingerprinting Approach for Location-Based Services
Published 2006“…Therefore, based on my knowledge and developed tools in localization, I can further develop a locationbased service system. …”
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Final Year Project -
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AI powered asthma prediction towards treatment formulation : An android app approach
Published 2022“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network
Published 2021“…The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. …”
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