104 research outputs found
Compressing High-Dimensional Data Spaces Using Non-Differential Augmented Vector Quantization
query processing times and space requirements. Database compression has been
discovered to alleviate the I/O bottleneck, reduce disk space, improve disk access speed,
speed up query, reduce overall retrieval time and increase the effective I/O bandwidth.
However, random access to individual tuples in a compressed database is very difficult to
achieve with most available compression techniques.
We propose a lossless compression technique called non-differential augmented vector
quantization, a close variant of the novel augmented vector quantization. The technique is
applicable to a collection of tuples and especially effective for tuples with many low to
medium cardinality fields. In addition, the technique supports standard database
operations, permits very fast random access and atomic decompression of tuples in large
collections. The technique maps a database relation into a static bitmap index cached
access structure. Consequently, we were able to achieve substantial savings in space by
storing each database tuple as a bit value in the computer memory.
Important distinguishing characteristics of our technique is that individual tuples can be
compressed and decompressed, rather than a full page or entire relation at a time, (b) the
information needed for tuple compression and decompression can reside in the memory or
at worst in a single page. Promising application domains include decision support systems,
statistical databases and life databases with low cardinality fields and possibly no text
field
Development of Wearable Systems for Ubiquitous Healthcare Service Provisioning
This paper reports on the development of a wearable system using wireless
biomedical sensors for ubiquitous healthcare service provisioning. The
prototype system is developed to address current healthcare challenges such as
increasing cost of services, inability to access diverse services, low quality
services and increasing population of elderly as experienced globally. The
biomedical sensors proactively collect physiological data of remote patients to
recommend diagnostic services. The prototype system is designed to monitor
oxygen saturation level (SpO2), Heart Rate (HR), activity and location of the
elderly. Physiological data collected are uploaded to a Health Server (HS) via
GPRS/Internet for analysis.Comment: 6 pages, 3 figures, APCBEE Procedia 7, 2013. arXiv admin note:
substantial text overlap with arXiv:1309.154
A Neural-CBR System for Real Property Valuation
In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the
increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based
reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed
the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However,
individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of
decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system
for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case-
Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the
system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case-
Based Reasoning systems
Meta-Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses for Open Distance Learning
This study aimed to apply the meta-analysis methodology to systematically synthesize results of primary studies to discover the main significant factors influencing student acceptance of massive open online courses (MOOCs) for open distance learning (ODL). An abundance of studies on MOOCs exists, but there is a lack of meta-analysis research on student acceptance of MOOCs, which is a novel contribution of the current study. The meta-analysis methodology was applied to investigate effect sizes, statistical heterogeneity, and publication bias across 36 primary studies involving 14233 participating students. The study findings show satisfaction to be the main significant factor influencing student acceptance of MOOCs. The findings can enlighten stakeholders in the decision-making process of implementing MOOCs for ODL and advance technology acceptance models. Moreover, this study has the potential to theoretically contribute to technology acceptance research by situating the widely known technology acceptance models in the context of education
IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS
Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social
attributes information of destinations is made a factor in the destination recommendation process
Classification of Eukaryotic Organisms Through Cepstral Analysis of Mitochondrial DNA
Accurate classification of organisms into taxonomical hierarchies based on genomic sequences is currently an open challenge, because majority of the traditional techniques have been found wanting. In this study, we employed mitochondrial DNA (mtDNA) genomic sequences and Digital Signal Processing (DSP) for accurate classification of Eukaryotic organisms. The mtDNA sequences of the selected organisms were first encoded using three popular genomic numerical representation methods in the literature, which are Atomic Number (AN), Molecular Mass (MM) and Electron-Ion Interaction Pseudopotential (EIIP). The numerically encoded sequences were further processed with a DSP based cepstral analysis to obtain three sets of Genomic Cepstral Coefficients (GCC), which serve as the genomic descriptors in this study. The three genomic descriptors are named AN-GCC, MM-GCC and EIIP-GCC. The experimental results using the genomic descriptors, backpropagation and radial basis function neural networks gave better classification accuracies than a comparable descriptor in the literature. The results further show that the accuracy of the proposed genomic descriptors in this study are not dependent on the numerical encoding methods
Constructing Frugal Sales System for Small Enterprises
In the current study, the authors report on the application of the design science methodology to construct, utilize, and evaluate a frugal information system that uses mobile devices and cloud computing resources for documenting daily sales transactions of very small enterprises (VSEs). Small enterprises play significant roles in the socioeconomic landscape of a community by providing employment opportunities and contributing to the gross domestic product. However, VSEs have very little access to innovative information technologies that could help them manage their challenges that are restricting their effective growth, sustainability, and participation in a knowledge economy. The results of a field-evaluation experiment, involving 22 VSE entrepreneurs using a newly constructed system, MobiSales, disclosed that user behavior, which demonstrates confidence, excitement, enthusiasm, energy, and trust varied when employing a mobile electronic device for social interactions, as compared to using it for business transactions
Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals
Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies
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