79 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
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
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
Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation
Lung cancer is one of the diseases responsible for a large number of cancer related death
cases worldwide. The recommended standard for screening and early detection of lung
cancer is the low dose computed tomography. However, many patients diagnosed die
within one year, which makes it essential to find alternative approaches for screening and
early detection of lung cancer. We present computational methods that can be implemented
in a functional multi-genomic system for classification, screening and early detection of lung
cancer victims. Samples of top ten biomarker genes previously reported to have the highest
frequency of lung cancer mutations and sequences of normal biomarker genes were
respectively collected from the COSMIC and NCBI databases to validate the computational
methods. Experiments were performed based on the combinations of Z-curve and tetrahedron
affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and
Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination
of computational methods to achieve improved classification of lung cancer biomarker
genes. Results show that a combination of affine transforms of Voss representation, HOG
genomic features and Gaussian RBF neural network perceptibly improves classification
accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving
low mean square erro
Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation
Lung cancer is one of the diseases responsible for a large number of cancer related death
cases worldwide. The recommended standard for screening and early detection of lung
cancer is the low dose computed tomography. However, many patients diagnosed die
within one year, which makes it essential to find alternative approaches for screening and
early detection of lung cancer. We present computational methods that can be implemented
in a functional multi-genomic system for classification, screening and early detection of lung
cancer victims. Samples of top ten biomarker genes previously reported to have the highest
frequency of lung cancer mutations and sequences of normal biomarker genes were
respectively collected from the COSMIC and NCBI databases to validate the computational
methods. Experiments were performed based on the combinations of Z-curve and tetrahedron
affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and
Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination
of computational methods to achieve improved classification of lung cancer biomarker
genes. Results show that a combination of affine transforms of Voss representation, HOG
genomic features and Gaussian RBF neural network perceptibly improves classification
accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving
low mean square erro
Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features
This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations
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
Smart city technology based architecture for refuse disposal management
Many modern cities are currently encumbered with various challenges among which is the need to promote the culture of environmental sanitation for healthy living. However, advances in information communications technology have given birth to the concept of smart city, which is rapidly being applied to address some of the challenges being faced in such cities. This paper presents the development of an architecture based on smart city technology, for refuse disposal management in communities. A proof of concept prototype was implemented for the proposed architecture using Arduino UNO microcontroller board, proximity sensor, breadboard, refuse bin and a personal computer. The proximity sensor was interfaced with the Arduino board to capture dataset that correspond to the five different positions calibrated on a refuse bin. The dataset was shown to be of good quality since the graph of the mean voltages against the distances is similar to the proximity sensor characteristic graph. To determine the appropriate classifier for realizing the pattern classification unit of the prototype, an experiment was performed using the acquired dataset to train five different variants of the K-NN classifier. The 1-NN classifier was nominated for the prototype because it is simple and it gave higher values of accuracy, precision and recal
- …