Association Rules Mining Based Clinical Observations

Healthcare institutes enrich the repository of patients' disease related information in an increasing manner which could have been more useful by carrying out relational analysis. Data mining algorithms are proven to be quite useful in exploring useful correlations from larger data repositories. In this paper we have implemented Association Rules mining based a novel idea for finding co-occurrences of diseases carried by a patient using the healthcare repository. We have developed a system-prototype for Clinical State Correlation Prediction (CSCP) which extracts data from patients' healthcare database, transforms the OLTP data into a Data Warehouse by generating association rules. The CSCP system helps reveal relations among the diseases. The CSCP system predicts the correlation(s) among primary disease (the disease for which the patient visits the doctor) and secondary disease/s (which is/are other associated disease/s carried by the same patient having the primary disease).Comment: 5 pages, MEDINFO 2010, C. Safran et al. (Eds.), IOS Pres

VAPI: Vectorization of Algorithm for Performance Improvement

This study presents the vectorization of metaheuristic algorithms as the first stage of vectorized optimization implementation. Vectorization is a technique for converting an algorithm, which operates on a single value at a time to one that operates on a collection of values at a time to execute rapidly. The vectorization technique also operates by replacing multiple iterations into a single operation, which improves the algorithm's performance in speed and makes the algorithm simpler and easier to be implemented. It is important to optimize the algorithm by implementing the vectorization technique, which improves the program's performance, which requires less time and can run long-running test functions faster, also execute test functions that cannot be implemented in non-vectorized algorithms and reduces iterations and time complexity. Converting to vectorization to operate several values at once and enhance algorithms' speed and efficiency is a solution for long running times and complicated algorithms. The objective of this study is to use the vectorization technique on one of the metaheuristic algorithms and compare the results of the vectorized algorithm with the algorithm which is non-vectorized.Comment: 21 page

TEduChain: A Platform for Crowdsourcing Tertiary Education Fund using Blockchain Technology

Blockchain is an emerging technology framework for creating and storing transaction in distributed ledgers with a high degree of security and reliability. In this paper we present a blockchain-based platform to create and store contracts in between students and their higher education sponsors. The sponsorship might be in any form, such as scholarship, donation or loan. The fund will be arranged and managed by a group of competitive agents (Fundraisers) who will hold the distributed ledgers and act as miners in the blockchain network

Time-dependent physicochemical characteristics of Malaysian residual soil stabilized with magnesium chloride solution

The effects of non-traditional additives on the geotechnical properties of tropical soils have been the subject of investigation in recent years. This study investigates the strength development and micro-structural characteristics of tropical residual soil stabilized with magnesium chloride (MgCl2) solution. Unconfined compression strength (UCS) and standard direct shear tests were used to assess the strength and shear properties of the stabilized soil. In addition, the micro-structural characteristics of untreated and stabilized soil were discussed using various spectroscopic and microscopic techniques such as X-ray diffractometry (XRD), energy-dispersive X-ray spectrometry (EDAX), field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR) and Brunauer, Emmett and Teller (BET) surface area analysis. From the engineering point of view, the results indicated that the strength of MgCl2-stabilized soil improved noticeably. The degree of improvement was approximately two times stronger than natural soil after a 7-day curing period. The results also concluded the use of 5 % of MgCl2 by dry weight of soil as the optimum amount for stabilization of the selected soil. In addition, the micro-structural study revealed that the stabilization process modified the porous network of the soil. The pores of the soils had been filled by the newly formed crystalline compounds known as magnesium aluminate hydrate (M-A-H).Ministry of Education Malaysia under the Fundamental Research Grant (FRGS) (R.J130000.7822.4F658); Universiti Teknologi Malaysia (UTM); Construction Research Centre UT

Sustainable Innovation and Creativity for Value Creation: A Study of Hospitality Enterprises in Jos Metropolis, Nigeria

Hospitality enterprises in Nigeria are currently experiencing serious stagnation. Their rate of value creation has been on the decline as a result of inadequate sustainable innovation, creative skills, emerging technology, and inadequate adoption of the automation system. This study aims to investigate the impacts of sustainable innovation on creativity for the creation of value in hospitality enterprises in Jos Metropolis. 108 individuals were selected as the sample size through the formula by Morgan and Krejcie. Furthermore, a quantitative approach was applied as the primary method of data collection. The formulated hypotheses were tested using Ordinary Least Squares (OLS) regression method. Based on this study’s findings, a significant association was proven between sustainable innovation and customers’ satisfaction, creativity skills, and competitiveness. However, there was no significant association between the utilisation of technology and the creation of value in hospitality enterprises. Lastly, it is recommended that owner-managers in hospitality enterprises improve their sustainable innovation for value creation and service delivery. They should focus on customers’ needs and satisfaction, the utilisation of technology, in the long run, improvement in the return on investment, increase in profit margins and revenue, and large market share for sustainable growth of the hospitality enterprises in Jos Metropolis. Keywords: Sustainable innovation, Creativity, Value creation, Hospitality enterprises, Jos, Metropolis DOI: 10.7176/EJBM/11-26-04 Publication date:September 30th 201

Principal Components Analysis of Raman Spectral Data for Screening of Hepatitis C Infection

In the current study, Raman spectroscopy is employed for the identification of the biochemical changes taking place during the development of Hepatitis C. The Raman spectral data acquired from the human blood plasma samples of infected and healthy individuals is analysed by Principal Components Analysis and the Raman spectral markers of the Hepatitis C Virus (HCV) infection are identified. Spectral changes include those associated with nucleic acidsat720 cm−1, 1077 cm−1 1678 (CO stretching mode of dGTP of RNA), 1778 cm−1 (RNA), with proteins at 1641 cm−1(amide-I), 1721 cm−1(CC stretching of proteins) and lipids at 1738 cm−1(CO of ester group in lipids). These differences in Raman spectral features of blood plasma samples of the patients and healthy volunteers can be associated with the development of the biochemical changes during HCV infection

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20×20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads

Investigation on Dielectric Properties of Sludge Waste from Water Treatment Using Microwave Non-Destructive Testing (MNDT)

The demand for water cleanup rises in tandem with a country's requirements and development. Recovery of purified water containing nutrients and other beneficial materials is a critical opportunity that must be taken advantage of. A challenge that needs to be tackled is the necessity for large capacity and high-value management of sludge waste following the water treatment process. The pH level and microwave frequencies influence were used as a starting point for assessing the content of the sludge waste. Microwave non-destructive testing (MNDT) is a microwave measurement that can be used to determine the dielectric characteristics of materials without destroying or modifying the sample's content. The methodology employs a free-space measurement technique with a frequency range of 8 to 12 GHz (X-band). Through S-parameters acquired, a correlation analysis was done to analyze the effect of frequencies with the sludge waste. A comparative investigation with peat soil samples in establishing if the sludge has similar attributes to normal soil is used to ensure the accuracy of the sludge waste data. It can be determined that the sludge waste has a high signal correlation towards the frequency band 8 GHz to 12 GHz, which is compatible with the MNDT approach. All of the sludge samples had a pH range that is appropriate for agricultural use

Investigation on Dielectric Properties of Sludge Waste from Water Treatment Using Microwave Non-Destructive Testing (MNDT)

The demand for water cleanup rises in tandem with a country's requirements and development. Recovery of purified water containing nutrients and other beneficial materials is a critical opportunity that must be taken advantage of. A challenge that needs to be tackled is the necessity for large capacity and high-value management of sludge waste following the water treatment process. The pH level and microwave frequencies influence were used as a starting point for assessing the content of the sludge waste. Microwave non-destructive testing (MNDT) is a microwave measurement that can be used to determine the dielectric characteristics of materials without destroying or modifying the sample's content. The methodology employs a free-space measurement technique with a frequency range of 8 to 12 GHz (X-band). Through S-parameters acquired, a correlation analysis was done to analyze the effect of frequencies with the sludge waste. A comparative investigation with peat soil samples in establishing if the sludge has similar attributes to normal soil is used to ensure the accuracy of the sludge waste data. It can be determined that the sludge waste has a high signal correlation towards the frequency band 8 GHz to 12 GHz, which is compatible with the MNDT approach. All of the sludge samples had a pH range that is appropriate for agricultural use

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature