Classifying Bio-Chip Data using an Ant Colony System Algorithm

Bio-chips are used for experiments on genes and contain various information such as genes, samples and so on. The two-dimensional bio-chips, in which one axis represent genes and the other represent samples, are widely being used these days. Instead of experimenting with real genes which cost lots of money and much time to get the results, bio-chips are being used for biological experiments. And extracting data from the bio-chips with high accuracy and finding out the patterns or useful information from such data is very important. Bio-chip analysis systems extract data from various kinds of bio-chips and mine the data in order to get useful information. One of the commonly used methods to mine the data is classification. The algorithm that is used to classify the data can be various depending on the data types or number characteristics and so on. Considering that bio-chip data is extremely large, an algorithm that imitates the ecosystem such as the ant algorithm is suitable to use as an algorithm for classification. This paper focuses on finding the classification rules from the bio-chip data using the Ant Colony algorithm which imitates the ecosystem. The developed system takes in consideration the accuracy of the discovered rules when it applies it to the bio-chip data in order to predict the classes.

Measuring Awareness of Waste Management among School Children using Rasch Model Analysis

The enormous amount of solid waste generated poses huge problems in waste management. It is therefore important to gauge the awareness of the public with regards to waste management. In this study, an instrument was developed to measure the beliefs, attitudes and practices about waste management of school children as an indication of their waste management awareness. This instrument has showed that a positive awareness towards waste management refers mainly to attitudes. However it is not easy for people to practice waste management as a reflection of their awareness.

Development of Subjective Measures of Interestingness: From Unexpectedness to Shocking

Knowledge Discovery of Databases (KDD) is the process of extracting previously unknown but useful and significant information from large massive volume of databases. Data Mining is a stage in the entire process of KDD which applies an algorithm to extract interesting patterns. Usually, such algorithms generate huge volume of patterns. These patterns have to be evaluated by using interestingness measures to reflect the user requirements. Interestingness is defined in different ways, (i) Objective measures (ii) Subjective measures. Objective measures such as support and confidence extract meaningful patterns based on the structure of the patterns, while subjective measures such as unexpectedness and novelty reflect the user perspective. In this report, we try to brief the more widely spread and successful subjective measures and propose a new subjective measure of interestingness, i.e. shocking.

Green Lean TQM Practices in Malaysian Automotive Companies

Green Lean Total Quality Management (TQM) System is a system comprises of Environmental Management System (EMS) practices which is integrated to TQM with Lean Manufacturing (LM) principles. The ultimate goal of this system is to focus on achieving total customer satisfaction and environmental care by removing eight wastes available in any process in an organization. A survey questionnaire was developed and distributed to 30 highly active automotive vendors in Malaysia and analyzed by SPSS v.17. It was found out that some vendors have been practicing TQM and LM while some have started to implement EMS. This study is only focusing on highly active companies that have been involved in MAJAICO Program and Proton Vendor Development Program. This is the first study conducted to know the current status of TQM, LM and EMS practices in highly active automotive companies in Malaysia. It was found out that EMS has been practiced by 16 companies out of 30. Within these 16 companies the approach is more holistic and green. This is a preliminary study that combined 4 awards practices, ISO/TS16949, Toyota Production System SAEJ4000, MAJAICO Lean Production System and EMS.

Technology Diffusion and Inclusive Development in Africa: A System Dynamics Perspective

Technology or lack of it will play an important role in Africa-s effort to achieve inclusive development. Although a key determinant of competitiveness, new technology can exacerbate exclusion of the majority from the mainstream economic activities. To minimise potential technology exclusion while leveraging its critical role in African-s development, requires insight into technology diffusion process. Using system dynamics approach, a technology diffusion model is presented. The frequency of interaction of people exposed to and those not exposed to technology, and the technology adoption rate - the fraction of people who embrace new technologies once they are exposed, are identified as the broad factors critical to technology diffusion to wider society enabling more people to be part of the economic growth process. Based on simulation results, it is recommends that these two broad factors should form part of national policy aimed at achieving inclusive and sustainable development in Africa.

Cascaded ANN for Evaluation of Frequency and Air-gap Voltage of Self-Excited Induction Generator

Self-Excited Induction Generator (SEIG) builds up voltage while it enters in its magnetic saturation region. Due to non-linear magnetic characteristics, the performance analysis of SEIG involves cumbersome mathematical computations. The dependence of air-gap voltage on saturated magnetizing reactance can only be established at rated frequency by conducting a laboratory test commonly known as synchronous run test. But, there is no laboratory method to determine saturated magnetizing reactance and air-gap voltage of SEIG at varying speed, terminal capacitance and other loading conditions. For overall analysis of SEIG, prior information of magnetizing reactance, generated frequency and air-gap voltage is essentially required. Thus, analytical methods are the only alternative to determine these variables. Non-existence of direct mathematical relationship of these variables for different terminal conditions has forced the researchers to evolve new computational techniques. Artificial Neural Networks (ANNs) are very useful for solution of such complex problems, as they do not require any a priori information about the system. In this paper, an attempt is made to use cascaded neural networks to first determine the generated frequency and magnetizing reactance with varying terminal conditions and then air-gap voltage of SEIG. The results obtained from the ANN model are used to evaluate the overall performance of SEIG and are found to be in good agreement with experimental results. Hence, it is concluded that analysis of SEIG can be carried out effectively using ANNs.

Study of Characteristics of Multi-Layer Piezoelectric Transformers by using 3-D Finite Element Method

Piezoelectric transformers are electronic devices made from piezoelectric materials. The piezoelectric transformers as the name implied are used for changing voltage signals from one level to another. Electrical energy carried with signals is transferred by means of mechanical vibration. Characterizing in both electrical and mechanical properties leads to extensively use and efficiency enhancement of piezoelectric transformers in various applications. In this paper, study and analysis of electrical and mechanical properties of multi-layer piezoelectric transformers in forms of potential and displacement distribution throughout the volume, respectively. This paper proposes a set of quasi-static mathematical model of electromechanical coupling for piezoelectric transformer by using a set of partial differential equations. Computer-based simulation utilizing the three-dimensional finite element method (3-D FEM) is exploited as a tool for visualizing potentials and displacements distribution within the multi-layer piezoelectric transformer. This simulation was conducted by varying a number of layers. In this paper 3, 5 and 7 of the circular ring type were used. The computer simulation based on the use of the FEM has been developed in MATLAB programming environment.

On the EM Algorithm and Bootstrap Approach Combination for Improving Satellite Image Fusion

This paper discusses EM algorithm and Bootstrap approach combination applied for the improvement of the satellite image fusion process. This novel satellite image fusion method based on estimation theory EM algorithm and reinforced by Bootstrap approach was successfully implemented and tested. The sensor images are firstly split by a Bayesian segmentation method to determine a joint region map for the fused image. Then, we use the EM algorithm in conjunction with the Bootstrap approach to develop the bootstrap EM fusion algorithm, hence producing the fused targeted image. We proposed in this research to estimate the statistical parameters from some iterative equations of the EM algorithm relying on a reference of representative Bootstrap samples of images. Sizes of those samples are determined from a new criterion called 'hybrid criterion'. Consequently, the obtained results of our work show that using the Bootstrap EM (BEM) in image fusion improve performances of estimated parameters which involve amelioration of the fused image quality; and reduce the computing time during the fusion process.

Analysis of Equal cost Adaptive Routing Algorithms using Connection-Oriented and Connectionless Protocols

This research paper evaluates and compares the performance of equal cost adaptive multi-path routing algorithms taking the transport protocols TCP (Transmission Control Protocol) and UDP (User Datagram Protocol) using network simulator ns2 and concludes which one is better.

New Enhanced Hexagon-Based Search Using Point-Oriented Inner Search for Fast Block Motion Estimation

Recently, an enhanced hexagon-based search (EHS) algorithm was proposed to speedup the original hexagon-based search (HS) by exploiting the group-distortion information of some evaluated points. In this paper, a second version of the EHS is proposed with a new point-oriented inner search technique which can further speedup the HS in both large and small motion environments. Experimental results show that the enhanced hexagon-based search version-2 (EHS2) is faster than the HS up to 34% with negligible PSNR degradation.

Service Architecture for 3rd Party Operator's Participation

Next generation networks with the idea of convergence of service and control layer in existing networks (fixed, mobile and data) and with the intention of providing services in an integrated network, has opened new horizon for telecom operators. On the other hand, economic problems have caused operators to look for new source of income including consider new services, subscription of more users and their promotion in using morenetwork resources and easy participation of service providers or 3rd party operators in utilizing networks. With this requirement, an architecture based on next generation objectives for service layer is necessary. In this paper, a new architecture based on IMS model explains participation of 3rd party operators in creation and implementation of services on an integrated telecom network.

Evolutionary Techniques Based Combined Artificial Neural Networks for Peak Load Forecasting

This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of neural networks has been trained with different architecture and training parameters. The networks are trained and tested for the actual load data of Chennai city (India). A set of better trained conventional ANNs are selected to develop a CANN module using different algorithms instead of using one best conventional ANN. Obtained results using CANN module confirm its validity.

Implementation and Analysis of Elliptic Curve Cryptosystems over Polynomial basis and ONB

Polynomial bases and normal bases are both used for elliptic curve cryptosystems, but field arithmetic operations such as multiplication, inversion and doubling for each basis are implemented by different methods. In general, it is said that normal bases, especially optimal normal bases (ONB) which are special cases on normal bases, are efficient for the implementation in hardware in comparison with polynomial bases. However there seems to be more examined by implementing and analyzing these systems under similar condition. In this paper, we designed field arithmetic operators for each basis over GF(2233), which field has a polynomial basis recommended by SEC2 and a type-II ONB both, and analyzed these implementation results. And, in addition, we predicted the efficiency of two elliptic curve cryptosystems using these field arithmetic operators.

Planning the Building Evacuation Routes by a Spatial Network

The previous proposed evacuation routing approaches usually divide the space into multiple interlinked zones. However, it may be harder to clearly and objectively define the margins of each zone. This paper proposes an approach that connects locations of necessary guidance into a spatial network. In doing so, evacuation routes can be constructed based on the links between starting points, turning nodes, and terminal points. This approach more conforms to the real-life evacuation behavior. The feasibility of the proposed approach is evaluated through a case of one floor in a hospital building. Results indicate that the proposed approach provides valuable suggestions for evacuation planning.

Using Services Oriented Architecture to Improve Efficient Web-Services for Postgraduate Students

The main aim of this paper is to present the research findings on the solution of centralized Web-Services for students by adopting a framework and a prototype for Service Oriented Architecture (SOA) Web-Services. The current situation of students- Web-based application services has been identified and proposed an effective SOA to increase the operational efficiency of Web-Services for them it was necessary to identify the challenges in delivering a SOA technology to increase operational efficiency of Web-Services. Moreover, the SOA is an emerging concept, used for delivering efficient student SOA Web-Services. Therefore, service reusability from SOA Web-Services is provided and logically divided services into smaller services to increase reusability and modularity. In this case each service is a modular unit by itself and interoperability services.

Influence of Silica Fume on High Strength Lightweight Concrete

The main objective of this paper is to determine the isolated effect of silica fume on tensile, compressive and flexure strengths on high strength lightweight concrete. Many experiments were carried out by replacing cement with different percentages of silica fume at different constant water-binder ratio keeping other mix design variables constant. The silica fume was replaced by 0%, 5%, 10%, 15%, 20% and 25% for a water-binder ratios ranging from 0.26 to 0.42. For all mixes, split tensile, compressive and flexure strengths were determined at 28 days. The results showed that the tensile, compressive and flexure strengths increased with silica fume incorporation but the optimum replacement percentage is not constant because it depends on the water–cementitious material (w/cm) ratio of the mix. Based on the results, a relationship between split tensile, compressive and flexure strengths of silica fume concrete was developed using statistical methods.

Optimal Sizing of a Hybrid Wind/PV Plant Considering Reliability Indices

The utilization of renewable energy sources in electric power systems is increasing quickly because of public apprehensions for unpleasant environmental impacts and increase in the energy costs involved with the use of conventional energy sources. Despite the application of these energy sources can considerably diminish the system fuel costs, they can also have significant influence on the system reliability. Therefore an appropriate combination of the system reliability indices level and capital investment costs of system is vital. This paper presents a hybrid wind/photovoltaic plant, with the aim of supplying IEEE reliability test system load pattern while the plant capital investment costs is minimized by applying a hybrid particle swarm optimization (PSO) / harmony search (HS) approach, and the system fulfills the appropriate level of reliability.

Arriving at an Optimum Value of Tolerance Factor for Compressing Medical Images

Medical imaging uses the advantage of digital technology in imaging and teleradiology. In teleradiology systems large amount of data is acquired, stored and transmitted. A major technology that may help to solve the problems associated with the massive data storage and data transfer capacity is data compression and decompression. There are many methods of image compression available. They are classified as lossless and lossy compression methods. In lossy compression method the decompressed image contains some distortion. Fractal image compression (FIC) is a lossy compression method. In fractal image compression an image is coded as a set of contractive transformations in a complete metric space. The set of contractive transformations is guaranteed to produce an approximation to the original image. In this paper FIC is achieved by PIFS using quadtree partitioning. PIFS is applied on different images like , Ultrasound, CT Scan, Angiogram, X-ray, Mammograms. In each modality approximately twenty images are considered and the average values of compression ratio and PSNR values are arrived. In this method of fractal encoding, the parameter, tolerance factor Tmax, is varied from 1 to 10, keeping the other standard parameters constant. For all modalities of images the compression ratio and Peak Signal to Noise Ratio (PSNR) are computed and studied. The quality of the decompressed image is arrived by PSNR values. From the results it is observed that the compression ratio increases with the tolerance factor and mammogram has the highest compression ratio. The quality of the image is not degraded upto an optimum value of tolerance factor, Tmax, equal to 8, because of the properties of fractal compression.

Experimental and Theoretical Investigation of Rough Rice Drying in Infrared-assisted Hot Air Dryer Using Artificial Neural Network

Drying characteristics of rough rice (variety of lenjan) with an initial moisture content of 25% dry basis (db) was studied in a hot air dryer assisted by infrared heating. Three arrival air temperatures (30, 40 and 500C) and four infrared radiation intensities (0, 0.2 , 0.4 and 0.6 W/cm2) and three arrival air speeds (0.1, 0.15 and 0.2 m.s-1) were studied. Bending strength of brown rice kernel, percentage of cracked kernels and time of drying were measured and evaluated. The results showed that increasing the drying arrival air temperature and radiation intensity of infrared resulted decrease in drying time. High bending strength and low percentage of cracked kernel was obtained when paddy was dried by hot air assisted infrared dryer. Between this factors and their interactive effect were a significant difference (p