Earticle

This paper is aim to predict nutrition status using by the Body Mass Index (BMI). BMI for men and women are obtained from the calorie needs. Calorie requirement is used to determine energy needs. The energy needs are determined by the needs of Protein, Carbohydrate, Fat, Vitamin and Mineral. The paper is made in the form of an application program using Mobile Programming. The tool used is a J2ME application on cell phone emulator having minimal CLDC, CDC 1.1, MIDP 2.0 and MIDlet. Main contribution, BMI as the simple, fast and easy to used method. BMI can be used to determine the nutrition status of individual. The benefit for individual reduce the cost of healthcare. The new result is the prediction of the nutrition status of individual using by BMI can be applied with new ways through the Mobile Application on Mobile Device, that is implemented to the cell phone. Mobile Application is a medium for the user can be accessed anytime and anywhere. It is known an application that can yield the rational decision in predicting the nutrition needs.

To promote molecular biology studies, public repositories for microarray data need to be constructed; the minimum contents for analysis of microarray experiment have been defined and standardized. Public repositories have been constructed by some researches which follow the standards such as MIAME-compliant data and MAGE-OM/ML. However, enough consideration has not been taken into the design of storage structure for the hierarchy of microarray data. In this paper, we propose alternative mapping strategy to mine the structural similarity and an advanced mapping rule from the algorithm. Object-relational mapping technique is used for extracting advanced storage design schema for microarray data and structural similarity of elements is evaluated for efficient storage construction. The mapping strategy reduced the number of relational tables remarkably. The strategy will contribute to design of the storage structure of microarray data and performance enhancement of a public repository.

BilIN (Biliary INtraepithelial Neoplasm) is a precursor lesion of Intrahepatic CholangioCarcinoma (ICC). It is important to diagnose ICC and distinguish its stages for the prevention of ICC and proper treatment for a patient. BilIN is classified into BilIN-1, BilIN-2, and BilIN-3 by the morphological change and loss of polarity of epithelial cell and the structural abnormality of epithelium. This paper proposes two quantitative features based pathological knowledge for distinguishing stages of BilIN. The first feature is LumenBoundaryAbnomality (LBA) to measure the abnormal structure of epithelium; the second feature is NucleiPolarity for determine loss of polarity of epithelial cells. The experiment performed the stage classification of BilIN using following features; non-epithelial nuclei features, epithelial nuclei features, proposed features, non-epithelial nuclei and proposed features, epithelial nuclei and proposed features. The classification learning algorithm is used back-propagation Artificial Neural Networks. The classification result showed classification accuracies of 35% with non-epithelial features, 40% with epithelial features, 74% with proposed features, and 35% with non-epithelial and proposed features, and 46% with epithelial and proposed features.

The environment is being polluted by humans & in doing so, not only air & water but land is also being contaminated. The major contaminant of soil is chemical fertilizer. The definition of soil quality encompasses physical, chemical and biological characteristics, and it is related to fertility and soil health. Due to heavily usage of chemical fertilizers and harmful pesticides on the crops, food security and safety became a daunting challenge. Indiscriminate and imbalanced use of chemical fertilizers, especially urea along with chemical pesticides and unavailability of organic manures has led to considerable reduction in soil health. Biodiversity performs a variety of ecological services beyond the production of food, including recycling of nutrients, regulation of microclimate and local hydrological processes, suppression of undesirable organisms and detoxification of noxious chemicals. In this paper the role of biodiversity in securing crop protection and soil fertility by linking diversity of soils. Soil biodiversity is a key parameter for maintaining the fertility and productivity of the soils - thereby safeguarding food production. This management systems provide the ideal environment for the re-establishment of ecosystem engineers such as earthworms and scarab beetle larvae, of saprophagous and litter transforming organisms such as termites and millipedes and of predator populations (pseudoscorpions, centipedes, Diplura and spiders), thus enhancing the system’s natural biological control and regulation mechanisms to maintain soil health and fertility.

Motion of thoracic and abdominal tumors induced by respiratory motion often exceeds more than one centimeter which can compromise dose conformality significantly. Motion-adaptive radiotherapy aims to deliver a conformal dose distribution to a tumor with minimal normal tissue exposure, by compensating for the tumor motion in real time. This requires prediction of respiratory motion to estimate the respiratory movement that has occurred during the system latency due to measurement and control. One of the most successful models for predicting respiratory motion is the local circular motion (LCM) model. It characterizes the local respiratory behavior with a circular motion in an augmented plane and captures the natural evolution of respiratory motion. In this paper, we utilize the first and second-order extended Kalman filters based on LCM model for predicting respiratory motion. We also optimize the parameters of the extended Kalman filters for each trace in an attempt to improve prediction accuracy. Numerical experiments are performed to evaluate and compare prediction accuracy of four different prediction schemes.

In modern society, aging and chronic disease is becoming common due to the increasing numbers of elderly patients. To best treat this growing segment of the population, medical care should be based on constant vital sign monitoring. In this study, we propose a mobile vital sign measurement and data collection system for chronic disease management. . And we implemented a middle ware using Multi-Agent platform in SOS (Self-Organizing System) platform that transmits patient clinical data for services. We also implemented a HL7 messaging interface for interoperability of clinical data exchange. We propose health services on a self-organized software platform.

Improving the contrast of computed tomography medical images is an essential issue since most of these images suffer from the low contrast phenomenon. This study confirms that adjusting the contrast of degraded CT images before beginning the restoration process is highly desired. A comparison between seven famous techniques was conducted likewise to choose the best method among the different popular contrast enhancement methods. Then, experiments were performed to prove that adjusting the contrast before restoring CT images would lead to better restoration results. Finally, a discussion and a conclusion are provided to highlight the important issues of this paper.

This paper proposes a novel method of connected component labeling and extraction that segments and removes dirt and interphase cells from the chromosome images. This technique was tested with a standard clinical database of human karyotype and excellent segmentation results were achieved. The process involves identifying the various connected components in the input image and assigning labels to create a Label Matrix using the Color Map for these connected components. The connected components or objects that have fewer than the predefined amount of pixels are removed from the image that produces another image where the chromosome except the dirt, stain and interphase cells are uniquely identified and removed. The segmented image is subtracted from the original image, leaving behind only the chromosomes with no dirt, stain and interphase cells, facilitating accurate karyotyping procedures. This technique is extremely helpful when the unwanted object to be segmented and removed shares common intensity levels with the desired information where traditional threshold based procedure will fail to accomplish precise segmentation results.

The data mining technique is applied in various fields as a method to extract information based on massive data, and Bayesian networks are also utilized as useful modeling technique. Accordingly, many algorithms in Bayesian networks such as K2, TAN in expansion have been proposed, and suitability of algorithm for each situation evaluation stage has been requested based on performance test result validation to selectively use optimum algorithm for certain situation. As massive various that affects the result exists in actual situation, acquired information through certain data mining technique is considerably limited. Also, the filmed medical images may positively affect the diagnosis but due to high weight on subjective judgment, it is an abstruse problem to process with automatic system. Through this, improved expansion model of search algorithm is proposed with the K2 or TAN in Bayesian networks, which is relatively advantageous in handling the complicated situation of reality and is based on multivariate probability model. Now, because of the nature of extended Bayesian network which greatly varies the performance depending on the type of applied search algorithm, realistic evaluation is required on performance and suitability of each techniques. So in this thesis, experimentation by using equivalent data on disease diagnosis in extended Bayesian network is conducted, and measured classification accuracy while giving changes in search algorithm such as K2 and TAN. In the experiment, comparative evaluation of performance is done based on the result analysis of 10-fold cross validation, and made it possible to distinguish high risk data through classifying HRCT images of patients with high risk of reoccurring of the disease.