Disease diagnosis and prognosis are two medical applications pose a great challenge to the researchers. The application of data mining and machine learning techniques has revolutionized the whole process of disease diagnosis. This paper intends to propose a hybridized approach, Correlative Framework (CF) algorithm for the implementation of diagnosing breast cancer, the most scare away disease. The major objective of this paper is to analyze and predict the vulnerability of disease in a patient. The association rule of data mining is deployed to correlate the interesting relation from the huge medical database. In this proposal, Linear Discriminant Analysis (LDA) is exploited for feature selection. Initially, base rule is generated, then for each rule feature support is computed which is followed by confidence. On the basis of feature supporting the base rule, supplementary rule and positive rule is generated. Regression is utilized for each rule with its corresponding feature value. Classification result is obtained based on minimum and maximum of residual support values. The significant performance of our newly devised algorithm is evaluated using confidence metrics. Experimental results expose that prediction level of our adduced work is more factual than other existing algorithms.
In recent times, a number of studies have been done on object tracking with wireless sensor networks (WSN) due to its broad applications. Most object tracking schemes uses prediction to minimize the energy consumption and to maintain low missing rate in a sensor network. However objects need to be localize, when object was found missing during tracking process. In this paper, we propose an inference system such as fuzzy Inference system (FIS) to calculate the edge weight of each sensor node and also we propose a searching technique such as simulated annealing to optimize the fuzzy inference system to accurately estimate the location of the missing object and Finally, we simulated our proposed method against the centroid and multilatertion methods to evaluate its performance in terms of network energy consumption and localization error.
Support Vector Machine (SVM) is a supervised learning algorithm, recommended for classification and nonlinear function approaches. The goal of SVM is to find the optimum separating hyperplane, which is able to classify data points as well as possible, and to again separate them into two classification points as much as possible. The hallmarks of this classification reasoning are the support vectors chosen from the training set. On the other hand, training SVM involves solving a constrained quadratic programming problem, which requires a large memory and enormous amounts of training time for large-scale problems. Therefore, when finding the optimum separating hyperplane only a small part of the training set is used. \nIn this paper, we propose a method for finding a set of the training data for the training of the SVM. For this purpose, we use Principal Component Analysis (PCA) technique for the elimination of non-critical training examples in the training set. By the help of PCA, the data in multi-dimensional space is converted into one-dimensional space. Then, by using the mean and the standard deviation of the one dimensional instances, the non-critical points are identified. Then, from the original training set, these non-critical instances are removed and the new reduced training set is used for the training process. Our experimental results show that our proposed method has a positive effect on computational time without degrading the classification results.
Stem cell research has raised expectations after novel cellular therapies of regenerative medicine came to light with the discovery of unexpected plasticity in stem cells. Stem cells offer a distinct prospect of changing the face of human medicine. However, although they have potential to develop into any tissue organization, they are still in the various stages of development as therapeutic interventions. In this article we have avoided the descriptions of various methodologies involved in collection, extraction, or purification of stem cells as several researches have already published or patented these technologies. The three most extensively used stem cell sources were umbilical cord blood, bone marrow and human embryos, subsequently other sources like human fatty tissues, hair follicles etc. have been documented. Advancement in stem cell medicine requires ethically sound and scientifically robust models to develop tomorrow’s remedies. Here, we describe the utility and application potential of human cord blood stem cells and its regenerative property.
Usability is a major concern in open source software (OSS). A pattern is used to provide solutions to a design problem within a certain context. Existing design patterns being used in OSS have certain issues such as lack of consideration of HCI rules, less users’ involvement in design, no style guidelines, and less focus on essential values and design invariant that can be encoded in software. An improved user design pattern is thus required to address these issues. This paper proposes a novel design pattern by studying and improving existing patterns. A case study has also been carried out to evaluate the proposed pattern.
Short vase life of orchid flowers due to the phytohormone ethylene is undesirable. To date, improvement of orchid plants with long vase life flowers by field breeding is considered time-consuming and laborious. Conversely, biotechnology to produce orchids resistant to ethylene by introducing an antisense ACC oxidase gene into the orchids is an alternative to overcome the disadvantages of the conventional method. The present study aimed at optimizing the condition for the delivery of an antisense ACC oxidase gene into Dendrobium aphyllum (Roxb.) Fischer mediated by Agrobacterium tumefaciens strain EHA105 (pCAMBIA1304). High transformation efficiency was achieved by cocultivating D. aphyllum protocorms in the bacterial suspension supplemented with 100 µM acetosyringone for 20 min. Cefotaxime and hygromycin concentrations of 300 and 25 mg/l, respectively, were considered effective for eliminating A. tumefaciens and selecting putative transformants. Hygromycin resistant protocorms showed the highest GUS activity of 79.26%. DNA integration was confirmed by PCR analysis and it was found that the sizes of amplified fragments were 180, 118 and 320 bp for the 35S, NOS and antisense ACC oxidase, respectively.
The species Aerides odorata Lour., a wild orchid native to the north and northeastern regions of Thailand, is likely to be to be depopulated due to the destruction and alteration of its natural environments, including over-collection of wild orchids. Even though germplasm conservation through traditional method is vital for maintenance of biodiversity and avoidance of genetic erosion, such method is time-consuming and difficult handle. Conversely, cryopreservation is an alternative to the traditional method to overcome the problem. The present study, therefore, aimed at establishing the protocol for the cryopreservation of A. odorata seeds by encapsulation-dehydration. The seeds were encapsulated in calcium-alginate before preculture in ND liquid medium supplemented with 0.5 M sucrose and maintained on a reciprocal shaker (100 rpm) at 25±2ºC under a long photoperiod (16 h light:8 h dark) with a photon dose of 40 µmol m-2 s-1 for 18 h.. The encapsulated seeds were then dehydrated by incubation in the sterile air flow of a laminar air flow cabinet for 0-6 h, and immediately plunged into liquid nitrogen (LN). After recovery from LN and rapid thawing in a waterbath (40 ºC), the germination rate and genetic alterations measured by flow cytometry were investigated. The highest germination rate (82%) was obtained from encapsulated seeds that were previously precultured in 0.5 M sucrose liquid medium for 18 h and sufficiently dehydrated for 6 h prior to storing in LN. Measured by flow cytometry, cryopreserved seeds did not show any evidence of genetic alterations.
The purpose of this study was to investigate the effectiveness of utilizing the multi-assessment strategy through a constructivist learning atmosphere with regard to perceptions of the prospective teachers. The participants were 98 third year (junior) prospective teachers attending to classroom management course in a public university in Turkey. Action research methodology and mixed method were utilized to collect data in this study. The results showed that classroom management field was acknowledged very positively by the most of the prospective teachers. The authentic activities utilized during the authentic instructions were positively recognized, although they admitted that all process was tiring and took long time. Although open ended questions yielded both positive and negative aspects, utilizing multi-assessment strategy was indicated mostly by the participants as highly effective. Findings indicated that employing constructivist assessment in teacher education may yield positive impacts especially when doing it learning by doing.