Expert Subjects Research Articles

Breast Cancer Screening Application Based on Android with the Certainty Factor Method

According to Globocan records, in Indonesia in 2020 there were 396.314 new cancer cases. And 234.511 people were declared dead. Women are a group with a high risk of developing cancer. If cancer is detected at an early stage, this can increase the chance of cure to 80-90%. Early detection of cancer can be done using several methods, for example, for breast cancer, the method of checking can be using the SADANIS (Clinical Breast Examination) and SADARI (Self Breast Examination) methods. In this research, a mobile application will be developed that can be used as a guide in carrying out early cancer detection independently. The early detection system uses an Android-based expert system and certainty factor method. The case study in this research is on breast cancer. Based on the results of accuracy testing with expert diagnosis as a reference, an accuracy value of 90% was obtained. The inaccuracy of this expert system is 10% which can be caused by several possibilities, namely the expert's subjectivity in providing confidence values for disease symptoms or the small number of symptoms entered.

How the cognitive features testing can assist in evaluating collective ontology engineering

The paper is devoted to the interplay issues between ontology engineering and cognitive psychology. It describes the study targeted at new visual structuring paradigm of ontology development. Such approach is formed with regard to personal cognitive features. The research was performed within the KOMET (knowledge and content structuring via methods of collaborative ontology design) project. Two phases of research have been done within the project. First was devoted to study of correlations between the expert's personal cognitive style features and the peculiarities of expert's subject domain ontology development procedure. The second deals with research on the relationship between the expert's individual cognitive style and the collective ontology design (including the design performed in groups consisting of experts either of similar or of different cognitive styles). The results of this research project can be applied to organising the collaborative ontology design, data and content structuring and other group analytical work.

Time Series Representation for Identification of Extremes

information from the huge time series is a challenging task. Databases are prepared by keeping in mind the type of information required. Indexing a time series is a difficult task, where shape may not be exact. Finding the minima and maxima of the time series is another difficult job dependent on the expert's subjectivity. In this information age algorithmic trading is the buzz word. For the identification of various patterns, a machine can be made intelligent by embedding some good algorithm in the trading module to identify the various patterns. In this paper an attempt has been made to identify the extremes, where profit probability is maximized.

국가적 차원의 유망연구영역 탐색: Scopus 데이터베이스를 이용한 과학계량학적 접근

급속도로 변화하는 과학기술 환경 속에서 기업들은 현존하는 기술의 발전을 모니터링함과 동시에 새롭게 부상하는 유망기술을 찾아야만 경쟁력을 가질 수 있다. 각 국가에서는 경쟁적으로 유망연구 영역을 찾고 있는데, 대부분의 국가에서는 전문가 평가를 기반으로 한 델파이 방법으로 유망기술을 탐색하고 있다. 그러나 델파이와 같은 전문가방식은 기본적으로 전문가의 주관적 판단에 의지하기 때문에 편향성과 이에 따른 문제가 발생한다. 본 연구에서는 델파이 방법의 기술예측방법이 가지는 문제점을 개선하기 위해 과학계량학적 방법으로 유망연구영역을 탐색하였다. 탐색과정에서는 다음 3가지의 특별한 노력을 통해 과학계량학적 방법의 성능을 제고하고자 하였다. 첫째, 데이터베이스 선정에 있어서 공학 등 저널수가 적은 기술분야에서도 유망연구영역의 도출이 가능한가를 고려하였다. 둘째, 과학기술 전분야를 대상으로 하는 분석에서 과학기술분야별로 가지고 있는 인용수의 차이로 인해 발생하는 문제점을 해결하고자 부분인용계상(fractional citation counting)과 이동평균을 이용한 선형회귀분석을 도입하였다. 셋째, 과학계량학적 분석으로 나온 결과를 정성적으로 검증하여 과학계량학적 방법에 의한 오류를 최소화 하였다. 최종 290개의 유망연구영역을 선정하였으며, 각 영역은 기술의 네트워크상에서 가시화하였다. 본 연구에서는 Scopus 데이터베이스가 사용되었으며, 데이터마이닝과 가시화에는 한국과학기술정보연구원에서 개발한 KnowledgeMatrix가 사용되었다. In todays environment in which scientific technologies are changing very fast than ever, companies have to monitor and search emerging technologies to gain competitiveness. Actually many nations try to do that. Most of them use Dephi approach based on experts review as a searching method. But experts review has been criticised for probability of inclination and its derivative problems in the sense that it is accomplished only by expert's subjectivity. To overcome such problems, we used Scientometric Method for identifying emerging technology that had been done by Delphi as a rule. We made three particular efforts in order to improve the Quality of the result. Firstly, we selected one alternative database between SCI and Scopus hoping to see evenly-distributing results in wide fields on the front burner. Secondly we used Fractional citation counting in counting citation number in the stage of linear regression analysis. Lastly, we verified Scientometric result with experts opinions to minimize probable errors in a Scientometric research. As a result, we derived 290 emerging technologies from Scientometric analysis with Scopus Database, and visualized them on 2-dimension map with data mining system named KnowledgeMatrix which was developed by KISTI.

SENSORY ANALYSIS OF RED WINES: DISCRIMINATION BY ADAPTIVE FUZZY PARTITION

ABSTRACT Classification models were established on a set of sensory profiles associated with red wine samples bottled from three vintages. These profiles were defined by eight assessors charged, for all wine samples, to assign to each of 17 sensory descriptors a note ranged from 1.0 to 9.0, with two assessments for each judge. A new and innovative method called adaptive fuzzy partition (AFP), derived from fuzzy logic (FL) concepts, was tested on the same data set. FL is particularly suitable to classify sensory analysis data sets, as it can represent the “fuzziness” linked to an expert's subjectivity in the characterization of wine sensory profiles. More precisely, after subdividing the 48 sensory profiles into training and test sets, the AFP method predicted correctly 75% of the test assessments. These very encouraging preliminary results show the proposed methods are worth investigating more thoroughly, testing large and diverse wine data sets. PRACTICAL APPLICATIONSA new and innovative method called adaptive fuzzy partition, derived from fuzzy logic (FL) concepts, is presented. FL is particularly suitable to classify sensory analysis data sets, as it can represent the “fuzziness” linked to an expert's subjectivity in the characterization of wine sensory profiles.

Comparing the Information Content of Two Large Olfactory Databases.

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Comparing the Information Content of Two Large Olfactory Databases

The expert's subjectivity in establishing an olfactory description can produce wide discrepancies in different databases listing the odor profile of identical compounds. A representative example is obtained by comparing the odorous compounds included in the "Perfumery Materials and Performance 2001" (PMP2001) database and in Arctander's books (1960 and 1969). To better assess this problem, classification models obtained by using the adaptive fuzzy partition method were established on subsets of these databases distributed into the same olfactory classes. The robustness and the prediction power of these models give a powerful criterion for evaluating the "quality" of their information content and for deciding which is the most trustable database.

Identification of Frontal Sinus by means of Form Factors and Fourier Descriptors

Individual identification based on radiographic frontal sinus comparison is considered a reliable means of establishing positive identification. An ante mortem craniography was compared to a post-mortem craniography. However, due to the position and distance at which the X-rays have been acquired, this comparison can not be carried out by a simple visual inspection. Digital Image Processing techniques are an important contribution in this discipline because they provide a quantitative description of the frontal sinus. The present work outlines a method of comparison of cranial cavities stemming from an analysis of Form Factors and of the most significant Fourier Descriptors. Simulated and real images resulting from digital X-rays were analysed. A range of numeric values was obtained from this procedure, which, in turn, allows for an indication of the similarity between two closed curves. This contribution helps minimize the expert's subjectivity when he is to carry out an identification that should be strictly accurate.