Effects of image processing techniques on mammographic phantom images: a pilot study

Breast cancer is one of the most important diseases among females. According to the Malaysian Oncological Society (Wahid, 2007), about 4% of women who are 40 years old and above are suffering from breast cancer. Masses and microcalcifications are two important signs for breast cancer diagnosis on mammography. In this research, the effects of different image processing techniques which include enhancement, restoration, segmentation, and hybrid methods on phantom images were studied. Three different phantom images, which were obtained at 25kv (63.2 MAS), 28kv (29.8 MAS) and 35kv (9.5 MAS), were manipulated using image processing methods. The images were scored by two expert radiologists and the results were compared to explore any significant improvements. Meanwhile, the Wilcoxen Rank test was used to compare the quality of the manipulated images with the original one (alpha=0.05). Each image processing method was found to be effective on some particular criteria for image quality. Some methods were effective on just one criterion while some others were effective on a few criteria. The statistical test showed that there was an average improvement of 41 percent when the images were manipulated using the histogram modification methods. It could be concluded that different image processing methods have different effects on phantom images which generally improve radiologists’ visualization. The results confirm that the histogram stretching and histogram equation methods lead to higher improvement in image quality as compared to the original image (p < 0.05)

Planning in Uncertain Temporal Domain

Planning is a fundamental aspect of human intelligence. Therefore it has become the fundamental problem in modelling intelligent behaviour within Artificial Intelligence. It is further complicated when the plan has to be done in uncertain temporal domain. This framework deals with linguistic fuzziness that often presents within temporal knowledge. An interval-based fuzzy membership representation is defined as an extension to the precise temporal interval. To deal with planning in this framework, a simple decision problem is shown. 1

Representation and Reasoning of Fuzzy Temporal Knowledge

Abstract—This paper proposes a model for fuzzy temporal knowledge representation and reasoning. It includes the frameworks that incorporate fuzziness with temporal intervals, representation of the knowledge and reasoning qualitatively and quantitatively. An interval-based fuzzy membership representation is defined as an extension to the precise temporal interval. To deal with planning and scheduling in this framework, a simple decision problem is shown. Keywords—temporal, reasoning, uncertainty, fuzzy I

Representation and reasoning of fuzzy temporal knowledge

This paper proposes a model for fuzzy temporal knowledge representation and reasoning. It includes the frameworks that incorporate fuzziness with temporal intervals, representation of the knowledge and reasoning qualitatively and quantitatively. An interval-based fuzzy membership representation is defined as an extension to the precise temporal interval. To deal with planning and scheduling in this framework, a simple decision problem is shown

Data Mining Application in Higher Learning Institutions

One of the biggest challenges that higher learning institutions face today is to improve the quality of managerial decisions. The managerial decision making process becomes more complex as the complexity of educational entities increase. Educational institute seeks more efficient technology to better manage and support decision making procedures or assist them to set new strategies and plan for a better management of the current processes. One way to effectively address the challenges for improving the quality is to provide new knowledge related to the educational processes and entities to the managerial system. This knowledge can be extracted from historical and operational data that reside in the educational organization's databases using the techniques of data mining technology. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper presents the capabilities of data mining in the context of higher educational system by i) proposing an analytical guideline for higher education institutions to enhance their current decision processes, and ii) applying data mining techniques to discover new explicit knowledge which could be useful for the decision making processes

Session F4B Application of Enhanced Analysis Model for Data Mining Processes in Higher Educational System

Abstract- One of the most important facts in higher education system is quality. It concerns with all the circumstances that allow decision makers to better evaluate and enhance the higher educational organizations. One way to reach the highest level of quality in higher education systems is by improving the decision making procedures on the various processes such as planning, counseling, evaluation and so on. This can be achieved by utilizing the managerial decision makers with valuable implicit knowledge, which is currently unknown to them. This knowledge is hidden among the educational data set and it is extractable through data mining technology. The meaningful knowledge, previously unknown and potentially useful information discovered from raw educational data through data mining techniques are used to assist decision makers to improve the decision-making procedure and to set more enhanced policies for the educational processes. This paper is designed to first present and justify the capabilities of data mining in the context of higher education system by offering an enhanced version of a recently proposed analysis model (DM_EDU) by the author, used for the application of data mining in higher educational system. Then one of the most important sections of the model, “student assessment ” sub-process under “evaluation ” will be implemented in a real world higher education, MMU in Malaysia, to present the ability of data mining in discovering useful patterns. The final result of this application aids managerial MMU decision makers to improve decision-making processes. Index Terms – Data Mining, knowledge gap, classification, decision tre