Andino Maseleno

Data on multicultural education and diagnostic information profiling: Culture, learning styles and creativity

This article contains data related to multicultural education and diagnostic information profiling preliminary findings. It includes the responses of 253 students. The data consists of six sections, i) culture: race, ethnicity, language and identity; ii) learning preferences: physiological and perceptual; iii) cognitive learning styles: physical, emotional and mental; iv) creativity skills and problem solving skills; v) motivation; and vi) students’ background knowledge. The data may be used as part of data analytics for specific personalized e-learning platform.

Skin infection detection using Dempster-Shafer theory

Skin infections still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin infections are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we built a Skin Infection Expert System for detecting skin infections and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.

Fuzzy logic and dempster-shafer theory to find kicking range of sepak takraw game

This research works considered how fuzzy logic and Dempster-Shafer theory can be applied for the sepak takraw game - for addressing uncertainty in kicking range of the ball. Uncertainty happens due to lack of knowledge or unpredictable factors. The salient aspect of the approach is the integration within a Tsukamotos fuzzy reasoning and inferences for evidential reasoning based on Dempster-Shafer theory. Sepak takraw is a highly complex net-barrier kicking sport that involves dazzling displays of quick reflexes, acrobatic twists, turns and swerves of the agile human body movement. Because of the humans involvement in the game, the fuzzy logic type reasoning are the most appropriate. We have chosen Tsukamotos fuzzy reasoning scheme, because the individual rule outputs are crisp numbers, and therefore, the functional relationship between the input vector and the system output can be relatively easily identified. Sepak takraw is played by two regus, each consisting of three players. One of the three players shall be at the back and he is called a “tekong”. The other two players shall be in front, one on the left and the other on the right. The result reveal that if tekong is far and front player is near then another regus player is far, if tekong is near and front player is far then another regus player is near, moreover possibility of kicking range is another regus player is far in kicking range.

Move Prediction in Start Kicking of Sepak Takraw Game Using Dempster-Shafer Theory

This paper presents Dempster-Shafer theory for move prediction in start kicking of the bicycle kick of sepak takraw game. Sepak takraw is a highly complex net-barrier kicking sport that involves dazzling displays of quick reflexes, acrobatic twists, turns and swerves of the agile human body movement. A Bicycle kick or Scissor kick is a physical move made by throwing the body up into the air, making a shearing movement with the legs to get one leg in front of the other without holding on to the ground. Specifically, this paper considers bicycle kick of sepak takraw game in start kicking of the ball with uncertainty where player has different awareness regarding the contingencies. We have chosen Dempster-Shafer theory because the advantages of the Dempster-Shafer theory which include the ability to model information in a flexible way without requiring a probability to be assigned to each element in a set, providing a convenient and simple mechanism for combining two or more pieces of evidence under certain conditions, it can model ignorance explicitly, moreover rejection of the law of additivity for belief in disjoint propositions.

Fuzzy Logic and Dempster-Shafer belief theory to detect the risk of disease spreading of African Trypanosomiasis

This research presents the risk of disease spreading of African Trypanosomiasis using Fuzzy logic and Dempster-Shafer belief theory. Integrating Fuzzy Logic and Dempster-Shafer belief theory by calculating the similarity between Fuzzy membership function in the context to detect the risk of disease spreading of African Trypanosomiasis and finally to develop a realistic and useful Web mapping for displaying maps on a screen to locate the risk of African Trypanosomiasis spreading. The risk of African Trypanosomiasis spreading is not classified according to higher population density which is equal to higher risk. This research has considered population changes in an area to detect the risk of African Trypanosomiasis spreading. The experts provide opinion in terms of basic probability assignment for interval or crisp focal elements. Those basic probability assignments are proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get basic probability assignment. The highest percentage of the risk of disease spreading of African Trypanosomiasis is 17%. In Angola and Zambia, the highest basic probability assignment of the risk of disease spreading of African Trypanosomiasis is very low which is equal to 0.173. The risk of disease spreading of African Trypanosomiasis in areas which include Angola, Botswana, Congo, Congo DRC, Malawi, Mozambique, Namibia, Tanzania, Zambia, and Zimbabwe.

Avian influenza (H5N1) expert system using Dempster-Shafer theory

Based on cumulative number of confirmed human cases of avian influenza (H5N1) reported to World Health Organization (WHO) in 2011 from 15 countries, Indonesia has the largest number of deaths because of avian influenza which 146 deaths. In this research, the researcher built an avian influenza (H5N1) expert system for identifying avian influenza disease and displaying the result of identification process. In this paper, we describe five symptoms as major symptoms which include depression, combs, wattle, bluish face region, swollen face region, narrowness of eyes, and balance disorders. We use chicken as research object. Dempster-Shafer theory is to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveals that avian influenza (H5N1) expert system has successfully identified the existence of avian influenza and displaying the result of identification process.