MATHEMATICAL THEORY OF EVIDENCE TO DENGUE FEVER DETECTION

This paper presents Dempster-Shafer Theory for dengue fever detection. Sustainable elimination of dengue fever as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we used Dempster-Shafer theory for detecting dengue fever diseasesand displaying the result of detection process. The Dempster-Shafer theory is a mathematical theory of evidence.Dengue fever diseases have the same symptoms withbabesiosis, lyme, malaria, and west nile. We describe six symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, and arthritis. Dempster-Shafer theory to quantify the degree of belief, 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

Knowledge-Based Systems Selection of Contraceptive Equipment for The Handling of Uncertainty

 Contraceptives is one of the products of the government program for controlling the population. The government has established the Department of Population Control and family planning and empowerment of women and child protection that specifically manages the dissemination and socialization of the apparatus. But to choose the appropriate contraceptives for himself The Community of people still feel trouble. Not only prospective of common people who feel difficulties, sometimes the KB officers also feel uncertain in giving advice of tool contraceptives. That is because, sometimes the condition of the user does not comply with the existing rules, the latest knowledge about the development of contraception has not been owned by the officer, thus resulting in uncertainty in the suggestion of selection of contraceptives. In this study proposed a knowledge-based system to assist the public in providing an overview of the type of contraceptive equipment suitable for theyself and can be used by the KB officers the as interactive media and in the handling of the uncertainty problem that mentioned before. Then for the handling of uncertatinty problems will use dempster shafer method. dempster shafer method is Chosen because this method can provide an estimate of the value of confidence against a result of the diagnosis, by conducting the calculation of the combination of the same symptoms will be obtained the highest confidence value, or the most dominant. In the testing process, there will be 40 cases compared to the results. This research aims to solve the uncertainty problems of the suggestion the selection of contraceptives tools. The results of this research can provide a consulting medium that is able to provide selection of contraceptives that solve the problem of uncertainty and confidence level of the system to the tool. The test showed an accuracy rate of 95

Developing integrated data fusion algorithms for a portable cargo screening detection system

Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.e. analyses, pre-processing and feature extraction is performed at this stage. In step two, using experimentally pre-determined thresholds, each feature decides on detection of cocaine or otherwise with a corresponding posterior probability. High level sensor fusion is then performed on this output locally to combine these decisions and their probabilities at time intervals. Output from every time interval is stored in the database and used as prior data for the next time interval. The final output is a decision on detection of cocaine. The key contributions of this thesis includes investigating the use of data fusion techniques as a solution for overcoming challenges in the real time detection of cocaine using fibre optic sensor technology together with an innovative user interface design. A generalizable sensor fusion architecture is suggested and implemented using the Bayesian and Dempster-Shafer techniques. The results from implemented experiments show great promise with this architecture especially in overcoming sensor limitations. A 5-fold cross validation system using a 12 13 - 1 Neural Network was used in validating the feature selection process. This validation step yielded 89.5% and 10.5% true positive and false alarm rates with 0.8 correlation coefficient. Using the Bayesian Technique, it is possible to achieve 100% detection whilst the Dempster Shafer technique achieves a 95% detection using the same features as inputs to the DF system