Decision-Making Support in .NET

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Decision-Making Support
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TIME 00.00.00: PERSON UNDER SCREENING 45 WARNING LEVEL 04 SPECIFICATION: DRUG OR ALCOHOL CONSUMPTION, LEVEL 03 LOCAL DATABASE MATCHING: POSITIVE PROPOSED DIALOGUE: QUESTION 1: DO YOU NEED MEDICAL
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ASSISTANCE ...
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QUESTION 10: DO YOU HAVE DRUGS IN YOUR LUGGAGE
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In our design concept, the results of automatically analyzing behavioral information are provided to the of cer; for example, in the following form:
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TIME 00.00.00: PERSON UNDER SCREENING 45 ALARM, LEVEL 04 SPECIFICATION: DRUG OR ALCOHOL CONSUMPTION, LEVEL 03 LOCAL DATABASE MATCHING: POSITIVE LEVEL OF TRUSTWORTHINESS OF QUESTION 10 IS 03: DO YOU
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HAVE DRUGS IN YOUR LUGGAGE
POSSIBLE ACTION: 1.DIRECT TO SPECIAL INSPECTION 2. CONTINUE CLARIFICATION BY QUESTIONS
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24.3.3 Decision-Support Assistants for Noninvasive Temperature Measure
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Consider a decision-support assistant for noninvasive temperature measurement that includes the following components (Figure 24.5):
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Figure 24.5. Decision-support assistant for noninvasive temperature measure.
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24.3 Decision-Making Support Assistant Design
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Sensor such as video and infrared cameras Preprocessing block for hyperspectral analysis Decision-making block Protocol generator
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This decision-support assistant implements the Bayesian model of belief. In Bayesian belief estimations, input data are the results of measurement in the hyperspectral band.
Decision Support During Interviewing
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Our study also concerned with studying dynamics of infrared images during interviewing. The interval of observation is used to record a thermal video and then analyze frames taken using regular intervals. The simplest analysis involves count of the number of pixels, corresponding to the low, medium, and high temperature and taken as a proportion to the total number of facial image pixels. The rst image in Figure 24.6b is taken at the beginning of performing the calculation, and the second
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Figure 24.6. Dynamics of thermal images due to a mental effort: (a) A thermal image and the
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histogram in which the region between 156 and 225 pixel values corresponds to the face region; (b) three-scale images (the images in which the pixels are distributed according to three temperature ranges: medium, high, and low, indicated by different colors) corresponding to the 20th and 100th frames of the thermal images, and the graph of proportions (called here the probability) of the pixels from three temperature ranges.
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Decision-Making Support
image is taken at the end. The proportion of the number pixels in each region to the total number of pixels (called probability) is changed during thermal video recording during calculation. We simpli ed our experimental study because of the complexity and high cost of real-world experiments: Instead of observing responses to questions, we asked the tested person to solve various mathematical calculations. Similarly to the questionnaire techniques, this required some intellectual effort. Based on this premise, we analyzed the dynamics of infrared images of people, participated in the study (Figure 24.6). The primary conclusion is that facial images in infrared band can distinguish people in the relaxing state and people making calculation tasks.
24.4 HYPERSPECTRAL ANALYSIS AND SYNTHESIS OF FACIAL SKIN TEXTURE
A decision-support assistant performs the face analysis (preprocessing phase, Figure 24.5) based on a model made up of two constituents: a face shape model (represented by a three-dimensional geometric mesh) and a skin model (generated from hyperspectral texture images in visible and infrared bands). In this section, we address the problem of skin modeling speci cally, the problem of extracting information helpful for early detection support from hyperspectral skin texture images.
Human Skin Modeling
Since the color of human skin can reveal distinct characteristics valuable for diagnostics, many authors have performed theoretical and experimental studies of the optical properties of human skin speci cally, the mechanism of skin color formation [33 37]. It has been demonstrated, in particular, that the dominant pigments in skin color formation are melanin and hemoglobin. Melanin and hemoglobin determine the color of the skin by selectively absorbing certain wavelengths of the incident light. The melanin has a dark brown color and predominates in the epidermal layer, while the hemoglobin has a reddish hue or purplish color, depending on the oxygenation, and is found mainly in the dermal layer. The quantities of melanin and hemoglobin pigments in the human skin were experimentally determined in reference 38 using multiple regression analysis, and the accuracy of the method was estimated by Monte Carlo simulation [39]. In reference 40, the melanin and hemoglobin content of the skin was experimentally analyzed based on diffuse re ectance spectroscopy in the visible and near-infrared bands. It was con rmed that it is possible to obtain quantitative information about hemoglobin and melanin by tting the parameters of an analytical model with re ectance spectra. An alternative, a fast- tting procedure based on a library search, was proposed in reference 41. Recent progress in imaging devices such as video CCD (including near-infrared and ultraviolet ranges) and thermal cameras employed in medical, surveillance, and security systems has stimulated the development of new approaches to human skin