To increase crowd evacuation efficiency in emergency situations, the authors developed a wearable computer, LifeBelt, to guide individuals from emergency situations to escape. The LifeBelt collects position and orientation information about individuals from embedded sensors, shares this information with a background emergency coordination system, receives an individualized optimized escape strategy, and guides the individual via vibro-tactile stimulation, indicating direction and distance to the recommended exit. Simulation models indicate that the number of successful evacuations per unit time improve significantly using the LifeBelt technology.

Diabetes mellitus, a chronic disease that has a significant influence on human lives, families, and communities globally, has reached alarming levels and is therefore a leading economic challenge of the twenty-first century. The International Diabetes Federation (IDF) reports that in 2019, 463 million adults aged 20 to 79 globally had diabetes, and predictive analyses estimate that the incidence will rise to 700 million in 2045. This chapter reviews leading studies of diabetes and machine learning techniques for its prevention. The chapter also examines analytical models and technologies for simulating and predicting the incidence of diabetes efficiently. These models help find the disease early to reduce medical costs and prevent more complex health problems, enabling decision-makers to develop solutions to reduce the negative impact and assess the future cost of treatment.

The simulation of urban mobility is a modeling challenge due to the complexity and scale. The complexity in modeling a social agent is due to three reasons: (i) the agent is behaviorally complex itself due to several interrelated/overlapping modeling aspects; (ii) the setting in which a social agent operates usually demands a multi-resolution approach; and (iii) the consideration of real spatial and population data is the underpinning that has to be realized. In this paper, we propose an agent-based parallel geo-simulation framework of urban mobility based on necessary modeling aspects. The aspect-oriented modeling paradigm relates the models vertically as well as horizontally and highlights the situations requiring multi-resolution interfacing. The framework takes into consideration the importance of technological footprints embedded with social behavior along with essential space and mobility features keeping focus on the importance of the city-scale scenario. We have used a real, high-quality raster map of a medium-sized city in central Europe converting it into a cellular automata (CA). The fine-grained CA readily supports pedestrian mobility and can easily be extended to support other mobility modes. The urban mobility simulation is performed on a real parallel and distributed hardware platform using a CA compatible software platform. Considering city-wide mobility in an emergency scenario, an analysis of the simulation efficiency and agent behavioral response is presented.

The effectiveness of an evacuation process in an emergency situation is heavily dependent on the understanding of the dynamics of crowds. Evidence has been delivered supporting the hypothesis that crowd behavior is self referential in the sense that movement patterns of individuals constitutes crowd movement, which in turn impacts the behavior of individuals. In this paper we build on this evidence and propose a belt like wearable device for vibro tactile directional guidance, LifeBelt, to notify individuals in panic about exits. To assess the potential improvement in evacuation efficiency when using the LifeBelt instead of when not, we empirically analyze the behavior of individuals in panic on the microscopic level, and parameterize large scale simulations (of up to 2000 individuals) with this evidence on the macroscopic level. Simulations show that LifeBelt based guidance can damp panic growth and increase the number of successful evacuations per unit time.

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.