An Intelligent Voice-Based eMarketplace for Visually Impaired People

Today eMarketplaces play a significant role in contemporary life by providing a lot of income and business opportunities to people and organizations throughout the world. Despite innovations in the field of IT, many of eMarketplaces lack the ability to provide appropriate services for people with special needs, especially the blind.? Therefore, this paper is focused on incorporating an interface for blind people to participate in the business of eMarketplaces. A proposed model of a voice-based eMarketplace has been introduced using voice recognition technology. Specific blind users of the system are uniquely identified using voice recognition technology to enable them to access the eMarketplace in a secure manner. Further work of this project involves building such as module on an existing eMarketplace.

A Decision Tree Approach for Predicting Smokers’Quit Intentions

This paper presents a decision tree approach for predicting smokers＇ quit intentions using the data from the International Tobacco Control Four Country Survey. Three rule-based classification models are generated from three data sets using attributes in relation to demographics, warning labels, and smokers＇ beliefs. Both demographic attributes and warning label attributes are important in predicting smokers＇ quit intentions. The model＇s ability to predict smokers＇ quit intentions is enhanced, if the attributes regarding smokers＇ internal motivation and beliefs about quitting are included.

Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?

Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.