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In machine learning, a key aspect is the acquisition of knowledge. As problems become more complex, and experts become scarce, the manual extraction of knowledge becomes very difficult. Hence, it is important that the task of knowledge acquisition be automated. This paper proposes a novel method that integrates neural network and expert system paradigms to(More)
In this paper, this paper seeks an answer to the question: can a vehicle automatically report to the emergency units its position and speed when an accident is occurred? It presents an approach that relates to an accident detection and prevention system. It is particularly related to a system that sends a message to designated parties (such as vehicle owner(More)
Although the special characteristics of Wireless Sensor Networks (WSNs) help in reducing the cost of sensor nodes manufacturing and deployment, they added new challenges that directly affect the network functionality. This is increases the probability of network functionality deviation from its norm operation and affects its' collected data accuracy.(More)
This proposal develops the concept of combining a handwritten signature, digital signature, and a smart card into an overall Public Key Infrastructure (PKI). The purpose of this proposed solution is to fulfill the cultural gap between traditional digital signatures and current smart card digital certificate/signature. It is achieved through the integration(More)
A new technique for identification of patients with Congestive Heart Failure (CHF) from normal controls is investigated in this paper using spectral analysis and neural networks. The data used in this work is obtained from Massachusetts Institute of Technology (MIT) databases. A data set of 17 CHF and 53 normal subjects is used as original learning data(More)
This paper discusses the challenges of teaching and design of embedded system course. A survey on different approaches of teaching embedded systems design course was presented. The embedded system design class/course is often the course in which students are exposed to fairly complex design problems. In this study, the authors explained the importance of(More)
Self-organizing maps are an unsupervised neural network model that lends itself to the cluster analysis of high dimensional input data. However, interpreting a trained map proves to be difficult because the features responsible for specific cluster assignment are not evident from resulting map representation. Paper presents an approach for automated(More)
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