Abstract-Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.Index Terms-Uncertainty, knowledge discovery, Bayesian network, image processing, decision making, privacy preservation, system reliability estimation. I. INTRODUCTIONUncertainty is a commonly faced problem in real world applications. Uncertainty can be described as an inadequate amount of information [1]. Nevertheless, uncertainty may also exist in situations that have enough amount of information [2]. Furthermore, uncertainty may be alleviated or eliminated with the addition of new information. Addition of more information in complex processes may lead to mining of limited knowledge. Uncertainty can be computed mathematically with probability theory. In uncertain situations, there is an involvement of possibility of states of attributes. Consequently, the models established on probabilistic inferences have the capability to assign a probabilistic value according to a defined principle. Accordingly, the prediction with large number of states in a model is accomplished. The question rises "how prediction is realized in the presence of large number of states in a model?" An answer to this question is the employment of Bayesian Network (BN) with several variables [3]- [5]. BNs, also known as belief networks, belong to the family of probabilistic graphical models. These graphical structures correspond to knowledge about an uncertain domain. More specifically, each node in the graphical structure represents a random variable, while the edges/arcs between the nodes represent conditional dependencies among nodes. These conditional dependencies are estimated by using acknowledged statistical and computational methods. Consequently, BNs incorporate concepts from graph and probability theory, computer science, and statistics.Since last two decades, BN is recognized as an important tool for a number of expert systems especially in domains involving uncertainty [6]. This recognition of BN has several reasons behind it. First, BN encodes the depen...

The application of 3D printing in medicine is the major area to concern in the nearest future. Namely, it is convenient to additively manufacture the Ankle-Foot Orthosis (AFO) by fused-deposition modeling 3D printer. AFO is the device, used in medicine, to help the patients rehabilitate from the foot drop disease. The shape of the AFO may vary depending on the leg and foot specifications of the patient. In this paper, three models of the AFO were designed to analyze both numerically and experimentally, those are fracture propagation, stress distribution, and deformation. The regions with the highest stress concentration were altered with the Nylon 12, and this contributed to stress reduction. Three different gait instances were considered for the numerical simulations FEA software. Then, the simplest model to prototype and its modified versions were tested by the compression machine, and the results were compared with the numerical ones. This work demonstrated the significance of the optimization of the multi-material 3D printed AFO’s performance and comfort for patients.

No abstract

This paper discusses the modeling and simulation results of a new multi-material for a cost-effective Selective Laser Sintering (SLS)-based 3D printer. As this technology utilizes several materials, the mechanical property analysis of multi-materials is crucial for manufacturing an object with the desired physical characteristics. Firstly, the development of a database of the SLS 3D printing materials is accomplished, and based on the mechanical properties of materials, this optimization technique is proposed. Secondly, enhancement of physical property by stiffeners is considered and based on the stiffening technology, and an alternative optimization method proposed. Finally, two different material minimization methods are discussed in this research. The first method is based on the embedded materials with desired mechanical properties for enhancing the mechanical properties of the printed objects, which are twice optimized by this method with increased material saving. The second method is designed to use stiffeners to improve the stiffness characteristics of the materials, and then, perform material optimization. This method is effective with more suitability to complex composite geometries. Thus, the methods help to reduce materials used as well as the production cost in 3D printing technology.

No abstract