Knowledge-based Decision System Research Articles

Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System.

Interest in sleep has been growing in the last decades, considering its benefits for well-being, but also to diagnose sleep troubles. The gold standard to monitor sleep consists of recording the course of many physiological parameters during a whole night. The human interpretation of resulting curves is time consuming. We propose an automatic knowledge-based decision system to support sleep staging. This system handles temporal data, such as events, to combine and aggregate atomic data, so as to obtain high-abstraction-levels contextual decisions. The proposed system relies on a semantic reprentation of observations, and on contextual knowledge base obtained by formalizing clinical practice guidelines. Evaluated on a dataset composed of 131 full night polysomnographies, results are encouraging, but point out that further knowledge need to be integrated.

Knowledge Base and Rule Base Safety Management Systems Development Strategies and Applications for Petrochemical Industries: A Brief Outline

This review paper discussed about the development tactics and applications of knowledge based and rule-based decision support systems for health and safety in oil and gas sector. It recommends the users on the extent of health and safety concerns for risk and hazard investigation and prevention in which the system developers and researchers mostly face issues during selection and adaptation of development approaches. A complete and systematic review and classification of articles from last seven years has been conducted and briefly analysed, to investigate various methodologies employed in previously developed rule based (RB) and knowledge based (KB) decision support systems. All major bibliographic databases covering a broad series of health and safety fields were searched in various oil and gas production sector related studies. Eighteen articles met the KB and RB study. Twelve of these met the methodological prospective of required investigation. In this review paper, researchers briefly explain about the methodological aspects and industrial applications of rule and knowledge-based decision support systems for reducing work place risks and hazards in safety and health domain. Conclusions that can be extracted from this analytical review paper of the methodological variation and applications of rule based and knowledge-based decision support system is that both inferences are interrelated to each other. RB and KB inferences techniques and methods are used in both interpretations for developing decision support systems in the context of oil and gas extraction and production sector. But safety and health consider KB decision systems as more effective for taking suitable decisions due to real time information and experts’ opinions which facilities safety professionals to take appropriate decision then RB reasoning decision support systems.

Knowledge-based decision system for automatic sleep staging using symbolic fusion in a turing machine-like decision process formalizing the sleep medicine guidelines

Automatic sleep staging is challenging since several issues need to be addressed. Traditional approaches from literature do not satisfy medical experts since they do not reflect the cognitive process they perform when scoring polysomnographic curves. We propose a new approach that is based on the implementation of medical knowledge by symbolic fusion. Medical knowledge coming from the international clinical practice guidelines for sleep medicine is formalized as a five-layer framework dedicated to data abstraction in order to deliver local and global propositions and support the interpretation of polysomnographic curves. Firstly, features are extracted from raw curves. Then these features are combined to recognize sleep events in accordance with guidelines. Sleep events are then fused into the criteria required to recognize the different sleep stages. Sleep is not homogeneous through the night. The physiological events observed during the night follow a dynamic that needs to be included into an automatic sleep staging system. In order to take this into account, decision rules are selected and applied to recognize a sleep stage according to the current context. Thereby, transitions are considered with interest. In this paper, we propose to use a Turing Machine-like decision process to handle transitions. To interpret the local observations and properly score a given state, the previous state which has been stored in a specific register is used as a context. One of the advantages of following the principles of symbolic fusion is to benefit from the full traceability of the decision. Hence, it makes possible to discuss each final — or intermediate — decision with an expert and check for relevance. The preliminary results are encouraging since agreement rates provided between decisions taken by our automatic approach and human experts are similar to those measured between human experts (average agreement rate = 54.60% / average Cohen’s kappa = 0.40) on a dataset of 131 full polysomnographic recordings.

Intelligent Collaborative Platform for Development of Personalized Surgical Orthopedic Guides

Currently, the processes of developing new medical devices (in particular, patient specific guides for orthopedic surgery being of interest in our research), is hampered by the difficulty of correctly and efficiently quantifying and translating the specific medical requirements in terms of technical specifications. This is caused by the intrinsic difficulty of the demarche, but also by a complicated communication between surgeon and engineer given the different practical and research perspectives, specific constraints, motivation, evaluation criteria and professional language barrier. The advent and development of new technologies with applications in the medical field, such as robotics, haptic-based virtual and augmented reality, additive manufacturing, collaborative modeling, knowledge-based support decision systems, etc., make now possible to bridge the gap between surgeons’ ideas and needs and their practical materialization into new customized medical devices. The paper enrolls in this research trend, presenting a general framework for the development of an intelligent e-health platform, which provides in a collaborative environment the necessary knowledge and computer-aided tools for translating surgeons’ needs into technical specifications for the design and manufacturing of patient-specific guides for orthopedic surgery. These guides can be used in the minimally invasive surgical procedures and for reducing the x-ray radiation exposure during surgery, and for increasing accuracy in performing different types of specific orthopedic surgical procedures such as cutting, drilling, tapping and aligning, by transferring the tools trajectories from computer-aided planning to surgery.

Advances on data, information, and knowledge in the internet of things

The internet of things (IoT) is a dynamic smart networked system with connected sensors, processors, and actuators that are designed to sense and interact with the physical world. Achieving the benefits of IoT requires the fusion and management of large scale heterogeneous data using knowledge-based decision systems and the integration of different technologies. Many challenges exist, including identifying things correctly and safely in IoT, modeling and integrating variety and volumes of data, acquiring knowledge automatically from the big data, and ensuring security and privacy, etc. This theme issue aims to explore these challenges through papers which address: (1) data models and big data and information processing in IoT; (2) how collaboration and interaction in IoT can be facilitated leveraging the best practices developed in social computing, social and community intelligence, and wireless sensor networks; and (3) the security and privacy issues in IoT and mobile computing. This issue is in collaboration with the international workshop on Identification, Information, and Knowledge in the Internet of Things (IIKI2013) held in October 2014, Beijing, China (http://ireg.bnu.edu.cn/IIKI2014/). Following a strict review process, we accept seventeen papers for this theme issue finally. Each of the papers was peer-reviewed by at least two experts in the field. In the following, we provide a brief introduction to each paper. Data dissemination is a challenging issue for mobile social activities in IoT. The paper titled ‘‘strengthen nodal cooperation for data dissemination in mobile social networks’’ by Guoliang Liu et al. proposes an incentive scheme to stimulate the users in a network to be more cooperative for data dissemination by considering selfishness factors of the users. Each node’s ability to fetch messages of a specific kind of interest is evaluated, and every single user can rent other nodes to help with obtaining the interested messages by paying credits. Extensive simulations on real traces are implemented to evaluate the proposed incentive scheme. The paper ‘‘self-Universum support vector machine’’ (SUSVM) by Dalian Liu et al. provides a theoretical explanation for an improved twin support vector machine based on the concept of Universum, which takes the positive class and negative class as Universum separately for the binary classification problem. Furthermore, SUSVM is improved by a special formulate of linear programming, which leads to the better generalization performance and less computational time. Data management and information organization play key roles in IoT realization. Yunchuan Sun et al. propose an extensible and active semantic information organization model for IoT in the paper titled ‘‘An extensible and active semantic model of information organizing for the Internet of Things.’’ The proposed model is well defined and involves two layers: the object layer and the event layer, which are both discussed in detail including the Y. Sun (&) R. Bie Beijing Normal University, Beijing, China e-mail: yunch@bnu.edu.cn

Uncertain variables and their applications in knowledge-based decision systems: New results and perspectives

The paper is concerned with a class of uncertain systems with relational knowledge representations. In the first part, a basic description of the uncertain variables and decision problems are presented. In the second part, a generalization in the form of soft variables, a learning system, and other problems concerning the applications of uncertain variables are described. © 2008 Wiley Periodicals, Inc.

A system for the acquisition of reproducible digital skin lesions images

A major issue concerning the design and implementation of an image acquisition system for skin lesions is its ability to capture reproducible images. The reproducibility is considered essential for image analysis and for the comparison of sequential images during follow-up studies. This paper describes a prototype image acquisition system that includes a standardized illumination and capturing geometry with polarizing filters and a series of software corrections: Calibration to Black, White and Color for color constancy, Internal camera Parameters adjustment and Pose extraction for stereo vision, Shading correction and Noise Filtering for color quality. The validity of the calibration procedure and the images' reproducibility were tested by capturing sample images in three different lighting conditions: dark, medium and intense lighting. For each case the average values of the three color planes RGB and their standard deviations were calculated; the measured error differences ranged between 0.7 and 12.9 (in the 0-255 scale). Preliminary experiments for stereo measurements provided repeatability of about 0.3 mm. The above results demonstrate the reproducibility of the captured images at a satisfactory level. The developed prototype was also evaluated clinically, for its ability to support the construction of knowledge-based decision systems and for telemedicine, thus to support telemedical sessions in dermatology.

Fuzzy behavior hierarchies for multi-robot control

Hierarchical approaches and methodologies are commonly used for control system design and synthesis. Well-known model-based techniques are often applied to solve problems of complex and large-scale control systems. The general philosophy of decomposing control problems into modular and more manageable subsystem control problems applies equally to the growing domain of intelligent and autonomous systems. However, for this class of systems, new techniques for subsystem coordination and overall system control are often required. This article presents an approach to hierarchical control design and synthesis for the case where the collection of subsystems is comprised of fuzzy logic controllers and fuzzy knowledge-based decision systems. The approach is used to implement hierarchical behavior-based controllers for autonomous navigation of one or more mobile robots. Theoretical details of the approach are presented, followed by discussions of practical design and implementation issues. Example implementations realized on various physical mobile robots are described to demonstrate how the techniques may be applied in practical applications involving homogeneous and heterogeneous robot teams. © 2002 Wiley Periodicals, Inc.

Decision Advisor - A Knowledge-Based Decision System

Decision Advisor® is a sophisticated knowledge-based intelligent decision system (IDS) that supports decision analysis and financial modeling of complex decisions.i i Decision Advisor partially a...

Knowledge-based decision system for goal directed military resource planning

This paper presents an knowledge-based decision system for structuring national force planning problem related to weapon systems. Mathematical models using Sequential Linear Goal Programming (SLGP) are formally described for efficiently determining procurement amount of military resources during five years.

Knowledge acquisition for knowledge based decision systems

Knowledge-based decision systems support the decision analysis process with decision analytic knowledge as well as domain-specific knowledge. The knowledge in the knowledgebased decision system is usually represented by influence diagrams. In the development of knowledge-based decision systems, knowledge acquisition is one of the most difficult problems. This article suggests a knowledge acquisition process using verbal protocol analysis and multi-attribute decision-making methodology. An environmental decision-making problem is used as an illustrative example to explain the developed knowledge acquisition process. The suggested process is so flexible that it can be expanded or applied to other problems with ease. Some guidelines for the knowledge-based decision system are also suggested.

Building an influence diagram in a knowledge-based decision system

Abstract In decision analysis, building a decision model such as an influence diagram is a burdensome problem requiring time and effort, and the resulting model is generally applicable to only one specific problem. In this research, an influence diagram is built using the technology of knowledge-based systems that utilizes the information learned in one decision problem to solve other similar problems. We designed and developed a knowledge-based decision system, KIDS (Knowledge-based Influence Diagram generating System), in Macintosh using Hypertalk. KIDS is applied to a subset of raw-material buyer problems of Cheil Synthetic Inc., which has produced textile products. KIDS decreases the amount of time and effort for modeling and analyzing decision problems, and it is believed to increase the acceptance of decision analysis as a useful tool.

Computer-assisted medical decision-making: interest growing

"C omputers can never replace physicians. Only physicians have the ability to reason logically and to mix that reason with intuition, experience, and ethics—something no machine can ever learn to do." Or so many physicians have been telling people proposing computer-assisted medical decision making (CMD) since the early 1960s, and controversy has continued. But now it looks like the two sides are nearing agreement: Computers may never replace physicians, but they can and will assist their decisions. "Assist" is the key word in CMD. Early advocates tended to overestimate the powers of the computer and promised complete clinical evaluation at the push of a button. It was natural for physicians to feel somewhat threatened. It was also natural that, with experience, such promises had to be modified. Today there still are only several functioning knowledge-based decision systems—called "knowledge-based" because they make inferences using a data base of medical information stored