Search results

Erythemato-squamous disease (ESD) is one of the complex diseases related to the dermatology field. Due to common morphological features, the diagnosis of ESDs become stringent and leads to inconsistency. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for ESD is essential to ensure credibility, consistency, to resolve lack of time, labor and competence and to diminish human error.

This paper presents the design of an automatic ontology framework through data mining techniques and subsequently depicts the diagnosis of ESD using the available knowledge- and rule-based system.

The rule language (Semantic Web Rule Language) and rule engine (Jess and Drools) have been integrated to explore the severity of the ESD and foresee the most appropriate class to be suggested.

In this paper, the authors identify the efficiency of the rule engine and investigate the performance of the computational techniques in predicting ESD using three different measures.

Primarily, the approach assesses transfer time for total number of axioms exported to rule engine (Jess and Drools) while the other approach measures the number of inferred axioms (process time) using the rule engine while the third measure calculates the time to translate the inferred axioms to OWL knowledge (execution time).

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale.

The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine.

By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem.

In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work.

The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

FOR ME there is only one serious drawback to the forthcoming marriage of the Prince of Wales to Lady Diana, and that is the prospect of our Poet Laureate flogging his dead muse to drool out another epithalamium similar to his O‐level effort for Princess Anne and Mark Phillips.

Changing scattered and dynamic business rules in business workflow systems has become a growing problem that hinders the use and configuration of workflow-based applications. There is a gap in the existing research studies which currently focus on solutions that are application specific, without accounting for the universal logical dependencies between the business rules and, as a result, do not support adaptation of the business rules in real time. The paper aims to discuss this issue.

To tackle the above problems, this paper adopts a bottom-up approach, which puts forward a component model of the business process workflows and then adds business rules which have clear logical semantics. This allows incremental development of the workflows and semantic indexing of the rules which govern them during the initial acquisition.

The paper introduces an event-driven model for development of business workflows which is purely logic-based and can be easily implemented using an object-oriented technology, together with a model of the business rules dependencies which supports incremental semantic indexing. It also proposes a two-level inference mechanism as a vehicle for controlling the business process execution and the process of adaptation of the business rules at real time based on propagating the dependencies.

The framework is strictly logical and completely domain-independent. It allows to account both synchronous and asynchronous triggering events as well as both qualitative and quantitative description of the conditions of the rules. Although our primary interest is to apply the framework to the business processes typical in the construction industry we believe our approach has much wider potential due to its strictly logical formalization and domain independence. In fact it can be used to control any business processes where the execution is governed by rules.

The framework could be applied to both large business process modelling tasks and small but very dynamic business processes like the typical digital business processes found in online banking or e-Commerce. For example, it can be used for adjusting security policies by adding the capability to adapt automatically the access rights to account for additional resources and new channels of operation which can be very interesting ion both B2C and B2B applications.

The potential scope of the impact of the research reported here is linked to the wide applicability of rule-based systems in business. Our approach makes it possible not only to control the execution of the processes, but also to identify problems in the control policies themselves from the point of view of their logical properties – consistency, redundancies and potential gaps in the logics. In addition to this, our approach not only increases the efficiency, but also provides flexibility for adaptation of the policies in real time and increases the security of the overall control which improves the overall quality of the automation.

The major achievement reported in this paper is the construction of a universal, strictly logic-based event-driven framework for business process modelling and control, which allows purely logical analysis and adaptation of the business rules governing the business workflows through accounting their dependencies. An added value is the support for object-oriented implementation and the incremental indexing which has been possible thanks to the bottom-up approach adopted in the construction of the framework.

This paper aims to present a detailed description of Agents for Enriching Services (AES), an agent-oriented framework that allows adapting a service in an information system. AES provides an adaptation logic that can be instantiated and extended to be useful in different domains. In previous works, we presented the adaptation mechanism of AES, which considers context aspects such as location, infrastructure; user aspects such as preferences and interests; and device aspects such as hardware and software features.

The first step was the definition of different profiles, mainly user and context profiles. Then the adaptation mechanism was defined, which considers these profiles. With this mechanism, the adaptation filters to apply them to the initial queries was specified. Finally, feedback was provided, which included implicit and explicit information from the user and the system. AES is an agent-based framework implemented in Java, using the multi-agent platform BESA and a rule-based engine Drools.

AES can be used as the starting point to adapt services by enriching them considering different stimulus whether they come from the environment, devices or user preferences.

This work was tested in an academic environment and was only applied to enhance queries by using keywords. AES uses the query mechanism implemented in the system that invokes it.

This paper focuses on: an integrated view of AES including its formal description and details about its implementation. Particularly, it includes an exhaustive and formal definition of the filters used to create the adaptation rules and three different scenarios of the application of AES to adapt content according to user and context features. Finally, a comparison analysis is presented to highlight the strengths of our framework, specially its capacity of integration with systems that require providing user- and context-oriented services.

It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule‐based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule‐based analysis.

The authors developed a java‐based data gathering tool with a rule‐based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.

The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study shows that a historical event creates buzz on Twitter and people's interests on the event are reflected in their Twitter activity.

Applying data‐mining techniques to the social network data has so much potential. A possible improvement to the Twitter Data Collecting Tool would be an adaptation of a built‐in data‐mining module.

This paper focuses on designing a system handling massive amounts of Twitter Data. This is the first approach to embed a rule engine for filtering and analyzing social data. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.

This paper describes computer‐aided design (CAD) and rapid prototyping (RP) systems for the fabrication of maxillofacial implant.

Design methods for medical RP of custom‐fabricated are presented in this paper. Helical computed tomography (CT) data were used to create a three‐dimensional model of the patient skull. Based on these data, the individual shape of the implant was designed in CAD environment and fabricate by RP process. One patient with a large mandible defect underwent reconstruction with individual prefabricated implant resulting from initial surgical failure with hand contoured reconstruction plate.

Results shows that the custom made implant fit well the defect. Overall, excellent mandible symmetry and stability were achieved with the custom made implants. The patient was able to eat. There was no saliva drooling after the reconstruction. The operating time was reduced.

The methods described above suffer from the expensive cost of RP technique.

This method allows accurate fabrication of the implant. The advantages of using this technique are that the physical model of the implant is fitted on the skull model so that the surgeon can plan and rehearse the surgery in advance and a less invasive surgical procedure and less time‐consuming reconstructive and an adequate esthetic can result.

The method improves the reconstructive surgery and reduces the risk of a second intervention, and the psychological stress of the patient will be eliminated.

Assistive living technologies provide support for specific activities, transforming a home into a smart home. The purpose of this paper is to present how to design, implement, deploy and install a personalized ambient support system for the elderly suffering from Alzheimer’s disease (AD) and nighttime wandering.

The intervention presented in this paper proceeds in two phases. During the monitoring phase, the system determines the profile of the person with AD, based on nighttime routines. Data are gathered from sensors dispatched in the smart home, coupled with physiological data obtained from sensors worn by the person. Data are then classified to determine engine rules that will provide assistance to the resident to satisfy their needs. During the second phase, smart assistance is provided to the person via environmental cues by triggering rules based on the person’s habits and the activities occurring during night.

The paper develops the architecture of a non-intrusive system that integrates heterogeneous technologies to provide a calm environment during night and limit wandering periods.

The goal is to help people age well at home as long as possible and recover a regular circadian cycle while providing more comfort to the caregiver.

The system presented in this paper offers a calm and personalized environment with music and visual icons to soothe persons with AD and encourage them to go back to bed. It is installed at the patient’s home using wireless technologies.