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Considering difficulties in on-site ADAMTS13 testing and the performance instability of PLASMIC score according to ethnicity, we developed a prediction tool, MED-TMA (machine learning (ML) method for differential diagnosis (DDx) of thrombotic microangiopathy (TMA)) to support clinical decision. Data from 319 patients visiting 31 hospitals in Korea clinically diagnosed with primary TMA was randomly separated by 2:1 into two groups – the development dataset (D-set, n = 212), the validation dataset (V-set, n = 107). Feature elimination was conducted to select optimal clinical predictors. We developed the model with the selected features using ML methods, verifying using V-set. After the feature elimination using 19 clinical variables, five variables were selected with high importance value. Among nine ML methods, four ML methods were chosen considering the Area Under the Curves (AUC) and the correlation between the methods using D-set. We developed MED-TMA based on an optimized ensemble model with the selected four ML methods resulting in AUC values of 0.945 and 0.924 in D-set and V-set, respectively. In addition to the binary outcome, MED-TMA was capable of providing a probability for DDx of TMA. The ensemble approach driven MED-TMA showed comparable accurate and intuitive decision support for DDx of TMA to that of the existing models based on a single ML method. We provide a web-based nomogram for the appropriate use of effective but costly therapeutics to treat TMA patients (http://hematology.snu.ac.kr/medtma/). • For differential diagnosis of primary TMA, we propose a decision support tool named MED-TMA. • MED-TMA improves the performance through ensemble with various ML methods. • MED-TMA provides accurate results with using simple laboratory data of patients. • MED-TMA provides more intuitive suggestion compared with the existing models. [ABSTRACT FROM AUTHOR]

Objective Manual evaluation of machine learning algorithms and selection of a suitable classifier from the list of available candidate classifiers, is highly time consuming and challenging task. If the selection is not carefully and accurately done, the resulting classification model will not be able to produce the expected performance results. In this study, we present an accurate multi-criteria decision making methodology (AMD) which empirically evaluates and ranks classifiers’ and allow end users or experts to choose the top ranked classifier for their applications to learn and build classification models for them. Methods and material Existing classifiers performance analysis and recommendation methodologies lack (a) appropriate method for suitable evaluation criteria selection, (b) relative consistent weighting mechanism, (c) fitness assessment of the classifiers’ performances, and (d) satisfaction of various constraints during the analysis process. To assist machine learning practitioners in the selection of suitable classifier(s), AMD methodology is proposed that presents an expert group-based criteria selection method, relative consistent weighting scheme, a new ranking method, called optimum performance ranking criteria, based on multiple evaluation metrics, statistical significance and fitness assessment functions, and implicit and explicit constraints satisfaction at the time of analysis. For ranking the classifiers performance, the proposed ranking method integrates Wgt.Avg.F-score, CPUTimeTesting, CPUTimeTraining, and Consistency measures using the technique for order performance by similarity to ideal solution (TOPSIS). The final relative closeness score produced by TOPSIS, is ranked and the practitioners select the best performance (top-ranked) classifier for their problems in-hand. Findings Based on the extensive experiments performed on 15 publically available UCI and OpenML datasets using 35 classification algorithms from heterogeneous families of classifiers, an average Spearman's rank correlation coefficient of 0.98 is observed. Similarly, the AMD method has showed improved performance of 0.98 average Spearman's rank correlation coefficient as compared to 0.83 and 0.045 correlation coefficient of the state-of-the-art ranking methods, performance of algorithms (PAlg) and adjusted ratio of ratio (ARR). Conclusion and implication The evaluation, empirical analysis of results and comparison with state-of-the-art methods demonstrate the feasibility of AMD methodology, especially the selection and weighting of right evaluation criteria, accurate ranking and selection of optimum performance classifier(s) for the user's application's data in hand. AMD reduces expert's time and efforts and improves system performance by designing suitable classifier recommended by AMD methodology. [ABSTRACT FROM AUTHOR]

Background Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. Methods We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Results Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. Conclusions We propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR . [ABSTRACT FROM AUTHOR]

Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owner’s information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsu’s algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4 dB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge constantly grows in scientific discourse and is revised over time by different stakeholders, either collaboratively or through institutionalized efforts. The body of knowledge gets structured and refined as the Communities of Practice concerned with a field of knowledge develop a deeper understanding of the issues. As a result, the knowledge model moves from a loosely clustered terminology to a semi-formal or even formal ontology. Change history management in such evolving knowledge models is an important and challenging task. Different techniques have been introduced in the research literature to solve the issue. A comprehensive solution must address various multi-faceted issues, such as ontology recovery, visualization of change effects, and keeping the evolving ontology in a consistent state. More so because the semantics of changes and evolution behavior of the ontology are hard to comprehend. This paper introduces a change history management framework for evolving ontologies; developed over the last couple of years. It is a comprehensive and methodological framework for managing issues related to change management in evolving ontologies, such as versioning, provenance, consistency, recovery, change representation and visualization. The Change history log is central to our framework and is supported by a semantically rich and formally sound change representation scheme known as change history ontology. Changes are captured and then stored in the log in conformance with the change history ontology. The log entries are later used to revert ontology to a previous consistent state, and to visualize the effects of change on ontology during its evolution. The framework is implemented to work as a plug-in for ontology repositories, such as Joseki and ontology editors, such as Protege. The change detection accuracy of the proposed system Change Tracer has been compared with that of Changes Tab, Version Log Generator in Protege; Change Detection, and Change Capturing of NeOn Toolkit. The proposed system has shown better accuracy against the existing systems. A comprehensive evaluation of the methodology was designed to validate the recovery operations. The accuracy of Roll-Back and Roll-Forward algorithms was conducted using different versions of SWETO Ontology, CIDOC CRM Ontology, OMV Ontology, and SWRC Ontology. Experimental results and comparison with other approaches shows that the change management process of the proposed system is accurate, consistent, and comprehensive in its coverage. [Copyright &y& Elsevier]

Abstract: Most healthcare data are available in XML format, which mainly focuses on the structure level and lacks support for data representation. Therefore, a variety of medical applications and medical semantic search engines have difficulty understanding and integrating healthcare data in a highly heterogeneous environment. OWL (Web Ontology Language) and Semantic Web technologies provide an infrastructure that can solve these problems. The aim of our study is to present a mechanism to ease the interpretation and automate the semantic transformation of XML healthcare data into the OWL ontology (S-Trans), which allows an easier and better semantic communication among hospital information systems. On the basis of the XML schemas (XSD or DTD), we extract the document structure and add more descriptions for XML elements. Moreover, to classify the semantic level of duplicate elements in an XML schema, we propose novel metrics to measure the similarity between them. Experimental results show that the proposed method reliably predicts semantic similarity of duplicates and produces a better-quality OWL ontology. [Copyright &y& Elsevier]

Abstract: In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan–Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast. [Copyright &y& Elsevier]

Abstract: Estimating location of mobile devices based on received signal strength (RSS) patterns is an attractive method to realize indoor positioning systems. Accuracy of RSS based location estimation, particularly in large target sites, is effected by several environmental factors. Especially the temporal or permanent absence of radio signals introduces null values rendering sparsity and redundancy in feature space. We present a visibility matrix based modular classification model which systematically caters for unavailable signals. This model is practically realized using two eminent classification methods: (1) multi-layer perceptron and (2) LVQ. In order to confirm robustness and applicability of this model, we developed two location systems at different sites. Experimental results in real-world environments demonstrate that modular classification model consistently achieves superior location accuracy. [Copyright &y& Elsevier]

Background: Standardized healthcare documents have a high adoption rate in today's hospital setup. This brings several challenges as processing the documents on a large scale takes a toll on the infrastructure. The complexity of these documents compounds the issue of handling them which is why applying big data techniques is necessary. The nature of big data techniques can trigger accuracy/semantic loss in health documents when they are partitioned for processing. This semantic loss is critical with respect to clinical use as well as insurance, or medical education.Methods: In this paper we propose a novel technique to avoid any semantic loss that happens during the conventional partitioning of healthcare documents in big data through a constraint model based on the conformance of clinical document standard and user based use cases. We used clinical document architecture (CDAR) datasets on Hadoop Distributed File System (HDFS) through uniquely configured setup. We identified the affected documents with respect to semantic loss after partitioning and separated them into two sets: conflict free documents and conflicted documents. The resolution for conflicted documents was done based on different resolution strategies that were mapped according to CDAR specification. The first part of the technique is focused in identifying the type of conflict in the blocks that arises after partitioning. The second part focuses on the resolution mapping of the conflicts based on the constraints applied depending on the validation and user scenario.Results: We used a publicly available dataset of CDAR documents, identified all conflicted documents and resolved all the them successfully to avoid any semantic loss. In our experiment we tested up to 87,000 CDAR documents and successfully identified the conflicts and resolved the semantic issues.Conclusion: We have presented a novel study that focuses on the semantics of big data which did not compromise the performance and resolved the semantic issues risen during the processing of clinical documents. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support.Methods and Materials: A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system.Results: We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy.Conclusion: Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers. [ABSTRACT FROM AUTHOR]

Cyber Physical System(CPS) allows to collect different sensor and alarm data from large number of facilities in industrial plants. Failure and faulty diagnosis is one of the most complicated and dynamic problems in the industrial plant management since most of failures are extremely ambiguous which needs to be solved based on an expert’s experience. This makes the solutions very subjective and requires too much time, efforts and monetary investment. In this paper, we are proposing new failure detection approach with machine learning and human expertise by using alarm data. As the first step of developing this new method, we collected several types of alarm data that detected functional failure in Hyundai Steel factory. We analyzed and processed the alarm data with 35 domain experts. Based on the data, we propose a knowledge based system which is Ripple Down Rule-based. This system acquires knowledge by machine learning which is maintained by human experts. The evaluation results showed that the proposed failure detection framework can reduce the time of human expertise acquisition and the cost of solving over-generalization and over-fitting problems by using machine learning techniques. [ABSTRACT FROM AUTHOR]

A huge array of personalized healthcare and wellness systems are introduced into the portfolio of digital health and quantified-self movement in recent years. These systems share common capabilities including self-tracking/monitoring and self-quantifications, based on the raw sensory data. These capabilities provide solid ground for the users to be more aware of their health; however, such measures are inefficient for changing the unhealthy habits of the users. In order to induce healthy habits in the users, a system must be capable of generating context-aware personalized recommendations. The main obstacle in this regard is the contextual interpretation of recommendations based on user's current context and contextual preferences. To resolve these issues, we propose a methodology of cross-context interpretation of recommendations (CCIR) for personalized health and wellness services. The CCIR method adds additional capabilities to the traditional reasoning methods and builds advanced form of the reasoning with the incorporation of contextual factors in the process of interpretations of the recommendations. With CCIR, the self-quantification systems can be enhanced to generate personalized recommendations in addition to tracking, quantifying, and monitoring user activities. In order to validate the proposed CCIR methodology, a set of 40 contextual scenarios and corresponding recommendations are presented for the evaluation collected from 40 different end users and 10 domain experts. Using chi-square test evaluation, the results demonstrated acceptable “goodness of fit” indices for the system developed on proposed CCIR methodology with respect to the end users’ opinion. Also from the statistical observation, it is found that there exists a higher level agreement towards the system between the participants of both end users and experts. [ABSTRACT FROM AUTHOR]

Medical students should be able to actively apply clinical reasoning skills to further their interpretative, diagnostic, and treatment skills in a non-obtrusive and scalable way. Case-Based Learning (CBL) approach has been receiving attention in medical education as it is a student-centered teaching methodology that exposes students to real-world scenarios that need to be solved using their reasoning skills and existing theoretical knowledge. In this paper, we propose an interactive CBL System, called iCBLS, which supports the development of collaborative clinical reasoning skills for medical students in an online environment. The iCBLS consists of three modules: (i) system administration (SA), (ii) clinical case creation (CCC) with an innovative semi-automatic approach, and (iii) case formulation (CF) through intervention of medical students' and teachers' knowledge. Two evaluations under the umbrella of the context/input/process/product (CIPP) model have been performed with a Glycemia study. The first focused on the system satisfaction, evaluated by 54 students. The latter aimed to evaluate the system effectiveness, simulated by 155 students. The results show a high success rate of 70% for students' interaction, 76.4% for group learning, 72.8% for solo learning, and 74.6% for improved clinical skills. [ABSTRACT FROM AUTHOR]

Objective Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows. Our proposed system provides a user-friendly authoring environment to create Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making by CDSS. Methods and materials Existing systems are not physician friendly and lack interoperability and shareability of knowledge. In this paper, we proposed Intelligent-Knowledge Authoring Tool (I-KAT), a knowledge authoring environment that overcomes the above mentioned limitations. Shareability is achieved by creating a knowledge base from MLMs using Arden Syntax. Interoperability is enhanced using standard data models and terminologies. However, creation of shareable and interoperable knowledge using Arden Syntax without abstraction increases complexity, which ultimately makes it difficult for physicians to use the authoring environment. Therefore, physician friendliness is provided by abstraction at the application layer to reduce complexity. This abstraction is regulated by mappings created between legacy system concepts, which are modeled as domain clinical model (DCM) and decision support standards such as virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We represent these mappings with a semantic reconciliation model (SRM). Results The objective of the study is the creation of shareable and interoperable knowledge using a user-friendly and flexible I-KAT. Therefore we evaluated our system using completeness and user satisfaction criteria, which we assessed through the system- and user-centric evaluation processes. For system-centric evaluation, we compared the implementation of clinical information modelling system requirements in our proposed system and in existing systems. The results suggested that 82.05% of the requirements were fully supported, 7.69% were partially supported, and 10.25% were not supported by our system. In the existing systems, 35.89% of requirements were fully supported, 28.20% were partially supported, and 35.89% were not supported. For user-centric evaluation, the assessment criterion was ‘ease of use’. Our proposed system showed 15 times better results with respect to MLM creation time than the existing systems. Moreover, on average, the participants made only one error in MLM creation using our proposed system, but 13 errors per MLM using the existing systems. Conclusion We provide a user-friendly authoring environment for creation of shareable and interoperable knowledge for CDSS to overcome knowledge acquisition complexity. The authoring environment uses state-of-the-art decision support-related clinical standards with increased ease of use. [ABSTRACT FROM AUTHOR]

In this study Ocimum basilicum L. ( OB ) and Ocimum tenuiflorum L. ( OT ) in vitro culture standardisation for increasing eugenol distribution, in comparison to their respective field grown parts was carried out. Eugenol was quantified using an optimised HPLC method and its relation with the total phenolic content (TPC) was measured. In vitro grown leaves and somatic embryos, of both OB and OT were found to contain similar quantities of eugenol (85 μg/g approximately), higher than OB and OT field-grown leaves (30.2 μg/g and 25.1 μg/g respectively). It was also determined that in vitro grown leaves were richer in TPC than the field-grown intact organs. Results demonstrated the prominence of in vitro cultures for eugenol extraction. This study underlines that important food flavouring metabolites (e.g. vanillin, vanillic acids) might be produced, via the eugenol pathway, in Ocimum species that may be a good potential source of eugenol. [ABSTRACT FROM AUTHOR]

Short Message Service (SMS) has become one of the most important media of communications due to the rapid increase of mobile users and it’s easy to use operating mechanism. This flood of SMS goes with the problem of spam SMS that are generated by spurious users. The detection of spam SMS has gotten more attention of researchers in recent times and is treated with a number of different machine learning approaches. Supervised machine learning approaches, used so far, demands a large amount of labeled data which is not always available in real applications. The traditional semi-supervised methods can alleviate this problem but may not produce good results if they are provided with only positive and unlabeled data. In this paper, we have proposed a novel semi-supervised learning method which makes use of frequent itemset and ensemble learning ( FIEL ) to overcome this limitation. In this approach, Apriori algorithm has been used for finding the frequent itemset while Multinomial Naive Bayes, Random Forest and LibSVM are used as base learners for ensemble learning which uses majority voting scheme. Our proposed approach works well with small number of positive data and different amounts of unlabeled dataset with higher accuracy. Extensive experiments have been conducted over UCI SMS spam collection data set, SMS spam collection Corpus v.0.1 Small and Big which show significant improvements in accuracy with very small amount of positive data. We have compared our proposed FIEL approach with the existing SPY-EM and PEBL approaches and the results show that our approach is more stable than the compared approaches with minimum support. [ABSTRACT FROM AUTHOR]

Abstract: Classification techniques are important in bioinformatics analysis as they can separate various bioinformatical data into distinct groups. To obtain good classifiers, accurate labeling of the training data is required. However labeling in practical bioinformatics applications might be erroneous due to various reasons. To identify those mislabeled data, an ensemble learning based scheme, single-voting has been widely used. It generates multiple classifiers and makes use of their voting to detect mislabeled data. Single-voting scheme mainly consists of two components: data partitioning component to generate multiple classifiers, and mislabeled detection component to identify mislabeled data. Existing works in this field mainly focus on mislabeled detection part and neglect data partitioning. However, our analysis shows that data partitioning plays an important role in single-voting scheme. This analysis helps us proposing a novel multiple-voting scheme. It is superior to traditional single-voting by reducing the unreliable influence from data partitioning. Empirical and theoretical evaluations on a set of bioinformatics datasets illustrate the utility of our proposed scheme. [Copyright &y& Elsevier]

Knowledge based systems have accomplished remarkable achievements in assisting evidence based decision making for complex problems. However, machine learning-driven, intelligent systems of today are dependent on the underlying knowledge model, which is acquired from domain experts, or the available datasets in a structured or unstructured format. Most of the existing literature utilized a single modal, while very few have combined multi-modalities (mainly two) for knowledge acquisition. In order to achieve a strong Artificial Intelligence, multi-domain and multi-modal knowledge acquisition, and consolidation is required. This paper presents the research work, driving the realization of such a comprehensive framework, in the field of healthcare. Using area specific, state-of-the-art machine learning techniques, we first extract knowledge from structured and unstructured data, which is consolidated with expert knowledge and managed through ripple down rules. Our presented technique shows an accuracy of 92.05%, which is much higher than single modal deep learning at 78.20%, naive bayes at 69.70%, logistic regression at 61.20%, expert driven knowledge at 86.02%, and naive knowledge combination at 70.86%. Thus, through the application of our proposed technique, we provide the foundations for an accurate and evolvable knowledge-base, that can greatly enhance decision making in the healthcare domain. [ABSTRACT FROM AUTHOR]

Abstract: As a research branch of grid computing, data grid focuses on the management of large-scale distributed data sets. Replica management is one of the most important issues in the data grid, which can offer fast data access time, high data availability and low bandwidth consumption. Computing Intelligent Algorithm (CIA) has been proved to be effective in the solution of large-scale distributed computing problems, whereas Quantum Evolutionary Algorithm (QEA) is one of these excellent optimization algorithms and little literatures are made for its application in Data Grid Replica Management (DGRM). This paper focuses on the application of the QEA in data grid replica creation strategy. A QEA-based global replica creation strategy is proposed after reviewing the replica creation strategies. The optimization model is divided into single and multi data replica creation two parts. The representation, evaluation and constraint procedure three key technologies problems for each part are discussed in detail. The detail algorithm of QEA based replica creation is provided. The experiments were carried out with OptorSim, and the results have shown that QEA-based replica creation strategy can effectively reduce the job response time and network bandwidth consumption, comparing to Genetic Algorithms (GAs), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) algorithms. Especially, its performance becomes better and better with the incensement of the number of jobs. The non-parametric statistical tests are used to verify the significant of QEA. [Copyright &y& Elsevier]

Abstract: Mappings are established among ontologies for resolving the terminological and conceptual incompatibilities among information networks and information systems. Accommodating new knowledge in domain ontology causes the ontology to change from one consistent state to another. This consequently makes existing mappings among ontologies unreliable and stale due to the changes in resources. Mapping evolution eliminates discrepancies in the existing mappings. The proposed approach offers the benefits of re-establishing mappings among the updated ontologies in less time than is required with existing systems. It only considers the changed resources and eliminates staleness from the mappings. This approach uses the change history to drastically reduce the time required for reconciling mappings among ontologies, as shown in the results. [Copyright &y& Elsevier]

Abstract: The trust network is a social network where nodes are inter-linked by their trust relations. It has been widely used in various applications, however, little is known about its structure due to its highly dynamic nature. Based on five trust networks obtained from the real online sites, we contribute to verify that the trust network is the small-world network: the nodes are highly clustered, while the distance between two randomly selected nodes is short. This has considerable implications on using the trust network in the trust-aware applications. We choose the trust-aware recommender system as an example of such applications and demonstrate its advantages by making use of our verified small-world nature of the trust network. [Copyright &y& Elsevier]

• The knowledge acquisition framework acquiring knowledge model from diverse sources. • The knowledge acquisition process applying formal verification process to ensure the consistence knowledge model. • The knowledge models are used for cancer domains and the process is easily applicable for other domains. • The knowledge acquisition process involves the stakeholders from diverse disciplines and it always ensures the valid knowledge model for specified clinical domain. Background and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification. Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models. [ABSTRACT FROM AUTHOR]

Objective: Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.Materials and Methods: To study the combined impact of personal behaviors, we have proposed a methodology to derive the comprehensive Healthy Behavior Index (HBI) consisting of two major processes: (1) Behaviors' Weight-age Identification (BWI), and (2) Healthy Behavior Quantification and Index (HBQI) modeling. The BWI process identifies the high ranked contributing behaviors through life-expectancy based weight-age, whereas HBQI derives a mathematical model based on quantification and indexing of behavior using wellness guidelines.Results: The contributing behaviors are identified through text mining technique and verified by seven experts with a Kappa agreement level of 0.379. A real-world user-centric statistical evaluation is applied through User Experience Questionnaire (UEQ) method to evaluate the impact of HBI service. This HBI service is developed for the Mining Minds, a wellness management application. This study involves 103 registered participants (curious about the chronic disease) for a Korean wellness management organization. They used the HBI service over 12 weeks, the results for which were evaluated through UEQ and user feedback. The service reliability for the Cronbach's alpha coefficient greater than 0.7 was achieved using HBI service whereas the stimulation coefficient of the value 0.86 revealed significant effect. We observed an overall novelty of the value 0.88 showing the potential interest of participants.Conclusions: The comprehensive HBI has demonstrated positive user experience concerning the stimulation for adapting the healthy behaviors. The HBI service is designed independently to work as a service, so any other wellness management service-enabled platform can consume it to evaluate the healthy behavior index of the person for recommendation generation, behavior indication, and behavior adaptation. [ABSTRACT FROM AUTHOR]

Abstract: Since finding whether a graph has a Hamiltonian path or Hamiltonian cycle are both NP-complete problems, researchers have been formulating sufficient conditions that ensure the path or cycle. Rahman and Kaykobad (2005)  presented a sufficient condition for determining the existence of Hamiltonian path. Three recent works–Lenin Mehedy, Md. Kamrul Hasan, Mohammad Kaykobad (2007) , Rao Li (2006) , Shengjia Li, Ruijuan Li, Jinfeng Feng (2007) –further used the same or similar condition to ensure Hamiltonian cycle with some exceptions. The three works, along with their unique findings, have some common results. This paper unifies the results and brings them under Rahman and Kaykobad’s condition. [Copyright &y& Elsevier]

Background: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors.Objective: In this paper, an approach of deep bimodal learning is introduced to leverage the performance of DR risk progression identification.Methods: In particular, we further involve valuable clinical information of fundus photography in addition to the aforementioned systemic attributes. Accordingly, a Trilogy of Skip-connection Deep Networks, namely Tri-SDN, is proposed to exhaustively exploit underlying relationships between the baseline and follow-up information of the fundus images and EMR-based attributes. Besides that, we adopt Skip-Connection Blocks as basis components of the Tri-SDN for making the end-to-end flow of signals more efficient during feedforward and backpropagation processes.Results: Through a 10-fold cross validation strategy on a private dataset of 96 diabetic mellitus patients, the proposed method attains superior performance over the conventional EMR-modality learning approach in terms of Accuracy (90.6%), Sensitivity (96.5%), Precision (88.7%), Specificity (82.1%), and Area Under Receiver Operating Characteristics (88.8%).Conclusions: The experimental results show that the proposed Tri-SDN can combine features of different modalities (i.e., fundus images and EMR-based numerical risk factors) smoothly and effectively during training and testing processes, respectively. As a consequence, with impressive performance of DR risk progression recognition, the proposed approach is able to help the ophthalmologists properly decide follow-up schedule and subsequent treatment plans. [ABSTRACT FROM AUTHOR]