Othman Ibrahim

A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS

Display Omitted A multi-criteria CF recommender system in tourism domain is proposed.Predictive accuracy of multi-criteria CF recommender systems is improved.EM algorithm, ANFIS and PCA are applied in the proposed method.PCA is applied for solving multi-collinearity problem.ANFIS is applied for developing the prediction models. In order to improve the tourist experience, recommender systems are used to offer personalized information for online users. The hotel industry is a leading stakeholder in the tourism sector, which needs to provide online facilities to their customers. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a users needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. They use a single rating as input. However, the multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects and they can be an appropriate choice for the tourist. In this paper, we propose a new hybrid method for hotel recommendation using dimensionality reduction and prediction techniques. Accordingly, we have developed the multi-criteria CF recommender systems for hotel recommendation to enhance the predictive accuracy by using Gaussian mixture model with Expectation Maximization (EM) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS). We have also used the Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF dataset. Our experiments confirmed that the proposed hybrid method achieved high accuracy for hotel recommendation for the tourism sector.

Clustering- and regression-based multi-criteria collaborative filtering with incremental updates

Abstract Recommender systems are a valuable means for online users to find items of interest in situations when there exists a large set of alternatives. Collaborative Filtering (CF) is a popular technique to build such systems which is based on explicit rating feedback on the items by a larger user community. Recent research has demonstrated that the predictive accuracy of CF based recommender systems can be measurably improved when multi-criteria ratings are available, i.e., when users provide ratings for different aspects of the recommendable items. Technically, in particular regression-based techniques have been shown to be a promising means to predict the user’s overall assessment of an item based on the multi-criteria ratings. Since in many domains customer subgroups (segments) exist that share similar preferences regarding the item features, we propose a novel CF recommendation approach in which such customer segments are automatically detected through clustering and preference models are learned for each customer segment. In addition, since in practical application constantly new rating information is available, the proposed method supports incremental updates of the preference models. An empirical evaluation of our method shows that the predictions of the resulting models are more accurate than previous multi-criteria recommendation methods.

Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.

A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments

The Multiple Criteria Decision Making (MCDM) utility determining approaches and fuzzy sets are considered to be new development approaches, which have been recently presented, extended, and used by some scholars in area of decision making. There is a lack of research regarding to systematic literature review and classification of study about these approaches. Therefore; in the present study, the attempt is made to present a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years. Regarding this, some major databases including Web of Science, Scopus and Google Scholar have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. In addition, the selected articles were classified based on authors, the year of publication, journals and conferences names, the technique and method used, research objectives, research gap and problem, solution and modeling, and finally results and findings. The results of this study can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria and uncertain environments. In addition, findings of this study help to practitioners and academic for adopting the new MCDM utility techniques such as WASPAS and SWARA in different application areas and presenting insight into literature.

Secure Multipath Routing Protocols in Wireless Sensor Networks: A Security Survey Analysis

Wireless Sensor Networks is usually consisting of huge number of limited sensor devices which are communicated over the wireless media. As sensor devices are limited, the networks exposed to various kinds of attacks and conventional defenses against these attacks are not suitable due to the resource constrained. Therefore, security in WSNs is a challenging task due to inheritance limitations of sensors. As a result, many secure routing protocols are introduced such as secure multipath routing protocols to reduce the attacks. In this paper we focus at security issues concerns in WSNs and present a matrix that analysed the security factor provided by the secure multipath routing protocols in wireless sensor networks.

A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques

Multi-criteria collaborative filtering (MC-CF) presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects of items. However, scalability and sparsity are two main problems in MC-CF which this paper attempts to solve them using dimensionality reduction and Neuro-Fuzzy techniques. Considering the user behavior about items’ features which is frequently vague, imprecise and subjective, we solve the sparsity problem using Neuro-Fuzzy technique. For the scalability problem, higher order singular value decomposition along with supervised learning (classification) methods is used. Thus, the objective of this paper is to propose a new recommendation model to improve the recommendation quality and predictive accuracy of MC-CF and solve the scalability and alleviate the sparsity problems in the MC-CF. The experimental results of applying these approaches on Yahoo!Movies and TripAdvisor datasets with several comparisons are presented to show the enhancement of MC-CF recommendation quality and predictive accuracy. The experimental results demonstrate that SVM dominates the K-NN and FBNN in improving the MC-CF predictive accuracy evaluated by most broadly popular measurement metrics, F1 and mean absolute error. In addition, the experimental results also demonstrate that the combination of Neuro-Fuzzy and dimensionality reduction techniques remarkably improves the recommendation quality and predictive accuracy of MC-CF in relation to the previous recommendation techniques based on multi-criteria ratings.

Accuracy Improvement for Predicting Parkinson’s Disease Progression

Parkinson’s disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson’s datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.

Recommendation quality, transparency, and website quality for trust-building in recommendation agents

A new model for trust-building in recommendation agents is proposed.We consider recommendation quality, transparency, and website quality in the model.We conducted empirical studies in the context of two popular e-commerce websites.The results highlight the importance of perceived transparency for trust-building. Trust is a main success factor for automated recommendation agents on e-commerce sites. Various aspects can contribute to the development of trust toward such an agent, including perceptions about the usefulness of the recommendations, the transparency of the recommendation process, and the general quality of the website. These factors have been analyzed in isolation in the literature though. We propose and evaluate a new trust model that integrates these factors, and allows us to assess their relative importance for trust-building. We conducted empirical studies in the context of two popular e-commerce websites. The findings suggest that transparency is equally important to consumers for building trust as recommendation quality, and that general we site quality contributes to the development of trust. The findings indicate that focusing on recommendation quality may be insufficient and higher levels of adoption of the recommendations can be achieved when several trust-building factors are considered.

A knowledge-based system for breast cancer classification using fuzzy logic method

A knowledge-based system is proposed for breast cancer disease classification.The knowledge-based system uses EM, PCA, CART and fuzzy rule-based methods.WDBC and Mammographic mass datasets are used for the method evaluation.The accuracies obtained by the method are respectively 0.932 and 0.941 for WDBC and Mammographic mass datasets. Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.

Government's ICT project failure factors: A revisit

Successful implementations of Information and Communication Technologies (ICT) projects act as strong foundations supporting government transformation programmes. The aim of this research is to revisit the failure factors linked to certain governments ICT projects. The study analyzes the current gap between failure factors in Malaysian government agencies (practice) as compared to the literature consulted (theory). Project management factors and process factors are the two major factors that contributed to ICT project failures in the Malaysian government. We believe that this finding should be studied further.