Norafida Ithnin

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.

Graphical Password: Comprehensive Study of the Usability Features of the Recognition Base Graphical Password Methods

Graphical passwords are an alternative authentication method to alphanumeric passwords in which users click on images to authenticate themselves rather than type alphanumeric strings. This research aims to study the usability features of the recognition base graphical password methods available and extract the usability features of the existing methods. In this paper we study the recognition base graphical password type with the available methods from the usability point of view according to previous studies and surveys. Then we match the usability features (General usability features, existing usability features for existing graphical password methods, and ISO usability features) to the existing graphical password methods and make a comparison study between these methods and the usability features. We have found that there is no method has the most important usability features. Thus, by completing this study a set of usability features is suggested to be in one graphical password system. This set includes the easy of use, memorize, creation, learning and satisfaction. Moreover, this work proposes to build a new system of graphical password system that provides promising usability features.

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.

Graphical Password: Prototype Usability Survey

Access to computer networks and systems is most often based on the use of conventional passwords nowadays. However, users have difficulty remembering a password that is long and random-appearing. So, they create short, simple, and insecure passwords. Graphical passwords have been designed to try to make passwords more memorable and easier for people to use, to create and, therefore, more usable and secure. Using a graphical password, users click on images rather than type conventional passwords. We have designed a usable graphical password system focusing in the usability features to the users, called Jetafida. In this paper we describe the Jetafida usability features, and the system usability features had been tested by questionnaire survey. This done by thirty computer students participates and uses the system for three times in different times to see the usability features achieved in the system. Where these features are ease of use, ease to create, ease to memorize, ease to learn and the overall system design and layout. The results show that the participants found that the proposed usable graphical password system has been achieving the usability features built in.

Factors influencing cloud computing adoption for e-government implementation in developing countries: Instrument development

Purpose This paper aims to develop a model and measurement to investigate the factors influencing cloud computing adoption as a part of developing countries’ alternatives to implement e-government services. Design/methodology/approach This study proposes a theoretical model based on the literature of technology adoption models. It constructs scale measurements for the proposed model constructs by extracting and adapting the items from the literature. The authors verify the scales’ content validity and reliability by applying face validity, pre-testing and pilot study. For the pilot study, the authors collect the data from 26 information technology staff in five public organizations in Yemen. The authors test the reliability of the scales using Cronbach’s alpha criterion, and then conduct exploratory factor analysis to evaluate the validity of the scales. Findings The results show that the scale measurements meet the conventional criteria of reliability and validity. Originality/value Theoretically, this paper provides an integrated model for examining cloud computing adoption for e-government implementation in developing countries. In addition, it develops an instrument to empirically investigate the influencing factors of cloud computing adoption in the context of developing countries’ e-government initiatives.

Broadcasting in Connected and Fragmented Vehicular Ad Hoc Networks

This paper starts with an overview of vehicular ad hoc networks (VANETs) and their characteristics. Then this paper reviews diverse applications of VANETs and the requirements of these applications. In addition it reviews VANETs standards, different broadcasting presented in a variety of studies, and also associated issues with data dissemination in connected and fragmented vehicular networks to solve broadcast storm problem and temporary disconnected VANETs. The discussion will be about the encountered challenges and presented solutions with respect to the related issues, based on the literature and strength and weakness of each protocol.

Parallel rsa encryption based on tree architecture

Parallelism is a technique to accelerate various applications. Nowadays, parallel operations are used to solve computer problems such as sort, search, and cryptography, which result in a reasonable speed. Sequential algorithms can be parallelized by being implemented on parallel architectures. Cryptography is the science of hiding information, which by the increase in the applications on insecure communication environments, has become one of the most important aspects of the digital world. In this article, we propose a parallel RSA utilizing parallel processing on RSA using tree architecture. RSA is a well-known public key cryptography which is not as fast as symmetric cryptographies. Parallelizing it, we can achieve speedup and more security. We also investigate the state of the art methods of RSA and figure out that their low speed can be faster with reasonable security using parallel architecture.

An Optimization of Tree Topology Based Parallel Cryptography

Public key cryptography has become of vital importance regarding the rapid development of wireless technologies. The RSA is one of the most important algorithms for secure communications in public-key cryptosystems. Since the RSA is expensive in terms of computational task which is modular exponentiation, parallel processing and architecture is a reasonable solution to speedup RSA operations. In this paper, taking into account pipelining and optimization, we improve throughput and efficiency of the TRSA method, a parallel architecture solution for RSA security based on tree topology. The optimization and pipelining of the tree based architecture increases its efficiency and throughput. The experimental results demonstrate that these pipelined and optimized approaches outperform the main TRSA.