Template-Type: ReDIF-Article 1.0 Author-Name: Manolis Maragoudakis Author-X-Name-First: Manolis Author-X-Name-Last: Maragoudakis Title: Bayesian feature construction for the improvement of classification performance Abstract: In this paper we are going to talk about the problem of the increase in validity, concerning the process of classification, but not through approaches having to do with the improvement of the ability to construct a precise classification model using any algorithm of machine learning. On the contrary, we approach this important matter by the view of a wider encoding of the training data and more specifically under the perspective of the creation of more features so that the hidden angles of the subject areas, which model the available data, are revealed to a higher degree. We suggest the use of a novel feature construction algorithm, which is based on the ability of the Bayesian networks to re-enact the conditional independence assumptions of features, bringing forth properties concerning their interrelation that are not clear when a classifier provides the data in their initial form. The results from the increase of the features are shown through the experimental measurement in a wide domain area and after the use of a large number of classification algorithms, where the improvement of the performance of classification is evident. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 43-75 Issue: 1 Volume: 12 Year: 2020 Keywords: machine learning; knowledge engineering methodologies; pattern analysis; statistical pattern recognition. File-URL: http://www.inderscience.com/link.php?id=105152 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:1:p:43-75 Template-Type: ReDIF-Article 1.0 Author-Name: Archana Purwar Author-X-Name-First: Archana Author-X-Name-Last: Purwar Author-Name: Sandeep Kumar Singh Author-X-Name-First: Sandeep Kumar Author-X-Name-Last: Singh Title: A novel ensemble classifier by combining sampling and genetic algorithm to combat multiclass imbalanced problems Abstract: To handle datasets with imbalanced classes is an exigent problem in the area of machine learning and data mining. Though a lot of work has been done by many researchers in the literature for two-class imbalanced problems, the multiclass problems still need to be explored. In this paper, we propose sampling and genetic algorithm based ensemble classifier (SA-GABEC) to handle imbalanced classes. SA-GABEC tries to find the best subset of classifiers for a given sample that is precise in predictions and can create an acceptable diversity in features subspace. These subsets of classifiers are fused together to give better predictions as compared to a single classifier. Moreover, this paper also proposes modified SA-GABEC which performs the feature selection before applying sampling and outperforms SA-GABEC. The performance of the proposed classifiers is evaluated and compared with GAB-EPA, Adaboost and bagging using minority class recall and extended G-mean. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 30-42 Issue: 1 Volume: 12 Year: 2020 Keywords: feature extraction; diversity; genetic algorithm; ensemble learning; multiclass imbalanced problems. File-URL: http://www.inderscience.com/link.php?id=105154 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:1:p:30-42 Template-Type: ReDIF-Article 1.0 Author-Name: Murat Yaşlıoğlu Author-X-Name-First: Murat Author-X-Name-Last: Yaşlıoğlu Title: Dynamics of the network economy: a content analysis of the search engine trends and correlate results using word clusters Abstract: Network economy is a relatively untouched area, strategic approach to the dynamics of this new economy is quite limited. Network economy is about the networks. Thus, it was decided to follow up the information on the internet including almost every kind of documentation. First, a deep relation analysis using trends was conducted to find out the related topics to new economy's dynamics: network effect, network externalities, interoperability, big data, open standards and social media. After the relation analysis, correlates of aforementioned keywords were analysed. Finally, all the clean 'top results' on the web were collected by the help of Linux command line tools into various, large text files. These files were analysed by the help of Nvivo qualitative analysis tool to form clusters. By the broad information available at hand, an extensive discussion on each result is written. It is believed that this new research approach will also guide many future researches on various subjects. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 1-29 Issue: 1 Volume: 12 Year: 2020 Keywords: network economy; network effect; network externalities; interoperability; big data; open standards; network strategy; methodology; analytics; word clusters; search engines. File-URL: http://www.inderscience.com/link.php?id=105162 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:1:p:1-29 Template-Type: ReDIF-Article 1.0 Author-Name: A. Senthilselvi Author-X-Name-First: A. Author-X-Name-Last: Senthilselvi Author-Name: R. Sukumar Author-X-Name-First: R. Author-X-Name-Last: Sukumar Author-Name: S. Senthilpandi Author-X-Name-First: S. Author-X-Name-Last: Senthilpandi Title: Hybrid fuzzy logic and gravitational search algorithm-based multiple filters for image restoration Abstract: In this paper, we present a multiple image filters for removal of impulse noises from test images. It utilises fuzzy logic (FL) approach to design a noise detector (ND) optimised by gravitational search algorithm (GSA) and utilises median filter (MF) for restoring. The proposed multiple filters used the FL approach to detect each pixels of a tests image are noise corrupted or not. If it is considered as noise-corrupted, the multiple filters restore it with the MF filter. Otherwise, it remains unchanged. We split the image into number of windows and each window apply the multiple filters. The filter output is used for the rule generation. The optimal rules are selected using GSA and given to the fuzzy logic system to detect the noise pixel. The experimental results are carried out using different noise level and different methods. The performance measured in terms of PSNR, MSE and visual quality. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 76-97 Issue: 1 Volume: 12 Year: 2020 Keywords: image restoration; impulse noise; fuzzy logic; multiple filters; median filter; standard test images; gravitational search algorithm; GSA. File-URL: http://www.inderscience.com/link.php?id=105182 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:1:p:76-97 Template-Type: ReDIF-Article 1.0 Author-Name: Kaparthi Srinivas Author-X-Name-First: Kaparthi Author-X-Name-Last: Srinivas Author-Name: Temberveni Venugopal Author-X-Name-First: Temberveni Author-X-Name-Last: Venugopal Title: Testing a file carving tool using realistic datasets generated with openness by a user level file system Abstract: During the development phase of a file carver, it is inappropriate to use a used hard disk as an input medium due to the fact that the file system does not provide openness regarding file fragmentation and location of data on the disk. In this paper, we propose a method that provides realistic datasets with openness which can be used to test carving tools. Realistic property of datasets is achieved by implementing a file system at user level. A large file is used to mimic a hard disk in this process. The large file, on the hard disk, is handled by the host file system. The same large file to mimic as a test hard disk is handled by a file system at user level and hence openness is achieved. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 155-171 Issue: 2 Volume: 12 Year: 2020 Keywords: metadata; digital forensics; data recovery; data analysis techniques; data analysis strategies; virtual disk; operating system; image file carving; user level file system; ULFS; script file; kernel level file system; realistic datasets; datasets with openness; software testing; data clusters. File-URL: http://www.inderscience.com/link.php?id=106639 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:2:p:155-171 Template-Type: ReDIF-Article 1.0 Author-Name: Jian-Bo Hu Author-X-Name-First: Jian-Bo Author-X-Name-Last: Hu Author-Name: Bing-Qi Liu Author-X-Name-First: Bing-Qi Author-X-Name-Last: Liu Author-Name: Kai Qiu Author-X-Name-First: Kai Author-X-Name-Last: Qiu Title: Fibre optic angle rate gyroscope performance evaluation in terms of Allan variance Abstract: Based on the analysis of the error-sources of the fibre optic angle rate gyroscope (FOARG), the Allan parameters are focused on calculation the Allan variances. The relationship between the Allan variance and the accuracy of FOARG is given. For the existences in the output of some-type FOARG, such as high noise, large volatilities in value and existing notable errors, a data-process algorithm is proposed with meaning and smoothing one. A lot of MATLAB blocks, such as data-sampling, meaning and smoothing, are designed to process some-type FOARG's dynamic data and static data and to evaluate its performance with Allan variance. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 114-126 Issue: 2 Volume: 12 Year: 2020 Keywords: fibre optic gyroscope; data-process; Allan variance. File-URL: http://www.inderscience.com/link.php?id=106640 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:2:p:114-126 Template-Type: ReDIF-Article 1.0 Author-Name: Aditya Khamparia Author-X-Name-First: Aditya Author-X-Name-Last: Khamparia Author-Name: Babita Pandey Author-X-Name-First: Babita Author-X-Name-Last: Pandey Title: A novel integrated principal component analysis and support vector machines-based diagnostic system for detection of chronic kidney disease Abstract: The alarming growth of chronic kidney disease has become a major issue in our nation. The kidney disease does not have specific target, but individuals with diseases such as obesity, cardiovascular disease and diabetes are all at increased risk. On the contrary, there is no such awareness about related kidney disease and its failure which affects individual's health. Therefore, there is need of providing advanced diagnostic system which improves health condition of individual. The intent of proposed work is to combine emerging data reduction technique, i.e., principal component analysis (PCA) and supervised classification technique support vector machine (SVM) for examination of kidney disease through which patients were being suffered from past. Variety of statistical reasoning and probabilistic features were encountered in proposed work like accuracy and recall parameters which examine the validity of dataset and obtained results. Experimental results concluded that SVM with Gaussian radial basis kernel achieved higher precision and performed better than other models in term of diagnostic accuracy rates. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 99-113 Issue: 2 Volume: 12 Year: 2020 Keywords: principal component analysis; PCA; support vector machine; SVM; classification; kidney disease; kernel; feature extraction. File-URL: http://www.inderscience.com/link.php?id=106641 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:2:p:99-113 Template-Type: ReDIF-Article 1.0 Author-Name: Mohit Kumar Author-X-Name-First: Mohit Author-X-Name-Last: Kumar Title: Measuring Pearson's correlation coefficient of fuzzy numbers with different membership functions under weakest t-norm Abstract: In statistical theory, the correlation coefficient has been widely used to assess a possible linear association between two variables and often calculated in crisp environment. In this study, a simplified and effective method is presented to compute the Pearson's correlation coefficient of fuzzy numbers with different membership functions using weakest triangular norm (t-norm)-based approximate fuzzy arithmetic operations. Different from previous research studies, the correlation coefficient computed in this paper is a fuzzy number rather than a crisp number. The proposed method has been illustrated by computing the correlation coefficient between the technology level and management achievement from a sample of 15 machinery firms in Taiwan. The correlation coefficient computed by proposed method has less uncertainty and obtained results are more exact. The computed results have also been compared with existing approaches. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 172-186 Issue: 2 Volume: 12 Year: 2020 Keywords: Pearson's correlation coefficient; fuzzy number; weakest t-norm arithmetic. File-URL: http://www.inderscience.com/link.php?id=106642 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:2:p:172-186 Template-Type: ReDIF-Article 1.0 Author-Name: S.P. Niranjan Author-X-Name-First: S.P. Author-X-Name-Last: Niranjan Author-Name: V.M. Chandrasekaran Author-X-Name-First: V.M. Author-X-Name-Last: Chandrasekaran Author-Name: K. Indhira Author-X-Name-First: K. Author-X-Name-Last: Indhira Title: Phase dependent breakdown in bulk arrival queueing system with vacation break-off Abstract: In this queueing model service process is split into two phases called first essential service and second essential service. Here the occurrence of breakdown during first essential service and second essential service are different. When the server got failure during first essential service, service process will be interrupted and sent to repair station immediately. On contrary during second essential service when the server got failure the service will not be interrupted, it performs continuously for current batch by doing some technical precaution arrangements. Server will be repaired after the service completion during renewal period. On service completion, if the queue length is less than 'a' then the server leaves for vacation. During vacation if the queue length reaches the value 'a' then the server breaks the vacation and performs preparatory work to start first essential service. Various performance measures and cost effective model with appropriate numerical solution of the model are presented. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 127-154 Issue: 2 Volume: 12 Year: 2020 Keywords: phase dependent breakdown; vacation break-off; dual control policy; bulk arrival; batch service; cost optimisation; renewal time; supplementary variable technique. File-URL: http://www.inderscience.com/link.php?id=106643 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:2:p:127-154 Template-Type: ReDIF-Article 1.0 Author-Name: Emmanouil D. Pratsinakis Author-X-Name-First: Emmanouil D. Author-X-Name-Last: Pratsinakis Author-Name: Symela Ntoanidou Author-X-Name-First: Symela Author-X-Name-Last: Ntoanidou Author-Name: Alexios Polidoros Author-X-Name-First: Alexios Author-X-Name-Last: Polidoros Author-Name: Christos Dordas Author-X-Name-First: Christos Author-X-Name-Last: Dordas Author-Name: Panagiotis Madesis Author-X-Name-First: Panagiotis Author-X-Name-Last: Madesis Author-Name: Ilias Eleftherohorinos Author-X-Name-First: Ilias Author-X-Name-Last: Eleftherohorinos Author-Name: George Menexes Author-X-Name-First: George Author-X-Name-Last: Menexes Title: Comparison of hierarchical clustering methods for binary data from molecular markers Abstract: Data from molecular markers used for constructing dendrograms, which are based on genetic distances between different plant species, are encoded as binary data. For dendrograms' construction, the most commonly used linkage method is the UPGMA in combination with the squared Euclidean distance. It seems that in this scientific field, this is the 'golden standard' clustering method. In this study, a review is presented on clustering methods used with binary data. Furthermore, an evaluation of the linkage methods and the corresponding appropriate distances (comparison of 163 clustering methods) is attempted using binary data resulted from molecular markers applied to five populations of the wild mustard <i>Sinapis arvensis</i> species. The validation of the various cluster solutions was tested using external criteria. The results showed that the 'golden standard' is not a 'panacea' for dendrogram construction, based on binary data derived from molecular markers. Thirty seven other hierarchical clustering methods could be used. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 190-212 Issue: 3 Volume: 12 Year: 2020 Keywords: dendrograms; proximities; linkage methods; Benzécri's chi-squared distance; correspondence analysis; categorical binary data; ISSR markers; Sinapis arvensis. File-URL: http://www.inderscience.com/link.php?id=108036 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:3:p:190-212 Template-Type: ReDIF-Article 1.0 Author-Name: Stratos Moschidis Author-X-Name-First: Stratos Author-X-Name-Last: Moschidis Author-Name: Efstratios Livanis Author-X-Name-First: Efstratios Author-X-Name-Last: Livanis Author-Name: Athanasios C. Thanopoulos Author-X-Name-First: Athanasios C. Author-X-Name-Last: Thanopoulos Title: Assessment of the awareness of Cypriot accounting firms level concerning cyber risk: an exploratory analysis Abstract: Technology development has made a decisive contribution to the digitisation of businesses, which makes it easier for them to work more efficiently. However, in recent years, data leakages have shown an increasing trend. To investigate the level of awareness among Cypriot accountancy firms about cyber-related risks, we use the data from a recent survey of Cypriot professional accountants' members of Institute of Certified Public Accountants of Cyprus (ICPAC). The categorical nature of the data and the purpose of our research led us to use methods of multidimensional statistical analysis. The emergence of intense differences between accounting companies in relation to the issue as we will present is particularly interesting. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 213-227 Issue: 3 Volume: 12 Year: 2020 Keywords: cyber risk; multiple correspondence analysis; MCA; Cypriot accounting firms; exploratory statistics. File-URL: http://www.inderscience.com/link.php?id=108037 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:3:p:213-227 Template-Type: ReDIF-Article 1.0 Author-Name: Angelos Markos Author-X-Name-First: Angelos Author-X-Name-Last: Markos Author-Name: Odysseas Moschidis Author-X-Name-First: Odysseas Author-X-Name-Last: Moschidis Author-Name: Theodore Chadjipantelis Author-X-Name-First: Theodore Author-X-Name-Last: Chadjipantelis Title: Sequential dimension reduction and clustering of mixed-type data Abstract: Clustering of a set of objects described by a mixture of continuous and categorical variables can be a challenging task. In the context of data reduction, an effective class of methods combine dimension reduction with clustering in the reduced space. In this paper, we review three approaches for sequential dimension reduction and clustering of mixed-type data. The first step of each approach involves the application of principal component analysis on a suitably transformed matrix. In the second step, a partitioning or hierarchical clustering algorithm is applied to the object scores in the reduced space. The common theoretical underpinnings of the three approaches are highlighted. The results of a benchmarking study show that sequential dimension reduction and clustering is an effective strategy, especially when categorical variables are more informative than continuous with regard to the underlying cluster structure. Strengths and limitations are also demonstrated on a real mixed-type dataset. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 228-246 Issue: 3 Volume: 12 Year: 2020 Keywords: cluster analysis; dimension reduction; correspondence analysis; principal component analysis; PCA; mixed-type data. File-URL: http://www.inderscience.com/link.php?id=108043 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:3:p:228-246 Template-Type: ReDIF-Article 1.0 Author-Name: N. Abdul Jaleel Author-X-Name-First: N. Abdul Author-X-Name-Last: Jaleel Author-Name: P. Vijaya Kumar Author-X-Name-First: P. Vijaya Author-X-Name-Last: Kumar Title: Implementation of an efficient FPGA architecture for capsule endoscopy processor core using hyper analytic wavelet-based image compression technique Abstract: To receive images of human intestine for medical diagnostics, wireless capsule endoscopy (WCE) is a state-of-the-art technology. This paper proposes implementation of efficient FPGA architecture for capsule endoscopy processor core. The main part of this processor is image compression, for which we proposed an algorithm called as hyper analytic wavelet transform (HWT). The hyper analytic wavelet transform (HWT) is quasi shift-invariant; it has a good directional selectivity and a reduced degree of redundancy. Huffman coding also used to reduce the amount of bits required to represent a string of symbols. This paper also provided forward error correction (FEC) scheme based on low density parity check codes (LDPC) to reduce bit error rate (BER) of the transmitted data. Compared to the similar existing works this paper proposed an efficient architecture. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 262-286 Issue: 3 Volume: 12 Year: 2020 Keywords: wireless capsule endoscopy; WCE; hyper analytic wavelet transform; HWT; Huffman coding; low density parity check codes; LDPC; forward error correction; FEC; quasi shift-invariant; bit error rate; BER. File-URL: http://www.inderscience.com/link.php?id=108056 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:3:p:262-286 Template-Type: ReDIF-Article 1.0 Author-Name: Dimitrios Stamovlasis Author-X-Name-First: Dimitrios Author-X-Name-Last: Stamovlasis Author-Name: Julie Vaiopoulou Author-X-Name-First: Julie Author-X-Name-Last: Vaiopoulou Author-Name: George Papageorgiou Author-X-Name-First: George Author-X-Name-Last: Papageorgiou Title: A comparative evaluation of dissimilarity-based and model-based clustering in science education research: the case of children's mental models of the Earth Abstract: In the present work, two different classification methods, a dissimilarity-based clustering approach (DBC) and the model-based latent class analysis (LCA), were used to analyse responses to a questionnaire designed to measure children's mental representation of the Earth. It contributes to an ongoing debate in cognitive psychology and science education research between two antagonistic theories on the nature of children's knowledge, that is, the coherent versus fragmented knowledge hypothesis. Methodology-wise the problem concerns the classification of response patterns into distinct clusters, which correspond to specific hypothesised mental models. DBC employs the partitioning around medoids (PAM) approach and selects the final cluster solution based on average silhouette width, cluster stability and interpretability. LCA, a model-based clustering method achieves a taxonomy by employing the conditional probabilities of responses. Initially, a brief presentation and comparison of the two methods is provided, while issues on clustering philosophies are discussed. Both PAM and LCA attained to detect merely the cluster which corresponds to the coherent scientific model and an artificial segment added on purpose in the empirical data. The two methods, despite the obvious deviations in cluster-membership assignment, finally provide sound findings as far as hypotheses tested, by converging to identical conclusions. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 247-261 Issue: 3 Volume: 12 Year: 2020 Keywords: mental model; latent class analysis; partitioning around medoids; dissimilarity-based clustering; coherent mental model hypothesis; fragmented knowledge hypothesis; science education; model-based clustering. File-URL: http://www.inderscience.com/link.php?id=108080 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:3:p:247-261 Template-Type: ReDIF-Article 1.0 Author-Name: James Otto Author-X-Name-First: James Author-X-Name-Last: Otto Author-Name: Chaodong Han Author-X-Name-First: Chaodong Author-X-Name-Last: Han Title: Data aggregation to better understand the impact of computerisation on employment Abstract: Data reduction methods are called for to address challenges presented by big data. Correlation of two variables may be less clear if data are organised at disaggregate levels in regression analysis. In this study, we apply data aggregation to regression analysis in the context of a study forecasting the impact of computerisation on jobs and wages. We show that data grouped by the ranked independent variable, versus random or other grouping schemes, provides a clearer pattern of the employment impacts of computerisation probability on job categories. The coefficient estimates are more consistent for groupings based on a ranked independent variable, than those provided by random grouping of the same independent variable. The improved estimations can have positive policy implications. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 299-317 Issue: 4 Volume: 12 Year: 2020 Keywords: data reduction methods; impact of computerisation; automation; computerisation probability; data grouping schemes; statistical regression; data aggregation; ranked regression; information reduction. File-URL: http://www.inderscience.com/link.php?id=111479 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:4:p:299-317 Template-Type: ReDIF-Article 1.0 Author-Name: Adilson Da Silva Author-X-Name-First: Adilson Da Author-X-Name-Last: Silva Author-Name: António Monteiro Author-X-Name-First: António Author-X-Name-Last: Monteiro Author-Name: Miguel Fonseca Author-X-Name-First: Miguel Author-X-Name-Last: Fonseca Title: Inference in mixed linear models with four variance components - Sub-D and Sub-DI Abstract: This work approaches the new estimators for variance components in mixed linear models Sub-D and its improved version Sub-DI, developed and tested by Silva (2017). Both estimators were deduced and tested in mixed linear models with two and three variance components; the authors gave the corresponding formulations in models with an arbitrary number of variance components but no one had ever tested their performances in models with more than three variance components. Particularly, here we aim to give the explicit formulations for both Sub-D and Sub-DI in models with four variance components, as well as a numerical example testing their performances. Tables containing the results of the numerical example will be given. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 318-334 Issue: 4 Volume: 12 Year: 2020 Keywords: orthogonal matrices; variance components; Sub-D; Sub-DI; mixed linear models. File-URL: http://www.inderscience.com/link.php?id=111482 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:4:p:318-334 Template-Type: ReDIF-Article 1.0 Author-Name: Admi Mohamed Author-X-Name-First: Admi Author-X-Name-Last: Mohamed Author-Name: El Fkihi Sanaa Author-X-Name-First: El Fkihi Author-X-Name-Last: Sanaa Author-Name: Rdouan Faizi Author-X-Name-First: Rdouan Author-X-Name-Last: Faizi Title: Detecting text in license plates using a novel MSER-based method Abstract: A new license plate detection method is proposed in this paper. The proposed approach consists of three steps: the first step aims to delete some details in the input image by converting it to a grey-level image and inverse it (negative) and then use MSER for the extraction of text in candidate regions. The second step is based on a dynamic grouped DBSCAN algorithm for a fast classification of the connected region, and the outer tangent of circles intersections for filtering regions with the same orientations. Finally, a geometrical and statistical character filter is used to eliminate false detections in the third step. Experimental results show that our approach performs better and achieves a better detection than that proposed by Yin et al. (2014). Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 335-348 Issue: 4 Volume: 12 Year: 2020 Keywords: text detection; MSER; circle overlapping; DBSCAN; license plate detection. File-URL: http://www.inderscience.com/link.php?id=111488 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:4:p:335-348 Template-Type: ReDIF-Article 1.0 Author-Name: Thomas Fotiadis Author-X-Name-First: Thomas Author-X-Name-Last: Fotiadis Title: A study of the effect of customer citizenship behaviour on service quality, purchase intentions and customer satisfaction Abstract: Customer citizenship behaviour constitutes a determinative factor of consumer behaviour. It shapes beliefs relating to the service quality offered by the enterprise and graduates the magnitude of customer satisfaction. This paper investigates customers' behaviour in the light of their intentions to provide information and feedback to the enterprise, to support it in their social circles, to advertise it through 'word-of-mouth', to communicate and interact with other customers and to exchange views, and to detect problems that may emerge due to, for example, delays or shortages in certain products. Additionally, the paper surveys the degree by which the aforementioned constituents affect, the perceived quality of the services rendered, the purchase intention and customer satisfaction. The implicative statistical analysis technique was used to analyse the data of the survey. Results show that feedback and interaction provided by customers shape purchase intention and that these parameters together determine the perceived service quality. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 349-366 Issue: 4 Volume: 12 Year: 2020 Keywords: consumer behaviour; customer citizenship behaviour; customer satisfaction; purchase intention; service quality. File-URL: http://www.inderscience.com/link.php?id=111496 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:4:p:349-366 Template-Type: ReDIF-Article 1.0 Author-Name: Amin Karami Author-X-Name-First: Amin Author-X-Name-Last: Karami Author-Name: Shafiq Urréhman Author-X-Name-First: Shafiq Author-X-Name-Last: Urréhman Author-Name: Mustansar Ali Ghazanfar Author-X-Name-First: Mustansar Ali Author-X-Name-Last: Ghazanfar Title: A novel centroids initialisation for K-means clustering in the presence of benign outliers Abstract: K-means is one of the most important and widely applied clustering algorithms in learning systems. However, it suffers from centroids initialisation that makes K-means algorithm unstable. The performance and the stability of the K-means algorithm may be degraded if benign outliers (i.e., long-term independence data points) appear in data. In this paper, we developed a novel algorithm to optimise K-means performance in the presence of benign outliers. We firstly identified the benign outliers and executed K-means across them, then K-means runs over all data points to re-locate clusters' centroids, providing high accuracy. The experimental results over several benchmarking and synthetic datasets confirm that the proposed method significantly outperformed some existing approaches with better accuracy based on applied performance metrics. Journal: Int. J. of Data Analysis Techniques and Strategies Pages: 287-298 Issue: 4 Volume: 12 Year: 2020 Keywords: clustering; K-means; centroid initialisation; benign outlier. File-URL: http://www.inderscience.com/link.php?id=111498 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:injdan:v:12:y:2020:i:4:p:287-298