These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Data Science (5 papers in press)
State Space and Box-Jenkins Approaches: A Comparison of Models Prediction Performance in Finance. by Obinna Adubisi, John Ikwuoche, Ogbaji Eka, Erinma Uduma Abstract: This paper describes a study that used data collected from the Central Bank statistical web database system in Nigeria to evaluate and compare the forecasting performance of the nonstationary linear state space model and Box-Jenkins (ARIMA) model at different historic time periods. The comparison uses data series on inflation rates (core and non-core) in Nigeria for a specified period. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE). The one-year forecast evaluation results indicated that predictions from the nonstationary linear state space model outperformed the seasonal ARIMA model at different time periods. Furthermore, the proposed nonstationary linear state space model captured the dynamic structure of the inflationary series reasonably and requires no new cycle of identification and model estimation given the availability of new data. Keywords: ARIMA; Filtering; Inflation rate; Smoothing; State space model.
Most Preferable Combination of Explicit Drift Detection Approaches with Different Classifiers for Mining Concept Drifting Data Streams by Ritesh Srivastava, Veena Tayal Abstract: Sensors in the real world applications are the major sources of big data streams with varying underlying data distribution. Continuously generated time varying data streams are commonly referred as concept drifting data streams or dynamic (non-stationary) data stream. Unlike to the stationary data, the learner of concept drifting data stream requires quick forgetting of out-dated concepts in order to learn the new concept whenever the concept drifts occur. Many concept drifting data mining algorithms explicitly utilize the drift detection algorithms for ensuring the forgetting condition. In concept drifting data streams, the accuracy of the learner depends on the accuracy of the drift detection algorithm. The drift detection algorithms are generally characterized by its accuracy in drifts detection and its promptness towards drifts detection. Usually, a significant drop in the average accuracy of the concept drifting data stream classifier signifies the occurrence of the concept drift. For achieving and maintaining a consistent high accuracy in the classification of concept drifting data stream, it is very important to understand the preferable combinations of drift detection algorithms with the classification algorithms. In order to explore such preferable combinations, this work presents an empirical evolution of some popular drift detection methods with some state-of-art classification algorithms. We utilized some standard benchmark datasets of real world to conduct our experiments. Keywords: Concept drifts; Online learning; Data stream mining;Big data; Machine learning; classification; Drift detection methods;.
Addressing Uncertainty in Buyer-Supplier Interfaces by Supply Chain Phase and Decision-Making Level: A Fuzzy Goal-Fitting Approach by Margaret Shipley, Ray (Qing) Cao, G. Jonathan Davis Abstract: This exploratory study addresses uncertainty in supply chain management interfaces required for effective buyer-supplier partnerships. Such partnerships require sharing of knowledge throughout the phases of the supply chain where more impact may be critical at different organizational levels of managerial decision making. The study considered the phases of Plan, Source, Make, and Deliver and the Operational, Tactical and Strategic levels of decision making as the points for interface importance. Fuzzy probabilities of degree of fit to goals set to statistical confidence intervals is detailed with application based on input from over 400 buyers comparing seven suppliers in the electronics industry. Survey questions are mapped to seminal works on performance criteria, where possible, to phase and decision-making level. Results showed that the Source, Plan, and Deliver phases at different levels are to varying degrees important in buyer-supplier interfaces. Interestingly, the Make phase was less important overall for interfacing. The results suggest a heuristic for managers to use in maximizing supply chain performance gains through limited attention to buyer-supplier partnership. Keywords: multi-criteria decision making; MCDM; data analytics; fuzzy sets; supply chain partnership; SCM; supplier selection; data science.
Maximal and Closed Frequent Itemsets Mining from Uncertain Database and Data Stream. by Maliha Momtaz, Abu Ahmed Ferdaus, Chowdhury Farhan Ahmed, Mohammad Samiullah Abstract: Frequent itemsets(FIs) mining from uncertain database is a very
popular research area nowadays. Many algorithms have been proposed to mine
FI from uncertain database. But in typical FI mining process, all the FIs have
to be mined individually, which needs a huge memory. Four trees are proposed
in this paper which are, i) MFU tree which contains only the maximal frequent
itemsets generated from uncertain database ii) CFU tree which contains only
closed frequent itemsets generated from uncertain data-base iii)MFUS tree which
contains maximal frequent itemsets generated from uncertain data stream and
iv) CFUS tree which contains closed frequent itemsets generated from uncertain
data stream. Experimental results are also presented which show that maximal
and closed frequent itemsets mining requires less time and memory than typical
frequent itemsets mining.
Keywords: Frequent itemset(FI); Uncertain database; Frequent itemset from uncertain database(FU); Maximal frequent itemset(MFI); Closed frequent itemset(CFI); Maximal frequent itemset from uncertain database(MFU); Closed frequent itemset from uncertain database(CFU); Frequent itemset from uncertain data stream(FUS); Maximal frequent itemset from uncertain data stream(MFUS); Closed frequent itemset from uncertain data stream(CFUS).
The Impact of Social Media on Human Interaction in an Organization Based on Real-time Social Media Data. by Sharifah Sakinah Syed Ahmad, Anis Naseerah Shaik Osman Abstract: The growth of online social networks around the world has created a new place of interaction and communication among people. Social media is a cumulative of online communications channels dedicated to nation-based interaction, input, collaboration and content-sharing. Individuals can share their knowledge, opinions, and experiences with one another through the features provided where it gives an impact on peoples behavior in terms of interaction, communication and decision making. Twitter is one of the example of social media provider that empowers users to send and read short messages called "tweets". Registered users can read and post tweets, but unregistered users can only read them. This shows that everyone can access the tweets and use them to examine and get a definite result of any intended purposes. By trying to connect to the outside world, the user would probably disconnect with people around them and this will also affect the human interaction in organization. The objectives of this research are twofold first to find out the components and variables involved in interaction and decision making processes in an organization through social media; second to identify the changes that social media has brought to the human interaction process, and a better understanding of communicators involved in an organization through questions of why, when, and how social media gives impact on human interaction. It may also offer possible insights for organization to identify the pitfalls and opportunity that lies in the new digital human interaction era. This research will attempt to discuss these issues drawing from social media interaction on physical/digital interactions based on data science approach. Keywords: Social media; data science; content-sharing; digital human interaction.