Forthcoming and Online First Articles

International Journal of Knowledge Engineering and Data Mining

International Journal of Knowledge Engineering and Data Mining (IJKEDM)

Forthcoming 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 Articles will not be published until the final proofs are validated by their authors.

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International Journal of Knowledge Engineering and Data Mining (3 papers in press)

Regular Issues

  • Applying Decision Trees on Road Traffic Accident Data for Predicting Survival Chance of Patients   Order a copy of this article
    by Parham Porouhan 
    Abstract: The main objective of this study is to generate decision tree (DT) models/graphs (i.e., a type of supervised machine learning (ML) research method) through RapidMiner Studio (i.e., a popular visual platform for predictive analytics). The dataset used in the study contains attributes regarding the car accidents such as 'gender', 'casualty class', 'age group' and 'type of vehicle'. These are important features to decide whether the 'survival chance' of traffic accident patients would be 'high' or 'low'. Therefore, our goal is to apply 'DTs' for predicting the 'survival attribute' with the purpose of identifying high risk groups within the dataset. The resulting 'DTs' show that whenever the attribute 'gender' has the value 'male', and the attribute 'casualty class' has the value 'passenger', and the attribute 'gender' has the value 'male', and the attribute 'age group' has the value 'teenager'; then the 'survival chance' of the traffic/accident patient would be extremely 'low'.
    Keywords: data mining; supervised machine learning; RapidMiner studio; decision tree; DT; Bayesian networks; predictive analytics; accident risk factors; survival rate.
    DOI: 10.1504/IJKEDM.2023.10051239
     
  • Amazigh Biographical Information Extraction   Order a copy of this article
    by Fadoua ATAA ALLAH, Siham BOULAKNADEL 
    Abstract: In the absence of an Amazigh knowledge database structuring biographical information, we propose a methodology to elaborate such database. In this paper, we give a brief overview of the concerned topics while introducing the respective approaches, and we present the process of making our biographical collection. Furthermore, we discuss the challenges we meet in finding the right balance between what we need to collect and what is available online. Our methodology focuses on biographical information extraction from press dispatches that have been annotated with named entities. It is based on linguistic patterns and lexical markers, while adopting 'Local Grammars' concept.
    Keywords: Amazigh Language; Biographical Information; Fact Relations; Information Extraction; Event Extraction; Relation Extraction; Fact Extraction; Named Entities; Lexical Markers; Local Grammars. .
    DOI: 10.1504/IJKEDM.2023.10053903
     
  • Algerian Arabizi rumour detection based on morphosyntactic analysis   Order a copy of this article
    by Chahnez ZAKARIA, Kamel Smaili, Besma SAHNOUN, Assia CHALA, Radjaa AGAGNA, Célia AMIRAT 
    Abstract: Social networks have become a customary news media source in recent times. However, the openness and unrestricted way of sharing information on social networks fosters spreading rumours which may cause severe damages economically, socially, etc. Motivated by this, our paper focuses on rumour detection problem in Algerian Arabizi. Studying linguistic rules of Algerian Arabizi, we propose a lemmatiser and a parser for analysing and standardising the text to produce better rumour detection models. An approach for classifying rumours and news in social networks based on emotions’ expression and users’ positions is proposed. The experiments were done on many ngram representations where the best one has reached more than 94% of f-score. In addition to that this research deals with resources creation for Algerian Arabizi which is an under-resourced dialect. A corpus and several lexicons have been built, which can be the subject of other works dealing with this dialect.
    Keywords: Social medias; Rumour detection; Lemmatiser; Parser; Arabizi; Machine learning; Arabizi corpus; Resource building.
    DOI: 10.1504/IJKEDM.2023.10054183
     

 

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