International Journal of Business Intelligence and Data Mining
- Editor in Chief
- Dr. M.A. Dorgham
- ISSN online
- ISSN print
- 8 issues per year
- CiteScore 1.2 (2022)
IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing.
Topics covered include
- Data extraction/reporting/cleaning/pre-processing
- OLAP, decision analysis, causal modelling
- Reasoning under uncertainty, noise in data
- Business intelligence cycle
- Model specification/selection/estimation
- Web technology, mining, agents
- Fuzzy, neural, evolutionary approaches
- Genetic algorithms, machine learning, expert/hybrid systems
- Bayesian inference, bootstrap, randomisation
- Exploratory/automated data analysis
- Knowledge-based analysis, statistical pattern recognition
- Data mining algorithms/processes
- Classification, projection, regression, optimisation clustering
- Information extraction/retrieval, human-computer interaction
- Multivariate data visualisation, tools
- Data extraction and reporting
- Data cleaning and pre-processing
- Decision analysis
- Causal modelling
- Reasoning under uncertainty
- Uncertainty and noise in data
- Business intelligence cycle, and model specification/selection/estimation
- Web technology, mining and agents
- Fuzzy, neural, and evolutionary approaches
- Genetic algorithms
- Machine learning
- Expert systems
- Hybrid systems
- Bayesian inference, bootstrap and randomisation
Data Analysis and Data Mining:
- Exploratory and automated data analysis
- Knowledge-based analysis
- Statistical pattern recognition
- Data mining algorithms and processes
- Classification, projection, regression, optimisation clustering
- Information extraction and retrieval
- Multivariate data visualisation
Applications and Tools:
- Visualisation tools
- Applications (e.g. commerce, engineering, finance, manufacturing, science)
- Human-computer interaction in intelligence data analysis
- Business intelligence and data analysis systems and tools
More on this journal...
Business intelligence and data mining share many common issues. IJBIDM aims to stimulate the exchange of ideas and interaction between these related fields of interest. It is intended to be the premier technical publication in the field, providing a resource collection relevant common methods and techniques and a forum for unifying the diverse constituent research communities in business intelligence and intelligent data analysis. Advances in data gathering, distribution and analysis have also created a need for an application of intelligent data analysis techniques to solve business modelling problems.
IJBIDM publishes original research results, surveys and tutorials of important areas and techniques, detailed descriptions of significant applications, technical advances and news items concerning use of intelligent data analysis technique in business applications. IJBIDM puts a heavy emphasis on new data analysis architectures, methodologies, and techniques and their applications in business.
IJBIDM provides a forum for the examination of issues related to the research and applications of intelligent data analysis in business. IJBIDM is targeted at academic, researchers, and IT professionals. This journal provides a vehicle to help business analysts and IT professionals to disseminate information and to learn from each other's work. Readers will be well informed of the latest development in research and practice in intelligent data analysis and data mining and its applications in business problems. Readers will be able to learn established knowledge in data analysis techniques through comprehensive survey articles. Readers will have the opportunity to learn future direction in business intelligence and data mining
IJBIDM is devoted to the publications of high quality papers on theoretical developments and practical applications in business intelligence, data analysis and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. Special issues are devoted to current issues in business intelligence and techniques. IJBIDM also publishes best papers from international conferences in the areas relevant to the journal.
Papers published in IJBIDM are geared heavily towards applications (use of intelligence data analysis and mining techniques in business applications), with an anticipated split of 70% of the papers published being applications-oriented, research and the remaining 30% containing more theoretical research.
IJBIDM is indexed in:
- Scopus (Elsevier)
- Compendex [formerly Ei] (Elsevier)
- Academic OneFile (Gale)
- ACM Digital Library
- cnpLINKer (CNPIEC)
- DBLP Computer Science Bibliography
- Expanded Academic ASAP (Gale)
- OneFile Business (Gale)
- General OneFile (Gale)
- Google Scholar
- Info Trac (Gale)
- Inspec (Institution of Engineering and Technology)
- io-port (FIZ Karlsruhe)
- Pascal (INIST-CNRS)
- ProQuest Advanced Technologies Database with Aerospace
IJBIDM is listed in:
- National Agency for Evaluation of the University and Research System (ANVUR)
- Cabell's Directory of Publishing Opportunities
- Taniar, David, Monash University, Australia
Editor in Chief
- Dorgham, M.A., International Centre for Technology and Management, UK
- Lee, Kee Khoon, Rolls Royce, Singapore
- Mungkasi, Sudi, Sanata Dharma University, Indonesia
- Pan, Yongping, National University of Singapore, Singapore
- Thakur, Nirmalya, University of Cincinnati, USA
- Angelov, Plamen, Lancaster University, UK
- Pedrycz, Witold, University of Alberta, Canada
- Rutkowski, Leszek, Czestochowa University of Technology, Poland
Editorial Board Members
- Barbiero, Alessandro, University of Milan, Italy
- Dovzan, Dejan, University of Ljubljana, Slovenia
- Gomide, Fernando, University of Campinas, Brazil
- Iglesias Martinez, Jose Antonio, Carlos III University of Madrid, Spain
- Koh, Yun Sing, University of Auckland, New Zealand
- Lian, Zhichao, Nanjing University of Science and Technology, China
- Lim, Chee Peng, Deakin University, Australia
- Lughofer, Edwin, Johannes Kepler University, Austria
- Oentaryo, Richard Jayadi, Singapore Management University, Singapore
- Perez, Javier Andreu, Imperial College London, UK
- Raghavan, Vijay, University of Louisiana at Lafayette, USA
- Rubio Avila, Jose De Jesus, Instituto Politécnico Nacional, Mexico
- Sayed-Mouchaweh, Moamar, Université de Reims Champagne-Ardenne, France
- Wang, Ning, Harbin Engineering University, China
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
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- All articles for this journal must be submitted using our online submissions system.
- View Author guidelines.
Data mining the dangers of self-medication
7 September, 2023
Self-medication for minor ailments and illnesses is common. Often the remedies people turn to are simple over-the-counter pharmacy medications or products available in other outlets that may or may not have proven physiological activity. There is a notion that self-medication may cause more harm than good, if a person with significant symptoms of disease opts for a shop-bought remedy rather than seeking professional medical advice. Ultimately, it might lead to a problem essentially being untreated and in the worst-case scenario could lead to a degradation of a person's health or even death. Research in the International Journal of Business Intelligence and Data Mining has used online social network data mining to investigate the phenomenon of dangerous self-medication. Reza Samizadeh, Mahsa Jadidi, and Sahar Vatankhah of Alzahra University, Morteza Khavanin Zadeh of the School of Medicine at Iran University of Medical Sciences Alzahra University, Tehran, Iran, and Mohammad Rezapour of the Iranian Ministry of Science, Research and Technology, have looked at social media updates on metabolic disease, obesity, and diabetes. Their data classification using text-mining algorithms and naive Bayes analysis was more accurate than a support vector machine approach, they explain [...]More details...