Research in the field of Medical, biomedical engineering and technology are further enhancing our understanding about this discipline and unfolding variety of opportunities for the experts to design innovative technical intervention that would offer easy and automated solutions to various biomedical problems. International Journal of Biomedical Data Mining is an interdisciplinary Biomedical system and technology journal that deals with various aspects of the field such as medical science, innovative emerging technologies, with an emphasis on biotechnology, bioengineering with their artificial manipulation and systems.
International Journal of Biomedical Data Mining open access is a peer reviewed journal for world class impact on the research work which includes Data mining, Artificial intelligence, Proteomics, Sequencing, Bioinformatics, Machine Learning, Data Analysis, Medical Informatics, Biostatistics and Cinical Informatics, system biology, protein sequencing,Transactions on Knowledge, Data Engineering etc.
Among all Biomedical technology journals, International Journal of Biomedical Data Mining having a good reach to researchers and scientific world. International Journal of Biomedical Data Mining is an academic journal that encompasses a wide range of current research under the scope of the journal which aims to offer a promising platform for the authors to make their valuable contributions towards the journal.
The editorial office follows peer review process of the manuscripts to ensure the quality of publishing. International Journal of Biomedical Data Mining is the best open access journals that publish the most comprehensive, relevant and reliable information based on the current research and development in the field in the form of original articles, review articles, case reports, short communications, etc.
Nida Tabassum Khan. In essence, data mining occurs when specific raw data is used to attempt to go from the known to the unknown in order to formulate predictions or pursue more in-depth trend analyses. Data mining goes beyond observation to include inference and modeling.
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One aspect of data mining deals with modeling the past to predict the future: we attempt to find hidden rules in a large volume of data relating to former customers so that we can apply them to new customers and make the best possible decisions. Therefore, data mining is both descriptive and predictive: the descriptive exploratory techniques bring to light information that exists but is buried in a mass of data as with automatic classification of individuals and discovering associations between products or drugs ; the predictive explanatory techniques aim to extrapolate new information from existing information, this new information can be either qualitative classification or scoring or quantitative prediction.
The first advance relates to the storage and calculation capacity offered by modern computing equipment and techniques: data warehouses capable of storing several tens of terabytes, parallel architectures and computers that are increasingly more powerful.
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These user-friendly algorithms provide a quality of output and possibilities for interactivity that were previously inconceivable. The third advance is a major evolution in the field of decision making: the implementation of data mining methods in production processes, ranging from periodic output of information to end users to automatic event triggering.