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Intelligent Decision Technologies (IDT) welcomes original research contributions on the fundamental concepts and applications of intelligent systems that support decision making. IDT is an official journal of KES International.

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Dear Colleague,

Welcome to our journal newsletter for Intelligent Decision Technologies (IDT). We are delighted to announce the publication of Volume 15, Issue 1.

The content from this new issue is included below, as well as details on how to submit your research to the journal.

Looking forward to working with you.

IDT newsletter

With kind regards,

Editors-in-Chief

George A. Tsihrintzis

Gloria Phillips-Wren

Lakhmi C. Jain

Junzo Watada

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15 years of the International Journal of Intelligent Decision Technologies (IDT): Reflection from the Editors

The inspiration for IDT grew out of conversations between Professor/Dr. Gloria Phillips-Wren and Professor/Dr. Lakhmi Jain at the Knowledge Engineering Systems (KES) International annual conferences. The mission of KES is stated as “dissemination, transfer, sharing and brokerage of knowledge” to address knowledge-intensive subjects. As a large professional scientific community, KES sponsors a broad spectrum of publications and activities for research scientists, academics, engineers and practitioners. Research in intelligent agents and intelligent systems in the timeframe of 2004–2006 at KES conferences was focused on algorithms and technical specifications. During the same general time period, the technology was becoming mature enough to be implemented into real-world decision support systems (DSS) for previously intractable problems.

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Free to Read – Editorial

Special issue on Intelligent Biomedical Data Analysis and Processing

Guest editors: Deepak Gupta, Oscar Castillo and Ashish Khanna

Today, humankind lives in the age of Information (and) Technology. Information is the key, the power and the engine that moves the world economy. The world is dependent on market data, medical/epidemiologic sets, Internet browsing records, geological survey data, complex engineering models, and so on. Health Sciences are highly dependent on Information Technology. Health Sciences and Biology are very complex fields and have covered a long distance since ancient times. In the early eighties, Artificial Intelligence in Medicine was a primary research area while developing medical expert systems in specialized medical domains to support diagnostic decision-making. The main problems addressed at this early stage of expert system research were concerned with knowledge acquisition, knowledge representation, reasoning, and explanation. Nowdays, there are many modern hospitals and health care institutions, which are well equipped with monitoring and other advanced data collection devices. The need for knowledge on the domain or the data analysis process becomes essential in biomedical applications, as medical decision making needs to be supported by arguments based on basic medical and pharmacological knowledge. The new tool for analyses of biomedical applications is “Intelligent Data Analysis (IDA)”.

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Volume 15, Issue 1

EDITORIAL
Special Issue on Intelligent Biomedical Data Analysis and Processing – Openly Available
Gupta, Deepak | Castillo, Oscar | Khanna, Ashish

RESEARCH ARTICLES
15 years of the International Journal of Intelligent Decision Technologies (IDT): Reflection from the Editors – Openly Available
Phillips-Wren, Gloria | Tsihrintzis, George A. | Jain, Lakhmi C. | Watada, Junzo

An intelligent medical decision support system for diagnosis of heart abnormalities in ECG signals – Openly Available
Revathi, J. | Anitha, J. | Hemanth, D. Jude

Sensory motor imagery EEG classification based on non-dyadic wavelets using dynamic weighted majority ensemble classification
Chaudhary, Poonam | Agrawal, Rashmi

An innovative method for cardiovascular disease detection based on nonlinear geometric features and feature reduction combination
Saeedi, Abdolkarim | Moridani, Mohammad Karimi | Azizi, Alireza

CAD diagnosis by predicting stenosis in arteries using data mining process
Singh, Akansha | Payal, Ashish

Machine learning for precision medicine forecasts and challenges when incorporating non omics and omics data
Susymary, J. | Deepalakshmi, P.

Whale optimization algorithm fused with SVM to detect stress in EEG signals
Gupta, Richa | Alam, M. Afshar | Agarwal, Parul

Uncertainty query sampling strategies for active learning of named entity recognition task
Agrawal, Ankit | Tripathi, Sarsij | Vardhan, Manu

Multi-objective techniques for feature selection and classification in digital mammography
Thawkar, Shankar | Singh, Law Kumar | Khanna, Munish

An intelligent unsupervised technique for fraud detection in health care systems
Kanksha, | Bhaskar, Aman | Pande, Sagar | Malik, Rahul | Khamparia, Aditya

Detection of white blood cells using optimized qGWO
Sharma, Prerna | Sharma, Moolchand | Gupta, Divij | Mittal, Nimisha

BioSignal modelling for prediction of cardiac diseases using intra group selection method
Kasturiwale, Hemant P. | Kale, Sujata N.

Machine learning for psychological disorder prediction in Indians during COVID-19 nationwide lockdown
Kumar, Akshi

BOOK REVIEW
Book Review – Innovations in Big Data Mining and Embedded Knowledge – Openly Available
Adhikari, Jhimli

Book Review – Machine Learning Paradigms Advances in Deep Learning-based Technological Applications – Openly Available

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Free to Read – Book Review

Innovations in Big Data Mining and Embedded Knowledge
Adhikari, Jhimli

Abstract
This is a review of a book that discusses knowledge discovery using data mining and knowledge embedding through models. A number of scheme are reported in the book to explain how data mining and discovered embedded knowledge can be beneficial to social organizations, domestic sphere and ICT market. Each chapter of the book presents a unique problem of data mining philosophies from an embedded point of view. It will help researchers to understand the current status of big data mining and embedded knowledge, discover new research opportunities and gain more information about this field.

“Innovations in Big Data Mining and Embedded Knowledge” by Anna Esposito (Editor), Antonietta M. Esposito (Editor), Lakhmi C. Jain (Editor), 1st edition (16 July 2019), Pages 276, ISBN 978-3-030-15938-2, Vol. 159 in Springer Book Series Intelligent Systems Reference Library

Free to Read – Book Review

Machine Learning Paradigms – Advances in Deep Learning-based Technological Applications Innovations in Big Data Mining and Embedded Knowledge
Edward Szczerbicki

Acting over the last three decades as an Editor and Associate Editor for a number of international journals in the general area of cybernetics and AI, as well as a Chair and Co-Chair of numerous conferences in this field, I have had the exciting opportunity to closely witness and to be actively engaged in the stimulating research area of machine learning and its important augmentation with deep learning techniques and technologies.

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Call for Papers

As a member of our research community, we would like to invite you to contribute your own work for publication in the journal. Intelligent Decision Technologies offers contributing authors many benefits, including:

· First class Editorial Board

· Rigorous peer review and speedy manuscript processing

View the detailed Instructions to Authors.

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