Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making [electronic resource] : Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019 /
Kahraman, Cengiz.editor.
Cebi, Selcuk.editor.
Cevik Onar, Sezi.editor.
Oztaysi, Basar.editor.
Tolga, A. Cagri.editor.
Sari, Irem Ucal.editor.
This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.
Bipolar models for a more realistic representation and processing of human judgments, intentions and preferences: a role of fuzzy logic -- An Improved Fuzzy Cognitive Mapping Method -- A new community detection algorithm based on fuzzy measures -- A predictive modeling approach to debt collection process -- Intelligent Visual Analysis in Employee Fraud Detection -- A linguistic system of defining and controlling project success -- A Hesitant Fuzzy Correspondence Analysis -- Customer Segmentation Method Determination Using Neutrosophic Sets -- Evaluating Innovation Projects in Air Cargo Sector with Fuzzy COPRAS -- Application of Fuzzy TOPSIS for credit scoring -- Supplier Selection Using Fuzzy Analytic Network Process -- A Fuzzy Based Risk Assessment Model with a Real Case Study -- Intuitionistic Fuzzy c-Control Charts Using Fuzzy Comparison Methods -- Fuzzy Evaluation of a New Search Engine Project to Fundraise NGOs -- Machine Criticality Level Assignment with Fuzzy Inference System for RCM -- Human Reliability Analysis in Healthcare: A Scenario Analysis.
This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.
Computational intelligence.
Engineering—Data processing.
Energy security.
Computational Intelligence.
Data Engineering.
Energy Security.