Intelligent Methods and Big Data in Industrial Applications - Bembenik, Robert; Skonieczny, Łukasz; Protaziuk, Grzegorz; Kryszkiewicz, Marzena; Rybinski, Henryk; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2019)
The inspiration for this book came from the Industrial Session of the ISMIS 2017 Conference in Warsaw. It covers numerous applications of intelligent technologies in various branches of the industry. Intelligent computational methods and big data foster innovation and enable the industry to overcome technological limitations and explore the new frontiers. Therefore it is necessary for scientists and practitioners to cooperate and inspire each other, and use the latest research findings to create new designs and products. As such, the contributions cover solutions to the problems experienced by practitioners in the areas of artificial intelligence, complex systems, data mining, medical applications and bioinformatics, as well as multimedia- and text processing. Further, the book shows new directions for cooperation between science and industry and facilitates efficient transfer of knowledge in the area of intelligent information systems. .
Nonlinear forecasting of energy futures -- Implementation of Generic Steering Algorithms for AI Agents in Computer Games -- The Dilemma of InnovationArticial Intelligence Trade-off -- Can we build recommender system for artwork evaluation?- Modelling OpenStreepMap data for determination of the fastest route under varying driving conditions -- Evolution algorithm for community detection in social networks using node centrality -- High Performance Computing By The Crowd -- Zero-overhead monitoring of remote terminal devices -- Asynchronous Specication of Production Cell Benchmark in Integrated Model of Distributed Systems -- Implementing the Bus Protocol of a Microprocessor in a Software-Dened Computer -- ISMIS 2017 Data Mining Competition: TradingBased on Recommendations - XGBoost approach with feature engineering -- Fast Discovery of Generalized Sequential Patterns -- Seismic attributes similarity in facies classication -- Ecient Discovery of Sequential Patterns from Event-Based Spatio-Temporal Data by Applying Microclustering Approach -- Unsupervised machine learning in classication of neurobiological data -- Incorporating Fuzzy Logic in Object-Relational Mapping Layer for Flexible Medical Screenings -- Multimodal learning determines rules of disease development in longitudinal course with Parkinson's patients -- Comparison of Methods for Real and Imaginary Motion Classication from EEG Signals -- Procedural Generation of Multilevel Dungeons for Application in Computer Games using Schematic Maps and L-system -- An HMM-Based Framework for Supporting Accurate Classication of Music Datasets -- Classication of musical genres by means of listening tests and decision algorithms -- Handwritten signature verication system employing wireless biometric pen -- Towards Entity Timeline Analysis in Polish Political News -- Automatic Legal Document Analysis: Improving the Results of Information Extraction Processes using an Ontology -- To improve, or not to improve; how changes in corpora inuence the results of machine learning tasks on the example of datasets used for paraphrase identication -- Context Sensitive Sentiment Analysis of Financial Tweets: A New Dictionary.
Keywords: Computational intelligence; Quantitative research; Artificial intelligence; Big data; Data mining; Computational Intelligence; Data Analysis and Big Data; Artificial Intelligence; Big Data; Data Mining and Knowledge Discovery