Advances in Intelligent Data Analysis and Its Applications

doi:10.3390/books978-3-03928-616-4

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Advances in Intelligent Data Analysis and Its Applications

Edited by

January 2024

542 pages

ISBN978-3-03928-615-7 (Hardback)
ISBN978-3-03928-616-4 (PDF)

https://doi.org/10.3390/books978-3-03928-616-4

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This book is a reprint of the Special Issue Advances in Intelligent Data Analysis and Its Applications that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Summary

This Special Issue sheds light on the escalating significance of intelligent data analysis and its applications in the era of burgeoning big data. It has emerged as a pivotal driver for progress across diverse domains, necessitating in-depth research and practical applications within this framework. This multifaceted endeavor facilitates the revelation of latent value within extensive datasets and provides robust support for innovation and progress across various industries. Consequently, the imperative nature of discussing and exploring the latest research developments in intelligent data analysis and its practical applications becomes apparent, representing a vital initiative to keenly discern and proactively address trends in the big data era. This compilation is dedicated to comprehensively exploring recent research advancements in intelligent data analysis, elucidating their specific manifestations in practical applications. Encompassing diverse and profound topics, including intelligent data mining algorithms and their applications, the integration of machine learning in intelligent data analysis, models for natural language processing, intelligent granular computing models, cognitive computing, and hybrid models, the overarching goal is to provide profound insights into the academic development of intelligent data analysis. This, in turn, extends innovative guidance for academic research and industrial applications, thereby nurturing the continuous advancement and evolution of intelligent data analysis technologies.

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Keywords

intuitionistic fuzzy concept; rough set; multi-granularity; relative macro-knowledge distance; multigranulation rough sets; optimistic approximation; pessimistic approximation; cost-sensitive; decision-making applications; context awareness; attention network; dynamic user preferences; next POI recommendation; IoT; unmanned aerial vehicle; anomaly detection; ALFA; CNN; orphan genes (OGs); hybrid features; machine learning; angiosperm; delay prediction model; lightweight neural network; lightweight attention mechanism; semi-supervised classification; co-training method; instance selection; granular computing; information granulation; variable structure of interacting multiple-model; symmetric model set optimization method; proportional reduction optimization method; expected model optimization method; granular computing; multi-granulation rough set; hesitant trapezoidal fuzzy set; air quality evaluation; dependency structure; graph convolution network; question answering; data mining; network clustering; protein complex detection; power-law distribution; topological characteristics; data enhancement; few-shot data; smart home; generative adversarial networks; intrusion detection; bilingual short text with emoji; emotional fusion; emotional fluctuation; emotional computation; question answering; knowledge graph embedding; chunked learning network; data-driven approach; picks wear state recognition; wavelet packet decomposition; Bayesian-LSTM; absolute positioning accuracy; deep belief network; differential evolution algorithm; industrial robot; off-line error compensation; sequential recommendation; local fluctuation; global stability; Stochastic Shared Embeddings; AUV; underwater navigation; dataset; inertial navigation; DVL; ultrasound imaging; plane-wave; beamforming; coherence factor; adaptive; spatio-temporally smoothed; delay multiply and sum beamforming; data augmentation; deep learning; data quality; big data; data mining; intrusion detection; deep learning; multi-head attention; BiLSTM; energy consumption; IoT-based power control systems; machine learning; optimization using sensor data; predictive control; pharmaceutical technology; process modeling; exploratory data analysis; multiscale neighbors; attentional mechanism; collaborative embedding; recommendation; computer science; artificial intelligence; machine learning; burnout; clinical reasoning; machine learning; deep learning; imbalanced datasets; stratified sampling; prediction; classification; accuracy; wrapper classes; target detection; YOLOV5s; attention mechanism; lightweighting; healthcare; imputation algorithms; incomplete data; neighborhood similarity; frequency synthesizer; direct frequency synthesizer; indirect frequency synthesizer; railway track circuit; n/a