Advances in Intelligent Data Analysis and Its Applications

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....

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Библиографические подробности
Формат:	Online
Язык:	английский
Опубликовано:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Предметы:	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 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 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 multi-head attention BiLSTM energy consumption IoT-based power control systems optimization using sensor data predictive control pharmaceutical technology process modeling exploratory data analysis multiscale neighbors attentional mechanism collaborative embedding recommendation computer science artificial intelligence burnout clinical reasoning 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 thema EDItEUR::U Computing and Information Technology::UY Computer science
Online-ссылка:	ONIX_20240514_9783039286157_50
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Описание
Итог:	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.

Advances in Intelligent Data Analysis and Its Applications

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