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231103 ||| eng |
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|a 9783036584867
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|a 9783036584874
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|a books978-3-0365-8487-4
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|a Li, Taiyong
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245 |
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|a Advanced Machine Learning Applications in Big Data Analytics
|h Elektronische Ressource
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260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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300 |
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|a 1 electronic resource (654 p.)
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|a machine learning
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|a DNA coding
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|a multi-strategy
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|a CBCFI
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|a extreme learning machine
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|a splicing model
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|a concept drift
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|a GM
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|a adversarial attacks
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|a feature selection
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|a variational mode decomposition
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|a financial time series forecasting
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|a short-term traffic-flow forecasting
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|a deep learning
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|a classification algorithms
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|a neural networks
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|a sliding window
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|a health
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|a improved matrix particle swarm optimization algorithm (IMPSO)
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|a complementary ensemble empirical mode decomposition (CEEMD)
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|a History of engineering & technology / bicssc
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|a long short-term memory network
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|a hierarchical clustering
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|a information system
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|a meta-heuristic
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|a pilot abnormal behavior
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|a swarm intelligence
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|a document classification
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|a Technology: general issues / bicssc
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|a talent stability prediction
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|a image encryption
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|a GA
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|a graph attention network
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|a data stream mining
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|a target detection
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|a DNA computing
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|a composite multi-scale dispersion entropy
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|a embedding propagation
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|a output optimization
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|a differential evolution
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|a PROPHET
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|a quantum dynamics
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|a dual-update strategy
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|a stock announcement news
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|a coupled map lattice
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|a convolutional neural networks
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|a blockchain consensus algorithm
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|a support vector machine
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|a event extraction
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|a fault diagnosis
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|a digital archives
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|a computer vision
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|a incremental learning
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|a event type
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|a ARMA
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|a bagging model
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|a Hemerocallis citrina Baroni
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|a overseas Chinese associations
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|a YOLOv5 algorithm
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|a lightweight neural networks
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|a image classification
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|a whale optimization algorithm
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|a transaction priority
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|a signal-to-noise ratio distance
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|a 1D quadratic chaotic system
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|a BaaS system
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|a emotion-cause pair extraction
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|a saving mileage
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|a deep feature
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|a hierarchical model
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|a infrared
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|a pixel level
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|a stock return
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|a KNN
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|a tomato leaf
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|a hybrid model
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|a confidentiality management
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|a particle swarm optimization
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|a heterogeneous graph
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|a border patrol
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|a quick access recorder
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|a neural architecture search
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|a mean absolute error
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|a forecasting
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|a ridge regression
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|a support vector machines
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|a warning system
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|a deep belief network
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|a energy storage
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|a n/a
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|a information systems
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|a multi-behavior recommendation
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|a event trigger words
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|a traffic flow forecasting
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|a service level agreement
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|a membership grade
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|a generative adversarial network
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|a Jaccard distance
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|a error coefficient
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|a PSO
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|a time series
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|a clustering algorithms
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|a DNA sequences design
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|a YOLOv4 algorithm
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|a hyperspectral image classification
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|a capacitated vehicle routing planning
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|a high-plateau flight
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|a attentional mechanisms
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|a stacking model
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|a disease classification
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|a cloud
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|a design science research
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|a behavior detection
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|a graph convolutional network
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|a spatial-temporal systems
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|a COVID-19
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|a portfolio optimization
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|a object detection
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|a principal component analysis
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|a peak shaving and frequency regulation
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|a model predictive control
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|a mean-semivariance model
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|a color image
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|a community clustering
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|a global optimization
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|a least squares method
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|a flight safety
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|a NLP
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|a sequential recommendation
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|a maturity detection
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|a graph neural network
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|a online learning
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|a time series classification
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|a gravity search
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|a crisscross search
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|a combined prediction model
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|a forex
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|a performance analysis
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|a opposition-based learning
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|a CNN
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|a butterfly optimization algorithm
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|a adaptive learning
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|a polymorphic mapping
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|a support vector machine swarm intelligence
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|a MultiRocket
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|a BP
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|a CBAM
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|a ELM
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|a hash function
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|a random replacement
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700 |
1 |
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|a Deng, Wu
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1 |
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|a Wu, Jiang
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1 |
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|a Li, Taiyong
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041 |
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|a eng
|2 ISO 639-2
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|b DOAB
|a Directory of Open Access Books
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500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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|a 10.3390/books978-3-0365-8487-4
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856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/113922
|z DOAB: description of the publication
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|u https://www.mdpi.com/books/pdfview/book/7765
|7 0
|x Verlag
|3 Volltext
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|a 720
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|a 576
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|a 330
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|a With the development of computer technology and communication technology, various industries have collected a large amount of data in different forms, so-called big data. How to obtain valuable knowledge from these data is a very challenging task. Machine learning is such a direct and effective method for big data analytics. In recent years, a variety of advanced machine learning technologies have emerged, and they continue to play important roles in the era of big data. Considering advanced machine learning and big data together, we have selected a series of relevant works in this Special Issue to showcase the latest research advancements in this field. Specifically, a total of thirty-three articles are included in this Special Issue, which can be roughly categorized into six groups: time series analysis, evolutionary computation, pattern recognition, computer vision, image encryption, and others.
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