Application of Machine Learning and Data Mining, 2nd Edition

Dear Colleagues,

Recent decades have seen a dramatic rise in the application of machine learning and data mining. Recent technologies, e.g., the Internet of things (IoT), neural networks, deep learning, and smart things, have brought new developments in machine learning and data mining to areas such as healthcare, manufacturing, automobiles, and agriculture. One important breakthrough in artificial intelligence techniques is deep learning, which includes a large family of neural computing methods, e.g., convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, that employ deep architectures composed of multiple non-linear transformations in order to model high-level abstractions of raw data. Recent studies have shown that deep neural networks significantly improve the performance of learning tasks such as object detection, image classification, and segmentation. As a consequence, many advanced real-world applications have developed increasingly close relationships with machine learning and data mining technologies.

This Special Issue aims to present cutting-edge techniques in the application of machine learning and data mining; this is also the aim of the 2024 International Conference on Neural Computing for Advanced Applications (NCAA 2024), which will be held in Guilin, China. The authors of outstanding papers selected from the NCAA 2024 will be invited to submit their extended technical papers for possible publication in the proposed Special Issue after an ordinary review process.

Prof. Dr. Mingbo Zhao
Prof. Dr. Haijun Zhang
Prof. Dr. Zhou Wu
Dr. Jianghong Ma
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• supervised, unsupervised, and self-learning methods
• large-scale data mining
• applicable neural networks and artificial intelligence
• neural network-based industrial applications
• neural model for natural language processing
• deep learning for health informatics and biomedical engineering
• graph convolutional neural networks and their applications
• deep reinforcement learning and its applications
• deep sparse and low-rank representation
• computer vision and pattern recognition techniques