Recent Advances in Artificial Intelligence and Machine Learning, 2nd Edition

Dear Colleagues,

With the rapid development of artificial intelligence (AI) and machine learning (ML), various intelligent models have been developed to solve practical problems in every imaginable domain, including, but not limited to, healthcare, engineering, finance, agriculture, and remote sensing. Currently, the application of intelligent systems for real-world applications is feasible and sound. AI and ML have a significant impact on human life, helping to transform life for the better in general. However, the implementation of AI and ML technologies faces several challenges, such as limited labeled samples, class imbalance, privacy issues, and model interpretability. There is a critical need for the development of advanced AL and ML methods in order to mitigate these challenges.

This Special Issue focuses on state-of-the-art research relating to the development and application of AI and ML technologies to enhance people’s lives. Topics of interest include, but are not limited to, the following:

1. The applications of artificial intelligence and machine learning models in various domains, such as smart health, smart cities, and smart factories;
2. Novel artificial intelligence and machine learning methods and algorithms;
3. Interpretable artificial intelligence and machine learning for the understanding of big data;
4. Artificial intelligence and machine learning for computer vision, such as image classification, object detection, segmentation, understanding, and generation;
5. Deep learning artificial intelligence and machine learning for intelligent speech (e.g., speech recognition, speaker verification, speech enhancement, and speech synthesis);
6. Artificial intelligence and machine learning for natural language processing;
7. Deepfake and anti-spoofing techniques.

Dr. Liang Zou
Dr. Liang Zhao
Prof. Dr. Yonghui Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

• artificial intelligence
• machine learning
• computer vision
• natural language processing
• intelligent speech
• interpretable algorithms
• deepfake
• Artificial intelligence
• Machine learning
• Deep learning
• Neural networks
• Natural language processing
• Computer vision
• Reinforcement learning
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Transfer learning
• Generative models
• Data augmentation
• Feature extraction
• Hyperparameter tuning
• Model interpretability
• Explainable AI
• Adversarial learning
• Federated learning
• Ethical AI
• Meta-learning
• Graph neural networks
• Evolutionary algorithms
• Swarm intelligence
• Decision trees
• Random forests
• Ensemble learning
• Bayesian networks
• Support vector machines
• Anomaly detection
• Clustering algorithms
• Dimensionality reduction
• Active learning
• Multi-task learning
• Self-supervised learning
• Data mining
• Knowledge representation
• Autonomous systems
• Robotics
• Cognitive computing

• Recent Advances in Artificial Intelligence and Machine Learning in Mathematics (13 articles)