Methods in Artificial Intelligence and Information Processing, 4th Edition
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 532
Special Issue Editors
Interests: digital telecommunication; quantization; compression; machine learning; coding
Special Issues, Collections and Topics in MDPI journals
Interests: speech processing; vocal biomarkers; machine learning; medical image processing
Special Issues, Collections and Topics in MDPI journals
Interests: nonclassical logic; applications of mathematical logic in computer science; artificial intelligence and uncertain reasoning; automated theorem proving; applications of heuristics to satisfiability problems and digitization of cultural and scientific heritage
Special Issues, Collections and Topics in MDPI journals
Interests: radiation effects; rad-hard design; radition sensors
Special Issues, Collections and Topics in MDPI journals
Interests: ICT; speech technologies; HCI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The area of artificial intelligence (AI), although introduced many years ago, has received considerable attention in recent years. This can be explained by the necessity to process large amounts of data, where efficient methods and algorithms are desirable. Most AI methods encountered in the literature are based on mathematical theories developed before the emergence of AI. Further research in this area will result in a better understanding of AI and will also provide its simplification through corresponding approximations. Namely, such simplification will provide a base for practical implementation, which is of crucial interest for engineers, researchers, and scientists dealing with transfer of scientific research results into commercial products and other applications. On the other hand, designing and analyzing processing algorithms using only very complex mathematical theories in AI and information processing (IP) would result in a loss of wide applicability (e.g., reduced possibility of hardware implementation).
Modern technology relies heavily on research in IP and AI, and a number of methods have been developed with the aim of solving problems in pattern recognition in signals (speech, image, audio, and biomedical signals), recognition of emotions, signal quality enhancement, detection of signals in the presence of noise, methods, and algorithms in wireless sensor networks, deep neural networks (DNNs), data compression, quantization in neural networks (NNs), and learning representations.
The implementation of DNNs on devices with constrained resources (edge devices, microcontrollers, Tiny ML, Tiny AI, etc.) is very important nowadays. Therefore, new solutions are required in the fields of normalization and coding, as well as in the compression of DNN parameters.
The topic of this Special Issue aims not only to address application of these methods but also to promote the independent and combined development in these two fields.
Potential topics include, but are not limited to, the following:
- Parametric and non-parametric machine learning algorithms;
- Deep learning algorithms;
- Entropy coding;
- Compression methods in neural networks (pruning and quantization);
- Acceleration of computing;
- Tiny AI;
- Speech and image processing;
- Biomedical signal/image processing;
- Object detection and face recognition;
- Formal reasoning about neuro-symbolic AI and entropy.
Prof. Dr. Zoran H. Perić
Dr. Vladimir Despotovic
Dr. Zoran Ognjanović
Dr. Marko S. Andjelković
Prof. Dr. Vlado Delić
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- neural networks
- compression
- entropy coding
- speech and image processing
- biomedical signal processing
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