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Neural Computing and Applications (NCA)

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Editor:
Springer
ISSN:
0941-0643
Vistas:
37813
Seguidores:
41

Solicitud de Artículos

Neural Computing and Applications (NCA) is an academic journal published by Springer. (ISSN 0941-0643, CCF C).

Overview Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: adaptive computing algorithms applicable neural networks theory applied statistics architectures artificial intelligence benchmarks case histories of innovative applications fuzzy logic genetic algorithms hardware implementations hybrid intelligent systems intelligent agents intelligent control systems intelligent diagnostics intelligent forecasting machine learning neural networks neuro-fuzzy systems pattern recognition performance measures self-learning systems software simulations supervised and unsupervised learning methods system engineering and integration Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews, and Announcements.
Última actualización por Dou Sun el

Special Issues

Special Issue on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2024) Día de Entrega: 2026-10-30 The "Internet of Things" heralds the connections of a nearly countless number of devices to the internet thus promising accessibility, boundless scalability, amplified productivity and a surplus of additional paybacks. The hype surrounding the IoT and its applications is already forcing companies to quickly upgrade their current processes, tools, and technology to accommodate massive data volumes and take advantage of insights. Since there is a vast amount of data generated by the IoT, a well-analysed data is extremely valuable. However, the large-scale deployment of IoT will bring new challenges and IoT security is one of them. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions. Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the IoT continues to grow, more algorithms will be needed to keep up with the rising sums of data that accompany this growth. One of the main challenges of the IoT security is the integration with communication, computing, control, and physical environment parameters to analyse, detect and defend cyber-attacks in the distributed IoT systems. The 5th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2024) is an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary agenda of Internet of things. This collection includes selected papers (with no less than 60% new content of the journal version) from SPIoT 2024, as well as an open call. Topics of interests include, but are not limited to: * Novel machine learning and big data analytics methods for IoT security. * Big data analytics/machine learning/deep learning for IoT security such as smart grid security analytics. * Data mining and statistical modelling for the secure IoT. * Machine learning and big data analytics architectures for IoT security. * Machine learning based security detecting protocols. * Machine learning experiments, test-beds and prototyping systems for IoT security. * Analytics and machine learning applications to IoT security. * Data based metrics and risk assessment approaches for IoT. * Data confidentiality and privacy in IoT. * Authentication and access control for data usage in IoT. * Data-driven co-design of communication, computing and control for IoT security. * Big data analytics/machine learning/deep learning edge/fog security. * Emerging standards for IoT security. A detailed call for papers is available at https://link.springer.com/journal/521/updates/27047404
Última actualización por Dou Sun el

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