Soft data mining, computational theory of perceptions, and rough-fuzzy approach

Data mining and knowledge discovery is described from pattern recognition point of view along with the relevance of soft computing. Key features of the computational theory of perceptions and its significance in pattern recognition and knowledge discovery problems are explained. Role of fuzzy-granulation (f-granulation) in machine and human intelligence, and its modeling through rough-fuzzy integration are discussed. Merits of fuzzy granular computation, in terms of performance and computation time, for the task of case generation in large scale case-based reasoning systems are illustrated through an example

The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning

Big Data Classification and Internet of Things in Healthcare

[EN] Every single day, a massive amount of data is generated by different medical data sources. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and data classification. The authors can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it. The fundamental purpose of this article is to summarize the role of Big Data analysis in healthcare, and to provide a comprehensive analysis of the various techniques involved in mining big data. This article provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future works. Therefore, in this article, the use of machine learning techniques is proposed for real-time diabetic patient data analysis from IoT devices and gatewaysRghioui, A.; Lloret, J.; Oumnad, A. (2020). Big Data Classification and Internet of Things in Healthcare. International Journal of E-Health and Medical Communications. 11(2):20-37. https://doi.org/10.4018/IJEHMC.2020040102203711