DGCC: data-driven granular cognitive computing

An Efficient and Adaptive Granular-Ball Generation Method in Classification Problem

Granular-ball computing (GBC) is an efficient, robust, and scalable learning method for granular computing. The granular ball (GB) generation method is based on GB computing. This article proposes a method for accelerating GB generation using division to replace k -means. It can significantly improve the efficiency of GB generation while ensuring an accuracy similar to that of the existing methods. In addition, a new adaptive method for GB generation is proposed by considering the elimination of the GB overlap and other factors. This makes the GB generation process parameter-free and completely adaptive in the true sense. In addition, this study first provides mathematical models for the GB covering. The experimental results on some real datasets demonstrate that the two proposed GB generation methods have accuracies similar to those of the existing method in most cases, while adaptiveness or acceleration is realized. All the codes were released in the open-source GBC library at https://www.cquptshuyinxia.com/GBC.html or https://github.com/syxiaa/gbc.

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.

M. Cognitive computing technological trends and future research directions in healthcare — A systematic literature review

BACKGROUND AND AIM

Cognitive Computing systems are the intelligent systems that thinks, understands and augments the capabilities of human brain by blending the technologies of Artificial Intelligence, Machine Learning and Natural Language Processing. In recent days, maintenance or enhancement of health by preclusion, prognosis, and analysis of diseases has become a challenging task. The increasing diseases and its causes becomes a big question before humanity. Limited risk analysis, meticulous training process, and automated critical decision-making are some of the issues of cognitive computing. To overcome this issue, cognitive computing in healthcare works like a medical prodigy which anticipates the disease or illness of the human being and helps the doctors with technological facts to take the timely action. The main aim of this survey article is to explore the present and futuristic technological trends of cognitive computing in healthcare. In this work, different cognitive computing applications are reviewed, and the best application is recommended to the clinicians. Based on this recommendation, the clinicians are able to monitor and analyze the physical health of patients.

METHODS

This article presents the systematic literature on the different aspects of cognitive computing in healthcare. Nearly seven online databases such as SCOPUS, IEEE Xplore, Google Scholar, DBLP, Web of Science, Springer and PubMed were screened and the published articles related to cognitive computing in healthcare is collected from 2014 to 2021. In total, 75 articles were selected, examined and their pros and cons are analyzed. The analysis is done with respect to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

The basic findings of this review article and their significance for theory and practice are mindmaps portraying the cognitive computing platforms, cognitive applications in healthcare, and use cases of cognitive computing in healthcare. A detailed discussion section highlighting the present issues, future research directions and recent applications of cognitive computing in healthcare. Accuracy analysis of different cognitive systems conclude that the Medical Sieve achieves 0.95 and Watson For Oncology (WFO) achieves 0.93 and hence proves to be the prominent computing systems for healthcare.

CONCLUSIONS

Cognitive computing, an evolving technology in healthcare augments the clinical thought process and enable the doctors to make the right diagnosis and preserve the patient's health in good condition. These systems provides timely care, optimal and cost-effective treatment. This article provides an extensive survey of the importance of cognitive computing in the health sector by highlighting the platforms, techniques, tools, algorithms, applications, and use cases. This survey also explores about the works in the literature on present issues and proposes the future research directions of applying cognitive systems in healthcare.

A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios

This paper presents a comprehensive model for representing and reasoning on situations to support decision makers in Intelligence analysis activities. The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the development of analysis methods and techniques to support Intelligence. This work made it possible to derive the characteristics of the model from previous case studies and applications with real data, and to link the reasoning techniques to concrete approaches used by intelligence analysts such as, for example, the Structured Analytic Techniques. The model allows to represent an operational situation according to three complementary perspectives: descriptive, relational and behavioral. These three perspectives are instantiated on the basis of the principles and methods of Granular Computing, mainly based on the theories of fuzzy and rough sets, and with the help of further structures such as graphs. As regards the reasoning on the situations thus represented, the paper presents four methods with related case studies and applications validated on real data.

Multi-granularity Complex Network Representation Learning

Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.