Cognitive Informatics

Wang, Yingxu; Howard, Newton; Kacprzyk, Janusz; Sheu, Phillip C.‐Y.; Fiorini, Rodolfo A.; Gavrilova, Marina L.; Patel, Shushma; Peng, Jun; Widrow, Bernard

doi:10.4018/ijcini.2018010101

Cited by 17 publications

(1 citation statement)

References 74 publications

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“…Computational intelligence found valuable applications in robotics and in combating organized terrorized network as a cognitive informatics [35][36]. Hung, Jayasumana and Bandara, (2019) [37] used graph trajectory analytics to understudy terrorist patterns with application in security warfare and other asymmetric warfare.…”

Section: Computational Approachmentioning

confidence: 99%

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Nwanga¹,

Okafor

Chukwudebe

et al. 2021

Int. J. Robot. Autom. Technol.

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Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.

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Section: Computational Approachmentioning

confidence: 99%

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Nwanga¹,

Okafor

Chukwudebe

et al. 2021

Int. J. Robot. Autom. Technol.

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An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems

França

Monteiro

Arthur

et al. 2022

Cognitive Intelligence and Big Data in Healthcare

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Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

Suthaharan

2018

WIREs Data Min & Knowl

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Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real‐world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state‐of‐the‐art discipline. An ignorance of observing the progression of this fast‐growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining

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Cognitive Informatics

Cited by 17 publications

References 74 publications

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems

Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

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