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Computational Neuroscience and Compartmental Modeling
Published in Bahman Zohuri, Patrick J. McDaniel, Electrical Brain Stimulation for the Treatment of Neurological Disorders, 2019
Bahman Zohuri, Patrick J. McDaniel
Although Cognitive Computing is a growing technology in today’s market, is that a road for the robots of the future to be smarter than their own creator is mainly, the human. As we stated previously in this chapter, Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works. The goal of cognitive computing is to create automated IT systems that are capable of solving problems without requiring human assistance (see Figure 3.5).
What Is Industry 5.0?
Published in Pau Loke Show, Kit Wayne Chew, Tau Chuan Ling, The Prospect of Industry 5.0 in Biomanufacturing, 2021
Omar Ashraf ElFar, Angela Paul A/p Peter, Kit Wayne Chew, Pau Loke Show
The industrial Internet of Things (IIoT) today enables the sharing of data associated with specific key elements such as a cyber-physical system, which is a mechanical device system that is run by computer-based algorithms. The Internet of Things (IoT) is an interconnected network of machine devices and vehicles embedded with computerized sensing, scanning, and monitoring capabilities. Cloud computing uses offsite network hosting and data backup. Cognitive computing is a technological platform that employs artificial intelligence.
Delineating the Key Capabilities of Cognitive Cloud Environments
Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022
Pethuru Raj, Anupama C. Raman, Harihara Subramanian
With the exponential growth of data, the arrival of AI algorithms, and the massive increase of computing power, data processing gets accelerated considerably to uncover and recover hidden patterns, beneficial associations, and fresh opportunities. Cognitive computing can disseminate this data to assist humans in decision making. It can weigh complex, conflicting, and changing information contextually to offer a best-fit solution. For example, a cognitive model may suggest a practical and achievable weight loss plan to manage diabetes.
Towards the cognitive and psychological perspectives of crowd behaviour: a vision-based analysis
Published in Connection Science, 2021
Elizabeth B. Varghese, Sabu M. Thampi
The term cognitive computing depicts technologies and methods that intelligently simulate human brain process and judgment. That is, the process of acquiring and rearranging a vast amount of data from the environment and operating them via the memory process in the human brain (Kim et al., 2015). This computing paradigm facilitates the creation of adaptive and contextual systems with self-learning capabilities. A smart surveillance system demands these capabilities to make smart decisions with minimum human involvement. Researchers and scientists explore human cognition to devise algorithms and methods for surveillance systems as well. In this context, this paper analyses the cognitive computing approaches for crowd behaviour analysis and crowd anomaly detection in a smart surveillance system. The cognitive computing technologies for crowd behaviour analysis that can mimic the human brain process namely machine learning, deep learning, and bio-inspired cognitive interaction are covered in this review.
Cognitive computing for big data systems over internet of things for enterprise information systems
Published in Enterprise Information Systems, 2020
Arun Kumar Sangaiah, Ankit Chaudhary, Chun-Wei Tsai, Jin Wang, Francesco Mercaldo
The paradigm of the Internet of Things (IoT) has become a key component for enterprise information systems (EIS). Low-cost sensing and actuation are available to the whole world. It enables seamless information exchange and networked interactions of physical and digital objects in enterprise computing. This interconnectivity together with large-scale data processing, advanced machine learning, robotics and new fabrication techniques steadily bring innovation and business models of the digital space into the physical world. Cognitive computing has broad horizons, which cover different characteristics of cognition. The field is highly trans-disciplinary in nature, combining ideas, principles and methods of psychology, computer and Internet technologies, linguistics, philosophy, neuroscience, etc. Cognitive computing is the creation of self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to solve complicated problems without constant human oversight. Cognitive computing will bring a high level of fluidity to analytics. Core Data Technologies are covering the underpinning mathematical, statistical and computer systems expertise and research concerned with new scalable approaches for capturing, storing, managing, analysing and visualising large scale, complex and diverse data from multiple sources. Data’s science techniques have been adopted to improve the IoT in terms of data throughput, optimisation and management. The data techniques and technologies will impact the future of the IoT, allowing researchers to reproduce scenarios, and optimise the acquisition, analysis and visualisation of the data acquired by IoT devices for EIS. One of the most ambitious and exciting challenges in data technologies is to model and replicate how people think and learn.