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About 10,000 papers for “Cognitive computing”

Cognitive Computing

University of California Office of the President · California Digital Library · 2007

Cited by 0Open access

Cognitive computing

Raghavan, Vijay V, Gudivada, Venkat N, Govindaraju, Venu et al. · CERN Document Server (European Organization for Nuclear Research) · 2016

Cited by 0

Cognitive Computing

Nilanjana Pradhan, Ajay Shankar Singh, Akansha Singh · 2020

Huge popularity of cognitive computing in both academic and industry is as a result of rapid development of computer software, hardware technologies and artificial intelligence. Logical methods are used by cognitive computing in psychology, biology, signal processing, information theory, mathematics and statistics to build machines which have reasonable ability similar to human brain. An effective cognitive computing architecture can be constructed using 5G network, robotics, deep learning, cloud and IOT infrastructures. Voice recognition, computer vision, cognitive healthcare, smart city, and smart transportation are some of the application areas. Cognitive computing framework connects with big data analytics as it requires huge amount of data to assimilate critical thinking capability of human being. Reinforcement learning is one of the machine learning techniques that can be used in cognitive computing architecture. Deep learning algorithms, open source frameworks and computer vision are also used to build an effective cognitive computing architecture. The architecture will define resource, workload and computation process rules which regulate application performance, It also helps in information hiding, autonomic scaling, optimize reliability and mobility of data which promotes DL/ML systems.

Cited by 4

Cognitive Computing

T Gunasekhar, Marella Surya Teja · 2021

The advancement of network-empowered sensors and Artificial Intelligence (AI) gives rise to different human-focused smart techniques that offer excellent quality services, for example, productive collaboration, self-governing driving, and brilliant medical services. Considering cognitive computing as a crucial innovation to build up these smart frameworks, this paper proposes human-focused cognitive computing supported by cloud computing. To begin with, we give a complete evolution description and analysis of the cognitive computing system including its advancement from data discovery, big data, and cognitive cloud. At that point, the architecture of cognitive computing is proposed, which comprises three basic innovations including IoT (networking), big data analytics (which enables technologies like reinforcement learning, deep learning, and image understanding), and cloud computing. At last, it portrays the applications of human-focused cognitive computing, involving chatbot creation, expressive communication structure like sentiment analysis & face/emotion detection, medical cognitive techniques, and risk assessment.

Cited by 2

Cognitive Computing

Haluk Demirkan, Seth Earley, Robert R. Harmon · IT Professional · 2017

Cognitive computing refers to smart systems that learn at scale, reason with purpose, and interact with humans and other smart systems naturally. This special issue seeks to provide readers with an overview of the current topics and practices related to cognitive computing, and looks at the future for IT professionals as these technologies become indispensable and essential enablers of product and service development and the creation of new markets.

Cited by 8

Cognitive Computing

Makarand R. Velankar, Parikshit N. Mahalle, Gitanjali R. Shinde · Innovations in Sustainable Technologies and Computing · 2024

Cited by 0

Cognitive Computing

J. Hemalatha, Bala Subramanian C, V. Vivek et al. · Advances in computational intelligence and robotics book series · 2022

During the present span, researchers are developing artificial intelligence and network-enabled sensor-based algorithms for various human-centric smart systems such as driverless cars, smart healthcare, virtual reality, and e-commerce personalized shopping. In the course of the last year, software entity hawkers are wrapping AI into business classes. This chapter is organized into three phases. In the first phase, the authors provide a deep view on architecture of cognitive computing. Then they provide some applications of Cognitive AI. Basically, knowledge-based suits are emerging over a couple of diverse functional usage scenarios: 1) computerizing everyday easy tasks, which consume time and 2) offering significant data to the user application. The application instances can pioneer possibilities to expand the yield and decision-making accuracy. In the third phase, the authors provide some fruitful ongoing research in cognitive computing.

Cited by 0

Cognitive Multimedia Computing

Sethuraman Panchanathan Fellow, Sethuraman Panchanathan, Fellow Ieee et al. · 2003

The future of computing will be largely defined by the degree to which computers and humans are able to collaborate in a symbiotic manner. In order to realize this vision, computing systems must evolve from being simply reactive, to being perceptive and cognitive. Today's multimedia applications operate mainly at the sensory level - collecting, preprocessing and storing raw input. What is needed is an ability to perceive visual and auditory content in a manner that parallels human perception. In order to do that, computers must be endowed with percepts and concepts that are similar to those used by humans to interpret sensory inputs. In order to accomplish this, it is vital that multimedia researchers make use of the knowledge collected by psychologists and cognitive scientists. Only with this knowledge will it be possible to bridge the perception-cognition gap. At the Center for Cognitive Ubiquitous Computing (CUbiC), Arizona State University, we are embarking on an interdisciplinary collaboration between computer scientists, psychologists, and cognitive modeling experts to develop natural and efficient human-machine interaction models. In order to test our perceptive and cognitive models, as well as our human-machine interaction models we have chosen to prototype assistive devices for people who are blind, vision impaired, or dyslexic. This paper presents the results of our recent research in the area of perceptive and cognitive multimedia computing.

Cited by 0

On Cognitive Computing

Yingxu Wang · IGI Global eBooks · 2012

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.

Cited by 0

On Cognitive Computing

Rui Wang, Caroline Sporleder · 2016

In this paper, we present our work on constructing a textual semantic relation corpus by making use of an existing treebank annotated with discourse relations. We extract adjacent text span pairs and group them into six categories according to the different discourse relations between them. After that, we present the details of our annotation scheme, which includes six textual semantic relations, backward entailment, forward entailment, equality, contradiction, overlapping, and independent. We also discuss some ambiguous examples to show the difficulty of such annotation task, which cannot be easily done by an automatic mapping between discourse relations and semantic relations. We have two annotators and each of them performs the task twice. The basic statistics on the constructed corpus looks promising: we achieve 81.17 % of agreement on the six semantic relation annotation with a.718 kappa score, and it increases to 91.21 % if we collapse the last two labels with a.775 kappa score. 1.

Cited by 0

On Cognitive Computing

Yingxu Wang · International Journal of Software Science and Computational Intelligence · 2009

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.

Cited by 196

Cognitive Computing Legal Apps

Kevin D. Ashley · Cambridge University Press eBooks · 2017

The prototype proposed in Section 11.5 would transform legal information retrieval into AR. If, as argued there, some of the legal knowledge representation frameworks of Part I's computational models of legal reasoning, argument, and prediction can be annotated automatically in case texts, then a legal app could accomplish more than conceptual legal information retrieval. It could support cognitive computing. This chapter describes a cognitive computing environment tailored to the legal domain in terms of tasks, interface, inputs, and outputs and explains how type systems and annotations based on the computational models will help humans frame hypotheses about legal arguments, make predictions, and test them against the documents in a corpus.

Cited by 0

Foundation of Cognitive Computing

R. Remya, M.G. Sumithra, M. Krishnamoorthi · 2022

This chapter focuses on the basics of cognitive computing methodology and explains how this acts as a platform to change the data to make the machine learn. The computer performs all the processes of evaluating and executing in a quicker manner. The cognitive system attempts to mimic the activity of the human with the support of the computer. Also, it generates a solution when more data has to be added using the machines' learning capacity. Furthermore, it explains how artificial intelligence and computers are involved in cognitive computing and its applications. Moreover, it presents an outline of cognitive computing technology.

Cited by 0

Emerging Cognitive Computing Areas

2012

Cognitive computing is beginning to have an impact on a number of different industries. This chapter identifies attributes of problem domains that make them well suited to similar transformations in other industries, and shows how some functional areas across all industries can also be transformed. Advanced systems make use of natural language processing (NLP) to capture meaning and nuances in text from publications. A cognitive system prevents biases from driving decision making. It is quite common for the most experienced professionals to select an approach based on their own best practices without taking into account that there is new data that they have not been exposed to. The chapter looks at a few market segments that are already using advanced analytics to improve their performance, and have leaders exploring cognitive computing solutions. Commercial network security is a concern for business continuity and general risk management in virtually every industry today.

Cited by 1

Cognitive Computing for Security.

Erik P. DeBenedictis, Fredrick Rothganger, James B. Aimone et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2015

Final report for Cognitive Computing for Security LDRD 165613. It reports on the development of hybrid of general purpose/ne uromorphic computer architecture, with an emphasis on potential implementation with memristors.

Cited by 1Open access