scholarly journals A Hybrid Intelligence-Based Cognitive Engine

2019 ◽  
Author(s):  
Martins Olaleye ◽  
Keshav Dahal ◽  
Zeeshan Pervez
Keyword(s):  
Cognitive Engine ◽  
Hybrid Intelligence

scholarly journals Hybrid intelligence in hospitals: towards a research agenda for collaboration

2021 ◽  
Author(s):  
Milad Mirbabaie ◽  
Stefan Stieglitz ◽  
Nicholas R. J. Frick
Keyword(s):  
Hospital Setting ◽  
Care Process ◽  
Future Research ◽  
Conversational Agents ◽  
Actual Application ◽  
Reasonable Approach ◽  
Hybrid Intelligence ◽  
Application Of Cas ◽  
Successful Collaboration

AbstractSuccessful collaboration between clinicians is particularly relevant regarding the quality of care process. In this context, the utilization of hybrid intelligence, such as conversational agents (CAs), is a reasonable approach for the coordination of diverse tasks. While there is a great deal of literature involving collaboration, little effort has been made to integrate previous findings and evaluate research when applying CAs in hospitals. By conducting an extended and systematic literature review and semi-structured expert interviews, we identified four major challenges and derived propositions where in-depth research is needed: 1) audience and interdependency; 2) connectivity and embodiment; 3) trust and transparency; and 4) security, privacy, and ethics. The results are helpful for researchers as we discuss directions for future research on CAs for collaboration in a hospital setting enhancing team performance. Practitioners will be able to understand which difficulties must be considered before the actual application of CAs.

A hybrid intelligence approach for sustainable service innovation of smart and connected product: A case study

2020 ◽  
Vol 46 ◽  
pp. 101163 ◽  
Author(s):  
Lingguo Bu ◽  
Chun-Hsien Chen ◽  
Geng Zhang ◽  
Bufan Liu ◽  
Guijun Dong ◽  
...  
Keyword(s):  
Service Innovation ◽  
Hybrid Intelligence ◽  
Intelligence Approach

An Online Quantified Safety Assessment Method for Train Service State Based on Safety Region Estimation and Hybrid Intelligence Technologies

2015 ◽  
Vol 25 (03) ◽  
pp. 493-511
Author(s):  
Yong Qin ◽  
Shan Yu ◽  
Yuan Zhang ◽  
Limin Jia ◽  
Xiaoqing Cheng
Keyword(s):  
Safety Assessment ◽  
Safety Analysis ◽  
Assessment Method ◽  
Rolling Bearings ◽  
Safety Risk ◽  
Hybrid Intelligence ◽  
Local Mean ◽  
Interval Type ◽  
Service State

Facing the important issues of safety analysis and assessment for the train service state, an online quantified safety assessment method based on the safety region estimation and hybrid intelligence technologies was proposed in this paper. First, the previous researches on the safety analysis and assessment were briefly reviewed for the train itself and its key equipment, and the existential problems were further pointed out. Then, using the safety monitoring data and the safety region estimation theory, a new online safety assessment method with data-driven was put forward, which was followed by a detailed description of the concrete implementation steps including the EMD (Local Mean Decomposition) and EM (Energy Moment) based safety risk evaluation index selection, Interval Type 2 Fuzzy C-Means (IT2FCM) clustering based safety region boundary calculation modeling and safety risk grading. Finally, in order to verify its performance through experiments, the above method was applied in analyzing and evaluating service states of the rolling bearings, the key equipment of the train, on the basis of mass field data. The experimental results indicate that this method is valid.

Secure Intelligent Radio for Trains (SIRT)

2017 ◽  
Author(s):  
K. R. Damindra S. Bandara ◽  
Satish Kolli ◽  
Duminda Wijesekara
Keyword(s):  
Critical Infrastructure ◽  
Adaptive Modulation ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Worker Safety ◽  
Radio Noise ◽  
Radio Communication ◽  
Radio System ◽  
Cognitive Engine ◽  
Prototype Development

American Railroads are planning to complete implementing their Positive Train Control (PTC) systems by 2020. Safety objectives of PTC are to avoid inter-train collisions, train derailments and ensuring railroad worker safety. Under published specifications of I-ETMS (the PTC system developed by Class I freight railroads), the on-board PTC controller communicates with two networks; namely, the Signaling network and the Wayside Interface Unit network to gather navigational information such as the positions of other trains, the status of critical infrastructure (such as switches) and any hazardous conditions that may affect the train path. By design, PTC systems are predicated on having a reliable radio network operating in reserved radio spectrum, although the PTC system itself is designed to be a real-time fail safe distributed control systems. Secure Intelligent Radio for Trains (SIRT) is an intelligent radio that is customized to train operations with the aim of improving the reliability and security of the radio communication network. SIRT has two tiers. The upper tier has the Master Cognitive Engine (MCE) which communicates with other SIRT nodes to obtain signaling and wayside device information. To do so, the MCE communicates with cognitive engines at the lower tier of SIRT; namely the Cryptographic Cognitive Engine (CCE) (that provide cryptographic security and threat detection) and the Spectrum Management Cognitive Engine (SCE) (that uses spectrum monitoring, frequency hopping and adaptive modulation to ensure the reliability of the radio communication medium). We presented the architecture and the prototype development of the CCE in [1]. This paper presents the design of the MCE and the SCE. We are currently developing a prototype of the SCE and the MCE and testing the performance of our cognitive radio system under varying radio noise conditions. Our experiments show that SIRT dynamically switches modulation schemes in response to radio noise and switches channels in response to channel jamming.

Implementation and On-Orbit Testing Results of a Space Communications Cognitive Engine

2018 ◽  
Vol 4 (4) ◽  
pp. 825-842 ◽  
Author(s):  
Timothy M. Hackett ◽  
Sven G. Bilen ◽  
Paulo Victor Rodrigues Ferreira ◽  
Alexander M. Wyglinski ◽  
Richard C. Reinhart ◽  
...  
Keyword(s):  
Space Communications ◽  
Cognitive Engine

Hybrid intelligence utilization for construction site layout

2002 ◽  
Vol 11 (5) ◽  
pp. 511-519 ◽  
Author(s):  
J.P. Zhang ◽  
L.H. Liu ◽  
R.J. Coble
Keyword(s):  
Construction Site ◽  
Site Layout ◽  
Hybrid Intelligence ◽  
Construction Site Layout

scholarly journals A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 633-643
Author(s):  
Niccolo Pescetelli
Keyword(s):  
Prediction Models ◽  
Collective Intelligence ◽  
Machine Intelligence ◽  
Future Research ◽  
Human Errors ◽  
Wide Range ◽  
Hybrid Intelligence ◽  
Forecasting System ◽  
Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

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