scholarly journals Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

2020 ◽  
Vol 12 (18) ◽  
pp. 7696 ◽  
Author(s):  
Shu-Hao Chang ◽  
Chin-Yuan Fan
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Market Development ◽  
Magnetic Storage ◽  
Main Subject ◽  
Development Trends ◽  
Digital Storage ◽  
Critical Technology ◽  
Research Gap ◽  
Application Fields

In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies.

A Study on the Development of Appointment Algorithm of CT With Big Data using AI (Artificial Intelligence)

2018 ◽  
Vol 20 (2) ◽  
pp. 1-5
Author(s):  
Sang-ho Jeon ◽  
Sung-yeul Yang ◽  
In-beom Shin ◽  
Dae-mok Son ◽  
Tae-han Kwon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data

scholarly journals Tarzan and chain: exploring the ICO jungle and evaluating design archetypes

2021 ◽  
Author(s):  
Nina M. Bachmann ◽  
Benedict Drasch ◽  
Gilbert Fridgen ◽  
Michael Miksch ◽  
Ferdinand Regner ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Market Performance ◽  
Peer To Peer ◽  
Secondary Market ◽  
Market Development ◽  
Use Case ◽  
Short Term ◽  
Funding Mechanism ◽  
Research Gap

AbstractThe phenomenon of a blockchain use case called initial coin offering (ICO) is drawing increasing attention as a novel funding mechanism. ICO is a crowdfunding type that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although more than $7bn has been raised globally via ICOs as at 2018, the concept and its implications are not yet entirely understood. The research lags behind in providing in-depth analyses of ICO designs and their long-term success. We address this research gap by developing an ICO taxonomy, applying a cluster analysis to identify prevailing ICO archetypes, and providing an outlook on the token value market performance for individual archetypes. We identify five ICO design archetypes and display their secondary market development from both a short-term and a long-term perspective. We contribute to an in-depth understanding of ICOs and their implications. Further, we offer practitioners tangible design and success indications for future ICOs.

scholarly journals Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

2021 ◽  
Author(s):  
Manish Kumar Tripathi ◽  
Abhigyan Nath ◽  
Tej P. Singh ◽  
A. S. Ethayathulla ◽  
Punit Kaur
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

2021 ◽  
Author(s):  
Marina Johnson ◽  
Rashmi Jain ◽  
Peggy Brennan-Tonetta ◽  
Ethne Swartz ◽  
Deborah Silver ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Driven

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

Sign in / Sign up

Export Citation Format

Share Document