China
Africa
Explore chapters and articles related to this topic
Survey of Biometric Tools and Big Data
Published in Rodgers Waymond, Artificial Intelligence in a Throughput Model, 2020
Leveraging Artificial Intelligence and advanced analytics in driving value creation and future growth can enhance an organization’s success. Some practical applications of Artificial Intelligence and analytics embrace: Tracking and forecasting relevant exponential technology trends. In addition, implementing data analytics along with Artificial Intelligence proactively can assist an organization in determining how and when to take action, to make better decisions, and stay ahead of competitors.Using predictive analytics to reduce decision choices grounded on intuition or outdated models. In other words, relying upon the highest paid individual’s opinion instead of relevant data.The capability to benchmark and track the progress and speed of individual’s innovative projects through development phases, and forecast future outcomes and revenues.
On the combination of simulated annealing and semi-supervised clustering for intrusion detection
Published in Amir Hussain, Mirjana Ivanovic, Electronics, Communications and Networks IV, 2015
Garg, A., Tai, K. & Sreedeep, S. 2014. An integrated SRMmulti-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes. Engineering Applications of Artificial Intelligence 30: 30-40.
Introduction
Published in Sugato Basu, Ian Davidson, Kiri L. Wagstaff, Constrained Clustering, 2008
Sugato Basu, Ian Davidson, Kiri L. Wagstaff
National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, pp. 792-299,1998.
A new hybrid Pythagorean fuzzy AHP and COCOSO MCDM based approach by adopting artificial intelligence technologies
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Thi Minh Hang Nguyen, V. P. Nguyen, D. T. Nguyen
(Priyadarshini & Cotton, 2021) In the twenty-first century, the development of computer capabilities, the amassing of vast amounts of data, and theoretical knowledge all contributed to the creation of AI technology. Significant progress has been achieved in applying AI research and technology to the development of practical goods. Currently, the most prominent applications of artificial intelligence are in huge data, visual services, natural language processing, and intelligent robots. The bulk of applications of artificial intelligence are found in business, finance, healthcare, and cars (Lovelock et al., 2018). Intelligent healthcare includes medical imaging, clinical decision support, voice recognition, medication research, health management, and pathology (Zheng et al., 2013). The application of AI in intelligent healthcare is possible. For instance, machine learning can predict medical performance, gene sequencing, and crystal shape. Natural language understanding facilitates the creation of electronic health records, intelligent queries, and support. Machine vision permits medical image recognition, lesion identification, and self-testing for skin conditions (Bern & Andrews, 2017, 2018). People’s health through enhancing the effectiveness of medical facilities and personnel and reducing medical expenses (Fast & Horvitz, 2017; Jean, 2020; Nguyen, 2022; Sadegh-Zadeh, 2015; Xu & Jia, 2021).
Artificial intelligence: a new clinical support tool for stress echocardiography
Published in Expert Review of Medical Devices, 2018
Maryam Alsharqi, Ross Upton, Angela Mumith, Paul Leeson
The first applications of artificial intelligence in healthcare were reported over three decades ago [5]. However it is only in the last few years, as artificial intelligence has become embedded within multiple areas of life, that there has been an exponential growth of interest in whether it can assist in automated diagnosis and personalized patient management. Artificial intelligence includes computational techniques that ‘learn’ from existing data to make future decisions. Deep learning is a method composed of many layers of highly interconnected processing elements, which are able to represent high levels of abstraction. The use of deep learning with imaging data is usually based on convolutional neural networks that mimic, to some extent, how the human ventral stream is structured [6]. These techniques facilitate rapid analysis of massive amounts of data [7]. Less fluid computational approaches are also possible such as support vector machines and random forests [8–10]. However, the objective of all these methods is to learn patterns from existing sets of clinical data, such as clinical notes, blood test results or images, to allow future sets of data to be automatically processed [5]. In medicine, applications of artificial intelligence have been innovative, particularly in medical imaging [11]. Medical images contain large sets of data that require intensive training and experience in order to detect abnormalities [6]. For clinical adoption, it is important the true impact of artificial intelligence systems, compared to operator-led analysis, on patient outcomes, including how changes in workflow and test accuracy impact on health economic costs, needs to be validated in clinical trials. However, machine-assisted interpretation of medical images offers the potential for more consistent decision-making that could improve patient outcome [11].