Filters








1,464 Hits in 8.9 sec

The development of a short-term liquidity decision model via protocol analysis and probabilistic neural networks

Sheng-Tun Li, Li-Yen Shue, W. Shiue
Proceedings of the 33rd Annual Hawaii International Conference on System Sciences  
A scheme for building a decision model of short-term liquidity analysis from domain experts is presented, which combines the features of both process tracing approach and output analysis approach.  ...  The output analysis component applies Probability Neural Network to build a decision model based on the predictions of the initial model.  ...  Concurrent Protocol Analysis and Probabilistic Neural Network The Concurrent Protocol Analysis is a well recognized method for using verbal data to study cognitive processes in many areas of science [  ... 
doi:10.1109/hicss.2000.926652 dblp:conf/hicss/LiSS00 fatcat:6khqbfqpxrahpes7leekkkg6si

Paper titles

2020 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)  
Perceived Temperature in Virtual Reality through Interactive Audio Markov Transition Field and Convolutional Long Short-Term Memory Neural Network for Manufacturing Quality Prediction Modeling of malware  ...  for Improving Deep Learning in Image Classification Problem Implementation of a Four-class Motor Imagery BCI using Long Short-term Memory Neural Network Implementation of Dynamic Task Assignment for Smartphone  ... 
doi:10.1109/icce-taiwan49838.2020.9258179 fatcat:2eheaztzhncixhbvp7nrbzml4m

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

María García-Pola, Eduardo Pons-Fuster, Carlota Suárez-Fernández, Juan Seoane-Romero, Amparo Romero-Méndez, Pia López-Jornet
2021 Cancers  
Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models.  ...  A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers13184600 pmid:34572831 fatcat:qm7gaw5bmvctnowrjkd2izmrte

Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Abdullah Aljumah
2021 Electronics  
This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and  ...  the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information.  ...  The findings of Table 3 and Figure 8 indicate that the models developed using the SVM, Neural Network, Naïve Bayes, K-NN, and Decision Table algorithms were successful in predicting verified and  ... 
doi:10.3390/electronics10151834 fatcat:byb7npucdfgwraksqrftldivfa

A Survey of Algorithms and Systems for Evacuating People in Confined Spaces

Huibo Bi, Erol Gelenbe
2019 Electronics  
The subject is now a multidisciplinary area of research where information and communication technologies (ICT), and in particular the Internet of Things (IoT), have a significant impact on sensing and  ...  Starting from the history of emergency management research, we identify the emerging challenges concerning system optimisation, evacuee behaviour optimisation and data analysis, and the additional energy  ...  patient. the velocity and the action of evacuees was determined by the social force model and the neural network model, respectively; the neural network had four inputs: the personality of an evacuee,  ... 
doi:10.3390/electronics8060711 fatcat:i6h2fpkyqrhgfpmjahvels5azu

Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

Wolfgang Maass
2014 Proceedings of the IEEE  
I will also describe why these results are paving the way for a qualitative jump in the computational capability and learning performance of neuromorphic networks of spiking neurons with noise, and for  ...  In addition, noise supports the self-organization of networks of spiking neurons, and learning from rewards. I will sketch here the main ideas and some consequences of these results.  ...  Acknowledgment The author would like to thank S. Habenschuss, Z. Jonke, R. Legenstein, D. Pecevski, D. Potzinger, and E. Rueckert for scientific advice and help with the figures.  ... 
doi:10.1109/jproc.2014.2310593 fatcat:54mgt3scqje5flvjqnad45okfi

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Ritika Chopra, Gagan Deep Sharma
2021 Journal of Risk and Financial Management  
input data, and nature of the study; and 'model characteristics' are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure.  ...  We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where 'study characteristics' are further categorized as the stock market covered,  ...  Conflicts of Interest: The authors declare no conflict of interest. J. Risk Financial Manag. 2021, 14, 526  ... 
doi:10.3390/jrfm14110526 fatcat:jthkt5lwnfa3vmh43vtgoo3kue

Artificial Intelligence for the Metaverse: A Survey [article]

Thien Huynh-The and Quoc-Viet Pham and Xuan-Qui Pham and Thanh Thi Nguyen and Zhu Han and Dong-Seong Kim
2022 arXiv   pre-print
In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse.  ...  In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and  ...  Recurrent neural network: RNN is one of the foundational neural network architectures from which various deep architectures, such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks  ... 
arXiv:2202.10336v1 fatcat:35isd745dbaqfnpzthnmbaosue

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

Ning Qiao, Hesham Mostafa, Federico Corradi, Marc Osswald, Fabio Stefanini, Dora Sumislawska, Giacomo Indiveri
2015 Frontiers in Neuroscience  
The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity.  ...  of computational neuroscience models and for building brain-inspired computing systems.  ...  Acknowledgments Some of the circuits described in this work were developed jointly with Chiara Bartolozzi, Elisabetta Chicca, and Srinjoy Mitra.  ... 
doi:10.3389/fnins.2015.00141 pmid:25972778 pmcid:PMC4413675 fatcat:oxoslvrbyneiraxcqcod5jphm4

IEEE Access Special Section Editorial: Mission-Critical Sensors and Sensor Networks (MC-SSN)

Qilian Liang, Tariq S. Durrani, Jinhwan Koh, Jing Liang, Yonghui Li, Xin Wang
2021 IEEE Access  
In MCSSN, the advantages of linking multiple electronic support measures and electronic attack assets to achieve improved capabilities across a networked mission-critical force have yet to be quantified  ...  To support critical missions, sensors and sensor networks need to be flexible and interactive and continuously work despite limited bandwidth, intermittent connectivity, and with a large number of devices  ...  This algorithm detects the changes in precipitation clouds for short-term forecasting and improves power spectrum.  ... 
doi:10.1109/access.2021.3068830 fatcat:mcmdtikg2vfqvokgu7pnm6xofq

SOCAIRE: Forecasting and Monitoring Urban Air Quality in Madrid

Rodrigo de Medrano, Víctor de Buen Remiro, José L. Aznarte
2021 Environmental Modelling & Software  
A new operational tool for air quality forecasting in Madrid is presented. • Produces probabilistic forecasts via a pipeline of statistical and neural models. • Can foresee the probability distribution  ...  Maciąg et al. (2019) is an example of an ensemble model with a neural network and an ARIMA, in a similar vein to what will be our proposal, applied successfully to a real urban environment in the city  ...  inertia and short-term correction inputs similar to the two previous ones.  ... 
doi:10.1016/j.envsoft.2021.105084 fatcat:z7jhx4xqjbanrhyayqm5mmxega

Can Biological Quantum Networks Solve NP-Hard Problems?

Göran Wendin
2019 Advanced Quantum Technologies  
This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness  ...  This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality.  ...  Acknowledgement This work has been partially supported by the Knut & Alice Wallenberg Foundation (KAW) (WACQT project), and by the EU Quantum Technologies Flagship (820363 -QpenSuperQ).  ... 
doi:10.1002/qute.201800081 fatcat:nud3r5wn3zbwbknq7hvbwamye4

Artificial Intelligence and Medicine: A literature review [article]

Chottiwatt Jittprasong
2022 arXiv   pre-print
This review of the literature will highlight the emerging field of artificial intelligence in medicine and its current level of development.  ...  Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine.  ...  (A) For the liver X receptors, we employed a deep learning model with a long-short term memory network.  ... 
arXiv:2205.00322v2 fatcat:5f2qcmezjrajbok56xdnl5n4ou

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics; TII Jan. 2020 202-214 Imran, A., see Hussain, B., TII Aug. 2020 4986-4996 Imran, M., see Fu, S., TII Sept. 2020 6013-6022  ...  J., see Sun, Q., TII Aug. 2020 5137-5149 Meng, K., see Zhang, Y., TII July 2020 4390-4402 Meng, K., Jia, Y., Yang, H., Niu, F., Wang, Y., and Sun, D., Motion Planning and Robust Control for the Endovascular  ...  Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

Cryptocurrency Trading: A Comprehensive Survey [article]

Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li, David Martinez-Regoband, Fan Wu
2022 arXiv   pre-print
In recent years, the tendency of the number of financial institutions including cryptocurrencies in their portfolios has accelerated.  ...  This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble  ...  Conclusions We provided a comprehensive overview and analysis of the research work on cryptocurrency trading. This survey presented a nomenclature of the definitions and current state of the art.  ... 
arXiv:2003.11352v5 fatcat:l7eih2yoazbq5i5lv4wh7c24ha
« Previous Showing results 1 — 15 out of 1,464 results