Real time computer based communication with conducting polymers: an artificial intelligence approach

1995 ◽  
Vol 32 (10) ◽  
pp. 405 ◽  
Author(s):  
Afshad Talaie ◽  
Nasser Esmaili ◽  
Farhad Talaie
Keyword(s):  
Artificial Intelligence ◽  
Conducting Polymers ◽  
Real Time ◽  
Computer Based ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

scholarly journals Why to Buy Insurance? An Explainable Artificial Intelligence Approach

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 137
Author(s):  
Alex Gramegna ◽  
Paolo Giudici
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Life Insurance ◽  
Insurance Coverage ◽  
Insurance Policy ◽  
Explainable Artificial Intelligence ◽  
Explainable Ai ◽  
Shapley Values ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.

Innovative Artificial Intelligence Approach in Vaca Muerta Shale Oil Wells for Real Time Optimization

2019 ◽  
Author(s):  
Adriana Romero Quishpe ◽  
Katherine Silva Alonso ◽  
Juan Ignacio Alvarez Claramunt ◽  
Jose Luis Barros ◽  
Pablo Bizzotto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Oil Wells ◽  
Shale Oil ◽  
Time Optimization ◽  
Vaca Muerta ◽  
Real Time Optimization ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

scholarly journals Artificial Intelligence Approach to Momentum Risk-Taking

2021 ◽  
Vol 9 (4) ◽  
pp. 58
Author(s):  
Ivan Cherednik
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Risk Taking ◽  
Expected Returns ◽  
Trading System ◽  
Share Prices ◽  
System A ◽  
Equity Trading ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility. Its implementation, a fully automated momentum equity trading system, is systematically discussed in this paper. It proved to be successful in extensive historical and real-time experiments. Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning. We begin with a new mathematical approach to news impact on share prices, which models well their power-type growth, periodicity, and the market phenomena like price targets and profit-taking. This theory generally requires Bessel and hypergeometric functions. Its discretization results in some tables of bids, basically, expected returns for main investment horizons, the key in our trading system. A preimage of our approach is a new contract card game. There are relations to random processes and the fractional Brownian motion. The ODE we obtained, especially those of Bessel-type, appeared to give surprisingly accurate modeling of the spread of COVID-19.

scholarly journals Real-time assessment of plant photosynthetic pigment contents with an artificial intelligence approach in a mobile application

2020 ◽  
Vol 51 (4) ◽  
pp. 220-228
Author(s):  
Kestrilia Rega Prilianti ◽  
Syaiful Anam ◽  
Tatas Hardo Panintingjati Brotosudarmo ◽  
Agus Suryanto
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Precision Agriculture ◽  
Mobile Application ◽  
Environmental Changes ◽  
Photosynthetic Pigment ◽  
Smart Devices ◽  
Pigment Contents ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

The assessment of the photosynthetic pigment contents in plants is a common procedure in agricultural studies and can describe plant conditions, such as their nutritional status, response to environmental changes, senescence, disease status and so forth. In this report, we show how the photosynthetic pigment contents in plant leaves can be predicted non-destructively and in real-time with an artificial intelligence approach. Using a convolutional neural network (CNN) model that was embedded in an Androidbased mobile application, a digital image of a leaf was processed to predict the three main photosynthetic pigment contents: chlorophyll, carotenoid and anthocyanin. The data representation, low sample size handling and developmental strategies of the best CNN model are discussed in this report. Our CNN model, photosynthetic pigment prediction network (P3Net), could accurately predict the chlorophyll, carotenoid and anthocyanin contents simultaneously. The prediction error for anthocyanin was ±2.93 mg/g (in the range of 0-345.45 mg/g), that for carotenoid was ±2.14 mg/g (in the range of 0-211.30 mg/g) and that for chlorophyll was ±5.75 mg/g (in the range of 0-892.25 mg/g). This is a promising result as a baseline for the future development of IoT smart devices in precision agriculture.

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