Artificial Neural Network
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Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.


2021 ◽  
Vol 2 (2) ◽  
pp. 27
Author(s):  
Catharina Natasa Bella Fortuna ◽  
Franky Chandra Satria Arisgraha, S.T., M.T. ◽  
Puspa Erawati

Based on various epidemiological studies, it is stated that blood lipids are the main risk factor for atherosclerosis that leads to coronary heart disease. In patients with blood lipid disorders, red blood cells undergo deformability so that their shape is flatter than normal red blood cells, which are round. The research entitled Application of Artificial Neural Network Method as Detection of Blood Fat Abnormalities in Image of Complete Blood Examination Results was conducted to help facilitate laboratory examinations. This research hopes that it will provide appropriate early detection to support the expert diagnosis. This research consists of two stages. The first stage is digital image processing to obtain area, perimeter, and eccentricity features. These three features will be used as input to the Backpropagation Neural Network program as the second stage. At this stage, blood lipid abnormalities are detected from features that have been obtained from image processing. The accuracy of detecting blood lipid abnormalities with ANN Backpropagation is 85%.


2021 ◽  
Vol 13 (23) ◽  
pp. 13292
Author(s):  
Xiaoyuan Wang ◽  
Yongqing Guo ◽  
Chenglin Bai ◽  
Quan Yuan ◽  
Shanliang Liu ◽  
...  

Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This study focuses on developing a driver’s intention emergence model with the involvement of driving emotion on two-lane urban roads. Driver emotions were generated using various ways, including visual stimuli (video and picture), material incentives, and spiritual rewards. Real and virtual driving experiments were conducted to collect the multi-source dynamic data of human–vehicle–environment. The driver intention emergence model was constructed based on an artificial neural network, to identify the influences of drivers’ emotions on intention, as well as the evolution characteristics of drivers’ intentions in different emotions. The results show that the proposed model can make accurate predictions on driver intention emergence. The findings of this study can be used to improve drivers’ behavior, in order to create more efficient and safe driving. It can also provide a theoretical foundation for the development of an active safety system for vehicles and an intelligent driving command system.


2021 ◽  
Author(s):  
Bianca Lento ◽  
Yannick Aoustin ◽  
Teresa Zielinska

Abstract Robotic exoskeletons inspired by the animal’s external covering are wearable systems that enhance human power, motor skills, or support the movement. The main difficulty, apart from the mechanical structure design, is the development of an exoskeleton control system, as it should recognize the movement intended by the user and assist in its execution. This work is devoted to the exoskeleton of the upper limbs that supports movement. The method of controlling the exoskeleton by means of electromyograms (EMG) was presented. EMG is a technique for recording and assessing the electrical activity produced by skeletal muscles. The main advantage of EMG based control is the ability to forecast intended motion, even if the user is unable to generate it. This work aims to define strategies for controlling the exoskeleton of the upper limb in children suffering from neuromuscular diseases. Such diseases gradually reduce the mobility of the lower and upper limbs. These children are wheelchair bound, so it was assumed that the upper limb exoskeleton could be attached to a wheelchair. EMG signals are recorded, amplified and filtered. An artificial neural network using fuzzy logic to process EMG was used. This network predicts movement trajectories. Using this forecast and taking into account the feedback information, the control system generates the appropriate drive torques.


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