A Novel Method for Medical Disease Diagnosis Using Artificial Neural Networks Based on Backpropagation Algorithm

2013 ◽  
Author(s):  
J.S. Bhalla ◽  
A. Aggarwal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Disease Diagnosis ◽  
Backpropagation Algorithm ◽  
Medical Disease ◽  
Novel Method ◽  
Artificial Neural

scholarly journals A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 124 ◽  
Author(s):  
Jianshen Zhu ◽  
Chenxi Wang ◽  
Aleksandar Shurbevski ◽  
Hiroshi Nagamochi ◽  
Tatsuya Akutsu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Compounds ◽  
Mixed Integer ◽  
Training Phase ◽  
Cycle Index ◽  
Inference Problem ◽  
Inverse Prediction ◽  
Novel Method ◽  
Artificial Neural

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method.

scholarly journals Structural Vibration Tests: Use of Artificial Neural Networks for Live Prediction of Structural Stress

2020 ◽  
Vol 10 (23) ◽  
pp. 8542
Author(s):  
Laura Wilmes ◽  
Raymond Olympio ◽  
Kristin M. de Payrebrune ◽  
Markus Schatz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Structure ◽  
Structural Vibration ◽  
Future Application ◽  
Different Types ◽  
Novel Method ◽  
Artificial Neural ◽  
Input Load ◽  
Vibration Tests

One of the ongoing tasks in space structure testing is the vibration test, in which a given structure is mounted onto a shaker and excited by a certain input load on a given frequency range, in order to reproduce the rigor of launch. These vibration tests need to be conducted in order to ensure that the devised structure meets the expected loads of its future application. However, the structure must not be overtested to avoid any risk of damage. For this, the system’s response to the testing loads, i.e., stresses and forces in the structure, must be monitored and predicted live during the test. In order to solve the issues associated with existing methods of live monitoring of the structure’s response, this paper investigated the use of artificial neural networks (ANNs) to predict the system’s responses during the test. Hence, a framework was developed with different use cases to compare various kinds of artificial neural networks and eventually identify the most promising one. Thus, the conducted research accounts for a novel method for live prediction of stresses, allowing failure to be evaluated for different types of material via yield criteria.

scholarly journals A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

2011 ◽  
Vol 222 (15) ◽  
pp. 2606-2614 ◽  
Author(s):  
Michael J. Watts ◽  
Yuxiao Li ◽  
Bayden D. Russell ◽  
Camille Mellin ◽  
Sean D. Connell ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Novel Method ◽  
Artificial Neural ◽  
Rocky Habitats

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

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