An internal information optimum algorithm for a multilayer perceptron and its generalization analysis

2004 ◽  
Vol 87 (5) ◽  
pp. 67-80
Author(s):  
Masanobu Tomomura ◽  
Kenji Nakayama
Keyword(s):  
Multilayer Perceptron ◽  
Optimum Algorithm ◽  
Internal Information

Sexual Preference Classification from Gaze Behavior Data using a Multilayer Perceptron

2006 ◽  
Author(s):  
S. Chartier ◽  
P. Renaud ◽  
S. Bouchard ◽  
J. Proulx ◽  
J. L. Rouleau ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Sexual Preference ◽  
Gaze Behavior

Insider Trading: A White-Collar Crime and its Impact on Share Market

Think India ◽  
2014 ◽  
Vol 17 (3) ◽  
pp. 22-24
Author(s):  
Sreekumar Ray
Keyword(s):  
Stock Market ◽  
Insider Trading ◽  
White Collar Crime ◽  
White Collar ◽  
Adverse Impact ◽  
High Ranking ◽  
Internal Information ◽  
Indian Stock Market ◽  
The Impact

Since inception, the growth of the Indian stock market has been constrained through unethical, illegal and self-actualized activities of swanky persons involved in different capacities in the market. The stock market was trying to retrieve itself from the devastating effect of Harshad Mehta share market scam, when within a gap of ten years it was once again pushed into the darkness of the dungeon by another demon-child of the country- Ketan Parekh. Corporations have been looted by the insider traders, diversifying internal information to an external in lieu of cash. Investigations in the majority cases have proved the involvement of the high ranking officers of the companies in the crime, sophistically referred to as white-collar crime. It has an adverse impact on the growth and sustainability of the share market. Under the light of the above issue, this paper endeavors to study the impact of such crime on the share market. It focuses on the mechanism behind the insider-trading, its impact on the share market and the regulators supervision on the issue. Finally, suggestions have been provided which will contribute towards the dream of every Indian-a fraud-free share market focusing towards the overall development of the country.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

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