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A novel approach for complex and subjective variable valuation in the COPPE-Cosenza fuzzy model applied to crude oil trading

Andrade, Gustavo B.; Andrade, Luciana B.; Conde, Edison; … - In: Decision analytics journal 7 (2023), pp. 1-10

Numerical algorithms and mathematical methods are regularly used in the commodities and stock markets. However, this so-called quantitative trading has been thus far limited to the paper markets, such as futures, swaps, and options, and excluded a large majority of the physical market....

Persistent link: https://www.econbiz.de/10014434170

A deep learning approach for assessing stress levels in patients using electroencephalogram signals

Bhatnagar, Shaleen; Khandelwal, Sarika; Jain, Shruti; … - In: Decision analytics journal 7 (2023), pp. 1-6

Stress can contribute to many health problems, such as high blood pressure, heart disease, obesity, and diabetes. Therefore, early stress detection is essential in preventing illness and health problems. This study proposes a music experimentation approach to identify stress levels among the...

Persistent link: https://www.econbiz.de/10014434143

A novel deep learning method for predicting athletes' health using wearable sensors and recurrent neural networks

Alghamdi, Wael Y. - In: Decision analytics journal 7 (2023), pp. 1-13

Good health is extremely important for athletes who engage in strenuous physical activities, such as football. They must develop a healthy body before participating in vigorous activities and competitions. Although researchers have presented a wide range of analytical approaches emphasizing...

Persistent link: https://www.econbiz.de/10014434151

A deep convolutional neural network for vibration-based health-monitoring of rotating machinery

Ong, Pauline; Tan Yean Keong; Lai Kee Huong; Chee Kiong Sia - In: Decision analytics journal 7 (2023), pp. 1-9

The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative...

Persistent link: https://www.econbiz.de/10014436400

A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction

Chen, Toly; Wang, Yu-Cheng - In: Decision analytics journal 7 (2023), pp. 1-11

Methods based on artificial neural network (ANN) or deep neural network (DNN) applications have been proposed to …-output relationship of an ANN (or DNN) is fitted by decision rules using a random forest approach. Then, domain knowledge is extracted …

Persistent link: https://www.econbiz.de/10014436432

An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management

Islam, Md Azharul; Rashid, Shawkh Ibne; Hossain, Niamat … - In: Decision analytics journal 7 (2023), pp. 1-13

Drones are used for post-flood disaster management and delivering relief goods to flood-affected areas. Autonomous drones are an alternative means of prioritizing assistance due to the lack of available technology and accessibility in many affected areas during floods. This study proposes a...

Persistent link: https://www.econbiz.de/10014436566

A predictive analytics model for designing deep underground foundations using artificial neural networks

Agbemenou, Atsou Komla Herve; Motamed, Ramin; … - In: Decision analytics journal 7 (2023), pp. 1-11

high-rise buildings. Different single hidden layer ANN architectures are utilized to predict the ultimate axial capacity of … total of 45 load tests were divided into 85% for training, and 15% for validation. The developed ANN model achieved good … a first-order estimate for the architecture of an ANN predictive model and the ultimate nominal axial capacity of …

Persistent link: https://www.econbiz.de/10014436673

DIDS : a Deep Neural Network based real-time Intrusion detection system for IoT

Vishwakarma, Monika; Kesswani, Nishtha - In: Decision analytics journal 5 (2022), pp. 1-9

The number of people using the Internet of Things (IoT) devices has exploded in recent years. The instantaneous development in deploying constrained devices in numerous areas makes them vulnerable to assaults due to limited resources. Advanced cryptography cannot be constructed in these modest...

Persistent link: https://www.econbiz.de/10014337042

A hybrid deep neural net learning model for predicting Coronary Heart Disease using Randomized Search Cross-Validation Optimization

Sharma, Neeraj; Malviya, Lokesh; Jadhav, Akshay; … - In: Decision analytics journal 9 (2023), pp. 1-10

Coronary Heart Disease (CHD) is a life-threatening public health problem. Many chronic CHDs and health risks can be avoided, reversed, and reduced with proper risk assessment. Medical professionals find it challenging to anticipate heart attacks and heart failures since it is a complex process...

Persistent link: https://www.econbiz.de/10014532396

An artificial neural network for exploring the relationship between learning activities and students' performance

Borhani, Kourosh; Wong, Richard T. K. - In: Decision analytics journal 9 (2023), pp. 1-19

This paper identifies the most significant learning factors impacting undergraduate academic performance using artificial neural networks (ANNs) and controlled student data collection. As higher education becomes increasingly common and important, finding the best ways to help students optimize...

Persistent link: https://www.econbiz.de/10014532501