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Hierarchical Method Based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.; Fainti, Rafik - In: International Journal of Monitoring and Surveillance … 4 (2016) 4, pp. 20-32

Plants (CCPPs) because of the quite efficient way these units are capable to produce electricity. Hence, the prediction of …

Persistent link: https://www.econbiz.de/10012046684

Analysis of the effects of cell temperature on the predictability of the solar photovoltaic power production

Al-Dahidi, Sameer; Al-Nazer, Salah; Ayadi, Osama; … - In: International Journal of Energy Economics and Policy : IJEEP 10 (2020) 5, pp. 208-219

Persistent link: https://www.econbiz.de/10012505992

THE COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR PREDICTIONS - ARTIFICIAL NEURAL NETWORKS

Bar, Mary Violeta - In: Annals of University of Craiova - Economic Sciences Series 2 (2014) 42, pp. 184-190

The computational intelligence techniques are used in problems which can not be solved by traditional techniques when there is insufficient data to develop a model problem or when they have errors.Computational intelligence, as he called Bezdek (Bezdek, 1992) aims at modeling of biological...

Persistent link: https://www.econbiz.de/10010897104

A neural network based system for predicting earthmoving production

Shi, Jonathan Jingsheng - In: Construction Management and Economics 17 (1999) 4, pp. 463-471

An artificial neural network based system (NN earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are modelled by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then...

Persistent link: https://www.econbiz.de/10005482616

Review on Feature Selection and Classification using Neuro-Fuzzy Approaches

Biswas, Saroj; Bordoloi, Monali; Purkayastha, Biswajit - In: International Journal of Applied Evolutionary … 7 (2016) 4, pp. 28-44

classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction …

Persistent link: https://www.econbiz.de/10012042704

ANN Modeling of Motional Resistance for Micro Disk Resonator

Sutagundar, Manjula A.; Sheeparamatti, Basavaprabhu G.; … - In: International Journal of Applied Evolutionary … 8 (2017) 4, pp. 14-31

This article describes how modeling is an integral part of design and development of any system that provides the theoretical characterization of the system and helps in understanding the relations between various parameters of the system, before the system is developed. The capability of an...

Persistent link: https://www.econbiz.de/10012042722

Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record

Sakizadeh, Mohamad; Rahmatinia, Hassan - In: International Journal of Agricultural and Environmental … 8 (2017) 4, pp. 37-53

network (ANN) for the classification and prediction of groundwater quality using a small data record in Malayer, Iran. For … positive predictive value and negative predictive value were 0.89 and 0.86, respectively. The prediction of water quality index …

Persistent link: https://www.econbiz.de/10012042780

A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology

Weckman, Gary R.; Dravenstott, Ronald W.; Young II, … - In: International Journal of Business Analytics (IJBAN) 3 (2016) 1, pp. 1-21

prediction model for the restaurant industry. Models were designed to forecast 1 week, 4 weeks, and 13 weeks into the future. The … model performed better than the benchmark methods, which included, an analyst prediction, multiple linear regression …, trading, and Buy and Hold trading strategies. The prediction accuracy of the ANN methodology presented reached accuracy …

Persistent link: https://www.econbiz.de/10012043160

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Walczak, Steven - In: International Journal of Computers in Clinical Practice … 3 (2018) 2, pp. 31-47

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into...

Persistent link: https://www.econbiz.de/10012043566

A Hybrid NIDS Model Using Artificial Neural Network and D-S Evidence

Lu, Chunlin; Li, Yue; Ma, Mingjie; Li, Na - In: International Journal of Digital Crime and Forensics (IJDCF) 8 (2016) 1, pp. 37-50

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and...

Persistent link: https://www.econbiz.de/10012043917

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