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Electricity demand forecasting using fuzzy hybrid intelligence-based seasonal models

Khashei, Mehdi; Chahkoutahi, Fatemeh - In: Journal of modelling in management 17 (2022) 1, pp. 154-176

Persistent link: https://www.econbiz.de/10012886288

Regularized Fuzzy Neural Networks to Aid Effort Forecasting in the Construction and Software Development

Benson-Emenike, Mercy E.; Ifeanyi-Reuben, Nkechi J. - 2019

Predicting the time to build software is a very complex task for software engineering managers. There are complex factors that can directly interfere with the productivity of the development team. Factors directly related to the complexity of the system to be developed drastically change the...

Persistent link: https://www.econbiz.de/10014109457

Dynamic modeling for product family evolution combined with artificial neural network based forecasting model : a study of iPhone evolution

Biswas, Sumana; Ali, Ismail; Chakrabortty, Ripon K.; … - In: Technological forecasting & social change : an … 178 (2022), pp. 1-21

Persistent link: https://www.econbiz.de/10013346701

Artificial intelligence in energy industry : forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system

Tutun, Salih; Tosyali, Ali; Sangrody, Hossein; … - In: Journal of business analytics 6 (2023) 1, pp. 59-76

Persistent link: https://www.econbiz.de/10013536148

An explainable deep-learning approach for job cycle time prediction

Wang, Yu-Cheng; Chen, Toly; Chiu, Min-Chi - In: Decision analytics journal 6 (2023), pp. 1-10

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, the prediction mechanism of a DNN is complex and difficult to communicate. This limits its acceptability (or practicability) in real-world applications. An explainable...

Persistent link: https://www.econbiz.de/10014337140

An Improved Intelligent Model for Stock Market Time Series Data Prediction Using Fuzzy Logic and Deep Neural Networks

Mousaie, Parniyan; Aghaeizadeh Saheli, Farzad - 2023

It is vitally crucial to establish a method that can accurately forecast prices on the stock exchange market because of the influence the stock market has on the country’s ability to raise capital and advance its economic growth. On the stock market, a great number of sensitivity factors are...

Persistent link: https://www.econbiz.de/10014348276

Bankruptcy prediction using fuzzy convolutional neural networks

Jabeur, Sami Ben; Serret, Vanessa - In: Research in international business and finance 64 (2023), pp. 1-19

Persistent link: https://www.econbiz.de/10014266324

Multivariate picture fuzzy time series : new definitions and a new forecasting method based on pi-sigma artificial neural network

Bas, Eren; Egrioglu, Erol; Tunc, Taner - In: Computational economics 61 (2023) 1, pp. 139-164

Persistent link: https://www.econbiz.de/10014228419

MCDA techniques used in optimization of weights and ratings of DRASTIC model for groundwater vulnerability assessment

Kumar, Prashant; Sharma, Reema Bera; Bhaumik, Subhasis - In: Data science and management : DSM 5 (2022) 1, pp. 28-41

DRASTIC is a very simple and common model used for the assessment of groundwater to contamination. This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment. The Ohio Water Well Association (OWWA) developed DRASTIC model in 1987....

Persistent link: https://www.econbiz.de/10014517935

Time prediction models of grapple skidder HSM 904 using multiple linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS)

Naghdi, Ramin; Ghajar, Ismael; Tsioras, Petros A. - In: Operational research : an international journal 16 (2016) 3, pp. 501-512

Persistent link: https://www.econbiz.de/10011724645