Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Sarat Chandra Nayak"'
Publikováno v:
Financial Innovation, Vol 10, Iss 1, Pp 1-43 (2024)
Abstract This study attempts to accelerate the learning ability of an artificial electric field algorithm (AEFA) by attributing it with two mechanisms: elitism and opposition-based learning. Elitism advances the convergence of the AEFA towards global
Externí odkaz:
https://doaj.org/article/f60abdccad4b4e9dbf9146fd4d9f6dc3
Publikováno v:
Ain Shams Engineering Journal, Vol 15, Iss 3, Pp 102462- (2024)
Compressive strength (CS) has been considered as the utmost critical parameter while designing concrete structures. Usually, it is determined through laboratory tests, which are expensive, time consuming, and requires consumptions of materials. There
Externí odkaz:
https://doaj.org/article/57177de2120d4ab581e22c5a4fefa215
Publikováno v:
IEEE Access, Vol 11, Pp 57693-57716 (2023)
Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a gro
Externí odkaz:
https://doaj.org/article/e3f6a417589e49d6a6fea1559c0fc89d
Publikováno v:
IEEE Access, Vol 10, Pp 130921-130943 (2022)
Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow
Externí odkaz:
https://doaj.org/article/fe566a552c924756b5ea6fc0c2173877
Publikováno v:
Financial Innovation, Vol 6, Iss 1, Pp 1-23 (2020)
Abstract Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for
Externí odkaz:
https://doaj.org/article/84ae9fe68c774cd7b7b582809a81ab8d
A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction
Publikováno v:
Financial Innovation, Vol 5, Iss 1, Pp 1-34 (2019)
Abstract Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain.
Externí odkaz:
https://doaj.org/article/e53784f83ccd454c9de7d0409a4f4524
Autor:
Sarat Chandra Nayak
Publikováno v:
EAI Endorsed Transactions on Energy Web, Vol 7, Iss 28 (2020)
Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with
Externí odkaz:
https://doaj.org/article/70187a27df5e45d9b2f0da6e3af1acc6
Publikováno v:
Financial Innovation, Vol 4, Iss 1, Pp 1-22 (2018)
Abstract Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task becau
Externí odkaz:
https://doaj.org/article/dfdc66237df04395a65d8baec4ddac8c
Autor:
Sarat Chandra Nayak
Publikováno v:
EAI Endorsed Transactions on Scalable Information Systems, Vol 6, Iss 22 (2019)
Precise and proficient modelling and forecasting financial time series has been paying attention of researchers,which leads to the development of various statistical and machine learning based models. Accuracy of a particularmethod is problem and dom
Externí odkaz:
https://doaj.org/article/cd2cf18835b141f5b821696a548e1212
Publikováno v:
Archives of Computational Methods in Engineering.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33b52a003725c64f8bcb0ce93fe21421
https://doi.org/10.1007/s11831-023-09942-9
https://doi.org/10.1007/s11831-023-09942-9