Neural network and genetic algorithm approaches for forecasting bangladeshi monsoon rainfall

Banik, Shipra; Chanchary, Farah; Khan, Khodadad; Rouf, Rifat Ara; Anwer, Mohammed

doi:10.1109/iccitechn.2008.4802997

Cited by 18 publications

(6 citation statements)

References 8 publications

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“…Furthermore, this ANN was trained using GA. By testing this network on XOR problem and hand-written pattern recognition, it was found that this network was better than BPA. Banik et al [17] proposed a model for forecasting rain in Bangladesh. They used hybrid neural model of ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS) and GA, which predicted better than BPA alone.…”

Section: Related Workmentioning

confidence: 99%

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

et al. 2015

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With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs), a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc.) while using Back Propagation Algorithm (BPA). In this paper, we have used the Genetic Algorithm (GA) for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.

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Section: Related Workmentioning

confidence: 99%

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

et al. 2015

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“…In [9,10], methods of hydrological analogy with the already studied territory or historical period are proposed for the restoration of hydrological data. The complexity of this method lies in the selection of an analogue river that would be subject to the same anthropogenic load as the studied watercourse.…”

Section: Introductionmentioning

confidence: 99%

Hydroecological data recovery using artificial intelligence

Nafikova¹,

Aleksandrov²,

Shaniyazova³

et al. 2023

E3S Web of Conf.

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Artificial neural networks and genetic algorithms have been tested for the restoration of missed hydroecological indicators (hydrological water quality parameters and bottom sediment quality parameters). Algorithms have been developed for recovering missing hydroecological data in the presence and absence of observation data from points upstream and downstream. Neural network models for the restoration of water quality indicators and bottom sediments were tested on the example of the catchment basin of the Bayda and Kidysh rivers in the territory of the Republic of Bashkortostan.

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“…Many researchers, for example, [1][2][3][4] and others have found that the empirical distribution of stock is significantly nonnormal and nonlinear. Stock market data are also observed in practice chaotic and volatile by nature (e.g., see [5][6][7][8]). That is why stock values are hard to predict.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, soft computing models are used to learn the nonlinear and chaotic patterns in the stock system. Several studies [7,11, and many others] have compared soft computing models and the traditional Box-Jenkins model. However, there are only a few comparative analyses (according to our knowledge) between soft computing models and standard time series statistical models [13] in case of Bangladeshi stock indices.…”

Section: Introductionmentioning

confidence: 99%

Modeling Chaotic Behavior of Chittagong Stock Indices

Banik

Anwer

Khan

2012

Applied Computational Intelligence and Soft Computing

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Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock investors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative study has been conducted to predict stock index values using soft computing models and time series model. Paying attention to the applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period from January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the adaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting models in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model is considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the considered time series model.

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Neural network and genetic algorithm approaches for forecasting bangladeshi monsoon rainfall

Cited by 18 publications

References 8 publications

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

Hydroecological data recovery using artificial intelligence

Modeling Chaotic Behavior of Chittagong Stock Indices

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