Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus

Nourani, Vahid; Molajou, Amir; Uzelaltinbulat, Selin; Sadıkoğlu, Fahreddin

doi:10.1007/s00704-019-02904-x

Cited by 46 publications

(23 citation statements)

References 35 publications

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“…Neural networks are widely used as an effective method for classifying patterns matching, but high volume computing and long learning cycles are far behind in many applications. In detecting maladaptive infiltration (unknown attacks) An artificial neural network is used, artificial neural network to identify unknown attacks and to avoid malicious intrusions has more advantages than other methods (based on statistics and rule-based) [11,12]. The intrusion detection system based on neural networks for a specific computer system includes the following three steps: a) Training Datasheets: Getting logs for each user in multi-day periods for each user through a vector shows what a user is doing.…”

Section: Neural Networkmentioning

confidence: 99%

“…The network between the NIDSs with the central management system can be private or that the existing infrastructure for data transmission is used. When an existing network is used to send management data, additional security is achieved by cryptography or virtual private VPN technology [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Bulletin Of Electrmentioning

confidence: 99%

“…An intrusion detection system can be set up. There are tools, methods, and documentation that help identify, identify, and report unregistered or unregistered activities under the network [5,8,[10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

Majidpour¹,

Hasanzadeh²

2020

Bulletin EEI

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Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.

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Section: Neural Networkmentioning

confidence: 99%

Section: Bulletin Of Electrmentioning

confidence: 99%

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Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

Majidpour¹,

Hasanzadeh²

2020

Bulletin EEI

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“…Emotional artificial neural network (EANN) models have recently been developed and deployed by integrating artificial emotions and the ANN technique as a new generation of traditional ANN-based models. Nourani et al (2019b) , proposed the first ever application of these models for multistep precipitation forecasting. Simultaneously, researchers have also suggested the long-term forecasting of precipitation using threshold-based hybrid data mining approach ( Nourani, Sattari & Molajou, 2017 ) and a novel data-intelligent approach ( Ali et al, 2020a ).…”

Section: Introductionmentioning

confidence: 99%

Multi-step rainfall forecasting using deep learning approach

Narejo

Jawaid

Talpur

et al. 2021

PeerJ Computer Science

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Rainfall prediction is immensely crucial in daily life routine as well as for water resource management, stochastic hydrology, rain run-off modeling and flood risk mitigation. Quantitative prediction of rainfall time series is extremely challenging as compared to other meteorological parameters due to its variability in local features that involves temporal and spatial scales. Consequently, this requires a highly complex system having an advance model to accurately capture the highly non linear processes occurring in the climate. The focus of this work is direct prediction of multistep forecasting, where a separate time series model for each forecasting horizon is considered and forecasts are computed using observed data samples. Forecasting in this method is performed by proposing a deep learning approach, i.e, Temporal Deep Belief Network (DBN). The best model is selected from several baseline models on the basis of performance analysis metrics. The results suggest that the temporal DBN model outperforms the conventional Convolutional Neural Network (CNN) specifically on rainfall time series forecasting. According to our experimentation, a modified DBN with hidden layes (300-200-100-10) performs best with 4.59E−05, 0.0068 and 0.94 values of MSE, RMSE and R value respectively on testing samples. However, we found that training DBN is more exhaustive and computationally intensive than other deep learning architectures. Findings of this research can be further utilized as basis for the advance forecasting of other weather parameters with same climate conditions.

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“…Comparison of the results showed that the discretization approaches had the highest effect on the results of the regression and counting methods, and the least effect on the multivariate conditional distribution method. Saadat and Asghari (2017) proposed a new method named reliability improved stochastic dynamic programming (RISDP) method to determine the reservoir operation rule curves. In proposed method, the infeasible solution was not created compared with the usual SDP method.…”

Section: -Introductionmentioning

confidence: 99%

Hybridizing ANN-NSGA-II Model with Genetic Programming Method for Reservoir Operation Rule Curve Determination (Case Study Zayandehroud Dam Reservoir)

Moeini

Nasiri

2021

Preprint

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One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy (SOP) method, stochastic dynamic programming (SDP), ANN model and, NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best- obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.

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Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus

Cited by 46 publications

References 35 publications

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

Multi-step rainfall forecasting using deep learning approach

Hybridizing ANN-NSGA-II Model with Genetic Programming Method for Reservoir Operation Rule Curve Determination (Case Study Zayandehroud Dam Reservoir)

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