Although many meteorological prediction models have been developed recently, their accuracy is still unreliable. Post-processing is a task for improving meteorological predictions. This study proposes a post-processing method for the Climate Forecast System Version 2 (CFSV2) model. The applicability of the proposed method is shown in Iran for observation data from 1982 to 2017. This study designs software to perform post-processing in meteorological organizations automatically. From another point of view, this study presents a decision support system (DSS) for controlling precipitation-based natural side effects such as flood disasters or drought phenomena. It goes without saying that the proposed DSS model can meet sustainable development goals (SDGs) with regards to a grantee of human health and environmental protection issues. The present study, for the first time, implemented a platform based on a graphical user interface due to the prediction of precipitation with the application of machine learning computations. The present research developed an academic idea into an industrial tool. The final finding of this paper is to introduce a set of efficient machine learning computations where the random forest (RF) algorithm has a great level of accuracy with more than a 0.87 correlation coefficient compared with other machine learning methods.

Intrusion detection systems (IDS) have been developed to identify and classify these attacks in order to prevent them from occurring. However, the accuracy and efficiency of these systems are still not satisfactory. In previous research, most of the methods used were based on ordinary neural networks, which had low accuracy. Therefore, this thesis, with the aim of presenting a new approach to intrusion detection and improving its accuracy and efficiency, uses long-term memory (LSTM) optimized with the Penguin optimization algorithm (EPO). In the proposed approach, first, the features were pre-processed by normalization, cleaning, and formatting in number format. In the next step, the linear discriminant analysis (LDA) method was used to reduce the dimensions of the processed features, and after that, the EPO algorithm was used to optimize the size of the hidden unit of the LSTM network. Finally, the optimized network was evaluated using the NSL-KDD dataset, which is a widely used benchmark dataset in the field of intrusion detection. The results obtained for the training and test datasets were 99.4 and 98.8%, respectively. These results show that the proposed approach can accurately identify and classify network intrusions and outperform many existing approaches. Keywords: Intrusion Detection Systems, Penguin Meta-Heuristic Algorithm, Long-Term Memory Neural Network, Linear Detection Analysis.