In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.

The Retailers all over the world are prospering from the burgeoning trend of online shopping. Kurdistan Regional Government is still struggling to grow its e-commerce markets. On the other hands e-commerce in Various countries in the Middle East have some of the world’s highest internet and mobile penetration rates. Alternative payments methods are quickly expanding, and having access to some of the world’s most coveted natural resources that allows countries in their region to have some of the highest GDP in the world. There are several challenges prevalent in the KRG Region market that will require international merchants to develop strategies based on innovation and vigilance. This unique region is plagued with complications many other countries have little to no experience with e-commerce, which highlights the need for retailers to have a deep understanding as to how this region operates before they can begin finding solutions. One of the biggest concerns today's consumers have is the risk of fraud when they are shopping online. With highly sophisticated malware and perceptive cybercriminals, customers' card and bank information can easily be stolen if a merchant does not take the proper security measures. In this paper we summarize all challenges need to be addressed in KRG in order to make correct steps to apply e-commerce in KRG. Finally, the recommendations and framework are proposed for e-commerce to encourage government, organizations, and people to take advantages from e-commerce.

Malaria is a dangerous viral disease caused by Plasmodium protozoan parasites that are spread by the bite of an infected female Anopheles mosquito. This pandemic disease's fast and precise identification is essential for effective treatment. The most reliable method for diagnosing malaria is a microscopic examination of a thick and thin blood smear, which looks for the parasite and counts the number of infected cells. The ability to wholly or partially automate the identification of the disease using the information in medical images highlights the critical role that computer-aided diagnosis plays in modern medicine, in which machine learning and deep learning play a critical role. In this study, we have presented an in-depth overview of the techniques and methods used to diagnose the malaria parasite through blood slides automatically. One of the techniques is using transfer learning models to detect the malaria parasite. We have compared the performance of transfer learning models on identifying infected malaria cells by feeding the models a large dataset of uninfected and parasite cell images. The results show that the DensNet models have the edge over the other models, with DenseNet-201 achieving the highest accuracy and F1 score of 0.9339 and 0.9321, respectively. Also, DenseNet-169 outperformed the other models with 0.9594 in precision, and finally, Densenet-121 had the highest recall with 0.9490.