In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. Among these algorithms, metaheuristics are an appropriate tool to solve these real problems. Also, ML is one of the advanced tools in Artificial Intelligence (AI) including different learning strategies to teach new tasks according to data. Therefore, proposing an efficient meta-heuristic to improve the inputs of the trainer in ML would be significant. In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. SGA is used in the process of solving optimization test problems by neural networks. The problems are solved by the same neural network with and without SGA, computational results prove the efficiency of SGA in neural networks. Finally, SGA is proposed to solve very extensive test problems including IoT optimization problems. Comparative results of applying the SGA on all of these problems with different sizes are included, and the proposed algorithm suggests effective solutions within a reasonable timeframe.

The main extraction parameters were statistically evaluated for 20 medicinal plants to obtain the optimum conditions for maximum extraction of total phenolic content (TPC) in each plant. Among various extraction parameters, pH, temperature, and concentration at different levels were studied. The results were analyzed using the analysis of variance to achieve the optimum conditions of phenolic extraction for all plants. Also, investigation of the optimum antioxidant (AnOX) activities using DPPH (2,2‐diphenyl‐1‐picrylhydrazyl) method and antibacterial potential against common pathogenic bacteria of Staphylococcus aureus, Escherichia coli, Pseudomonas spp., and Candida spp. through disk diffusion method for the extract of all plants under the optimum total phenolic concentration of each plant extract confirmed a direct relation among bioactivity and TPC.

In this study, we address the pressing need to improve e-government services by conducting a comprehensive evaluation of document management practices within the Ministry of Higher Education in the Kurdistan Region of Iraq. Employing structural equation modeling and regression modeling techniques, we assess the effectiveness of key variables using the six factors (digitalization, utility, reliability, efficiency, user satisfaction, and accessibility) under the e-government assessment model. Our findings reveal that user satisfaction and ease of access are pivotal for the success of e-government services, with digitization and efficiency significantly impacting operational effectiveness. Furthermore, it also concerns that without improving the overall user satisfaction, the level of acceptance and use of e-government remains low. High user satisfaction can make a lot of difference in overall engagement besides improving service delivery among organizations. Through insights specific to the Kurdistan Region, we underscore the importance of balancing technical infrastructure with user experience. Our proposed six-factor model offers valuable guidance for policymakers and practitioners globally, aiming to enhance e-government services.

The process of producing electricity from sources of energy is known as electricity production. Electric also isn't freely accessible in environment, thus it should be "manufactured" (i.e., converting another kinds of energy to electrical energy) by utilities with in electricity industry (transportation, distributing, and so on).Moreover, the objective of this study is to compared of Brown’s as well as Holt’s Double Exponential Smoothing also build a best forecasting time series model among two smoothing model forecasting, as well as focuses on optimizing characteristics to use the golden section technique. This exponential smoothing approach has been one of the time series forecasting methods that would be used to forecast (Generation Electrical) with in Kurdistan area. The issue that arises with this technique is determining the appropriate parameters to reduce predict inaccuracy. In addition, Data used in this paper are (Generation Electrical) in Kurdistan region for (132) months from 2010 to 2020. The study revealed that such data is trending modeled, indicating that a double exponential smoothing (DES) approach from Brown & Holt can be used with the (Stratigraphic & Minitab) software. There are the same results but the Result of analysis more depend on the R-program. The difference among the forecast findings acquired with optimum parameters as well as the assaying data was utilized to assess the feasibility of the forecast by completing normality and randomness tests. Ultimately, the outcomes of parameterization show that the optimal value of α that in DES Brown is (0.22) as well as the optimal MAPE is 9.23616 percent, whereas in DES Holt the optimal is (0.95) as well as the optimal β is (0.05) via the optimal MAPE of 8.08586 percent. This MAPE of a DES Brown technique is greater than the MAPE of a DES Holt approach. Feasibility experiments revealed that both approaches are capable of predicting. Depending on the value of MAPE as well as evaluation process, DES Holt's was recognized as the main prediction model.