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The effective selection of protein features and the accurate method for predicting protein structural class (PSP) is an important aspect in protein folding, especially for low-similarity sequences. Many promising approaches are proposed to solve this problem, mostly via computational intelligence methods. One of the main aspect of the prediction is the extraction of an excellent representation of a protein sequence. An integrated vector of dimensions 71 was extracted using secondary and hydropathy information in this study Using newly developed strategies for categorizing proteins into their respective main structures classes, which are all-α, all-β, α/β, and α+β. Support Vector Machine (SVM) and Differential Evolution (DE) were combined using the wrapper method to select the top N features based on the level of their respective importance. The classification can be made more accurate by tuning the kernel parameters for the SVM in the training phase. In this study, the mean of the classification rate from using the SVM classifier was used to evaluate the selected subset of features. This study was tested using two low - similarity data sets (D640 and ASTRAL). A comparison between the proposed (SVM + DE) based on DE feature selection approach and (SVM+DE) based on grid search (a traditional method to search for parameters) forms the core of this work. The proposed SVM+DE model is competitive and highly reliable in terms of time and performance accuracy compared with other reported methods in literature.

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Nowadays the use of mobile devices has increased dramatically as they have been integrated into different learning facilities. In this paper, the opinions of high school students and their teachers will be evaluated in order to get a better understanding of how mobile devices are used in the learning environment. A qualitative and quantitative method was used in this study. Multiple cases for the purpose of understanding the level of students' use of these devices in schools. Through the results of this study, it can be determined whether spending on textbooks and supplies is necessary compared to replacing it with technology. This model can be divided into five categories. (MLIS) mobile phone by developing a mobile learning model in Iraqi secondary schools (MLIS). This model can be divided into five categories, including mobile learning, drivers, process, community, and influencing factors. Each of the categories is related to each other, as well as related to planning and goals. However, both students and teachers believe that using mobile devices in an educational setting can help increase overall achievement, improve student motivation, and create a positive learning environment in schools. This study also helps enrich the existing literature on mobile technology in schools, where knowledge is lacking in the Iraqi educational system.