The current work aims to predict and assess a PV/T system using ANN models based on an experimental dataset in Oman. The PV/T system with weather station and data acquisition was installed in Sohar, Oman. The weather and electrical data has been recorded. A novel mathematical and ANN model for examining the performance of PV/T systems has been developed. The experimental results show improvement in PVT power production (68.6132 W) compared to the conventional PV (66.7827 W). The results demonstrate that the three proposed models (MLP, SOFM, and SVM) achieved excellent MSE results for generating the current values of the PV system (0.00043, 0.00030, 0.00041) and PV/T system (0.00719, 0.00683, 0.00763), respectively. Also, the proposed models delivered excellent MSE results for simulating the power values of the PV system (0.04457, 0.05006, 0.13816) and PV/T system (0.04457, 0.05006, 0.13816), respectively. The proposed models result validated with experimental data using descriptive statistics and Evaluation Metrics. Finally, the proposed neural models can generate future figures for any needed period that accurately fit the actual datasets.

The driver’s face is detected and extracted extra features of the eyes and mouth areas. Start the analysis process to determine the status of these parameters. Second, the Kalman filter method is used to track and manipulate the difference in the size and orientation of the captured features. The technique checks all image states such as brightness, shadows, and clarity. Third, the blur control system provides different alert sounds based on the information tracked from the face, eyes, and mouth. The proposed method uses real-time data recorded by a webcam tool in the MatlabR2016a environment. The data sample contains videos of different users of different races, whether they wear glasses, gender, and various lighting backgrounds. The proposed system achieved an accuracy of up to 94.5% in the detection driver status.

This work developed two-stage labeling models. The first stage is to process the text before execution by removing the suffixes attached to it. And then the second stage is to design models by applying mathematical models using Multilayer Perceptron (MPL), Full Recurrent Neural Network (FRNN), and Support Vector Machines (SVM). The current system helps classify words and allocate the correct parts of speech according to their wholesale position. To test the effectiveness of the proposed models using two different languages (Arabic and Hindi). The results showed the effectiveness of the proposed models is successfully solving the problem of clarification of words for the Arabic text. Also, compared to previous studies, the proposed models achieved high accuracy by classifying the parts of speech with an accuracy of up to (99%).

This paper aims to compare social and telepresence robots in the teaching industry in the near future. Both a literature survey and a comparison segment were produced. It was concluded that the future of educational environments would incorporate both of these technologies since they serve different functions. This is to provide the best experience for as wide of a range of people as possible, wherever they are living and whatever disabilities/ challenges they are experiencing.

This work is concerned with proposing mathematical models characterized by accuracy and ease in predicting the number of diabetics type 2 in the Sultanate of Oman. By analyzing the proposed mathematical models of the current work (1, 2, and 3), it was found that the proposed mathematical model in Equation 6 can accurately predict the number of diabetics in Oman up to 2050. In order to test the model’s accuracy and validity, we revised it with actual data. The results prove the accuracy of the proposed model in predicting future data of 99%. Lastly, several recommendations were recorded that could help to reduce the prevalence of diabetes type 2 in Oman.

This paper investigated and reviewed the current big data methods and tools in solar energy production. It discusses the comprehensive two-stage design and evaluation for examining the optimal structure for renewable energy systems. In the design stage, technical and economic aspects are discussed based on a robust analysis of all input/output variables for determining the highest performance. Next, assess and evaluate the effectiveness of each method under different circumstances conditions. Then convert each qualitative indicator into a quantitative measure using extensive data analysis methods to determine the overall performance of the various qualitative variables. The paper also provides an in-depth analysis of the mathematical techniques used in measuring the efficiency of the renewable energy production system and discussing future axes of work in the field of specific energy.