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Renewable energy sources are considered ubiquitous and drive the energy revolution. Energy producers suffer from inconsistent electricity generation. They often struggled with the unpredictability of the weather. Thus, making it challenging to balance supply and demand. Technologies like artificial intelligence (AI) and machine learning are effective ways to forecast, distribute, and manage renewable photovoltaic (PV) solar supplies. AI will make the energy forecasting system more connected, intelligent, reliable, and sustainable. AI can innovate how energy is used and help find solutions for decarbonizing energy systems. There are potential advantages to total energy forecasting. AI can support the growth and integration of PV solar energy. The article’s main objective is to use AI to forecast the output consumed power of the Yarmouk University PV solar system in Jordan. The total actual yield is 5548.96 MW h, and the performance ratio (PR) is 95.73%. Many techniques are used to predict the consumed solar power. The random forest model obtains the best results of root mean squared error and mean absolute error are 172.07 and 68.7, respectively. This accurate prediction allows for the maximum use of solar power and the minimal use of grid power. This work guides the operators to learn trends embedded in Yarmouk University’s historical data. These understood trends can be used to predict the consumption of solar power output. Thus, the control system and grid operators have advanced knowledge of the expected consumption of solar power at each hour of the day.

In online social networks, spam profiles represent one of the most serious security threats over the Internet; if they do not stop producing bad advertisements, they can be exploited by criminals for various purposes. This article addresses the nature and the characteristics of spam profiles in a social network like Twitter to improve spam detection, based on a number of publicly available language-independent features. In order to investigate the effectiveness of these features in spam detection, four datasets are extracted for four different language contexts (i.e. Arabic, English, Korean and Spanish), and a fifth is formed by combining them all. We conduct our experiments using a set of five well-known classification algorithms in spam detection field, k -Nearest Neighbours (k -NN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT) (J48) and Multilayer Perceptron (MLP) classifiers, along with five filter-based feature selection methods, namely, Information Gain, Chi-square, ReliefF, Correlation and Significance. The results show oscillating performance of each classifier across all datasets, but improved classification results with feature selection. In addition, detailed analysis and comparisons are carried out on two different levels: in the first level, we compare the selected features' importance among the feature selection methods, whereas in the second level, we observe the relations and the importance of the selected features across all datasets. The findings of this article lead to a better understanding of social spam and improving detection methods by considering the various important features resulting from the different lingual contexts. [ABSTRACT FROM AUTHOR]