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Cadmium telluride (CdTe)/cadmium sulfide (CdS) solar cell is a promising candidate for photovoltaic (PV) energy production, as fabrication costs are compared by silicon wafers. We include an analysis of CdTe/CdS solar cells while optimizing structural parameters. Solar cell capacitance simulator (SCAPS)-1D 3.3 software is used to analyze and develop energy-efficient. The impact of operating thermal efficiency on solar cells is highlighted in this article to explore the temperature dependence. PV parameters were calculated in the different absorber, buffer, and window layer thicknesses (CdTe, CdS, and SnO2). The effect of the thicknesses of the layers, and the fundamental characteristics of open-circuit voltage, fill factor, short circuit current, and solar energy conversion efficiency were studied. The results showed the thickness of the absorber and buffer layers could be optimized. The temperature had a major impact on the CdTe/CdS solar cells as well. The optimized solar cell has an efficiency performance of >14% when exposed to the AM1.5 G spectrum. CdTe 3000 nm, CdS 50 nm, SnO2 500 nm, and (at) T 300k were the I-V characteristics gave the best conversion open circuit voltage (Voc)=0.8317 volts, short circuit current density (Jsc)=23.15 mA/cm2, fill factor (FF)%=77.48, and efficiency (η)%=14.73. The results can be used to provide important guidance for future work on multi-junction solar cell design.

The rapid expansion of e-learning platforms, where students can share their opinions and express their thoughts, has become a rich source of data for opinion mining and sentiment analysis. This study aims to develop an effective model for predicting students' attitudes about e-learning, with a focus on mining opinions that indicate positive or negative sentiments. The study was implemented in two stages. The first stage aimed to discover the most popular platform used in e-learning at the University of Mosul to collect the largest amount of data through comments posted within the platforms, also to identify trends in students' opinions towards e-learning. The results show that the focus of both lecturers and students revolved around well-known platforms such as Google Classroom and Google Meet, both of which had relative importance (45.33% and 42.29%, respectively). The second stage uses a machine-learning algorithm on the data collected to determine the impact of e-learning on students. Also, two feature selection approaches, Information Gain IG and CHI statistics, were explored and enhanced in addition to HMM and SVM-based hybrid learning strategy. As a result, an opinion mining method was used to assist developers in improving and promoting the quality of relevant services.