An Ultrafast Maximum Power Point Setting Scheme for Photovoltaic Arrays Using Model Parameter Identification حسب Zhaohui Cen (17217391)

"…In order to separate the search algorithms from converter operation, a model parameter identification approach is presented to estimate insolation conditions of each PV panel and build a real-time overallP-Icurve of PV arrays. …"

Using Machine Learning Algorithms to Forecast Solar Energy Power Output حسب Ali Jassim Lari (22597940)

"…We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. …"

Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands حسب Peng, Wang

"…Moreover, different types of renewable energy sources including wind turbine, solar panel and fuel cell are modeled and considered in the scheduling process of the hybrid microgrid. …"
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A comprehensive analysis of control strategies for enhancing regulation in standalone photovoltaic systems حسب Aishworya Roy (19437826)

Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques حسب Kais Abdulmawjood (17947784)

Fault Diagnosis Based Machine Learning and Fault Tolerant Control of Multicellular Converter Used in Photovoltaic Water Pumping System حسب B. Rouabah (17947820)

Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks حسب Sharma, Neetan

"…The proposed ANN based algorithm has been used for unswerving petite term forecasting. …"
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Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort حسب Mohamed Adil Shah Khoodoruth (14589828)

"…Linear regression analysis revealed that MLR and clozapine treatment were significantly correlated with the severity of schizophrenia symptoms. The Random Forest model, a supervised machine learning algorithm, efficiently differentiated between cases and controls and between TRS and NTRS, with accuracies of 86.87 % and 88.41 %, respectively. …"

Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition حسب Abboud, Ralph

"…Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. …"
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