Integrating renewable energy sources with new technologies such as artificial intelligence (AI) is important to balance energy supply and demand. The predictability of variable energy sources, such as solar energy, plays an important role in maintaining the stability and efficiency of power grids. This study examines the use of various algorithms in AI applications within renewable energy systems. The study critically evaluates existing methods and proposes an innovative approach for AI prediction in solar energy systems using advanced machine learning techniques. It focuses on the effectiveness of MLP, Ridge, and RF algorithms in forecasting Direct Current (DC). The results showed that the RF algorithm achieved the highest R² value (0.9999) and the lowest error RMSE (0.0024) and MAE (0.0006) measurements to demonstrate the superior ability of the models to explain variance in the data and make accurate predictions. In addition, the model developed with SHAP and LIME explainable AI algorithms is interpreted.

Integrating renewable energy resources with new technologies such as artificial intelligence (AI) is critical for balancing energy supply and demand. The predictability of variable energy sources, such as solar Energy, plays a vital role in maintaining the stability and efficiency of electrical grids. In this context, this study examines the use of various algorithms in AI applications within renewable energy systems. A comprehensive literature analysis reveals the advantages and challenges of popular AI techniques such as Artificial Neural Networks, Support Vector Machines, and Decision Trees in energy prediction and optimization. This study mainly focuses on the effectiveness of boosting algorithms such as XGBoost, CatBoost, AdaBoost, LightGBM algorithms and Ensemble learning in estimating alternating current (AC). The results have shown that the Ensemble model achieved the highest R2 value (0.942) and the lowest error metrics MAE (0.040) to demonstrate its superior ability to explain variance in data and make precise predictions. This paper critically evaluates existing methods and proposes an innovative approach to AC prediction in solar power systems using advanced machine-learning techniques.

Epilepsy is a neurological disorder in which involuntary contractions, sensory abnormalities, and changes occur as a result of abrupt and uncontrolled discharges in the neurons in the brain, which disrupt the systems regulated by the brain. In epilepsy, abnormal electrical impulses from cells in various brain areas are noticed. The accurate interpretation of these electrical impulses is critical in the illness diagnosis. This study aims to use different machine-learning algorithms to diagnose epileptic seizures. The frequency components of EEG data were extracted using parametric approaches. This feature extraction approach was fed into machine learning classification algorithms, including Artificial Neural Network (ANN), Gradient Boosting, and Random Forest. The ANN classifier was shown to have the most significant test performance in this investigation, with roughly 97% accuracy and a 91% F1 score in recognizing epileptic episodes. The Gradient Boosting classifier, on the other hand, performed similarly to the ANN, with 96% accuracy and a 93% F1 score.

ChatGPT is a language model developed by OpenAI that uses generative artificial intelligence (GenAI) algorithms for natural language processing (NLP) tasks. It is a popular Chatbot due to its impressive ability to respond consistently to users' various prompts. In this study, the Public Personnel Selection Examination (KPSS) questions, organized by ÖSYM (Turkish Student Selection and Placement Center) every year, were asked to ChatGPT, and its performance was evaluated. The evaluation was made based on fundamental text questions asked in the last three years (2021-2023) in Biology, History, Geography, Turkish Language Literature, and Social Studies in KPSS. This study also aims to investigate the accuracy and reliability of the questions asked to ChatGPT in the field of KPSS educational sciences. According to the research results, ChatGPT's KPSS knowledge and interpretation skills are significantly lower than the university graduate candidates who took the exam. Some suggestions are included for better Chatbot performance, more effective prompt engineering and ethical use of these tools.