The development of generative artificial intelligence and large language models (LLMs) has enabled significant progress in automatic code generation and the application of AI systems in education and software engineering. This paper presents fundamental concepts of generative artificial intelligence, the importance of prompt engineering, prompting techniques and strategies for improving the quality of responses generated by large language models. Special attention is given to AI tools for code generation, their characteristics, operating principles and application in software development. The paper also analyzes studies that evaluate the performance, accuracy and quality of AI-generated code, as well as the potential applications of these tools in programming education. In addition to the advantages, challenges such as inaccurate responses, model bias, academic ethics and the need for human supervision are also discussed.
Branislava Radu, Ljubica Kazi· 16th International Symposium...· 0 citations
Air pollution data processing is a relevant aspect of urban-life-quality monitoring. This study relates the values of air pollutants to meteorological factors with prediction models created with multiple linear regression (MLR) in the R program. Fragmented data were obtained in the years 2023/24 from the heaviest-urban-traffic location in Belgrade, Serbia. A Serbian-comparable list of air pollutants (PM10, PM2.5, SO2, NO2, CO, and O3) was created according to an analysis of sensor availability at monitoring stations. With the prediction models and seasonally clustered data (spring and summer data clusters were incomplete), each of these pollutants (except O3) was related to all measurable meteorological factors (atmospheric pressure, wind speed, relative humidity, and temperature). All independent (meteorological) factors showed a moderate level (Multiple R2 < 0.7) of impact on the prediction of air pollutant values (dependent factors). The second group of prediction models related pollutant triplets, and fourteen of the created models reached R2 > 0.7. The prediction model with the highest relevant R2 value (CO~PM10 + NO2) was used for CO pollutant prediction and in-sample fit analysis. The obtained mean absolute error is ~0.13 mg/m3. This case study contributes to the examination of the role of meteorological factors in urban-traffic air-quality monitoring within a seasonal context.
Z. Kazi, Ljubica Kazi, S. Filip· Applied Sciences· 0 citations
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