Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

We introduce a new method for dimensionality reduction by attribute extraction and evaluate its impact on text classification. The textual contents in body sections of the news in Reuters-21758 are the selected attributes for classification. Using the offered method, high dimension of attributes- words extracted from the news bodies- are projected onto a new hyper plane having dimensions equal to the number of classes. Results show that processing times of classification algorithms dramatically decrease with the attribute extraction method we offer. This is achieved by the fall of the number of attributes given to classifiers. Accuracies of the classification algorithms also increase compared to tests run without using the proposed method.

ARNetMiT R Package: association rules based gene co-expression networks of miRNA targets

Regression testing is an important process for software quality. Test case reduction is one of the widely used techniques for regression testing, which can dramatically decrease the testing costs. However, it is an NP-complete problem and big test cases cannot be accomplished in reasonable amount of time. For this reason, we propose a test suite reduction approach by using greedy and genetic algorithms. The greedy algorithm found a wide usage in previous studies thanks to its simplicity, but we already know that it sticks to local optima and does not benefit from metaheuristics. Thus, we used genetic algorithm to overcome its weaknesses. Our experimental results prove that metaheuristics and evolutionary algorithms perform better than the greedy approaches.

It is very important to forecast the electric prices in deregulated markets for both producers and brokers. This information is crucial to make effective decisions concerning to production, purchase, maintenance and investment. In this study, we built two different systems for short-term prediction of electricity price in Turkish Electric Market. One of the systems built on ARIMA model, while the other employs a feed forward neural network. Both systems use calendar and historical price information as input. Performance of both systems are compared and it is shown that it is possible to forecast weekly electric price with an average error rate of %8.5.

Manual code review is essential for software quality, but often slows down development cycles due to the high time demands on developers. In this study, we propose an automated solution for Python (version 3.13) projects that generates code review comments by combining Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). To achieve this, we first curated a dataset from GitHub pull requests (PRs) using the GitHub REST Application Programming Interface (API) (version 2022-11-28) and classified comments into semantic categories using a semi-supervised Support Vector Machine (SVM) model. During the review process, our system uses a vector database to retrieve the top-k most relevant historical comments, providing context for a diverse spectrum of open-weights LLMs, including DeepSeek-Coder-33B, Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, Mistral-Instruct-7B, and Phi-3-Mini. We evaluated the system using a multi-step validation that combined standard metrics (BLEU-4, ROUGE-L, cosine similarity) with an LLM-as-a-Judge approach, and verified the results through targeted human review to ensure consistency with expert standards. The findings show that retrieval augmentation improves feedback relevance for larger models, with DeepSeek-Coder’s alignment score increasing by 17.9% at a retrieval depth of k = 3. In contrast, smaller models such as Phi-3-Mini suffered from context collapse, where too much context reduced accuracy. To manage this trade-off, we built a hybrid expert system that routes each task to the most suitable model. Our results indicate that the proposed approach improved performance by 13.2% compared to the zero-shot baseline (k = 0). In addition, our proposed system reduces hallucinations and generates comments that closely align with the standards expected from the experts.