Makaleler

• "The detection of unaccounted natural gas consumption: A neural networks and subscriber-based solution", Engineering Science and Technology, an International Journal, 2024.
  (SCI-E)

  Abstract: Energy is a paramount expense for nations, emphasizing the significance of energy access and conservation. This study proposes using neural networks and statistical methods to identify unaccounted natural gas consumption. Companies gauge gas usage through real-time calculations with A-type remote measurement stations (RMS-A) and monthly reflected bills. Unaccounted consumption, resulting in economic losses, stems from the disparity between these methods. The causes for this can include gas leaks, pressure changes, inactive subscribers, and faulty meters. This study focuses on unaccounted consumption at gas meters faulty meters. Big data, including subscriber data, meteorology, and calendar information, was used for predictions. Two neural network models estimated subscriber consumption, with gas meter replacement details influencing the predictions. Statistical techniques categorized subscribers based on MAPE values into safe, transition, and abnormal zones. Subscribers in the abnormal zone are pointed out to have a high risk of lost and unaccounted natural gas consumption.

• "An Effective Feature Extraction Method for Olive Peacock Eye Leaf Disease Classification", European Food Research and Technology, 2024.
  (SCI-E)

  Abstract: Early diagnosis of plant diseases is one of the key elements determining plant productivity. The productivity and quality of plants are significantly reduced when plant diseases are not identified and prevented in a timely manner, which results in major financial losses for producers. Olive is a plant with high added value. While the fruit and oil of olive are consumed as food, its oil is used in cosmetics, medicine, etc. It is also used in industries. In addition, active substances such as oleuropein, triterpene, maslinic acid, and flavonoid found in olive leaves are also used in the pharmaceutical industry. Considering all these valuable uses of olive, the importance of productivity is understood. Plant diseases are one of the most significant factors affecting the yield of olives. Among these diseases, fungal disease called peacock eye can spread to the whole tree through the leaves. This disease causes reduced crop production, defoliation, and rot of tree branches. In this study, an efficient method was developed to detect peacock eye disease from olive leaves. In the first stage, an original dataset of healthy and diseased leaves was created. Then, by extracting deep features from this dataset with CNN models, diseased and healthy leaf classification was performed with the transfer learning approach. As a result of the experiments, very satisfactory results were obtained around 98.63%.