Browsing by Author "Balci, Z."

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Artificial Intelligence Based Determination of Cracks in Eggshell Using Sound Signals
(Sakarya University, 2022) Balci, Z.; Yabanova, İ.
Although the egg is a cheap food source, it is one of the valuable nutritional sources for people because of its rich nutritional values. It is also among the most consumed foods in daily nutrition. With the increase in egg production, it is very difficult to collect them with the human power in the egg production farms, to classify them according to their weights and to separate the defective (dirty and broken) eggs. Therefore, the mechanization has become a necessity in large capacity production farms. Cracks and fractures may occur in the egg shell as a result of exposure to external factors such as the transportation of eggs. The cracks or fractures that are formed leave the egg vulnerable to disease-causing micro-organisms. Before the egg sorting and packing, the broken and cracked eggs must be separated. This process is commonly carried out with manpower by which it is very difficult to obtain the necessary efficiency. In this study, the egg crack detection was performed by using Support Vector Machines (SVM) and Artificial Neural Network (ANN). As a result of the application of studied methods, the accuracy values of crack detection process were 0.99 for ANN and 1 for SVM. In addition, a data acquisition and processing program was developed in LABVIEW environment to detect cracks in real time. © 2022, Sakarya University. All rights reserved.
Comparative Evaluation of Al/Chlorophyll Device Under Varying Irradiance Intensity: Machine Learning Modeling Vs. Experimental Data
(Elsevier Science Sa, 2025) Kaya, F. S.; Orak, I.; Balci, Z.; Kilic, Z.
This study investigated the effect of chlorophyll, a natural photosynthetic pigment, on solar cell characteristic parameters. Chlorophyll thin film layers were analyzed using scanning electron microscopy (SEM) and atomic force microscopy (AFM). The current-voltage (I-V) characteristics of an Al/p-Si/chlorophyll/Al structure were investigated to understand the device's behavior under different light intensities. The fill factor (FF) and efficiency values (eta), which are the characteristic parameters of the solar cell, were calculated from the current values predicted by machine learning (ML) models using the voltage values of the Al/p-Si/chlorophyll/Al devices produced and the results obtained were compared. The results demonstrate the light intensity-dependent electrical response of the Al/p-Si/chlorophyll/Al structure and provide valuable insights for further developments in organic photodetectors and solar cell technology.
Enhanced Photoacoustic Signal Processing Using Empirical Mode Decomposition and Machine Learning
(Taylor and Francis Ltd., 2025) Balci, Z.; Mert, A.
In this study, we propose a robust photoacoustic (PA) signal processing framework for a material independent defect detection using empirical mode decomposition (EMD) and machine learning algorithms. First, a database of the PA signals with 960 samples has been obtained from aluminium, iron, plastic and wood materials using a laser, microphone and data acquisition board-based PA apparatus. Second, the EMD based time and time-frequency domain techniques are proposed to extract robust cross-material feature space focusing on laser induced acoustic signal, and the decomposed intrinsic mode (IMF) with 14 extracted features are performed on totally 960 samples PA signals to evaluate k-nearest neighbour (k-NN), decision tree (DT) and support vector machine (SVM) classifiers. Inter- material and cross-material evaluations are performed, and the accuracy rates up to 100% for SVM and 97.77% for k-NN are yielded. © 2024 Informa UK Limited, trading as Taylor & Francis Group.