WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Now showing 1 - 4 of 4
  • Conference Object
    Citation - Scopus: 1
    PCB Component Recognition With Semi-Supervised Image Clustering
    (IEEE, 2021-06-09) Unal, Ahmet Emin; Tasdemir, Kasim; Bahcebasi, Akif
    Classification of surface mounted devices plays an important role on automated inspection systems of printed component board production. Limited number of publicly available datasets which the components are labeled and high intraclass variance in these datasets causes the supervised approches to be inefficient. In this study a deep learning method, enhanced with an unsupervised clustering system, which uses a small set of labeled data is proposed. The method compared with the current studies and the supervised systems. Most optimized setting reached high accuracy results by outrunning current classification methods.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 2
    Combining Classifiers for Protein Secondary Structure Prediction
    (IEEE, 2017) Aydin, Zafer; Uzut, Ommu Gulsum
    Protein secondary structure prediction is an important step in estimating the three dimensional structure of proteins. Among the many methods developed for predicting structural properties of proteins, hybrid classifiers and ensembles that combine predictions from several models are shown to improve the accuracy rates. In this paper, we train, optimize and combine a support vector machine, a deep convolutional neural field and a random forest in the second stage of a hybrid classifier for protein secondary structure prediction. We demonstrate that the overall accuracy of the proposed ensemble is comparable to the success rates of the state-of-the-art methods in the most difficult prediction setting and combining the selected models have the potential to further improve the accuracy of the base learners.
  • Article
    AI-Enhanced PV Power Forecasting Using Cloud Thickness and Motion in Kayseri, Türkiye
    (Wiley, 2025-01) Yavuz, Levent; Onen, Ahmet; Awad, Ahmed; Ahshan, Razzaqul; Al-Badi, Abdullah
    The incorporation of renewable energy in photovoltaic (PV) systems has made significant progress. The inherent intermittency nature of PV generation, nevertheless poses an obstacle to accurate energy forecasting. Historical PV production plus meteorological data such as temperature, humidity, and atmospheric pressure are largely utilized in present methods of forecasting. However, cloud thickness and dynamics-integrated system, has not been investigated and tested in real-world examples yet.This research seeks to fill this gap in research through the development of a new AI-based PV forecasting model that incorporates cloud thickness, cloud motion, and solar position into the forecasting model. Cloud properties and their impact on solar radiation are computed through a deep learning-based panel-shadowing model. For cloud movement forecasting, a gated recurrent unit (GRU) is used, while multiple convolutional neural networks (CNNs) are used for estimating cloud thickness. These outcomes are then integrated with measurements from environmental sensors to improve the accuracy of the predictions.The system was implemented and tested at Abdullah G & uuml;l University and exhibited a remarkable improvement in forecasting accuracy compared to current models. The results prove that cloud motion and thickness improve the accuracy of PV predictions, which is important for energy market stability and power grid operations.
  • Conference Object
    Citation - Scopus: 1
    A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models
    (IEEE, 2025-05-23) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, Abdulkadir
    The steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores.