Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet

Mesut Hakses; Kemal Adem

doi:10.54047/bibted.2010306

TR EN

Pedilen Ayak Görüntülerinde Derin Öğrenme Tabanlı Hatalı Ürün Sınıflandırması

Abstract

Endüstriyel üretim süreçlerinde hatalı ürünlerin erken ve doğru biçimde belirlenmesi kalite güvencesi, maliyetlerin azaltılması ve müşteri memnuniyeti açısından önem taşımaktadır. Pedilen ayak üretiminde kimyasal karışımın yetersizliği veya enjeksiyon sırasında oluşan hava kabarcıkları nedeniyle çukur ve kabarcık biçiminde yüzey kusurları meydana gelebilmektedir. Bu çalışmada, bir Pedilen ayak üreticisinden elde edilen görüntüler kullanılarak hatalı ve hatasız ürünlerin derin öğrenme tabanlı ikili görüntü sınıflandırması gerçekleştirilmiştir. Görüntü işleme/veri artırma işlemleri sonrasında oluşturulan 1200 görüntülük çalışma veri seti üzerinde Xception, InceptionResNetV2, DenseNet201, NASNetMobile ve EfficientNetV2S modelleri 10 ve 20 epoch ayarlarında karşılaştırılmıştır. Mevcut deney protokolünde InceptionResNetV2 modeli 20 epoch için 0.9972 (%99.72) doğruluk ile en yüksek doğruluk değerini sağlamıştır. Aynı yapılandırma için raporlanan kesinlik ve geri çağırma değerleri 0.99 olup, bu değerlerle matematiksel olarak uyumlu F1-skoru 0.99 olarak sunulmuştur. Sonuçlar, Pedilen ayak yüzeylerindeki kusurların görüntü sınıflandırma yaklaşımıyla ayırt edilebileceğini göstermektedir. Bununla birlikte, veri artırma öncesi kaynak görüntü ayrımının geriye dönük olarak doğrulanamaması ve aynı hold-out değerlendirme kümesinin model karşılaştırmasında kullanılması nedeniyle bulgular mevcut veri seti ve deney koşulları kapsamındaki içsel karşılaştırma sonuçları olarak değerlendirilmelidir.

Keywords

Derin Öğrenme, Pedilen Ayak, Görüntü Sınıflandırma, CNN, Hatalı Ürün Sınıflandırması

Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet

Abstract

Early and accurate identification of defective products is important for quality assurance, cost reduction, and customer satisfaction in industrial production. During Pedilen foot manufacturing, pit and bubble surface defects may occur because of insufficient chemical mixing or air trapped during injection. In this study, deep learning-based binary image classification was used to distinguish defective and non-defective products using images obtained from a Pedilen foot manufacturer. A working dataset of 1,200 images generated after image-processing/data-augmentation operations was evaluated with Xception, InceptionResNetV2, DenseNet201, NASNetMobile, and EfficientNetV2S under 10- and 20-epoch configurations. Under the existing experimental protocol, InceptionResNetV2 achieved the highest accuracy, 0.9972 (99.72%), at 20 epochs. For the same configuration, the reported precision and recall were 0.99, and the F1-score is presented as 0.99 to ensure mathematical consistency with these reported values. The findings indicate that surface defects in Pedilen foot images can be distinguished using an image-classification approach. However, because source-image-level separation before augmentation cannot be retrospectively verified and the same hold-out evaluation subset was used for model comparison, the results should be interpreted as internal comparative performance under the present dataset and experimental conditions.

Keywords

Image Classification, Deep Learning, CNN, Pedilen Foot, Defective Product, Classification

References

Adem, K. (2020). Diagnosis of breast cancer with Stacked autoencoder and Subspace kNN. Physica A: Statistical Mechanics and its Applications, 551, 124591.
Adem, K. (2022). Impact of activation functions and number of layers on detection of exudates using circular Hough transform and convolutional neural networks. Expert Systems with Applications, 203, 117583.
Adem, K., Ozguven, M. M., & Altas, Z. (2023). A sugar beet leaf disease classification method based on image processing and deep learning. Multimedia Tools and Applications, 82(8), 12577–12594.
Adem, K. (2013). Tetra Brik aseptik kartonlardaki şerit dikişlerin sayısal görüntüleme tabanlı kalite kontrolü (Yüksek lisans tezi). Gaziosmanpaşa Üniversitesi, Fen Bilimleri Enstitüsü, Tokat.
Akar, M. (2022). COVID-19’a karşı insansız hava aracı ile derin öğrenme tabanlı maske tespiti (Yüksek lisans tezi). Karamanoğlu Mehmetbey Üniversitesi, Karaman.
Akdoğan, S. (2002). Diz altı amputelerde farklı ayakların yürüyüş ve ambulasyon üzerine etkisi (Yüksek lisans tezi). Hacettepe Üniversitesi, Sağlık Bilimleri Enstitüsü, Ankara.
Alkhder, H. (2021). Face mask detection (Yüksek lisans tezi). Başakşehir Üniversitesi, Bilgisayar Mühendisliği Ana Bilim Dalı, İstanbul.
Alqadiri, Y. (2022). Face mask detection using deep learning methods (Yüksek lisans tezi). Bahçeşehir Üniversitesi, Bilgisayar Mühendisliği Ana Bilim Dalı, İstanbul.
Ameri, R., Hsu, C.-C., & Band, S. S. (2024). A systematic review of deep learning approaches for surface defect detection in industrial applications. Engineering Applications of Artificial Intelligence, 130, 107717. https://doi.org/10.1016/j.engappai.2023.107717
Artan, C. T. (2019). Car damage analysis for insurance market using convolutional neural networks (Yüksek lisans tezi). Istanbul Technical University, Graduate School of Science Engineering and Technology, Istanbul.

Arvas, M. (2022). Derin öğrenme algoritmaları kullanılarak döner kanatlı insansız hava aracı görüntülerinden haşhaş, kenevir ve tütün bitkilerinin tespiti (Yüksek lisans tezi). Van Yüzüncü Yıl Üniversitesi, Fen Bilimleri Enstitüsü, Van.
Ashames, M. M. A. (2020). Derin öğrenme yöntemleri kullanılarak osteoporozun belirlenmesi (Yüksek lisans tezi). Konya Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, Konya.
Atcılı, A. (2020). Yapay sinir ağlarında kullanılan optimizasyon algoritmaları. Machine Learning Türkiye. https://medium.com/machine-learning-t%C3%BCrkiye/yapay-sinir-a%C4%9Flar%C4%B1nda-kullan%C4%B1lan-optimizasyon-algoritmalar%C4%B1-3e87cd738cb5 (URL-1)
Ba Alawi, A. E., & Qasem, A. M. (2021). Lightweight CNN-based models for masked face recognition. 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1–5. https://doi.org/10.1109/ICOTEN52080.2021.9493424
Bozkurt, F. (2021). Derin öğrenme tekniklerini kullanarak akciğer X-ray görüntülerinden COVID-19 tespiti. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Özel Sayı 2021 (ARACONF), 149–156.
c3.ai (2022). Model training. c3.ai. https://c3.ai/glossary/data-science/model-training/ (URL-2)
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258.
Çarkacı, N. (2018). Derin öğrenme uygulamalarında en sık kullanılan hiper-parametreler. Medium. https://medium.com/deep-learning-turkiye/derin-ögrenme-uygulamalarinda-en-sık-kullanılan-hiper-parametreler-ece8e9125c4 (URL-3)
Çayırlı, M. (2006). Yanma olayının modellenmesi ve görüntü işleme yoluyla yanma performansının optimizasyonu (Yüksek lisans tezi). Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Isparta.
Çoşkun, H. (2022). Endüstriyel karo üretiminde kalite kontrol sürecinin yapay görme ve derin öğrenme teknikleri ile dijitalleştirilmesi (Yüksek lisans tezi). Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Isparta.
Degadwala, S., Vyas, D., Chakraborty, U., Dider, A. R., & Biswas, H. (2021). YOLO-v4 deep learning model for medical face mask detection. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 209–213. https://doi.org/10.1109/ICAIS50930.2021.9395857
Dengiz, O., Alaboz, P., Saygın, F., Adem, K., & Yüksek, E. (2024). Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition. Advances in Space Research, 74(11), 5514–5529.
Doğan, Ö. (2020). CNN (convolutional neural networks) nedir? Teknoloji.org. https://teknoloji.org/cnn-convolutional-neural-networks-nedir/ (URL-4)
Dümen, S., Kavalcı Yılmaz, E., Adem, K., & Avaroglu, E. (2024). Performance of vision transformer and swin transformer models for lemon quality classification in fruit juice factories. European Food Research and Technology, 250(9), 2291–2302.
Edizer, E. (2006). Sayısal görüntü işleme yöntemi ile tane boyut dağılımı analizi (Yüksek lisans tezi). Çukurova Üniversitesi, Fen Bilimleri Enstitüsü, Adana.
Harriat Christa, G., Jesica, J., Anisha, K., & Sagayam, K. M. (2021). CNN-based mask detection system using OpenCV and MobileNetV2. 2021 3rd International Conference on Signal Processing and Communication (ICPS), 115–119. https://doi.org/10.1109/ICSPC51351.2021.9451688
Hayıt, T., & Çınarer, G. (2022). X-ray görüntülerini kullanarak GLCM ve derin özniteliklerin birleşimine dayalı COVID-19 sınıflandırılması. İnönü Üniversitesi Sağlık Hizmetleri Meslek Yüksekokulu Dergisi, 10(1), 313–325.
Herdian, Kusuma, G. P., & Suharjito (2019). Classification of C2C e-commerce product images using deep learning algorithm. International Journal of Advanced Computer Science and Applications, 10(9). https://doi.org/10.14569/IJACSA.2019.0100925
Hoang, V.-T., & Jo, K.-H. (2021). Practical analysis on architecture of EfficientNet. 2021 14th International Conference on Human System Interaction (HSI), 1–4. https://doi.org/10.1109/HSI52170.2021.9538782
Jasim, A. A. (2022). Gerçek zamanlı yüz maskesi algılama uyarı sistemi (Yüksek lisans tezi). Kırşehir Ahi Evran Üniversitesi, İleri Teknolojiler Ana Bilim Dalı, Kırşehir.
Jha, S. B., & Babiceanu, R. F. (2023). Deep CNN-based visual defect detection: Survey of current literature. Computers in Industry, 148, 103911. https://doi.org/10.1016/j.compind.2023.103911
Kaçmaz, R. N. (2020). Mining colonoscopy images for abnormality detection (Doktora tezi). Abdullah Gül Üniversitesi, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı, Kayseri.
Kassani, S. H., Kassani, P. H., Khazaeinezhad, R., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019). Diabetic retinopathy classification using a modified Xception architecture. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 1–6.
Katirci, R., Adem, K., Tatar, M., & Ölmez, F. (2025). Comparison of the performance of classical and quantum machine-learning methods on the detection of sugar beet Cercospora leaf disease. Plant Pathology, 74(2), 507–518.
Kavalcı Yılmaz, E., Adem, K., & Zontul, M. (2026). A hybrid approach based on ViT and capsule networks for classification of agricultural plant diseases. Applied Fruit Science, 68(1), 64.
Kaya, B., & Önal, M. (2021). COVID-19 tespiti için akciğer BT görüntülerinin bölütlenmesi. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Özel Sayı 2021 (ICAENS), 1296–1303. https://doi.org/10.31590/ejosat.1015061
Khanam, R., Hussain, M., Hill, R., & Allen, P. (2024). A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access, 12, 94250–94295. https://doi.org/10.1109/ACCESS.2024.3425166
Koç, Y. (2022). Hasarlı güneş paneli hücrelerinin derin öğrenme yöntemleriyle sınıflandırılması (Yüksek lisans tezi). Amasya Üniversitesi, Fen Bilimleri Enstitüsü, Amasya.
Konca, M. S. (2020). Evrişimli sinir ağları (convolutional neural networks—CNN). Medium. https://mustafaserdarkonca.medium.com/evri%C5%9Fimli-sinir-a%C4%9Flar%C4%B1-convolutional-neural-networks-cnn-74b3d4a567f9 (URL-5)
Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. Proceedings of ICML, 30(1), 3.
Militante, S. V., & Dionisio, N. V. (2020). Deep learning implementation of facemask and physical distancing detection with alarm systems. 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), 1–5. https://doi.org/10.1109/ICVEE50212.2020.9243183
Özbay, E., & Altunbey Özbay, F. (2020). Derin öğrenme ve sınıflandırma yaklaşımları ile BT görüntülerinden COVID-19 tespiti. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(2), 211–219. https://doi.org/10.24012/dumf.812810
Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19, 100360.
Ramakrishna, S., Mayer, J., Wintermantel, E., & Leong, K. W. (2001). Biomedical applications of polymer-composite materials: A review. Composites Science and Technology, 61, 1189–1224.
Rubik’s Code (2020). Introduction to convolutional neural networks. Rubik’s Code. https://rubikscode.net/2018/02/26/introduction-to-convolutional-neural-networks/ (URL-6)
Shwetha, T. R., Thomas, S. A., Kamath, V., & Niranjana, K. B. (2019). Hybrid Xception model for human protein atlas image classification. 2019 IEEE 16th India Council International Conference (INDICON), 1–4. https://doi.org/10.1109/INDICON47234.2019.9030333
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Sutaji, D., & Yıldız, O. (2022). LEMOXINET: Lite ensemble MobileNetV2 and Xception models to predict plant disease. Ecological Informatics, 70, 101698.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826.
Şeker, A., & Yüksek, A. G. (2017). Stacked autoencoder method for fabric defect detection. Cumhuriyet Science Journal, 38(2), 342–354. https://doi.org/10.17776/cumuscij.300261
Tan, M., & Le, Q. V. (2021). EfficientNetV2: Smaller models and faster training (arXiv:2104.00298). arXiv. https://arxiv.org/abs/2104.00298
Tang, P. C. Y., Ravji, K., Key, J. J., Mahler, D. B., Blume, P. A., & Sumpio, B. (2008). Current prosthesis options for leg and foot amputees. Journal of the American College of Surgeons, 206(3), 548–560.
Taşçı, M. (2011). FPGA kontrollü robotik göz (Yüksek lisans tezi). Balıkesir Üniversitesi, Fen Bilimleri Enstitüsü, Balıkesir.
Türkoğlu, M., Hanbay, K., Saraç Sivrikaya, I., & Hanbay, D. (2020). Derin evrişimsel sinir ağı kullanılarak kayısı hastalıklarının sınıflandırılması. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(1), 334–345. https://doi.org/10.17798/bitlisfen.562101
Uras, A. (2006). Pullu uç demirlerindeki aşınmaların belirlenmesinde görüntü işleme tekniğinden yararlanma olanakları (Yüksek lisans tezi). Uludağ Üniversitesi, Fen Bilimleri Enstitüsü, Bursa.
Yavuz, M. (2020). Derin öğrenme modellerinin hücre veri seti üzerinde eğitilerek kıyaslanması ve mobil ortama uyarlanması (Yüksek lisans tezi). Sakarya Uygulamalı Bilimler Üniversitesi, Biyomedikal Mühendisliği Ana Bilim Dalı, Sakarya.
Yılmaz, A. (2007). Kamera kullanılarak görüntü işleme yoluyla gerçek zamanlı güvenli uygulaması (Yüksek lisans tezi). Haliç Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
Yılmaz, B. (2009). Karbon fiber kompozit ayak protezi tasarım ve imalatı (Yüksek lisans tezi). Ege Üniversitesi, Fen Bilimleri Enstitüsü, İzmir.
Yöntem, M. K., Adem, K., Ilhan, T., & Kılıçarslan, S. (2019). Divorce prediction using correlation based feature selection and artificial neural networks. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 9(1), 259–273.
Yurdusev, A. A., Adem, K., & Hekim, M. (2023). Detection and classification of microcalcifications in mammograms images using difference filter and YOLOv4 deep learning model. Biomedical Signal Processing and Control, 80, 104360.
Yüksek, E., Adem, K., & Elmabruk, K. (2025). A novel approach to atmospheric turbulence detection in free-space optical communication links using DCGAN with hyperparameter optimization and transfer learning models. Optics & Laser Technology, 187, 112816.
Zhang, E. (2021). A real-time deep transfer learning model for facial mask detection. 2021 Integrated Communications Navigation and Surveillance Conference (ICNS), 1–7. https://doi.org/10.1109/ICNS52807.2021.9441582

Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Mesut Hakses
0000-0001-7804-8117
Türkiye

Kemal Adem ^*
0000-0002-3752-7354
Türkiye

Publication Date

November 3, 2026

Submission Date

August 4, 2026

Acceptance Date

October 5, 2026

Published in Issue

Year 2026 Volume: 6 Number: 2

DOI

https://doi.org/10.54047/bibted.2010306

IZ

https://izlik.org/JA67NJ66ZT

APA

Hakses, M., & Adem, K. (2026). Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet. Bilgisayar Bilimleri Ve Teknolojileri Dergisi, 6(2). https://doi.org/10.54047/bibted.2010306

AMA

1.Hakses M, Adem K. Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet. BIBTED. 2026;6(2). doi:10.54047/bibted.2010306

Chicago

Hakses, Mesut, and Kemal Adem. 2026. “Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet”. Bilgisayar Bilimleri Ve Teknolojileri Dergisi 6 (2). https://doi.org/10.54047/bibted.2010306.

EndNote

Hakses M, Adem K (November 1, 2026) Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet. Bilgisayar Bilimleri ve Teknolojileri Dergisi 6 2

IEEE

[1]M. Hakses and K. Adem, “Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet”, BIBTED, vol. 6, no. 2, Nov. 2026, doi: 10.54047/bibted.2010306.

ISNAD

Hakses, Mesut - Adem, Kemal. “Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet”. Bilgisayar Bilimleri ve Teknolojileri Dergisi 6/2 (November 1, 2026). https://doi.org/10.54047/bibted.2010306.

JAMA

1.Hakses M, Adem K. Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet. BIBTED. 2026;6. doi:10.54047/bibted.2010306.

MLA

Hakses, Mesut, and Kemal Adem. “Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet”. Bilgisayar Bilimleri Ve Teknolojileri Dergisi, vol. 6, no. 2, Nov. 2026, doi:10.54047/bibted.2010306.

Vancouver

1.Mesut Hakses, Kemal Adem. Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet. BIBTED. 2026 Nov. 1;6(2). doi:10.54047/bibted.2010306

Aim & Scope

The main aim of the Journal of Computer Science and Technologies is to contribute to academic knowledge by publishing original and high-quality scientific studies in the fields of computer science, software engineering, artificial intelligence, data science, cybersecurity, information technologies, and related disciplines.

The journal seeks to strengthen scientific communication among researchers, academicians, industry professionals, and students, while encouraging the dissemination of current technological developments and supporting interdisciplinary studies.

All submissions are evaluated through a Double-Blind Peer Review process in accordance with the principles of scientific impartiality and publication ethics.

The scientific, ethical, and legal responsibility of the published articles belongs entirely to the authors.

The following types of manuscripts are accepted in our journal:

Research Article: Scientific studies presenting original research findings.
Review Article: Comprehensive studies reviewing the literature on a specific subject.
Industrial Article: Studies focusing on industrial applications, innovative products, and technological developments.
Thesis-Based Study: Articles derived from graduate theses and adapted into scientific manuscript format.
Short Paper: Studies presenting preliminary findings, innovative ideas, or concise research outputs.
Book Review/Comment: Evaluative writings on recent scientific books relevant to the field.

The Journal of Computer Science and Technologies covers theoretical, applied, and interdisciplinary studies in the fields of computer science, software, and information technologies.

The scope of the journal includes the following areas:

Artificial Intelligence and Intelligent Systems
Machine Learning
Data Science and Data Mining
Algorithms and Theory of Computation
Computer Networks
Cybersecurity
Cryptography
Computer Architecture
Operating Systems
Parallel and Distributed Systems
Computer Graphics
Computer Vision
Image and Signal Processing
Natural Language Processing
Scientific Computing
Modeling and Simulation
Mobile and Cloud Computing Systems
Bioinformatics
Medical Informatics
Information Retrieval
Human–Computer Interaction
Software Engineering

The journal welcomes innovative and scientifically significant studies addressing current developments in computer science and technologies, without being limited to the fields listed above.

Author Guidelines

General Principles for Authors

Manuscripts submitted to the Journal of Computer Science and Technologies must meet at least one of the following criteria:

Research studies presenting original scientific findings,
Studies addressing practical applications through scientific methods and approaches,
Review studies comprehensively evaluating current developments in a particular field.

Submitted manuscripts must not have been previously published or simultaneously submitted to another journal or publication for evaluation.

It is assumed that all authors have agreed upon the order of authorship in the manuscript. The scientific, ethical, and legal responsibility for published studies belongs entirely to the authors.

Published studies, in whole or in part, may not be used without proper citation. Manuscripts may not be published elsewhere or presented as conference papers without the permission of the Editorial Board.

Submission of Manuscripts

Manuscripts must be submitted electronically through the DergiPark submission system. Submission and upload procedures should be completed via the following address:

https://dergipark.org.tr/tr/journal/3293/submission/start

Manuscript Preparation Guidelines

Manuscripts must be prepared in accordance with the journal’s formatting and writing guidelines. Authors are encouraged to use the sample manuscript template during the preparation process.

📝 Sample Manuscript File (docx)

Documents Required for Manuscript Submission

Mandatory Documents:

Plagiarism Report
As of 2017, all manuscripts submitted to the journal are required to be uploaded together with a plagiarism report (iThenticate, Turnitin, etc.).
Manuscripts without a plagiarism report may be rejected by the Editorial Board without further evaluation. In addition, submitted manuscripts may be re-examined using iThenticate or similar software when deemed necessary by the Editorial Board.
Copyright Transfer Form
The Copyright Transfer Form must be completed, signed, and uploaded to the system in PDF format.
The signed form must be scanned and uploaded to the system. Manuscripts submitted without this document cannot be published.
📝 Copyright Transfer Form (doc)

Optional Documents:

Reviewer Suggestion Form
Authors may upload a Reviewer Suggestion Form during the submission process in order to contribute to the evaluation procedure. The Editorial Board reserves the full right to decide whether or not to consider the proposed reviewers during the review process.
📝 Reviewer Suggestion Form (doc)

Contact

Prof. Dr. Erdinç AVAROĞLU
Editor-in-Chief

E-mail: eavaroglu@mersin.edu.tr

Ethical Principles and Publication Policy

PUBLICATION ETHICS

The publication processes applied in the Journal of Computer Science and Technologies constitute the basis for the development and distribution of information in an impartial and respectful manner. The processes implemented in this direction are directly reflected in the quality of the work of the authors and the institutions that support the authors. Peer-reviewed studies are studies that embody and support the scientific method. At this point, it is important that all stakeholders of the process (authors, readers and researchers, publishers, referees and editors) comply with the standards of ethical principles. All stakeholders are expected to bear the following ethical responsibilities within the scope of the publication ethics of the Journal of Computer Science and Technologies.

The ethical missions and responsibilities below have been prepared by taking into account the guidelines and policies published by the Committee on Publication Ethics (COPE) as open access.
The editors of the Journal of Computer Science and Technologies have the following ethical missions and responsibilities on the basis of the "COPE Code of Conduct and Best Practice Guidelines for Journal Editors" and "COPE Best Practice Guidelines for Journal Editors" published by the Committee on Publication Ethics (COPE) must be.

Plagiarism İnspection;

The Journal of Computer Science and Technologies will check plagiarism for all submitted articles before they are previously published. If plagiarism content is detected by editors, the author will be notified for rewriting. Plagiarism will be prevented by scanning the entire article by iThenticate®. In any case, if more than 20% of the original article submitted is plagiarism, then the article will be rejected and the author will also be notified. We strongly recommend that you check your content before submitting it to the Journal of Computer Science and Technologies for publication. While plagiarism is not entirely reliable, some free plagiarism programs are recommended.

Ethical Responsibilities of Authors;

1- The works submitted by the author (s) are expected to be original. If the author (s) benefit from other studies or use other studies, they must reference and / or cite completely and accurately.
2- People who do not contribute to the content intellectually in the creation of the study should not be specified as a writer.
3- The situations and relationships that may constitute a conflict of interest, if any, should be explained.
4- Raw data regarding the articles can be requested from the author (s) within the framework of the evaluation processes; In such a case, the author (s) should be ready to present the expected data and information to the editorial board.
5- The author (s) must have the document showing that they have the right to use the data used and the necessary permissions for research / analysis.
6- If the author (s) notices an error or error related to his published, early-view or evaluation phase, there is an obligation to inform the editor of the journal or the publisher, to cooperate with the editor in correction or withdrawal.
7- Authors cannot keep their works in the process of application of more than one journal at the same time. Each application can be started following the completion of the previous application. The study published in another journal cannot be sent to the Journal of Computer Science and Technologies.

Ethical Responsibilities of Referees;

1- It should only agree to evaluate studies of its area of expertise.
2- It should evaluate in neutrality and confidentiality.
3- If he thinks that he is facing a conflict of interest during the evaluation process, he should refuse to review the study and inform the journal editor.
4- In accordance with the principle of confidentiality, it should destroy the studies they have examined after the evaluation process. They can only use the final versions of the studies they have studied only after their publication.
5- It should make the assessment objectively only in relation to the content of the study. Nationality should not allow gender, religious beliefs, political beliefs, and commercial concerns to influence evaluation.
6- He should make the assessment in a constructive and kind language. It should not make humiliating personal comments that include hostility, slander and insults.
7- They should carry out the studies they accepted to evaluate in a timely manner and with the above ethical responsibilities.

Ethical Responsibilities of Editors;

1- All publications are evaluated based on the intellectual content of the author, regardless of gender, race, ethnicity, religion, citizenship and political values.
2- This information about the articles is kept confidential.
3- The conflict of interest observed for the article should be disclosed.
4- While the editorial board evaluates the article, the journal is based on editorial board policies, legally prohibited articles against plagiarism, insults and copyright infringement and takes responsibility for decisions to publishing for submitted articles.

Price Policy

Indexes

Journal Boards

Editor

Erdinç AVAROĞLU

Information and Computing Sciences, Cryptography, Engineering, Electrical Engineering

Editorial Board or Advisory Board

AA

Ali ARI

Decision Support and Group Support Systems, Deep Learning, Neural Networks, Artificial Reality

ÖT

Ömer TÜRK

Image Processing, Deep Learning, Machine Vision , Information Security and Cryptology

ES

Esra SARAÇ

Machine Learning Algorithms, Machine Learning (Other), Information Retrieval and Web Search, Natural Language Processing, Evolutionary Computation, Artificial Intelligence (Other)

SY

Selman YAKUT

Algorithms and Calculation Theory, Information Security and Cryptology, Cryptography, Data and Information Privacy, Computer Software

TT

Taner TUNCER

Image Processing, Deep Learning

Kemal ADEM

Image Processing, Artificial Intelligence (Other)

Faruk SERIN

Information and Computing Sciences, Image Processing, Machine Learning Algorithms, Natural Language Processing, Artificial Intelligence (Other), Engineering

İsmail KOYUNCU

Embedded Systems

JB

Jale BEKTAŞ

Image Processing, Computational Imaging, Pattern Recognition

Technical Editor

OA

Oben ADAK

Image Processing, Deep Learning, Machine Learning Algorithms, Natural Language Processing

Language Editor

JB

Jale BEKTAŞ

Image Processing, Computational Imaging, Pattern Recognition

Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet

Pedilen Ayak Görüntülerinde Derin Öğrenme Tabanlı Hatalı Ürün Sınıflandırması

Abstract

Keywords

Deep Learning-Based Classification of Defective Product in Images of Pedilen Feet

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Aim & Scope

Author Guidelines

General Principles for Authors

Submission of Manuscripts

Manuscript Preparation Guidelines

Documents Required for Manuscript Submission

Contact

Ethical Principles and Publication Policy

Price Policy

Indexes

Other Indexes

Journal Boards

Editor

Editorial Board or Advisory Board

Technical Editor

Language Editor