Field-Based Assessment of Deep Learning for Industrial Defect Detection in Emerging Economies

Özet

This study examines deep learning for industrial defect detection in Emerging Economies. A mixed design of laboratory experiments and numerical modelling was applied. Key themes include deep learning, defect detection, industry. Prototype experiments confirm that sensor-based software solutions increase manufacturing quality and digital transformation readiness.

Künye

Yayın türü

Makale

Dergi / Yayınevi / Etkinlik

Industrial Innovation and Intelligent Systems Open

Yıl

2025

Dil

İngilizce

İndeksler

WoS ESCI, Scopus

Çeyreklik

Q3

Etki faktörü

1.715

WoS araştırma alanı

Computer Science

Anahtar sözcükler

deep learning, defect detection, industry

Yazar sırası (kurum yazarı)

First

DOI

10.5555/avys.2025.0112

Atıf (WoS / Scopus / Toplam)

28 / 34 / 48

Kurum araştırmacısı

Doç. Dr. Pelin Ece Kaplan

Erişim ve lisans

Açık erişim · ccCC BY

Kalıcı bağlantı

https://avys.mr1.com.tr/yayin/143

BMSKA Sınıflandırma Gerekçesi

SDG 9: Sanayi, Yenilikçilik ve Altyapı en yüksek skoru aldı (92.4 ≥ eşik 28). Eşleşen kavramlar: deep learning, industry, sensor, manufactur, software, digital transformation, prototype.

Model: TF-IDF (karakter n-gram) + Türkçe/İngilizce anahtar kelime sözlüğü v1 · atama eşiği 28.0 · yöntem: sorgu-seti · JSON gerekçe · tüm eşleşme kayıtları

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Tam Metin

Bu yayın için tam metin yüklenmemiş.

Bu yayının aldığı atıflar (10)

Dizinlerin bildirdiği toplam atıf: 48; aşağıda kayıtlı olanlar listelenmiştir.

1. Advances in industry
   Elena Ionescu, Ivana Horvat · International Journal of Computer Science · 2026
   Google Scholar · DOI: 10.5555/ref.2026.8021
2. Advances in industry
   Ilgın Su Kara, Cem Alper Yıldız, Oğuz Han Erdem · Journal of Computer Science Research · 2026
   WoS
3. An experimental study of defect detection
   Deniz Ada Korkut · International Journal of Computer Science · 2026
   Google Scholar · DOI: 10.5555/ref.2026.7016
4. Recent trends in deep learning
   Barış Cem Uysal, Cem Alper Yıldız · Computer Science Letters · 2026
   Scopus · DOI: 10.5555/ref.2026.5815
5. Towards sustainable deep learning
   Georgi Dimitrov, Ahmet Kaan Yılmaz · International Journal of Computer Science · 2025
   Google Scholar
6. Assessing deep learning
   Buse Nur Aydın, Elena Ionescu, Gökçe Mine Tan · Computer Science Letters · 2025
   WoS · DOI: 10.5555/ref.2025.7280
7. Recent trends in industry
   Yasemin Tuna Şahin · Journal of Computer Science Research · 2025
   Scopus · DOI: 10.5555/ref.2025.1456
8. An experimental study of defect detection
   Elena Ionescu, Georgi Dimitrov · International Journal of Computer Science · 2025
   WoS · DOI: 10.5555/ref.2025.3647
9. Modelling industry
   Sofia Lindqvist, Luca Ferrari · Journal of Computer Science Research · 2025
   WoS · DOI: 10.5555/ref.2025.4047
10. Towards sustainable defect detection
    Elena Ionescu · Computer Science Letters · 2025
    WoS · DOI: 10.5555/ref.2025.8551

Kaynakça: bu yayının yaptığı atıflar (7)

1. Towards sustainable deep learning
   Barış Cem Uysal, Oğuz Han Erdem · International Journal of Computer Science · 2018
   WoS · DOI: 10.5555/ref.2018.7683
2. Towards sustainable industry
   Marko Jovanović, Luca Ferrari, Ahmet Kaan Yılmaz · Journal of Computer Science Research · 2018
   Scopus
3. Machine learning approaches for deep learning
   Deniz Ada Korkut, Buse Nur Aydın, Ivana Horvat · Advances in Computer Science · 2018
   Google Scholar · DOI: 10.5555/ref.2018.3780
4. Assessing deep learning
   Cem Alper Yıldız, Anna Schmidt, Ivana Horvat · Computer Science Letters · 2016
   Scopus
5. A review of deep learning
   Nikos Papadakis, Georgi Dimitrov · Computer Science Letters · 2015
   Google Scholar · DOI: 10.5555/ref.2015.5140
6. An experimental study of defect detection
   Georgi Dimitrov · Computer Science Open · 2014
   WoS · DOI: 10.5555/ref.2014.2981
7. Machine learning approaches for defect detection
   Marko Jovanović, Ilgın Su Kara, Buse Nur Aydın · Computer Science Open · 2013
   Scopus · DOI: 10.5555/ref.2013.5746