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T. Ince

22 papers hereh-index 349.9k citations119 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author20

Across the 22 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CV7
  • eess.SP6
  • cs.SD4
  • cs.LG3
  • cs.NE1
  • eess.IV1
same name
  • T. Ince — 115 papers
  • T. Ince — 22 papers
  • T. Ince — 5 papers
  • T. Ince — 4 papers
  • T. Ince — 3 papers
  • T. Ince — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192024
most citedRobust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks

51 citations · 113 across the 18 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

eess.SP2019

1D Convolutional Neural Networks and Applications: A Survey

Serkan Kiranyaz, Onur Avci, Osama Abdeljaber +3

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Ar…

cs.CV2019★ 23 cited

Colorectal cancer diagnosis from histology images: A comparative study

Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth +4

Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approach…

eess.SP2019★ 10 cited

Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks

Serkan Kiranyaz, Morteza Zabihi, Ali Bahrami Rad +4

The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnosti…

cs.CV2019

Operational Neural Networks

Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis +1

Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning perf…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.