no code implementations • 17 Dec 2023 • Serkan Kiranyaz, Ozer Can Devecioglu, Amir Alhams, Sadok Sassi, Turker Ince, Onur Avci, Moncef Gabbouj
One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations.
no code implementations • 19 Apr 2023 • Ozer Can Devecioglu, Mete Ahishali, Fahad Sohrab, Turker Ince, Moncef Gabbouj
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide.
no code implementations • 19 Apr 2023 • Sertac Kilickaya, Mete Ahishali, Fahad Sohrab, Turker Ince, Moncef Gabbouj
Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
1 code implementation • 30 Dec 2022 • Turker Ince, Serkan Kiranyaz, Ozer Can Devecioglu, Muhammad Salman Khan, Muhammad Chowdhury, Moncef Gabbouj
In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it.
1 code implementation • 12 Dec 2022 • Serkan Kiranyaz, Ozer Can Devecioglu, Amir Alhams, Sadok Sassi, Turker Ince, Osama Abdeljaber, Onur Avci, Moncef Gabbouj
To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics.
2 code implementations • 29 Jan 2022 • Serkan Kiranyaz, Ozer Can Devecioglu, Turker Ince, Junaid Malik, Muhammad Chowdhury, Tahir Hamid, Rashid Mazhar, Amith Khandakar, Anas Tahir, Tawsifur Rahman, Moncef Gabbouj
Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult.
1 code implementation • 30 Sep 2021 • Moncef Gabbouj, Serkan Kiranyaz, Junaid Malik, Muhammad Uzair Zahid, Turker Ince, Muhammad Chowdhury, Amith Khandakar, Anas Tahir
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors.
no code implementations • 30 Sep 2021 • Turker Ince, Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz, Onur Avci, Levent Eren, Moncef Gabbouj
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs.
no code implementations • 30 Sep 2021 • Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few.
no code implementations • 28 Sep 2021 • Ozer Can Devecioglu, Junaid Malik, Turker Ince, Serkan Kiranyaz, Eray Atalay, Moncef Gabbouj
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain.
no code implementations • 3 Aug 2021 • Serkan Kiranyaz, Junaid Malik, Mehmet Yamac, Mert Duman, Ilke Adalioglu, Esin Guldogan, Turker Ince, Moncef Gabbouj
In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection.
no code implementations • 29 Dec 2020 • Muhammad Uzair Zahid, Serkan Kiranyaz, Turker Ince, Ozer Can Devecioglu, Muhammad E. H. Chowdhury, Amith Khandakar, Anas Tahir, Moncef Gabbouj
Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99. 83% F1-score, 99. 85% recall, and 99. 82% precision.
no code implementations • 10 Nov 2020 • Mete Ahishali, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach.
no code implementations • 21 Aug 2020 • Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj
As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.
2 code implementations • 24 Apr 2020 • Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj
However, Greedy Iterative Search (GIS) method, which is the search method used to find optimal operators in ONNs takes many training sessions to find a single operator set per layer.
no code implementations • 9 May 2019 • Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, Daniel J. Inman
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations.
no code implementations • 27 Mar 2019 • Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth, Turker Ince, Somaya Al-Maadeed, Ridha Hamila, Moncef Gabbouj
Moreover, we conduct quantitative comparative evaluations among the traditional methods, transfer learning-based methods and the proposed adaptive approach for the particular task of cancer detection and identification from scarce and low-resolution histology images.
no code implementations • 15 Feb 2019 • Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj
In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called Operational Neural Networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data.