Log in

Relevant bibliographies by topics / Residual neural network (ResNet) / Journal articles

To see the other types of publications on this topic, follow the link: Residual neural network (ResNet).

Journal articles on the topic 'Residual neural network (ResNet)'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Residual neural network (ResNet).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Sabba, Sara, Meroua Smara, Mehdi Benhacine, Loubna Terra, and Zine Eddine Terra. "Residual Neural Network in Genomics." Computer Science Journal of Moldova 30, no. 3(90) (2022): 308–34. http://dx.doi.org/10.56415/csjm.v30.17.

Full text

Abstract:

Residual neural network (ResNet) is a Deep Learning model introduced by He et al. in 2015 to enhance traditional convolutional neural networks proposed to solve computer vision problems. It uses skip connections over some layer blocks to avoid vanishing gradient problem. Currently, many researches are focused to test and prove the efficiency of the ResNet on different domains such as genomics. In fact, the study of human genomes provides important information on the detection of diseases and their best treatments. Therefore, most of the scientists opted for bioinformatics solutions to get resu

APA, Harvard, Vancouver, ISO, and other styles

2

Lei, Xia, Jia-Jiang Lin, Xiong-Lin Luo, and Yongkai Fan. "Explaining deep residual networks predictions with symplectic adjoint method." Computer Science and Information Systems, no. 00 (2023): 47. http://dx.doi.org/10.2298/csis230310047l.

Full text

Abstract:

Understanding deep residual networks (ResNets) decisions are receiving much attention as a way to ensure their security and reliability. Recent research, however, lacks theoretical analysis to guarantee the faithfulness of explanations and could produce an unreliable explanation. In order to explain ResNets predictions, we suggest a provably faithful explanation for ResNet using a surrogate explainable model, a neural ordinary differential equation network (Neural ODE). First, ResNets are proved to converge to a Neural ODE and the Neural ODE is regarded as a surrogate model to explain the deci

APA, Harvard, Vancouver, ISO, and other styles

3

Meng, Zhe, Lingling Li, Xu Tang, Zhixi Feng, Licheng Jiao, and Miaomiao Liang. "Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification." Remote Sensing 11, no. 16 (2019): 1896. http://dx.doi.org/10.3390/rs11161896.

Full text

Abstract:

Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models. Based on the fact that very deep residual networks (ResNets) behave like an ensemble of relatively shallow networks, our proposed network, called multipath ResNet (MPRN), employs multiple residual functions in the residual blocks to make the network wider, rather than deeper. The proposed network consists of shorter-medium paths for efficient gr

APA, Harvard, Vancouver, ISO, and other styles

4

Choi, Kanghae, Hokyoung Ryu, and Jieun Kim. "Deep Residual Networks for User Authentication via Hand-Object Manipulations." Sensors 21, no. 9 (2021): 2981. http://dx.doi.org/10.3390/s21092981.

Full text

Abstract:

With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple d

APA, Harvard, Vancouver, ISO, and other styles

5

Varghese, Prathibha, and Arockia Selva Saroja. "Biologically inspired deep residual networks." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1873. http://dx.doi.org/10.11591/ijai.v12.i4.pp1873-1882.

Full text

Abstract:

<p>Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagonal

APA, Harvard, Vancouver, ISO, and other styles

6

Varghese, Prathibha, and Arockia Selva Saroja. "Biologically inspired deep residual networks." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1873–82. https://doi.org/10.11591/ijai.v12.i4.pp1873-1882.

Full text

Abstract:

Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagona

APA, Harvard, Vancouver, ISO, and other styles

7

Yang, Ruizhao, Yun Li, Binyi Qin, Di Zhao, Yongjin Gan, and Jincun Zheng. "Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy." RSC Advances 12, no. 3 (2022): 1769–76. http://dx.doi.org/10.1039/d1ra06905e.

Full text

Abstract:

We proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy.

APA, Harvard, Vancouver, ISO, and other styles

8

Chavan, Mahesh, Vijayakumar Varadarajan, Shilpa Gite, and Ketan Kotecha. "Deep Neural Network for Lung Image Segmentation on Chest X-ray." Technologies 10, no. 5 (2022): 105. http://dx.doi.org/10.3390/technologies10050105.

Full text

Abstract:

COVID-19 patients require effective diagnostic methods, which are currently in short supply. In this study, we explained how to accurately identify the lung regions on the X-ray scans of such people’s lungs. Images from X-rays or CT scans are critical in the healthcare business. Image data categorization and segmentation algorithms have been developed to help doctors save time and reduce manual errors during the diagnosis. Over time, CNNs have consistently outperformed other image segmentation algorithms. Various architectures are presently based on CNNs such as ResNet, U-Net, VGG-16, etc. Thi

APA, Harvard, Vancouver, ISO, and other styles

9

Nurlinda, Nurlinda, Erfan Hasmin, and Jufri Jufri. "DETEKSI PENYAKIT RUMPUT LAUT DENGAN RESIDUAL NEURAL NETWORK." Jurnal Teknik Informasi dan Komputer (Tekinkom) 7, no. 2 (2024): 637. https://doi.org/10.37600/tekinkom.v7i2.1621.

Full text

Abstract:

This research aims to detect seaweed diseases using the Residual Neural Network (ResNet) deep learning model. Seaweed, or Thallus, is a crucial fishery commodity in Indonesia, but it is often threatened by diseases such as Ice-ice and Bulu Kucing, which are challenging to distinguish visually. The dataset used in this study consists of images of healthy and diseased seaweed, which undergo preprocessing steps like resizing, augmentation, and data splitting. The ResNet model is trained on this processed data and evaluated using a Confusion Matrix, achieving an accuracy of 96.78% and a validation

APA, Harvard, Vancouver, ISO, and other styles

10

Jayashri Musale, Nikita Hajare, Shruti Garud, Radhika Chaudhari, and Dr. Pramod Ganjewar. "Human Emotion Recognition Using ResNet Architechture." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 07 (2025): 3285–93. https://doi.org/10.47392/irjaeh.2025.0483.

Full text

Abstract:

Emotion detection plays a crucial role in enabling systems to accurately interpret and respond to human emotions, thereby enhancing human-computer interaction. This re- search leverages the Residual Neural Network (ResNet) architecture—a deep learning model specifically designed to tackle challenges like the vanishing gradient problem in deep networks—to deliver an improved approach to emotion detection. By leveraging ResNet’s ability to learn residuals, the proposed system achieves superior accuracy in classifying emotions from facial expressions, outperforming traditional models. Com- pared

APA, Harvard, Vancouver, ISO, and other styles

11

Ajel, Ahmed R., Ayad Qasim Al-Dujaili, Zaid G. Hadi, and Amjad Jaleel Humaidi. "Skin cancer classifier based on convolution residual neural network." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6240. http://dx.doi.org/10.11591/ijece.v13i6.pp6240-6248.

Full text

Abstract:

Accurate automatic classification of skin lesion images is a great challenge as the image features are very close in these images. Convolution neural networks (CNN) promise to provide a potential classifier for skin lesions. This work will present dermatologist-level classification of skin cancer by using residual network (ResNet-50) as a deep learning convolutional neural network (DLCNN) that maps images to class labels. It presents a classifier with a single CNN to automatically recognize benign and malignant skin images. The network inputs are only disease labels and image pixels. About 320

APA, Harvard, Vancouver, ISO, and other styles

12

Chen, Yunhua, Jin Du, Qian Liu, Ling Zhang, and Yanjun Zeng. "Robust and energy-efficient expression recognition based on improved deep ResNets." Biomedical Engineering / Biomedizinische Technik 64, no. 5 (2019): 519–28. http://dx.doi.org/10.1515/bmt-2018-0027.

Full text

Abstract:

Abstract To improve the robustness and to reduce the energy consumption of facial expression recognition, this study proposed a facial expression recognition method based on improved deep residual networks (ResNets). Residual learning has solved the degradation problem of deep Convolutional Neural Networks (CNNs); therefore, in theory, a ResNet can consist of infinite number of neural layers. On the one hand, ResNets benefit from better performance on artificial intelligence (AI) tasks, thanks to its deeper network structure; meanwhile, on the other hand, it faces a severe problem of energy co

APA, Harvard, Vancouver, ISO, and other styles

13

Xie, Xuping, Clayton Webster, and Traian Iliescu. "Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network." Fluids 5, no. 1 (2020): 39. http://dx.doi.org/10.3390/fluids5010039.

Full text

Abstract:

Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require drastic ROM mode truncations. We propose a deep residual neural network (ResNet) closure learning framework for ROMs of nonlinear systems. The novel ResNet-ROM framework consists of two steps: (i) In the first step, we use ROM projection to filter the given nonlinear system and construct a spatially filtered ROM. This filtered ROM is low-dimensional, but is not closed. (ii) In the second step, we use ResNet to close t

APA, Harvard, Vancouver, ISO, and other styles

14

Lin, Hongbo, Jinghua Zhao, Shuang Liang, and Huilin Kang. "Prediction model for stock price trend based on convolution neural network." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 4999–5008. http://dx.doi.org/10.3233/jifs-179985.

Full text

Abstract:

Aiming at the image features of stock data, considering the picture features of stock data and the characteristics of CNN’s good at extracting picture features, the paper proposed a stock price trend prediction model CNN-M based on a Convolutional Neural Network. At the same time, based on the excellent image feature extraction ability of the residual network, this paper proposed a residual network-based stock price trend prediction model ResNet-M based on the Conventional Neural Network. The experimental results showed that the prediction ability of the improved residual network-based predict

APA, Harvard, Vancouver, ISO, and other styles

15

Chen, Yu, and Ziyue Yu. "Research on signal modulation identification method based on residual neural network." Journal of Computing and Electronic Information Management 12, no. 3 (2024): 4–11. http://dx.doi.org/10.54097/00qdkms1.

Full text

Abstract:

Aiming at the problems of large number of parameters, high complexity and low recognition accuracy of the current modulation recognition model based on high-performance neural network, a lightweight signal modulation recognition algorithm based on residual neural network (ResNet) is proposed. Firstly, the ResNet network model is built, which contains two residual units and three fully connected layers. Secondly, the pruning method of network slimming is introduced to compress the neural network model without affecting the recognition accuracy. Finally, the model was trained using modulated sig

APA, Harvard, Vancouver, ISO, and other styles

16

Andre, Citro Febriliyan Lanyak, Prasetiadi Agi, Budi Widodo Haris, Hisyam Ghani Muhammad, and Athallah Abiyan. "Dental caries detection using faster region-based convolutional neural network with residual network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2027–35. https://doi.org/10.11591/ijai.v13.i2.pp2027-2035.

Full text

Abstract:

Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are

APA, Harvard, Vancouver, ISO, and other styles

17

Lanyak, Andre Citro Febriliyan, Agi Prasetiadi, Haris Budi Widodo, Muhammad Hisyam Ghani, and Abiyan Athallah. "Dental caries detection using faster region-based convolutional neural network with residual network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2027. http://dx.doi.org/10.11591/ijai.v13.i2.pp2027-2035.

Full text

Abstract:

Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are

APA, Harvard, Vancouver, ISO, and other styles

18

Yadav, Jyoti Deshwal, Vivek K. Dwivedi, and Saurabh Chaturvedi. "ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System." Wireless Communications and Mobile Computing 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/2697932.

Full text

Abstract:

Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been develo

APA, Harvard, Vancouver, ISO, and other styles

19

Вера, А. Ч., Сергей Анатольевич Жерлицын, and В. Б. Александр. "Neural network architecture of a client for biometric personal identification based on the iris of the eye." Scientific works of KubSTU, no. 1 (June 6, 2024): 88–100. http://dx.doi.org/10.26297/2312-9409.2024.1.10.

Full text

Abstract:

Публикация фокусируется на разработке и применении технологии идентификации личности на основе нормализованного изображения радужки глаза с использованием остаточных нейронных сетей (ResNet). Представленный метод предобработки изображения включает сегментацию и нормализацию с последующей классификацией признаков при помощи модифицированной архитектуры ResNet. Основные этапы предобработки включают сегментацию зрачка, бинаризацию изображения, обнаружение границ с применением алгоритма Кэнни и использование преобразования Хафа для локализации зрачка. После этого изображение подвергается нор

APA, Harvard, Vancouver, ISO, and other styles

20

Xu, Junjie, Binbin Li, Yang Zhao, and Shijie Xue. "Sound source localization based on data and neural network model." Journal of Physics: Conference Series 2816, no. 1 (2024): 012085. http://dx.doi.org/10.1088/1742-6596/2816/1/012085.

Full text

Abstract:

Abstract In this study, the integration of sound source localization with neural network techniques is proposed. Firstly, the development history and basic principle of neural networks are introduced. Combined with the characteristics of sound source location, the residual neural network is analyzed, and the residual neural network is used as the neural network model. The sound signal received by the microphone array is computed by PHAT-SCOT and the output is trained as the input feature of ResNet. Finally, the results of indoor sound source location are obtained in the test set. Experimental

APA, Harvard, Vancouver, ISO, and other styles

21

Udekwe, Daniel. "EMBEDDED RESIDUAL NEURAL NETWORKS FOR REAL-WORLD PLANT DISEASE IDENTIFICATION IN DIGITAL AGRICULTURE." International Journal of Software Engineering and Computer Systems 10, no. 2 (2025): 149–58. https://doi.org/10.15282/ijsecs.10.2.2024.12.0130.

Full text

Abstract:

This study addresses the challenge of real-time plant disease identification on resource-constrained embedded platforms, a critical need for improving agricultural productivity. Using the NVIDIA Jetson Orin Developer Kit and the PlantVillage dataset, the research evaluates Residual Neural Networks (ResNets), focusing on ResNet-50, ResNet-101, and ResNet-152. The study highlights the balance between model depth, batch size, accuracy, and computational efficiency. ResNet-101, optimized with a batch size of 64, achieved 90.62% accuracy and an average identification time of 17.6 milliseconds, emer

APA, Harvard, Vancouver, ISO, and other styles

22

Li, Haoxuan. "The advance of neural ordinary differential ordinary differential equations." Applied and Computational Engineering 6, no. 1 (2023): 1283–87. http://dx.doi.org/10.54254/2755-2721/6/20230709.

Full text

Abstract:

Differential methods are widely used to describe complex continuous processes. The main idea of ordinary differential equations is to treat a specific type of neural network as a discrete equation. Therefore, the differential equation solver can be used to optimize the solution process of the neural network. Compared with the conventional neural network solution, the solution process of the neural ordinary differential equation has the advantages of high storage efficiency and adaptive calculation. This paper first gives a brief review of the residual network (ResNet) and the relationship of R

APA, Harvard, Vancouver, ISO, and other styles

23

Liu, Jingyu. "Face recognition technology based on ResNet-50." Applied and Computational Engineering 39, no. 1 (2024): 160–65. http://dx.doi.org/10.54254/2755-2721/39/20230593.

Full text

Abstract:

Face recognition technology is progressively finding its place across diverse domains. In pursuit of enhancing the efficacy of face recognition systems, this study employs a ResNet-50 deep convolutional neural network. The dataset is meticulously gathered and processed via OpenCV, thus amplifying the precision and utility of face recognition. ResNet, an advanced convolutional neural network, incorporates the concept of residual connections, bridging convolutional layers through shortcut connections. These connections facilitate the addition of input to output, forming residual blocks. Conseque

APA, Harvard, Vancouver, ISO, and other styles

24

Ahmad Badruzzaman and Aniati Murni Arymurhty. "A Comparative Study of Convolutional Neural Network in Detecting Blast Cells for Diagnose Acute Myeloid Leukemia." Journal of Electronics, Electromedical Engineering, and Medical Informatics 6, no. 1 (2024): 84–91. http://dx.doi.org/10.35882/jeeemi.v6i1.354.

Full text

Abstract:

Understanding blood plays a crucial role in obtaining information for monitoring health conditions and diagnosis of hematologic diseases such as acute myeloid leukemia. It is characterized by irregular expansion of immature white blood cells called blast cells in the blood and bone marrow. To diagnose acute myeloid leukemia, a sample of bone marrow is necessary to be examined under a microscope through bone marrow examination. As for minimizing human subjectivity and automating medical screening, this study performed image classification for detecting blast cells in leukocytes from microscopic

APA, Harvard, Vancouver, ISO, and other styles

25

Park, Junsang, Jin-kook Kim, Sunghoon Jung, Yeongjoon Gil, Jong-Il Choi, and Ho Sung Son. "ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks." Applied Sciences 10, no. 18 (2020): 6495. http://dx.doi.org/10.3390/app10186495.

Full text

Abstract:

Accurate electrocardiogram (ECG) interpretation is crucial in the clinical ECG workflow because it is most likely associated with a disease that can cause major problems in the body. In this study, we proposed an ECG-signal multi-classification model using deep learning. We used a squeeze-and-excitation residual network (SE-ResNet), which is a residual network(ResNet) with a squeeze-and-excitation block. Experiments were performed for seven different types of lead-II ECG data obtained from the Korea University Anam Hospital in South Korea. These seven types are normal sinus rhythm, atrial fibr

APA, Harvard, Vancouver, ISO, and other styles

26

ROMERO LUGO, ALEXANDRA, ANDREA MAGADAN SALAZAR, JORGE FUENTES PACHECO, RAUL PINTO ELIAS, and NIMROD GONZALEZ FRANCO. "RESIDUAL NEURAL NETWORKS FOR MONOCULAR DEPTH ESTIMATION IN NATURAL ENVIRONMENTS." DYNA NEW TECHNOLOGIES 11, no. 1 (2024): [11P.]. http://dx.doi.org/10.6036/nt11086.

Full text

Abstract:

ABSTRACT: Presently, depth estimation research has focused on deep learning, mainly on monocular depth estimation, because this technique has proven to be an excellent alternative for methods that use expensive sensors or require high computational consumption, providing higher performance and accuracy. Despite there being a large number of works, most have focused on indoor and urban datasets. Due to this, we review the variants of the residual networks being trained with images of natural environments with a high presence of vegetation. We proposed a new dataset specialized in natural enviro

APA, Harvard, Vancouver, ISO, and other styles

27

Rajashree, Y. Patil, Gulvani Sampada, B. Waghmare Vishal, and K. Mujawar Irfan. "Image based anthracnose and red-rust leaf disease detection using deep learning." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 6 (2022): 1256–63. https://doi.org/10.12928/telkomnika.v20i6.24262.

Full text

Abstract:

Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design

APA, Harvard, Vancouver, ISO, and other styles

28

Chen, Xuejing, Luyuan Xie, Yonghong He, et al. "Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning." Analyst 144, no. 14 (2019): 4312–19. http://dx.doi.org/10.1039/c9an00913b.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Anni and Suharjito. "Lip-Reading with Visual Form Classification using Residual Networks and Bidirectional Gated Recurrent Units." HighTech and Innovation Journal 4, no. 2 (2023): 375–86. http://dx.doi.org/10.28991/hij-2023-04-02-010.

Full text

Abstract:

Lip-reading is a method that focuses on the observation and interpretation of lip movements to understand spoken language. Previous studies have exclusively concentrated on a single variation of residual networks (ResNets). This study primarily aimed to conduct a comparative analysis of several types of ResNets. This study additionally calculates metrics for several word structures included in the GRID dataset, encompassing verbs, colors, prepositions, letters, and numerals. This component has not been previously investigated in other studies. The proposed approach encompasses several stages,

APA, Harvard, Vancouver, ISO, and other styles

30

Zeng, Jingxiao. "Diagnostic Study of MBR Membrane Fouling based on CA-ResNet18." Frontiers in Computing and Intelligent Systems 3, no. 3 (2023): 102–6. http://dx.doi.org/10.54097/fcis.v3i3.8578.

Full text

Abstract:

In this paper, we propose a diagnostic model for addressing difficult-to-diagnose factors that influence membrane fouling, using a residual neural network (ResNet) optimized with a coordinate attention mechanism. After pre-processing fouling data from the membrane bioreactor using Principal Component Analysis (PCA) to derive nine categories of fouling factors, we determined the residual neural network structure and optimized it using the Coordinate Attention Mechanism (CA) to enhance feature extraction, improve diagnosis accuracy, and establish a stable and reliable diagnostic model for membra

APA, Harvard, Vancouver, ISO, and other styles

31

Varuna, Shree. N., and Kumar TNR. "The Segmentation of Oral Cancer MRI Images using Residual Network." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 1891–98. https://doi.org/10.5281/zenodo.8000545.

Full text

Abstract:

The segmentation of tumour from a cancer MRI images in image processing is classic research area of interest and a tedious task. Manually segmenting the MRI images is very time consuming and liable to errors. Many researchers have done investigation using deep neural network in segmenting the oral MRI images as they poses higher performance in segmenting the oral cancer images automatically. Owing to their gradient dissemination and complexity issues, the CNN takes more time and excess computational power in training the images. Our aim is build an automated technique for the segmentation of o

APA, Harvard, Vancouver, ISO, and other styles

32

Luo, Yuanqing, Yuhang Yang, Shuang Kang, Xueyong Tian, Shiyue Liu, and Feng Sun. "Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention." Actuators 13, no. 10 (2024): 401. http://dx.doi.org/10.3390/act13100401.

Full text

Abstract:

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced

APA, Harvard, Vancouver, ISO, and other styles

33

Noor, M. Al-Moosawi‬‏, and S. Khudeyer Raidah. "ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neural network." TELKOMNIKA 21, no. 05 (2023): 1051–59. https://doi.org/10.12928/telkomnika.v21i5.24515.

Full text

Abstract:

Diabetic retinopathy (DR) is a progressive eye disease associated with diabetes, resulting in blindness or blurred vision. The risk of vision loss was dramatically decreased with early diagnosis and treatment. Doctors diagnose DR by examining the fundus retinal images to develop lesions associated with the disease. However, this diagnosis is a tedious and challenging task due to growing undiagnosed and untreated DR cases and the variability of retinal changes across disease stages. Manually analyzing the images has become an expensive and time-consuming task, not to mention that training new s

APA, Harvard, Vancouver, ISO, and other styles

34

Lippl, Samuel, Benjamin Peters, and Nikolaus Kriegeskorte. "Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification." PLOS ONE 19, no. 3 (2024): e0293440. http://dx.doi.org/10.1371/journal.pone.0293440.

Full text

Abstract:

Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are useful in many domains, so they might provide good solutions for neural networks to learn. However, principled methods for measuring and manipulating iterative convergence in neural networks remain lacking. Here we address this gap by 1) quantifying the degree to which ResNets learn iterative solutions and 2) introducing a regularization approach that encourages the learning of iterative solutions. Iterative methods are characterized by two prop

APA, Harvard, Vancouver, ISO, and other styles

35

Pratama, Naufal Harsa, Ema Rachmawati, and Gamma Kosala. "CLASSIFICATION OF DOG BREEDS FROM SPORTING GROUPS USING CONVOLUTIONAL NEURAL NETWORK." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 7, no. 4 (2022): 1080–87. http://dx.doi.org/10.29100/jipi.v7i4.3208.

Full text

Abstract:

The use of convolutional neural networks has been applied to various applications. Such as image clas-sification, object detection and recognition, and others. One of the most popular uses for neural networks is image classification. Image classification mainly identifies and categorizes images according to the specified group. One application is to distinguish between one type of dog to another. Classification of dog breeds has its challenges because several kinds of dogs have similar physical characteristics, espe-cially those that belong to the same group. This study explains how to develop

APA, Harvard, Vancouver, ISO, and other styles

36

Hu, Yuan, Xifan Hua, Wei Liu, and Jens Wickert. "Sea Ice Detection from GNSS-R Data Based on Residual Network." Remote Sensing 15, no. 18 (2023): 4477. http://dx.doi.org/10.3390/rs15184477.

Full text

Abstract:

Sea ice is an important component of the polar circle and influences atmospheric change. Global navigation satellite system reflectometry (GNSS-R) not only realizes time-continuous and wide-area sea ice detection, but also greatly reduces the cost of sea ice remote sensing research, which has been a hot topic in recent years. To tackle the challenges of noise interference and the reduced accuracy of sea ice detection during the melting period, this paper proposes a sea ice detection method based on a residual neural network (ResNet). ResNet addresses the issue of vanishing gradients in deep ne

APA, Harvard, Vancouver, ISO, and other styles

37

D. Suma, Mrs. "AI Generated Image Detection Using Neural Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem29037.

Full text

Abstract:

This abstract introduces a breakthrough in image detection using Convolutional Neural Networks (CNNs). Renowned for their ability to recognize local features, share weights, and employ pooling mechanisms, CNNs form the foundation of this innovative approach. The study presents a novel method that harnesses CNNs' inherent strengths to elevate image detection. Central to this method is the incorporation of a specialized module called "ShortCut3- ResNet," inspired by the Residual Network (ResNet) concept. This module enhances the network's capacity to capture intricate image details, thereby faci

APA, Harvard, Vancouver, ISO, and other styles

38

Huang, Xuehua. "Improved Model Based on GoogLeNet and Residual Neural Network ResNet." International Journal of Cognitive Informatics and Natural Intelligence 16, no. 1 (2022): 1–19. http://dx.doi.org/10.4018/ijcini.313442.

Full text

Abstract:

To improve the accuracy of image classification, a kind of improved model is proposed. The shortcut is added to GoogLeNet inception v1 and several other ways of shortcut are given, and they are GRSN1_2, GRSN1_3, GRSN1_4. Among them, the information of the input layer is directly output to each subsequent layer in the form of shortcut. The new improved model has the advantages of multi-size and small convolution kernel in the same layer in the network and the advantages of shortcut to reduce information loss. Meanwhile, as the number of inception blocks increases, the number of channels is incr

APA, Harvard, Vancouver, ISO, and other styles

39

He, Zehui. "Applying multi-layer perceptron and ResNet for handwritten digits recognition." Applied and Computational Engineering 18, no. 1 (2023): 16–22. http://dx.doi.org/10.54254/2755-2721/18/20230956.

Full text

Abstract:

The task of handwritten digit recognition is to recognizing the handwritten digits from pictures. Applying machine learning based models to automatically perform handwritten digit recognition task can significantly improve efficiency. This paper applies two machine learning based models, including multi-layer perceptron and residual neural network, for such a task. Firstly, this paper introduces the basic concept of the simple multi-layer perceptron model and then presents the structure of the residual neural network model. Subsequently, such two models are trained on the MNIST corpus, one of

APA, Harvard, Vancouver, ISO, and other styles

40

Waheed Javed, Gulnaz Parveen, and Sobia Bilal. "Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms." Lahore Garrison University Research Journal of Computer Science and Information Technology 7, no. 01 (2023): 7–13. http://dx.doi.org/10.54692/lgurjcsit.2023.0701361.

Full text

Abstract:

Deep learning is an artificial intelligence subfield within machine learning. Now- a-days, deep learning has been used in various applications like computer vision, natural language processing, speech recognition, social network filtering, neural machine translation, etc. Deep learning, Convolutional Neural Network (CNN) is a set of deep neural networks mainly designed for image analysis. Deep learning strong ability is mainly due to multiple feature extraction. In this pa- per, we will discuss and compare AlexNet,VGGNet-16,Residual Network(ResNet-50,101,152).

APA, Harvard, Vancouver, ISO, and other styles

41

Man, Ke, Ruilin Liu, Xiaoli Liu, et al. "Water Leakage and Crack Identification in Tunnels Based on Transfer-Learning and Convolutional Neural Networks." Water 14, no. 9 (2022): 1462. http://dx.doi.org/10.3390/w14091462.

Full text

Abstract:

In order to solve the problems of long artificial time consumption, the inability to standardize the degree of damage, and the difficulty of maintaining data in traditional tunnel disease detection methods, this paper proposes the use of Residual Network (ResNet) models for tunnel water leakage and crack detection. ResNet proposes a residual learning framework to ease the training of networks that are deeper than those previously used. Furthermore, ResNet explicitly reformulates the layers as learning the residual functions of the reference layer inputs, rather than learning the unreferenced f

APA, Harvard, Vancouver, ISO, and other styles

42

Suryakanth, B., and S. A. Hari Prasad. "3D CNN-Residual Neural Network Based Multimodal Medical Image Classification." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (October 31, 2022): 204–14. http://dx.doi.org/10.37394/23208.2022.19.22.

Full text

Abstract:

Multimodal medical imaging has become incredibly common in the area of biomedical imaging. Medical image classification has been used to extract useful data from multimodality medical image data. Magnetic resonance imaging (MRI) and Computed tomography (CT) are some of the imaging methods. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. In comparison to 1D and 2D images, 3D images have a very clear vision. The proposed method use

APA, Harvard, Vancouver, ISO, and other styles

43

Khan, Fahad, Xiaojun Yu, Zhaohui Yuan, and Atiq ur Rehman. "ECG classification using 1-D convolutional deep residual neural network." PLOS ONE 18, no. 4 (2023): e0284791. http://dx.doi.org/10.1371/journal.pone.0284791.

Full text

Abstract:

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic

APA, Harvard, Vancouver, ISO, and other styles

44

Mohammad, Amimul Ihsan Aquil, and Hussain Wan Ishak Wan. "Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 467–75. https://doi.org/10.11591/ijai.v10.i2.pp467-475.

Full text

Abstract:

Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by compar

APA, Harvard, Vancouver, ISO, and other styles

45

Zhao, Feng, and Shao-Lun Huang. "On the Universally Optimal Activation Function for a Class of Residual Neural Networks." AppliedMath 2, no. 4 (2022): 574–84. http://dx.doi.org/10.3390/appliedmath2040033.

Full text

Abstract:

While non-linear activation functions play vital roles in artificial neural networks, it is generally unclear how the non-linearity can improve the quality of function approximations. In this paper, we present a theoretical framework to rigorously analyze the performance gain of using non-linear activation functions for a class of residual neural networks (ResNets). In particular, we show that when the input features for the ResNet are uniformly chosen and orthogonal to each other, using non-linear activation functions to generate the ResNet output averagely outperforms using linear activation

APA, Harvard, Vancouver, ISO, and other styles

46

Tu, Fengmiao, Suixian Yang, and Jingyuan Yang. "Fault Diagnosis for Rotating Machine Based on Mel Spectrogram and Residual Neural Network." Proceedings of the International Conference on Condition Monitoring and Asset Management 2024, no. 1 (2024): 12–23. https://doi.org/10.1784/cm2024.1b3.

Full text

Abstract:

Current methods for processing acoustic signals for fault diagnosis are commonly based on the method of processing the vibration signals. It is difficult to obtain the information only existing in the acoustic signal. Therefore, considering the fault diagnosis based on the acoustic signal, a method based on the Logarithmic Mel (LM) spectrogram and a residual neural network (ResNet) is proposed. Firstly, to correspond with the auditory perception, the acoustic signal is processed and linearly represented in the LM spectrogram. Secondly, the ResNet model is introduced to extract the fault inform

APA, Harvard, Vancouver, ISO, and other styles

47

Sirohi, Sonam. "ECG Heartbeat Classification Using Ensemble of Efficient Machine Learning Approaches." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48871.

Full text

Abstract:

Abstract— Electrocardiogram, an established method for cardiac health analysis, has attracted significant research interest in accurate heartbeat classification. Despite numerous studies in this field, achieving high accuracy scores remains a challenge. This paper employs and fine-tunes well-known machine learning techniques, comparing them with additional cutting-edge techniques. The study utilizes highly imbalanced datasets, addressing this issue by adjusting the Artificial Neural Network (ANN) and Residual Network (ResNet) models loss value through class weight assignment. Two enriched ECG

APA, Harvard, Vancouver, ISO, and other styles

48

Kassylkassova, Kamila, Zhanna Yessengaliyeva, Gayrat Urazboev, and Ayman Kassylkassova. "OPTIMIZATION METHOD FOR INTEGRATION OF CONVOLUTIONAL AND RECURRENT NEURAL NETWORK." Eurasian Journal of Mathematical and Computer Applications 11, no. 2 (2023): 40–56. http://dx.doi.org/10.32523/2306-6172-2023-11-2-40-56.

Full text

Abstract:

Abstract In recent years, convolutional neural networks have been widely used in image processing and have shown good results. Particularly useful was their ability to automatically extract image features (textures and shapes of objects). The article proposes a method that improves the accuracy and speed of recognition of an ultra-precise neural network based on image recognition of people’s faces. At first, a recurrent neural network is introduced into the convolutional neural network, thereby studying the characteristics of the image more deeply. Deep image characteristics are studied in par

APA, Harvard, Vancouver, ISO, and other styles

49

Trinh, Tan Dat, Pham Cung Le Thien Vu, and Pham The Bao. "Comparing Mask R-CNN backbone architectures for human detection using thermal imaging." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3962. http://dx.doi.org/10.11591/ijece.v14i4.pp3962-3970.

Full text

Abstract:

We introduce a method for detecting humans in thermal imaging using an end-to-end deep learning model. Our objective is to optimize the human detection process in thermal imaging by investigating the mask region-based convolutional neural network (Mask R-CNN). The model, an advancement of the faster region-based convolutional neural network (Faster R-CNN), not only captures bounding boxes encompassing human subjects but also delineates segmentation masks around them. Our investigation extends to the evaluation and comparison of various convolutional neural networks for feature learning, like r

APA, Harvard, Vancouver, ISO, and other styles

50

Yao, Dechen, Qiang Sun, Jianwei Yang, Hengchang Liu, and Jiao Zhang. "Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model." Shock and Vibration 2020 (November 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8823050.

Full text

Abstract:

The present work aimed at the problems of less negative samples and more positive samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol inspection tasks. Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. First, GAN was used to track the distribution of rail fastener failure data. To study the noise distribution, the mapping relationship between image

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!