Relevant bibliographies by topics / Residual neural network (ResNet)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Residual neural network (ResNet)'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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

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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.

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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
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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.

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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
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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.

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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
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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.

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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
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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.

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<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
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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.

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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
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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.

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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.
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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.

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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
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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.

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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
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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.

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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
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Dissertations / Theses on the topic "Residual neural network (ResNet)"

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Zhang, Huizhen. "Alpha Matting via Residual Convolutional Grid Network." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39467.

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Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and image synthesis. The conventional approaches for alpha matting perform unsatisfactorily when they encounter complicated background and foreground. It is also difficult for them to extract alpha matte accurately when the foreground objects are transparent, semi-transparent, perforated or hairy. Fortunately, the rapid development of deep learning techniques brings new possibilities for solving alpha matting problems. In this thesis, we pro
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Yu, Xiafei. "Wide Activated Separate 3D Convolution for Video Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39974.

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Video super-resolution (VSR) aims to recover a realistic high-resolution (HR) frame from its corresponding center low-resolution (LR) frame and several neighbouring supporting frames. The neighbouring supporting LR frames can provide extra information to help recover the HR frame. However, these frames are not aligned with the center frame due to the motion of objects. Recently, many video super-resolution methods based on deep learning have been proposed with the rapid development of neural networks. Most of these methods utilize motion estimation and compensation models as preprocessing to
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Prochorenko, I., N. Tymoshenko, and S. Galchenko. "Neural network model for predicting the level of residual knowledge of the subjects of study." Thesis, Aviation in the XXI-st century. Safety in aviation and space technologies: the seventh world congress, 19-21 of september 2016: abstracts. – K., 2016. – V.1. – P. 1.1.4-1.1.6, 2016. http://er.nau.edu.ua/handle/NAU/29423.

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The task is to build a neural network model depending on residual knowledge the trainees with whom they come into the labor market. Neural network model makes it possible with enough precision to predict the level of professional training according to their individual abilities.
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Truzzi, Stefano. "Event classification in MAGIC through Convolutional Neural Networks." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1216295.

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The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes are able to detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic known objects, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by imaging the Cherenkov light produced by the charged superluminal leptons in the extended air shower originated when the primary particle interacts with the atmosphere. These Cherenkov flashes brighten the night sky for short times in the nanosecond scale. From the image topology an
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Galassi, Andrea. "Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12859/.

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Le reti neurali artificiali, grazie alle nuove tecniche di Deep Learning, hanno completamente rivoluzionato il panorama tecnologico degli ultimi anni, dimostrandosi efficaci in svariati compiti di Intelligenza Artificiale e ambiti affini. Sarebbe quindi interessante analizzare in che modo e in quale misura le deep network possano sostituire le IA simboliche. Dopo gli impressionanti risultati ottenuti nel gioco del Go, come caso di studio è stato scelto il gioco del Mulino, un gioco da tavolo largamente diffuso e ampiamente studiato. È stato quindi creato il sistema completamente sub-simbolico
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Iuliano, Luca. "Analisi delle prestazioni di una rete neurale convoluzionale per la super-resolution di un'immagine." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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In questa tesi viene trattato il problema inverso della Super-Resolution di un’immagine singola (SISR), che consiste nel ricostruire un’immagine ad alta risoluzione a partire dalla conoscenza di una sua versione a bassa risoluzione, e vengono analizzate nel dettaglio tecniche di ricostruzione basate sul Deep-Learning. Oggi in molte applicazioni, la risoluzione delle immagini è diventata molto importante. Per esempio, nell’ambito della video-sorveglianza, una risoluzione più elevata nelle telecamere può consentire di vedere molti più dettagli, che potrebbero, ad esempio, facilitare un'indagi
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Le, Ngan Thi Hoang. "Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1166.

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Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation, augmented reality systems, semantic searching, medical imaging are on the rise, requiring more accurate and efficient segmentation mechanisms. In recent years, deep learning approaches based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have dramatically emerged as the dominant paradigm for solving many problems in computer vision and machine learning. The main focus of this thes
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Nikzad, Dehaji Mohammad. "Structural Improvements of Convolutional Neural Networks." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410448.

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Over the last decade, deep learning has demonstrated outstanding performance in almost every application domain. Among different types of deep frameworks, convolutional neural networks (CNNs), inspired by the biological process of the visual system, can learn to extract discriminative features from raw inputs without any prior manipulation. However, efficient information circulation and the ability to explore effective new features are still two key and challenging factors for a successful deep neural network. In this thesis, we aim at presenting novel structural improvements of the CNN framew
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Siegel, David. "Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382372576.

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Elander, Filip. "Semantic segmentation of off-road scenery on embedded hardware using transfer learning." Thesis, KTH, Mekatronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301154.

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Real-time semantic scene understanding is a challenging computer vision task for autonomous vehicles. A limited amount of research has been done regarding forestry and off-road scene understanding, as the industry focuses on urban and on-road applications. Studies have shown that Deep Convolutional Neural Network architectures, using parameters trained on large datasets, can be re-trained and customized with smaller off-road datasets, using a method called transfer learning and yield state-of-the-art classification performance. This master’s thesis served as an extension of such existing off-r
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Book chapters on the topic "Residual neural network (ResNet)"

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Khan, Saquib Nawaz, S. Narendran, R. Mahaveerakannan, and K. Sudhakar. "Improving Plant Disease Detection Accuracy Using Optimized Convolutional Neural Networks (CNN) Compared to Residual Networks (ResNet)." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3244-2_29.

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Ren, Haoyu, Mostafa El-khamy, and Jungwon Lee. "DN-ResNet: Efficient Deep Residual Network for Image Denoising." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20873-8_14.

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Suriya Prakash, B., and N. Velmurugan. "Leaf Disease Detection Using ResNet Deep Neural Network." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69982-5_32.

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Cheng, Wen Xin, P. N. Suganthan, and Rakesh Katuwal. "Oblique Random Forests on Residual Network Features." In Neural Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_26.

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Elboher, Yizhak Yisrael, Elazar Cohen, and Guy Katz. "Neural Network Verification Using Residual Reasoning." In Software Engineering and Formal Methods. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17108-6_11.

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Mhapsekar, Mandar, Prathamesh Mhapsekar, Aniket Mhatre, and Vinaya Sawant. "Implementation of Residual Network (ResNet) for Devanagari Handwritten Character Recognition." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_14.

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Guo, Changlu, Márton Szemenyei, Yugen Yi, Wei Zhou, and Haodong Bian. "Residual Spatial Attention Network for Retinal Vessel Segmentation." In Neural Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63830-6_43.

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Raja, Kondaveti, K. V. Nageswari, and G. Archana. "Bone Fracture Detection Using Residual Neural Network." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003641537-4.

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Zhuge, Ruibin, Haiying Xia, Haisheng Li, and Shuxiang Song. "Fast Single Image De-raining via a Weighted Residual Network." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04224-0_22.

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Anu, K. P., and J. V. Bibal Benifa. "Human Activity Recognition a Comparison Between Residual Neural Network and Recurrent Neural Network." In Artificial Intelligence: Theory and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8479-4_9.

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Conference papers on the topic "Residual neural network (ResNet)"

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Yang, Hao, Deng Chen, and Xiancheng Feng. "Abnormality Monitoring and Recognition of Surveillance Video Based on ResNet Residual Network." In 2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT). IEEE, 2024. http://dx.doi.org/10.1109/aicit62434.2024.10730173.

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Ghai, Mohit, Shobha Bhatt, and Divya Taneja. "Speaker Recognition for Hindi Language Using Convolution-ResNet Neural Network." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739196.

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Liu, Yansong, and Pin Wang. "Network intrusion detection method based on CAE-ResNet-BiLSTM." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049566.

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Kant, Vishnu. "Automated Corn Leaf Disease Diagnosis using ResNet-based Convolutional Neural Network." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696710.

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Abdallah, Abdallah S., Ahmed A. ElSharkawy, and Mohamed W. Fakhr. "Interference Mitigation in Automotive Radar using ResNet Deep Neural Network Models." In 2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2024. http://dx.doi.org/10.1109/ccece59415.2024.10667065.

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Atul, Sadikul Hasan Mrida, Mohammad Sakib, Md Isthiakul Hasan, Md Saiful Islam, Omar Faruq Khan, and Abdur Rahim. "Potato Leaf Disease Detection System Using the Convolutional Neural Network (ResNet)." In 2024 IEEE Conference on Engineering Informatics (ICEI). IEEE, 2024. https://doi.org/10.1109/icei64305.2024.10912342.

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Chauhan, Shanvi. "ResNet-18 for Automated Eye Disease Diagnosis: A Neural Network Solution." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911395.

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Chauhan, Shanvi. "ResNet-18 for Automated Eye Disease Diagnosis: A Neural Network Solution." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774939.

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Mridha Atul, Sadikul Hasan, Md Isthiakul Hasan, Md Musfiqur Rahman, Farhana Sultana, Omar Faruq Khan, and Md Saiful Islam. "Rice Leaf Disease Detection System Using the Convolutional Neural Network (ResNet)." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013852.

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Alpan, Kezban, Bardia Arman, and Kamil Dimililer. "Effect of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Breast Cancer Detection Using Residual Network (ResNet)." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013776.

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Reports on the topic "Residual neural network (ResNet)"

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl
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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened att
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SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGH- TEMPERATURE SCENES BASED ON NEURAL NETWORK. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.

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Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting ther
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