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Relevant bibliographies by topics / Fully convoluted neural network

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Academic literature on the topic 'Fully convoluted neural network'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Fully convoluted neural network"

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Kotlyar, D. I., and A. N. Lomanov. "SEGMENTATION OF PICTURES CONTAINING BLADE EDGE OF A GAS TURBINE ENGINE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 227 (May 2023): 3–10. http://dx.doi.org/10.14489/vkit.2023.05.pp.003-010.

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The article describes common techniques for semantic segmentation pictures containing edges of gas turbine engines blades for detecting left and right borders for further using in forming trajectory algorithms with direct metal deposition. For analysis such metrics, as pixel accuracy, mean pixel accuracy, intersection over union, frequency weighed intersection over union are used. Classic method of computer vision with threshold filters, border segmentation neural network method, fully convoluted neural network for semantic segmentation are focused on. The classic method of computer vision pro

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Mruthyunjaya and Suresh Kumar Mandala. "A brain tumor identification using convolution neural network and fully convolution neural network." MATEC Web of Conferences 392 (2024): 01130. http://dx.doi.org/10.1051/matecconf/202439201130.

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Brain tumor identification, along with an investigation, is harmful to the patient. Segmentation, therefore, of paying attention to near-neighborhood growth remains accurate, effective, and healthy. Fully Convolution Neural Network (FCNN) is a reliable picture model to capitulate the hide quality. The form of the multifaceted with the incessant pixels taught with the crest state and the symbolic picture taught. In this research, the making of a totally convoluted method to obtain the participation of a random element and the production of correspondingly large-scale output with a resourceful a

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S., Muthamil Selvan, Pudhota Maneesh, Gunnam Sridhar, and Kumar GP Kaushik. "Movie Recommendation Based on Posters and Still Frames using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1747–50. https://doi.org/10.35940/ijeat.D7255.049420.

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Movie recommendation system has become a key part in online movie services to gain and maintain the huge market. While within the preceding studies works Convolution neural network (CNN) concept is employed to spot the various movies with similar posters or stills to recommend the users. Using CNN, similar posters and stills are classified into group and any hard cash within the poster may place it out of the group. But the CNN method isn't fully connected and uses back propagation technique which could be a touch slow within the poster identification and more over just with posters the fi

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Saida, D., KLSDT Keerthi Vardhan, and P. Premchand. "Effective Brain Tumor Classification Using Deep Residual Network-Based Transfer Learning." International journal of electrical and computer engineering systems 14, no. 6 (2023): 625–34. http://dx.doi.org/10.32985/ijeces.14.6.2.

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Brain tumor classification is an essential task in medical image processing that provides assistance to doctors for accurate diagnoses and treatment plans. A Deep Residual Network based Transfer Learning to a fully convoluted Convolutional Neural Network (CNN) is proposed to perform brain tumor classification of Magnetic Resonance Images (MRI) from the BRATS 2020 dataset. The dataset consists of a variety of pre-operative MRI scans to segment integrally varied brain tumors in appearance, shape, and histology, namely gliomas. A Deep Residual Network (ResNet-50) to a fully convoluted CNN is prop

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Hou, Yuanyuan, Shiyu Wang, Bing Bai, H. C. Stephen Chan, and Shuguang Yuan. "Accurate Physical Property Predictions via Deep Learning." Molecules 27, no. 5 (2022): 1668. http://dx.doi.org/10.3390/molecules27051668.

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Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such

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Shi, Cuiping, Xinlei Zhang, Tianyi Wang, and Liguo Wang. "A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification." Remote Sensing 14, no. 13 (2022): 3184. http://dx.doi.org/10.3390/rs14133184.

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The large intra-class difference and inter-class similarity of scene images bring great challenges to the research of remote-sensing scene image classification. In recent years, many remote-sensing scene classification methods based on convolutional neural networks have been proposed. In order to improve the classification performance, many studies increase the width and depth of convolutional neural network to extract richer features, which increases the complexity of the model and reduces the running speed of the model. In order to solve this problem, a lightweight convolutional neural netwo

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Luo, Honglin, Lin Bo, Chang Peng, and Dongming Hou. "An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System." Machines 10, no. 7 (2022): 503. http://dx.doi.org/10.3390/machines10070503.

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More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic accuracy. In the improved CNN architecture, the feature maps of all the convolutional and pooling layers are globally convoluted into a corresponding one-dimensional feature sequence, and then all the feature sequences are concatenated into the fully connected layer. On this basis, t

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Herbert, Christoph, Joan Francesc Munoz-Martin, David Llaveria, Miriam Pablos, and Adriano Camps. "Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission." Remote Sensing 13, no. 7 (2021): 1366. http://dx.doi.org/10.3390/rs13071366.

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Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission lau

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Коваленко, Ю. Ф., Е. А. Шулаева, И. А. Табаков та М. Д. Мамлеев. "Моделирование безопасного проведения процесса сжигания абгазов с использованием нейронных сетей". Южно-Сибирский научный вестник, № 6(58) (31 грудня 2024): 207–12. https://doi.org/10.25699/sssb.2024.58.6.036.

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Использование технологий обнаружения возгораний с помощью нейросетей становится всё более востребованным в промышленности благодаря их эффективности и точности. Однако оптимизация работы таких систем остаётся сложной задачей из-за сложного характера взаимодействия факторов, таких как изменения освещения, задымление и другие визуальные шумы. В данном тексте представлен обзор методов, используемых для анализа и разработки систем на основе нейросетей для обнаружения огня. Для реализации такой системы был проведён обзор существующих подходов в области компьютерного зрения и машинного обучения, а т

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Kishimoto, Masashi, Yodai Matsui, and Hiroshi Iwai. "Conditional GAN for Generation of 3D SOFC Electrode Microstructure Dataset." ECS Meeting Abstracts MA2023-01, no. 54 (2023): 82. http://dx.doi.org/10.1149/ma2023-015482mtgabs.

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Synthetic three-dimensional porous structures of solid oxide fuel cell anode are generated using the deep convolutional conditional generative adversarial neural network (DCCGAN). The developed network consists of a generator that produces an artificial structure dataset from random numbers, so-called latent variables, and a discriminator that judges whether the input structure dataset is real or fake. The generator and discriminator are alternately trained to improve their performance in an adversarial manner so that the generator can eventually create realistic structures indistinguishable f

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Dissertations / Theses on the topic "Fully convoluted neural network"

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Liu, Jin. "Fully parallel learning neural network chip for real-time control." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/22214.

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Hussain, Saed. "Fault tolerant flight control : an application of the fully connected cascade neural network." Thesis, University of Central Lancashire, 2015. http://clok.uclan.ac.uk/12123/.

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The endurance of an aircraft can be increased in the presence of failures by utilising flight control systems that are tolerant to failures. Such systems are known as fault tolerant flight control systems (FTFCS). FTFCS can be implemented by developing failure detection, identification and accommodation (FDIA) schemes. Two of the major types of failures in an aircraft system are the sensor and actuator failures. In this research, a sensor failure detection, identification and accommodation (SFDIA); and an actuator failure detection, identification and accommodation (AFDIA) schemes are develope

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Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

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Phillips, Adon. "Melanoma Diagnostics Using Fully Convolutional Networks on Whole Slide Images." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36929.

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Semantic segmentation as an approach to recognizing and localizing objects within an image is a major research area in computer vision. Now that convolutional neural networks are being increasingly used for such tasks, there have been many improve- ments in grand challenge results, and many new research opportunities in previously untennable areas. Using fully convolutional networks, we have developed a semantic segmentation pipeline for the identification of melanocytic tumor regions, epidermis, and dermis lay- ers in whole slide microscopy images of cutaneous melanoma or cutaneous metastati

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Yuan, Yuchen. "Advanced Visual Computing for Image Saliency Detection." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17039.

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Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations abov

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Fischer, Christian [Verfasser], and Barbara [Akademischer Betreuer] Conradt. "Computational analysis of mitochondria in Caenorhabditis elegans using a Fully Convolutional Neural Network and common feature detectors / Christian Fischer ; Betreuer: Barbara Conradt." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1225682924/34.

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Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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This work proposed a deep learning approach to a multi-class classification problem. In particular, our project goal is to establish whether there is a connection between olive oil molecular composition and its geographical origin. To accomplish this, we implement a method to transform structured data into meaningful images (exploring the existing literature) and developed a fine-tuned Convolutional Neural Network able to perform the classification. We implement a series of tailored techniques to improve the model.

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Lang, Matěj. "Detekce vad vláknitého materiálu užitím metod strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400649.

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Cílem této diplomové práce je automatizace detekce vad ve vláknitých materiálech. Firma SILON se již přes padesát let zabývá výrobou jemné vaty z recyklovaných PET lahví. Tato vata se následně používá ve stavebnictví, automobilovém průmyslu, ale nejčastěji v dámských hygienických potřebách a dětských plenách. Cílem firmy je produkovat co nejkvalitnější výrobek a proto je každá dávka testována v laboratoři s několika přísnými kritérii. Jednám z testů je i množství vadných vláken, jako jsou zacuchané smotky vláken, nebo nevydloužená vlákna, která jsou tvrdá a snadno se lámou. Navrhovaný systém s

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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more

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Salem, Mostafa. "Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/668990.

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This thesis is focused on developing novel and fully automated methods for the detection of new multiple sclerosis (MS) lesions in longitudinal brain magnetic resonance imaging (MRI). First, we proposed a fully automated logistic regression-based framework for the detection and segmentation of new T2-w lesions. The framework was based on intensity subtraction and deformation field (DF). Second, we proposed a fully convolutional neural network (FCNN) approach to detect new T2-w lesions in longitudinal brain MR images. The model was trained end-to-end and simultaneously learned both the DFs

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Book chapters on the topic "Fully convoluted neural network"

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Kundu, Prerana, Pabitra Kundu, Sohini Mallik, et al. "Facial Expression Recognition Using Convoluted Neural Network (CNN)." In Cyber Intelligence and Information Retrieval. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4284-5_8.

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Izabachène, Malika, Renaud Sirdey, and Martin Zuber. "Practical Fully Homomorphic Encryption for Fully Masked Neural Networks." In Cryptology and Network Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31578-8_2.

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Leite, Ernesto, Fabrice Mourlin, and Pierre Paradinas. "Fully Distributed Deep Neural Network: F2D2N." In Mobile, Secure, and Programmable Networking. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52426-4_15.

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Schmidhuber, Jürgen. "Reinforcement Learning with Interacting Continually Running Fully Recurrent Networks." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_97.

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Ji, Shanshan, Te Li, Shiqiang Zhu, Qiwei Meng, and Jianjun Gu. "Multi-branch Fusion Fully Convolutional Network for Person Re-Identification." In Neural Information Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92238-2_14.

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Chen, Zijing, Jun Li, Zhe Chen, and Xinge You. "Generic Pixel Level Object Tracker Using Bi-Channel Fully Convolutional Network." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_69.

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Yang, Guorun, and Zhidong Deng. "End-to-End Disparity Estimation with Multi-granularity Fully Convolutional Network." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_25.

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Kapdi, Rupal A., Jigna A. Patel, and Jitali Patel. "Brain Tumor Segmentation Using Fully Convolution Neural Network." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9876-8_1.

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Yu, Ruiguo, Xuzhou Fu, Han Jiang, et al. "Remote Sensing Image Segmentation by Combining Feature Enhanced with Fully Convolutional Network." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_37.

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Chen, Mingjian, Hao Zheng, Changsheng Lu, Enmei Tu, Jie Yang, and Nikola Kasabov. "A Spatio-Temporal Fully Convolutional Network for Breast Lesion Segmentation in DCE-MRI." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04239-4_32.

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Conference papers on the topic "Fully convoluted neural network"

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Niranjana, S., D. Vidyanadha Babu, K. Malathy, S. Durga Devi, K. Kishore Babu, and C. Karthikeyan. "CDNN: A Novel Methodology Development to Detect Online Social Network Cyberbullying Threats Using Convoluted Deep Neural Network Principle." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910787.

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Jiang, Xiaoyang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Jingtong Ma, and Renjing Xu. "Fully Spiking Neural Network for Legged Robots." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890793.

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Lance, Romain, Anas Skalli, Xavier Porte, and Daniel Brunner. "A fully optical C-band neural network for telecommunication applications." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027689.

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Zhang, Shaochuan, Fengji Li, Li Wang, Jie Zhou, and Haijun Niu. "Tongue Model-Driven Method Based on Fully Connected Neural Network." In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800371.

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Yang, Wen, and Yunting Liao. "AFCNNM: Accelerating Fully Connected Neural Network on Mesh-based Optical Network-on-Chip." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00303.

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Zhang, Bingqian, Zhihong Yan, and Dong Wang. "Fully Neural Network Low-Bit Quantization and Adjustment of Activation Distributions." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846012.

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Dutta, Jayati, Priyanka Peri, and Rohith Malkuchi. "A Novel ZUC-PRN Generator Using a Fully-Connected Neural Network." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683011.

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Natarajan, Gayathri, S. Shaik Mohamed Badhusha, R. Monisha, T. Rajalakshmi, and U. Snekhalatha. "Detection of face skin cancer using deep convoluted neural network." In EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0072455.

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Alghazal, M. A., and D. Krinis. "Data-Driven Modeling of Oil Saturation from Dielectric Logs Using Ensemble Regression, Dimensionality Reduction and Anomaly Detection Machine Learning Algorithms." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216430-ms.

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Abstract Dielectric is a specialized logging tool employed to measure the oil saturation independently of water salinity, enabled by the convoluted multi-frequency measurements of the formation's dielectric properties. Conventional resistivity and salinity-dependent tools are more commonly used but with high measurement uncertainty in variable salinity environments. In this paper, we developed a unique data-driven model encapsulating several supervised and unsupervised machine learning algorithms to predict the dielectric-based saturation using readily available reservoir and well data. More t

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Ayhan, Tuba, and Mustafa Altun. "Approximate Fully Connected Neural Network Generation." In 2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD). IEEE, 2018. http://dx.doi.org/10.1109/smacd.2018.8434843.

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Reports on the topic "Fully convoluted neural network"

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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can

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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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Fernández-Villaverde, Jesús, Joël Marbet, Galo Nuño, and Omar Rachedi. Inequality and the zero lower bound. Banco de España, 2024. http://dx.doi.org/10.53479/36133.

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This paper studies how household inequality shapes the effects of the zero lower bound (ZLB) on nominal interest rates on aggregate dynamics. To do so, we consider a heterogeneous agent New Keynesian (HANK) model with an occasionally binding ZLB and solve for its fully non-linear stochastic equilibrium using a novel neural network algorithm. In this setting, changes in the monetary policy stance influence households’precautionary savings by altering the frequency of ZLB events. As a result, the model features monetary policy non-neutrality in the long run. The degree of long-run non-neutrality

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe

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Panta, Manisha, Padam Thapa, Md Hoque, et al. Application of deep learning for segmenting seepages in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49453.

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Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To th

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