Relevant bibliographies by topics / Fully connected neural network (FCNN)

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

Academic literature on the topic 'Fully connected neural network (FCNN)'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Fully connected neural network (FCNN)"

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Zhang, Jiayuan. "Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast." Applied and Computational Engineering 96, no. 1 (2024): 210–17. http://dx.doi.org/10.54254/2755-2721/96/20241281.

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Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully conn
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Wang, Wenqi, Donghao Yang, Binkai Sun, et al. "Deep Learning-Based Magnetic Core Loss Prediction Using a Fully Connected Neural Network." Academic Journal of Science and Technology 14, no. 2 (2025): 174–79. https://doi.org/10.54097/29ccz526.

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This paper proposes a core loss prediction model based on a fully connected neural network (FCNN). After pre-processing the data, seven key features are extracted, and feature importance is sorted. The excitation waveform features are converted into five variables and combined with four additional features to form the final input feature set. Based on this, the FCNN prediction model is constructed, the early stop method is used in the training process to prevent overfitting, and the generalization ability is evaluated using cross-validation[1]. The R2 of the model training set and the test set
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Zhang, Zhikui, and Lina Wu. "Research on Continuous Pipeline Life Prediction Method Based on Fully Connected Neural Network." Academic Journal of Science and Technology 8, no. 3 (2023): 69–73. http://dx.doi.org/10.54097/fcqfsz74.

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Aiming at the low accuracy of traditional empirical formulas in predicting the fatigue life of continuous oil pipelines, a fully connected neural network is utilized to predict the low-week fatigue life of continuous oil pipelines. Considering the influence of internal pressure on the fatigue life of continuous oil pipeline during operation, a prediction method combining the fully connected neural network and gated recirculation unit is proposed, and the experiment proves that the FCNN-GRU neural network performs better in terms of prediction accuracy and stability compared with the BP neural
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Alwan, Ali H., and Ali H. Kashmar. "Block Ciphers Analysis Based on a Fully Connected Neural Network." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 1 (2023): 415–27. http://dx.doi.org/10.30526/36.1.3058.

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With the development of high-speed network technologies, there has been a recent rise in the transfer of significant amounts of sensitive data across the Internet and other open channels. The data will be encrypted using the same key for both Triple Data Encryption Standard (TDES) and Advanced Encryption Standard (AES), with block cipher modes called cipher Block Chaining (CBC) and Electronic CodeBook (ECB). Block ciphers are often used for secure data storage in fixed hard drives, portable devices, and safe network data transport. Therefore, to assess the security of the encryption method, it
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Kumar Reddy, Pottipati Dileep, and Kota Venkata Ramanaiah. "Field-programmable gate array implementation of efficient deep neural network architecture." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3863. http://dx.doi.org/10.11591/ijece.v14i4.pp3863-3875.

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Deep neural network (DNN) comprises multiple stages of data processing sub-systems with one of the primary sub-systems is a fully connected neural network (FCNN) model. This fully connected neural network model has multiple layers of neurons that need to be implemented using arithmetic units with suitable number representation to optimize area, power, and speed. In this work, the network parameters are analyzed, and redundancy in weights is eliminated. A pipelined and parallel structure is designed for the fully connected network information. The proposed FCNN structure has 16 inputs, 3 hidden
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Dileep, Kumar Reddy Pottipati, and Ramanaiah Kota Venkata. "Field-programmable gate array implementation of efficient deep neural network architecture." Field-programmable gate array implementation of efficient deep neural network architecture 14, no. 4 (2024): 3863–75. https://doi.org/10.11591/ijece.v14i4.pp3863-3875.

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Deep neural network (DNN) comprises multiple stages of data processing sub-systems with one of the primary sub-systems is a fully connected neural network (FCNN) model. This fully connected neural network model has multiple layers of neurons that need to be implemented using arithmetic units with suitable number representation to optimize area, power, and speed. In this work, the network parameters are analyzed, and redundancy in weights is eliminated. A pipelined and parallel structure is designed for the fully connected network information. The proposed FCN
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Shang, Hongchun, Lanjie Niu, Zhongwang Tian, Chenyang Fan, Zhewei Zhang, and Yanshan Lou. "Multi-Scale Anisotropic Yield Function Based on Neural Network Model." Materials 18, no. 3 (2025): 714. https://doi.org/10.3390/ma18030714.

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The increasingly complex form of traditional anisotropic yield functions brings difficulties to parameter calibration and finite element application, and it is necessary to establish a unified paradigm model for engineering applications. In this study, four traditional models were used to calibrate the anisotropic behavior of a 2090-T3 aluminum alloy, and the corresponding yield surfaces in σxx,σyy,σxy and α,β,r spaces were studied. Then, α and β are selected as input variables, and r is regarded as an output variable to improve the prediction and generalization capabilities of the fully conne
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Chowdhury, Mohammad Samin Nur, Arindam Dutta, Matthew Kyle Robison, Chris Blais, Gene Arnold Brewer, and Daniel Wesley Bliss. "Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG." Sensors 20, no. 21 (2020): 6090. http://dx.doi.org/10.3390/s20216090.

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Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each tria
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Hu, Jinlong, Lijie Cao, Tenghui Li, Bin Liao, Shoubin Dong, and Ping Li. "Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder." Computational and Mathematical Methods in Medicine 2020 (May 18, 2020): 1–12. http://dx.doi.org/10.1155/2020/1394830.

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Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to c
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Judith, J., R. Tamilselvi, M. Parisa Beham, et al. "Remote Sensing Based Crop Health Classification Using NDVI and Fully Connected Neural Networks." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-G-2025 (July 28, 2025): 739–47. https://doi.org/10.5194/isprs-archives-xlviii-g-2025-739-2025.

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Abstract. Accurate crop health monitoring is not only essential for improving agricultural efficiency but also for ensuring sustainable food production in the face of environmental challenges. Traditional approaches often rely on visual inspection or simple NDVI measurements, which, though useful, fall short in detecting nuanced variations in crop stress and disease conditions. In this research, we propose a more sophisticated method that leverages NDVI data combined with a Fully Connected Neural Network (FCNN) to classify crop health with greater precision. The FCNN, trained using satellite i
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Dissertations / Theses on the topic "Fully connected neural network (FCNN)"

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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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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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Hensman, Paulina. "Intra-prediction for Video Coding with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224197.

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Intra-prediction is a method for coding standalone frames in video coding. Until now, this has mainly been done using linear formulae. Using an Artificial Neural Network (ANN) may improve the prediction accuracy, leading to improved coding efficiency. In this degree project, Fully Connected Networks (FCN) and Convolutional Neural Networks (CNN) were used for intra-prediction. Experiments were done on samples from different image sizes, block sizes, and block contents, and their effect on the results were compared and discussed. The results show that ANN methods have the potential to perform be
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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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(8784458), Qin He. "Learning Lighting Models with Shader-Based Neural Networks." Thesis, 2020.

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<p>To correctly reproduce the appearance of different objects in computer graphics applications, numerous lighting models have been proposed over the past several decades. These models are among the most important components in the modern graphics pipeline since they decide the final pixel color shown in the generated images. More physically valid parameters and functions have been introduced into recent models. These parameters expanded the range of materials that can be represented and made virtual scenes more realistic, but they also made the lighting models more complex and dependent on me
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Ting, YI-SIANG, and 丁弈翔. "Real-time Driver’s Eyes Tracking System using Semantics-based Vague Image Representation and Fully Connected Neural Network on Single Chip." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4c3zjh.

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碩士<br>國立中正大學<br>電機工程研究所<br>107<br>Abstract For a smart vehicle system design, driver’s attention monitoring system is essential to advanced driver assistance system (ADAS). Such technology can be achieved by using face tracking to detect distraction of driver. The computing process involves complicated feature extraction and pattern recognition so that design concepts of small dimension, high computing performance, and low power consumption are required in order to be implement in a vehicle. For this, this study focuses on the way to realize an efficient driving eyes tracking algorithm on a si
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Monteiro, Nelson Rodrigo Carvalho. "End-to-End Deep Learning Approach for Drug-Target Interaction Prediction." Master's thesis, 2019. http://hdl.handle.net/10316/87296.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>A descoberta de potenciais Interações Fármaco-Alvo é uma etapa determinante no processo de descoberta e reposicionamento de fármacos, uma vez que a eficácia do tratamento antibiótico disponível está a diminuir, provocado pelo aumento da sua utilização indevida. Apesar dos esforços colocados nos métodos tradicionais in vivo ou in vitro, o investimento financeiro farmacêutico foi reduzido ao longo dos anos. Desta forma, estabelecer métodos computacionais eficazes, é decisivo para
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Book chapters on the topic "Fully connected neural network (FCNN)"

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Savioli, Nicoló, Giovanni Montana, and Pablo Lamata. "V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation." In Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12029-0_30.

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Bird, Jordan J., Anikó Ekárt, Christopher D. Buckingham, and Diego R. Faria. "Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22871-2_52.

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Zhang, Qintian, Shenao Xu, and Zhiwei Xu. "Handwritten Digit Recognition Application Based on Fully Connected Neural Network." In Proceedings of the 11th International Conference on Computer Engineering and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6554-7_9.

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Wang, Zenghui, and Yanxia Sun. "Fully Connected Multi-Objective Particle Swarm Optimizer Based on Neural Network." In Advanced Intelligent Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24728-6_23.

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Shrivastava, Vineet, and Suresh Kumar. "Movie Recommendation Based on Fully Connected Neural Network with Matrix Factorization." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4831-2_44.

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Devi, M. Shyamala, Penikalapati Sai Akash Chowdary, Muddangula Krishna Sandeep, and Yeluri Praveen. "Quad Mount Fabricated Deep Fully Connected Neural Network Based Logistic Pricing Prediction." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_43.

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Liu, Yanfei, Yunqiao Yang, Yi Lin, et al. "Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Network." In Cerebral Aneurysm Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_9.

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Shyamala Devi, M., J. Arun Pandian, P. S. Ramesh, et al. "Oversampled Deep Fully Connected Neural Network Towards Improving Classifier Performance for Fraud Detection." In Advances in Data and Information Sciences. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5292-0_34.

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Yang, Dengjie, Junjie Cao, YingZhi Ma, Jiawei Yu, Shikun Jiang, and Liang Zhou. "Circular FC: Fast Fourier Transform Meets Fully Connected Layer for Convolutional Neural Network." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8126-7_38.

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Alexander, Kevin, Arya Wicaksana, and Ni Made Satvika Iswari. "Labeling Algorithm and Fully Connected Neural Network for Automated Number Plate Recognition System." In Applied Computing and Information Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25217-5_10.

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Conference papers on the topic "Fully connected neural network (FCNN)"

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Numpradit, Jeerasak, Pongsarun Boonyopakorn, and Suebsakul Charoensawat. "Malware Detection and Analysis Using Deep Learning Through Fully Connected Neural Network (FCNN)." In 2025 IEEE International Conference on Cybernetics and Innovations (ICCI). IEEE, 2025. https://doi.org/10.1109/icci64209.2025.10987292.

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Akhtar, Mohd Zubair, Christian Kreiner, Maximilian Schmid, Andreas Zippelius, Ulrich Tetzlaff, and Gordon Elger. "Fully Connected Neural Network (FCNN) Based Validation Framework for FEA Post Processing to Improve SAC Solder Reliability Analysis." In 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC). IEEE, 2024. http://dx.doi.org/10.1109/estc60143.2024.10712023.

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Tung, Wei-Cheng, Brijesh Patel, and Po Ting Lin. "Multi-Fidelity Design Optimization (MFDO) for Fully Connected Deep Neural Network (FCDNN)." In 2024 20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE, 2024. http://dx.doi.org/10.1109/mesa61532.2024.10704915.

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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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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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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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Li, Shuyi, Meng Deng, and Yi Wang. "Predicting Supercontinuum Generation in Silicon Waveguides with a Fully Connected Neural Network." In 2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR). IEEE, 2024. http://dx.doi.org/10.1109/cleo-pr60912.2024.10676449.

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Lu, Xiaochi, Haotian Li, and Dexin Zhao. "High Isolation Base Station Antenna Array Based on Fully Connected Neural Network Optimization." In 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618487.

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Hsin, Hsi-Chin, Chien-Kun Su, and Cheng-Ying Yang. "Fully Connected Neural Network Based Lifting Scheme with Adaptive Split for Image Compression." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674265.

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Baskar, N., Bhoomika S S, Mohammed Al-Farouni, Gotte Ranjith Kumar, and K. Deiwakumari. "Fully Connected Deep Convolutional Neural Network and Improved SURF for Land Cover Classification." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721645.

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Reports on the topic "Fully connected neural network (FCNN)"

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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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