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

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Strijhak, Sergei, Daniil Ryazanov, Konstantin Koshelev, and Aleksandr Ivanov. "Neural Network Prediction for Ice Shapes on Airfoils Using iceFoam Simulations." Aerospace 9, no. 2 (2022): 96. http://dx.doi.org/10.3390/aerospace9020096.

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In this article the procedure and method for the ice accretion prediction for different airfoils using artificial neural networks (ANNs) are discussed. A dataset for the neural network is based on the numerical experiment results—obtained through iceFoam solver—with four airfoils (NACA0012, General Aviation, Business Jet, and Commercial Transport). Input data for neural networks include airfoil and ice geometries, transformed into a set of parameters using a parabolic coordinate system and Fourier series expansion. Besides input features include physical parameters of flow (velocity, temperatu

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H, Archana. "Enhancing Text Classification Using Advanced Natural Language Processing Techniques." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04058.

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Abstract—This study evaluates the performance of var- ious neural network models and a pre-trained trans- former model in the task of quote classification. The models analyzed include Fully Connected Neural Networks (FCNN), Long Short-Term Memory (LSTM), Gated Re- current Units (GRU), Bi-directional LSTM (BiLSTM), and DistilBERT. The goal is to identify the most effective model for the given dataset based on key performance metrics such as accuracy, precision, and recall. DistilBERT, a lightweight transformer-based model, is also assessed for its efficiency and accuracy compared to traditional

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Wang, Bo, Wenyu Ma, Hui Jiang, and Shaowen Huang. "Research on Soft-Sensing Method Based on Adam-FCNN Inversion in Pichia pastoris Fermentation." Sensors 25, no. 13 (2025): 4105. https://doi.org/10.3390/s25134105.

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To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in Pichia pastoris fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. Firstly, a non-deterministic mechanism model is constructed to characterize the dynamic coupling relationships among multiple variables in the fermentation process, and the reversibility of the system and the construction method of the inverse extended model are analyzed. Further, by leveraging the n

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Zhou, Wen-Ying, Zhong-Lei Mei, Mai Lu, and Ya-Bo Zhu. "Two-Step Decoupling Design of a Microstrip Antenna Array by Using Waveguided Complementary Split-Ring Resonators and a Fully Connected Neural Network." International Journal of Antennas and Propagation 2023 (April 20, 2023): 1–10. http://dx.doi.org/10.1155/2023/1734637.

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To suppress the mutual coupling between closely-spaced patches, we propose a two-step decoupling design approach for a microstrip antenna array with the dimensions of 44.9 × 30.495 mm2. The first step is designing a decoupling unit on the basis of waveguided complementary split-ring resonators (WCSRRs) to improve isolation. The second step is presenting an optimization method by using a fully connected neural network (FCNN) to enhance design efficiency. By inserting WCSRRs structure between two patches with the edge-to-edge distance of 0.24λ0 (port-to-port distance with 0.66λ0), where λ0 is th

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Navale, Mr Nilesh Dagadu. "Cardiovascular Disease Detection in ECG Images: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 985–1008. https://doi.org/10.22214/ijraset.2025.73108.

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Cardiovascular diseases (CVDs) represent a major global health burden, necessitating accurate and early diagnosis for effective treatment. Traditional methods of ECG analysis rely heavily on expert interpretation, which is often time-consuming and prone to variability. This study proposes a hybrid deep learning model that combines visual analysis of electrocardiogram (ECG) images with structured clinical data to enhance cardiovascular disease detection. Convolutional neural networks (CNNs) such as AlexNet and SqueezeNet are utilized to extract salient features from ECG images, while a fully co

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Zhao, Linna, Shu Lu, and Dan Qi. "Improvement of Maximum Air Temperature Forecasts Using a Stacking Ensemble Technique." Atmosphere 14, no. 3 (2023): 600. http://dx.doi.org/10.3390/atmos14030600.

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Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The deep learning neural networks are more flexible but with high variance. Here, we proposed a stacking ensemble model named FLT, which consists of a fully connected neural network with embedded layers (ED-FCNN), a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to overcome the high variance of a single neural network a

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Yun, Jinhyeok, and Jungil Lee. "Prediction of Near-Wake Velocity in Laminar Flow over a Circular Cylinder Using Neural Networks with Instantaneous Wall Pressure Input." Applied Sciences 13, no. 12 (2023): 6891. http://dx.doi.org/10.3390/app13126891.

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In the present study, to predict the transverse velocity field in the near-wake of laminar flow over a circular cylinder at the Reynolds numbers of 60 and 300, we construct neural networks with instantaneous wall pressures on the cylinder surface as the input variables. For the two-dimensional unsteady flow at Re=60, a fully connected neural network (FCNN) is considered. On the other hand, for a three-dimensional unsteady flow at Re=300 having spanwise variations, we employ two different convolutional neural networks based on an encoder–FCNN (CNN-F) or an encoder–decoder (CNN-D) structure. Num

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Gustafsson, Erik, and Magnus Andersson. "Investigating the Effects of Labeled Data on Parameterized Physics-Informed Neural Networks for Surrogate Modeling: Design Optimization for Drag Reduction over a Forward-Facing Step." Fluids 9, no. 12 (2024): 296. https://doi.org/10.3390/fluids9120296.

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Physics-informed neural networks (PINNs) are gaining traction as surrogate models for fluid dynamics problems, combining machine learning with physics-based constraints. This study investigates the impact of labeled data on the performance of parameterized physics-informed neural networks (PINNs) for surrogate modeling and design optimization. Different training approaches, including physics-only, data-only, and several combinations of both, are evaluated using fully connected (FCNN) and Fourier neural network (FNN) architectures. The test case focuses on reducing drag over a forward-facing st

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Yuan, Deren, Xiaochun Xie, Gao Gao, and Ju Xiao. "Advances in Hyperspectral Image Classification with a Bottleneck Attention Mechanism Based on 3D-FCNN Model and Imaging Spectrometer Sensor." Journal of Sensors 2022 (August 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/7587157.

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Deep learning approaches have significantly enhanced the classification accuracy of hyperspectral images (HSIs). However, the classification process still faces difficulties such as those posed by high data dimensions, large data volumes, and insufficient numbers of labeled samples. To enhance the classification accuracy and reduce the data dimensions and training needed for labeled samples, a 3D fully convolutional neural network (3D-FCNN) model was developed by including a bottleneck attention module. In such a model, the convolutional layer replaces the downsampling layer and the fully conn

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Jiang, Bohui, and Weifeng Zhou. "Fishing operation type recognition based on multi-branch convolutional neural network using trajectory data." PeerJ Computer Science 11 (July 22, 2025): e3020. https://doi.org/10.7717/peerj-cs.3020.

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Accurate identification of fishing vessel operations is vital for sustainable fishery management. Existing methods inadequately exploit spatiotemporal contextual information in vessel trajectories and fail to effectively fuse multimodal data. To address this, this study proposes a novel framework integrating Geohash-based geocoding with embedding techniques inspired by natural language processing to extract spatiotemporal features from trajectory sequences. We develop a multi-branch 1D convolutional neural network (MB-1dCNN) to minimize feature engineering dependency while enhancing operationa

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Chu, Hongyang, Xinwei Liao, Peng Dong, Zhiming Chen, Xiaoliang Zhao, and Jiandong Zou. "An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)." Energies 12, no. 15 (2019): 2846. http://dx.doi.org/10.3390/en12152846.

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The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test mode

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Yang, Xiyun, Tianze Ye, Qile Wang, and Zhun Tao. "Diagnosis of Blade Icing Using Multiple Intelligent Algorithms." Energies 13, no. 11 (2020): 2975. http://dx.doi.org/10.3390/en13112975.

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The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm

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Al Imran, Ibrahim, and Mamun Rabbani. "Comparison of Deep Learning & Adaptive Algorithm Performance for De-Noising EEG." Journal of Physics: Conference Series 2325, no. 1 (2022): 012038. http://dx.doi.org/10.1088/1742-6596/2325/1/012038.

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Abstract Various forms of artifacts can readily contaminate an electroencephalogram recorded using surface electrodes. A comparison of several electroencephalogram (EEG) de-noising methods is shown here. Five distinct forms of noise are reduced using three different strategies, and the results are compared. These three procedures are Recursive Least Squares (RLS) adaptive algorithm, Least Mean Squares (LMS) method, and Fully Connected Neural Network (FCNN). The results are shown using time-domain plots of the real EEG signal, noisy EEG signal, and forecasted EEG signal. For comparing the perfo

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Alvarez-Gomez, Julio, Hubert Roth, and Jürgen Wahrburg. "Intraoperative Registration of 2D C-arm Images with Preoperative CT Data in Computer Assisted Spine Surgery: Motivation to Use Convolutional Neural Networks for Initial Pose Generator." Current Directions in Biomedical Engineering 5, no. 1 (2019): 25–28. http://dx.doi.org/10.1515/cdbme-2019-0007.

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AbstractIn this paper, we present an approach for getting an initial pose to use in a 2D/3D registration process for computer-assisted spine surgery. This is an iterative process that requires an initial pose close to the actual final pose. When using a proper initial pose, we get registrations within two millimeters of accuracy. Consequently, we developed a fully connected neural network (FCNN), which predicts the pose of a specific 2D image within an acceptable range. Therefore, we can use this result as the initial pose for the registration process. However, the inability of the FCNN for le

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Razumov, T. E., D. V. Churikov, and O. V. Kravchenko. "Application of convolutional neural networks in optical text recognition to junk data filtering." Journal of Physics: Conference Series 2127, no. 1 (2021): 012024. http://dx.doi.org/10.1088/1742-6596/2127/1/012024.

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Abstract In this paper, the problem of constructing a model for detecting and filtering unwanted spam messages is solved. A fully connected convolutional neural network (FCNN) was chosen as the model of the classifier of unwanted emails in email. It allows you to divide emails into two categories: spam and not spam. The main result of the research is a software application in the C++ language, which has a micro-service architecture and solves the problem of image classification. The app can handle more than 106 requests per minute in real-time.

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Rodimkov, Yury, Evgeny Efimenko, Valentin Volokitin, et al. "ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy." Entropy 23, no. 1 (2020): 21. http://dx.doi.org/10.3390/e23010021.

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When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected N

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Awais, Muhammad, Waqar Hussain, Nouman Rasool, and Yaser Daanial Khan. "iTSP-PseAAC: Identifying Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC." Current Bioinformatics 16, no. 5 (2021): 700–709. http://dx.doi.org/10.2174/1574893615666210108094431.

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Background: The uncontrolled growth due to accumulation of genetic and epigenetic changes as a result of loss or reduction in the normal function of Tumor Suppressor Genes (TSGs) and Pro-oncogenes is known as cancer. TSGs control cell division and growth by repairing of DNA mistakes during replication and restrict the unwanted proliferation of a cell or activities, those are the part of tumor production. Objectives: This study aims to propose a novel, accurate, user-friendly model to predict tumor suppressor proteins, which would be freely available to experimental molecular biologists to assi

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Andriyanov, Nikita A., David A. Petrosov, and Andrey V. Polyakov. "SELECTING AN ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR ASSESSING THE STATE OF A GENETIC ALGORITHM POPULATION IN THE PROBLEM OF STRUCTURAL-PARAMETRIC SYNTHESIS OF SIMULATION MODELS OF BUSINESS PROCESSES." SOFT MEASUREMENTS AND COMPUTING 12, no. 73 (2023): 70–81. http://dx.doi.org/10.36871/2618-9976.2023.12.007.

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This article proposes a study aimed at determining the architecture of artificial neural networks to solve the problem of determining the population state of a genetic algorithm adapted to solve the problem of structuralparametric synthesis of simulation models of business processes. As the initial data for training the artificial neural network, we used the results of computational experiments obtained when operating a genetic algorithm model based on mathematical nested Petri nets, which solves the problem of synthesizing business process models (Petri net models) based on a given behavior.

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Su, Guanpeng. "Analysis of the attack effect of adversarial attacks on machine learning models." Applied and Computational Engineering 6, no. 1 (2023): 1212–18. http://dx.doi.org/10.54254/2755-2721/6/20230607.

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The use of neural networks has produced outstanding results in a variety of domains, including computer vision and text mining. Numerous investigations in recent years have shown that using adversarial attacks technology to perturb the input samples weakly can mislead most mainstream neural network models, for example Fully Connected Neural Networks (FCNN) and Convolutional Neural Networks (CNN), to make wrong judgment results. Adversarial attacks can help researchers discover the potential defects of neural network models in terms of robustness and security so that people can comprehend the n

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Babu, Katta Rajesh, K. Charan Subhash, S. Sumanth, Ainala Karthik, G. Megana Ram, and D. Rajendra Prasad. "Performance Analysis of Face Forgery Recognition and Classification Using Advanced Deep Learning Methods." Metallurgical and Materials Engineering 31, no. 4 (2025): 355–67. https://doi.org/10.63278/1444.

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The adoption of web technology has come to be accompanied by a number of worrying security issues, one of which is deep fakes that are now counted among the top visual deceits in the field. The need for identifying such manipulations which is on the rise is the need for stronger methods that can be used to identify such manipulations. This article deals with the usage of fully connected neural networks (FCNN), convolutional neural networks (CNN), and deep convolutional neural networks (DCNN) to determine if a presented facial image is original or fake. In this case, the methods apply the use o

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Wang, Xun, Zhiyuan Zhang, Chaogang Zhang, Xiangyu Meng, Xin Shi, and Peng Qu. "TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture." International Journal of Molecular Sciences 23, no. 8 (2022): 4263. http://dx.doi.org/10.3390/ijms23084263.

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Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation s

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Broz, Matic, Marko Jukič, and Urban Bren. "Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning." Molecules 28, no. 20 (2023): 7046. http://dx.doi.org/10.3390/molecules28207046.

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Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper

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Passagem dos Santos, João, and Hugo Algarvio. "A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations." Energies 18, no. 6 (2025): 1467. https://doi.org/10.3390/en18061467.

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The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology

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De Filippi, Francesco Maria, Matteo Ginesi, and Giuseppe Sappa. "A Fully Connected Neural Network (FCNN) Model to Simulate Karst Spring Flowrates in the Umbria Region (Central Italy)." Water 16, no. 18 (2024): 2580. http://dx.doi.org/10.3390/w16182580.

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In the last decades, climate change has led to increasingly frequent drought events within the Mediterranean area, creating an urgent need of a more sustainable management of groundwater resources exploited for drinking and agricultural purposes. One of the most challenging issues is to provide reliable simulations and forecasts of karst spring discharges, whose reduced information, as well as the hydrological processes involving their feeding aquifers, is often a big issue for water service managers and researchers. In order to plan a sustainable water resource exploitation that could face fu

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Li, Haiyu, Heungjin Chung, Zhenting Li, and Weiping Li. "Compressive Strength Prediction of Fly Ash-Based Concrete Using Single and Hybrid Machine Learning Models." Buildings 14, no. 10 (2024): 3299. http://dx.doi.org/10.3390/buildings14103299.

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The compressive strength of concrete is a crucial parameter in structural design, yet its determination in a laboratory setting is both time-consuming and expensive. The prediction of compressive strength in fly ash-based concrete can be accelerated through the use of machine learning algorithms with artificial intelligence, which can effectively address the problems associated with this process. This paper presents the most innovative model algorithms established based on artificial intelligence technology. These include three single models—a fully connected neural network model (FCNN), a con

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Yuliansyah, Diannata Rahman, Min-Chun Pan, and Ya-Fen Hsu. "Sensor-to-Image Based Neural Networks: A Reliable Reconstruction Method for Diffuse Optical Imaging of High-Scattering Media." Sensors 22, no. 23 (2022): 9096. http://dx.doi.org/10.3390/s22239096.

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Imaging tasks today are being increasingly shifted toward deep learning-based solutions. Biomedical imaging problems are no exception toward this tendency. It is appealing to consider deep learning as an alternative to such a complex imaging task. Although research of deep learning-based solutions continues to thrive, challenges still remain that limits the availability of these solutions in clinical practice. Diffuse optical tomography is a particularly challenging field since the problem is both ill-posed and ill-conditioned. To get a reconstructed image, various regularization-based models

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D.Kiruthika and Judith J. "Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Modified Fully connected Convolutional Neural Networks." International Journal of Innovative Science and Research Technology 7, no. 12 (2022): 569–77. https://doi.org/10.5281/zenodo.7490772.

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Knee Osteoarthritis (OA) is an extremely common and degenerative musculoskeletal disease worldwide which creates a significant burden on patients with reduced quality of life and also on society because of its financial impact. Therefore, technical try and efforts to reduce the burden of the disease could help both patients and society. In this paper, an automated novel method is proposed with a supported combination of joint shape and modified Fully connected neural network (FCNN) based bone texture features, to differentiate between the knee radiographs with and without osteoarthritis. Moreo

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Chen, Kun, Ting Liu, and Xiaoming Wang. "GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System." Electronics 13, no. 21 (2024): 4178. http://dx.doi.org/10.3390/electronics13214178.

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The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)–assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (

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Haggag, Sayed, Fahmi Khalifa, Hisham Abdeltawab, et al. "An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images." Sensors 21, no. 16 (2021): 5457. http://dx.doi.org/10.3390/s21165457.

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Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically s

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Tejal Shah. "Deep Learning for Non-Linear Black-Scholes Model in an Illiquid Financial Market with Transaction Costs." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 320–28. https://doi.org/10.52783/cana.v32.3678.

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A topic of interest in financial mathematics is the Black-Scholes model. However, the underlying asset price in the stock market may not be satisfied by this linear model, which was developed under a number of assumptions, including liquidity and the absence of transaction costs. The linear model has restricted its precision in actual market conditions. We study the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model. Using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlin

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Hamidreza Emami, Et al. "Improving Traffic Sign Recognition by Using Wavelet Convolutional Neural Network." Power System Technology 47, no. 4 (2023): 183–95. https://doi.org/10.52783/pst.169.

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Traffic sign recognition (TSR) considered as a challenging subject in image processing for many years. Nowadays, after achievements in processing power of processors and easily accessible datasets, many researches has been done by using convolutional neural networks (CNN) In many applications including TSR. CNN is a popular deep learning method that has a reasonable functionality in image classification and pattern recognition. Important factors in performance of a CNN can be written as follows: accuracy, efficiency and the precision. Therefore, in this paper we try to achieve better results i

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Xiang, Yiran, Songting Li, and Lihu Chen. "Adaptive Beamforming for On-Orbit Satellite-Based ADS-B Based on FCNN." Sensors 24, no. 21 (2024): 7065. http://dx.doi.org/10.3390/s24217065.

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Digital multi-beam synthesis technology is generally used in the on-orbit satellite-based Automatic Dependent Surveillance–Broadcast (ADS-B) system. However, the probability of successfully detecting aircraft with uneven surface distribution is low. An adaptive digital beamforming method is proposed to improve the efficiency of aircraft detection probability. The current method has the problem of long operation time and is not suitable for on-orbit operation. Therefore, this paper proposes an adaptive beamforming method for the ADS-B system based on a fully connected neural network (FCNN). The

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Chen, Changhe, and Rongbo Zhang. "Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification." Theoretical and Natural Science 28, no. 1 (2023): 78–84. http://dx.doi.org/10.54254/2753-8818/28/20230401.

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Respiratory diseases are one of the leading causes of death around the world and they severely affect patient quality of life. Auscultation is an essential method for diagnosing respiratory diseases, and it is low-cost and convenient. However, auscultation requires experts who are highly experienced. Medical trainees suffer from misdiagnosis inevitably. To address this issue, a novel machine learning model is proposed, which consists of upsampling convolutional neural network (CNN), a long short-term memory network (LSTM), and a fully connected network (FCNN) with embedding layers to classify

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Vizel, Miki, Roger Alimi, Daniel Lahav, Moty Schultz, Asaf Grosz, and Lior Klein. "Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms." Applied Sciences 15, no. 2 (2025): 964. https://doi.org/10.3390/app15020964.

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We use an array of nine elliptical Planar Hall Effect (PHE) sensors and machine learning algorithms to map the magnetic signal generated by a magnetic source. Based on the obtained mapping, the location and nature of the magnetic source can be determined. The sensors are positioned at the vertices of a symmetrical and evenly spaced 3 × 3 grid. The main electronic card orchestrates their measurement by supplying the required driving current and amplifying and sampling their output in a synchronized manner. A two-dimensional interpolation of the data collected from the nine sensors fails to yiel

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Wang, Ying, Zhansheng Mao, Hexian Jin, Abbas Shafi, Zhenyu Wang, and Dan Liu. "Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces." Agronomy 14, no. 5 (2024): 938. http://dx.doi.org/10.3390/agronomy14050938.

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Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the fields of urban planning and environmental management. This complexity arises from two main challenges: the limitations in acquiring high-density, high-precision data, and the difficulties traditional methods face in capturing and modeling the complex nonlinear relationships between environmenta

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Li, Xudong, Yanjun Li, Yuyuan Cao, Shixuan Duan, Xingye Wang, and Zejian Zhao. "Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization." Applied Sciences 12, no. 17 (2022): 8562. http://dx.doi.org/10.3390/app12178562.

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Contrapose the highly integrated, multiple types of faults and complex working conditions of aircraft electro hydrostatic actuator (EHA), to effectively identify its typical faults, we propose a fault diagnosis method based on fusion convolutional neural networks (FCNN). First, the aircraft EHA fault data is encoded by gram angle difference field (GADF) to obtain the fault feature images. Then we build a FCNN model that integrates the 1DCNN and 2DCNN, where the original 1D fault data is the input of the 1DCNN model, and the feature images obtained by GADF transformation are used as the input o

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Liu, Na, Haiming Mou, Jun Tang, Lihong Wan, Qingdu Li, and Ye Yuan. "Fully Connected Hashing Neural Networks for Indexing Large-Scale Remote Sensing Images." Mathematics 10, no. 24 (2022): 4716. http://dx.doi.org/10.3390/math10244716.

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With the emergence of big data, the efficiency of data querying and data storage has become a critical bottleneck in the remote sensing community. In this letter, we explore hash learning for the indexing of large-scale remote sensing images (RSIs) with a supervised pairwise neural network with the aim of improving RSI retrieval performance with a few binary bits. First, a fully connected hashing neural network (FCHNN) is proposed in order to map RSI features into binary (feature-to-binary) codes. Compared with pixel-to-binary frameworks, such as DPSH (deep pairwise-supervised hashing), FCHNN

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Song, Yuxuan, Dezhi Wang, Xiaodan Xiong, Xinghua Cheng, Lingzhi Huang, and Yichao Zhang. "The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model." Journal of Marine Science and Engineering 12, no. 12 (2024): 2262. https://doi.org/10.3390/jmse12122262.

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At present, unmanned maritime equipment has become the main force in the implementation of marine exploration tasks. However, due to the complexity of the marine environment, equipment is susceptible to damage and loss. This is why achieving more effective search and rescue (SAR) of unmanned maritime equipment plays an extremely important role. The drifting trajectory and range predicted by the traditional methods are normally no longer corrected dynamically, which results in a low SAR efficiency. In this work, we propose a trajectory prediction and dynamic correction method based on a fully c

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Jiang, Jiazhou. "Research on the Application of Convolutional Neural Networks on MNIST Datasets." Applied and Computational Engineering 109, no. 1 (2024): 189–96. https://doi.org/10.54254/2755-2721/2024.18132.

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This paper explores the application of Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks (FCNNs) on the MNIST dataset for handwritten digit recognition. The study underscores the significance of digit recognition in various sectors, including automation, accessibility, data validation, security, education, and as a foundation for complex AI systems. The research compares the performance of FCNNs and CNNs, highlighting the latter's superiority due to its ability to capture spatial features and resist overfitting. The paper details the structure and evaluation of both netw

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Hangawatta, Dilan C., Ameen Gargoom, and Abbas Z. Kouzani. "Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data." Energies 18, no. 1 (2024): 128. https://doi.org/10.3390/en18010128.

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Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. This study addresses this gap by proposing a novel method that employs a fully connected neural network (FCNN) to predict household phases from energy consumption data. The research utilizes the IEEE European Low Voltage Testing Feeder dataset, which includes one-minute energy consumption readings for 55 households over a full

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