Relevant bibliographies by topics / Fully connected Neural Network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Fully connected Neural Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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Journal articles on the topic "Fully connected Neural Network"

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Zhang, Wei, Zhi Han, Xiai Chen, Baichen Liu, Huidi Jia, and Yandong Tang. "Fully Kernected Neural Networks." Journal of Mathematics 2023 (June 28, 2023): 1–9. http://dx.doi.org/10.1155/2023/1539436.

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In this paper, we apply kernel methods to deep convolutional neural network (DCNN) to improve its nonlinear ability. DCNNs have achieved significant improvement in many computer vision tasks. For an image classification task, the accuracy comes to saturation when the depth and width of network are enough and appropriate. The saturation accuracy will not rise even by increasing the depth and width. We find that improving nonlinear ability of DCNNs can break through the saturation accuracy. In a DCNN, the former layer is more inclined to extract features and the latter layer is more inclined to classify features. Therefore, we apply kernel methods at the last fully connected layer to implicitly map features to a higher-dimensional space to improve nonlinear ability so that the network achieves better linear separability. Also, we name the network as fully kernected neural networks (fully connected neural networks with kernel methods). Our experiment result shows that fully kernected neural networks achieve higher classification accuracy and faster convergence rate than baseline networks.
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Chen, Qipin, and Wenrui Hao. "A homotopy training algorithm for fully connected neural networks." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2231 (2019): 20190662. http://dx.doi.org/10.1098/rspa.2019.0662.

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In this paper, we present a homotopy training algorithm (HTA) to solve optimization problems arising from fully connected neural networks with complicated structures. The HTA dynamically builds the neural network starting from a simplified version and ending with the fully connected network via adding layers and nodes adaptively. Therefore, the corresponding optimization problem is easy to solve at the beginning and connects to the original model via a continuous path guided by the HTA, which provides a high probability of obtaining a global minimum. By gradually increasing the complexity of the model along the continuous path, the HTA provides a rather good solution to the original loss function. This is confirmed by various numerical results including VGG models on CIFAR-10. For example, on the VGG13 model with batch normalization, HTA reduces the error rate by 11.86% on the test dataset compared with the traditional method. Moreover, the HTA also allows us to find the optimal structure for a fully connected neural network by building the neutral network adaptively.
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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 connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generalization capabilities. The conclusion is that neural network models generally have better results than traditional algorithms in house price prediction, and the neural network model of CNN composite has significantly better prediction performance.
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Erichsen, R., W. K. Theumann, and D. R. C. Dominguez. "Categorization in fully connected multistate neural network models." Physical Review E 60, no. 6 (1999): 7321–31. http://dx.doi.org/10.1103/physreve.60.7321.

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Hsu, K. Y., H. Y. Li, and D. Psaltis. "Holographic implementation of a fully connected neural network." Proceedings of the IEEE 78, no. 10 (1990): 1637–45. http://dx.doi.org/10.1109/5.58357.

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Sergeev, Fedor, Elena Bratkovskaya, Ivan Kisel, and Iouri Vassiliev. "Deep learning for quark–gluon plasma detection in the CBM experiment." International Journal of Modern Physics A 35, no. 33 (2020): 2043002. http://dx.doi.org/10.1142/s0217751x20430022.

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Classification of processes in heavy-ion collisions in the CBM experiment (FAIR/GSI, Darmstadt) using neural networks is investigated. Fully-connected neural networks and a deep convolutional neural network are built to identify quark–gluon plasma simulated within the Parton-Hadron-String Dynamics (PHSD) microscopic off-shell transport approach for central Au+Au collision at a fixed energy. The convolutional neural network outperforms fully-connected networks and reaches 93% accuracy on the validation set, while the remaining only 7% of collisions are incorrectly classified.
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Li, Gang, Xing San Qian, Chun Ming Ye, and Lin Zhao. "A Clustering Method for Pruning Fully Connected Neural Network." Advanced Materials Research 204-210 (February 2011): 600–603. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.600.

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This paper focuses mainly on a clustering method for pruning Fully Connected Backpropagation Neural Network (FCBP). The initial neural network is fully connected, after training with sample data, a clustering method is employed to cluster weights between input to hidden layer and from hidden to output layer, and connections that are relatively unnecessary are deleted, thus the initial network becomes a PCBP (Partially Connected Backpropagation) Neural Network. PCBP can be used in prediction or data mining more efficiently than FCBP. At the end of this paper, An experiment is conducted to illustrate the effects of PCBP using the submersible pump repair data set.
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Qian, Wei, and Yijie Wang. "Analyzing E-Commerce Market Data Using Deep Learning Techniques to Predict Industry Trends." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/joeuc.342093.

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Faced with challenges in sales predicting research, this article combines the capabilities of deep learning algorithms in handling complex tasks and unstructured data. Through analyzing consumer behavior, it selects factors influencing sales, including images, prices and discounts, and historical sales, as input variables for the model. Three different types of neural network models-fully connected neural networks, convolutional neural networks, and recurrent neural networks-are employed to process structured data, image data, and sales sequence data, respectively. This forms a deep neural network for feature representation. Subsequently, based on the outputs of these three types of deep neural networks, a fully connected neural network is employed to train the sales prediction model. Ultimately, experimental results demonstrate that the proposed sales prediction method outperforms exponential regression and shallow neural networks in terms of accuracy.
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GUSSO, M., C. MARANGI, G. NARDULLI, and G. PASQUARIELLO. "DYNAMICS OF FULLY CONNECTED NEURAL NETWORKS WITH SIGN CONSTRAINTS." International Journal of Modern Physics C 03, no. 06 (1992): 1221–33. http://dx.doi.org/10.1142/s0129183192000841.

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We consider fully connected neural networks near saturation, trained by a modified Edinburgh algorithm, with sign constraints on the synaptic couplings. We study the domains of attraction of the stored patterns for both the balanced and the unbalanced case (excess of positive over negative constraints). A comparison with the dilute network is also included.
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Shapalin, Vitaliy Gennadiyevich, and Denis Vladimirovich Nikolayenko. "Comparison of the structure, efficiency, and speed of operation of feedforward, convolutional, and recurrent neural networks." Research Result. Information technologies 9, no. 4 (2024): 21–35. https://doi.org/10.18413/2518-1092-2024-9-4-0-3.

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This article examines the efficiency of fully connected, recurrent, and convolutional neural networks in the context of developing a simple model for weather forecasting. The architectures and working principles of fully connected neural networks, the structure of one-dimensional and two-dimensional convolutional neural networks, as well as the architecture, features, advantages, and disadvantages of recurrent neural networks—specifically, simple recurrent neural networks, LSTM, and GRU, along with their bidirectional variants for each of the three aforementioned types—are discussed. Based on the available theoretical materials, simple neural networks were developed to compare the efficiency of each architecture, with training time and error magnitude serving as criteria, and temperature, wind speed, and atmospheric pressure as training data. The training speed, minimum and average error values for the fully connected neural network, convolutional neural network, simple recurrent network, LSTM, and GRU, as well as for bidirectional recurrent neural networks, were examined. Based on the results obtained, an analysis was conducted to explore the possible reasons for the effectiveness of each architecture. Graphs were plotted to show the relationship between processing speed and error magnitude for the three datasets examined: temperature, wind speed, and atmospheric pressure. Conclusions were drawn about the efficiency of specific models in the context of forecasting time series of meteorological data.
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Dissertations / Theses on the topic "Fully connected Neural Network"

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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 developed. These schemes are developed using the artificial neural network (ANN). A number of techniques can be found in the literature that address FDIA in aircraft systems. These techniques are, for example, Kalman filters, fuzzy logic and ANN. This research uses the fully connected cascade (FCC) neural network (NN) for the development of the SFDIA and AFDIA schemes. Based on the study presented in the literature, this NN architecture is compact and efficient in comparison to the multi-layer perceptron (MLP) NN, which is a popular choice for NN applications. This is the first reported instance of the use of the FCC NN for fault tolerance applications, especially in the aerospace domain. For this research, the X-Plane 9 flight simulator is used for data collection and as a test bed. This simulator is well known for its realistic simulations and is certified by the Federal Aviation Administration (FAA) for pilot training. The developed SFDIA scheme adds endurance to an aircraft in the presence of failures in the aircraft pitch, roll and yaw rate gyro sensors. The SFDIA scheme is able to replace a faulty gyro sensor with a FCC NN based estimate, with as few as 2 neurons. In total, 105 failure experiments were conducted, out of which only 1 went undetected. In the developed AFDIA scheme, a FCC NN based roll controller is employed, which uses just 5 neurons. This controller can adapt on-line to the post failure dynamics of the aircraft following a 66\% loss of wing surface. With 66\% of the wing surface missing, the NN based roll controller is able to maintain flight. This is a remarkable display of endurance by the AFDIA scheme, following such a severe failure. The results presented in this research validate the use of FCC NNs for SFDIA and AFDIA applications.
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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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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 better or on par with the video coder High Efficiency Video Coding (HEVC) in the intra-prediction task. The proposed ANN designs perform better on smaller block sizes, but different designs could lead to better performance on larger block sizes. It was found that training one network for each HEVC mode and using the most suitable network to predict each block improved performance of the ANN approach.<br>Intra-prediktion är en metod för kodning av stillbilder i videokodning. Hittills har detta främst gjorts med hjälp av linjära formler. Användning av artificialla neuronnät (ANN) skulle kunna öka prediktionsnoggrannheten och ge högre effektivitet vid kodning. I detta examensarbete användes fully connected networks (FCN) och convolutional neural networks (CNN) för att utföra intra-prediktion. Experiment gjordes på prover från olika bildstorlekar, blockstorlekar och blockinnehåll, och de olika parametrarnas effekt på resultaten jämfördes och diskuterades. Resultaten visar att ANN-metoder har potential att prestera bättre eller lika bra som videokodaren High Efficiency Video Coding (HEVC) för intra-prediktion. De föreslagna ANN-designerna presterar bättre på mindre blockstorlekar, men andra ANN-designs skulle kunna ge bättre prestanda för större blockstorlekar. Det konstaterades att prestandan för ANN-metoderna kunde ökas genom att träna ett nätverk för varje HEVC-mode och använda det mest passande nätverket för varje block.
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Cun, Xiao Dong. "Image splicing localization via semi-global network and fully connected conditional random fields." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950634.

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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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Bingham, Philip R. "The effect of message length distribution on the performance of fully connected switches." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15389.

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Shishani, Basel. "Segmentation of connected text using constrained neural networks." Thesis, Queensland University of Technology, 1997.

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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 metastatic melanoma. This pipeline includes processes for annotating and preparing a dataset from the output of a tissue slide scanner to the patch-based training and inference by an artificial neural network. We have curated a large dataset of 50 whole slide images containing cutaneous melanoma or cutaneous metastatic melanoma that are fully annotated at 40× ob- jective resolution by an expert pathologist. We will publish the source images of this dataset online. We also present two new FCN architectures that fuse multiple deconvolutional strides, combining coarse and fine predictions to improve accuracy over similar networks without multi-stride information. Our results show that the system performs better than our comparators. We include inference results on thousands of patches from four whole slide images, reassembling them into whole slide segmentation masks to demonstrate how our system generalizes on novel cases.
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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 above, we have proposed multiple novel models with favorable performances. Specifically, we first systematically reviewed the developments of saliency detection and its related works, and then proposed four new methods, with two based on low-level image features, and two based on DNNs. The regularized random walks ranking method (RR) and its reversion-correction-improved version (RCRR) are based on conventional low-level image features, which exhibit higher accuracy and robustness in extracting the image boundary based foreground / background queries; while the background search and foreground estimation (BSFE) and dense and sparse labeling (DSL) methods are based on DNNs, which have shown their dominant advantages in high-level image feature extraction, as well as the combined strength of multi-dimensional features. Each of the proposed methods is evaluated by extensive experiments, and all of them behave favorably against the state-of-the-art, especially the DSL method, which achieves remarkably higher performance against sixteen state-of-the-art methods (including ten conventional methods and six learning based methods) on six well-recognized public datasets. The successes of our proposed methods reveal more potential and meaningful applications of saliency detection in real-life computer vision tasks.
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Books on the topic "Fully connected Neural Network"

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NoteBooks, Sappuris. Teacher Planner, High Technology Neural Network Connected Cells with Links Cover. Independently Published, 2021.

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Smith, Matthew Wilson. The Nervous System. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190644086.003.0004.

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During the 1860s, a cult of sensation took full hold of cultures across much of Western Europe and North America; as the term “sensation” implies, this craze was linked to developments in neurology. This chapter focuses on one particular network in the construction of the modern neural subject, a network that connects elements as seemingly diverse as railway trains, changing notions of risk and trauma, and the newly popular form of melodrama dubbed “sensation drama,” with the emblematic scenario of the person tied to the train tracks and rescued in the nick of time. This “railway rescue” scenario emerged in the late 1860s, spread like wildfire, and continued in our collective consciousness to the present. This chapter traces the explosive rise and iconic significance of this scenario, and concludes by reading a Dickens short story that reflects on melodrama and the ghostly traumas of industrialized sensation.
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Steane, Andrew. The Human Being. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824589.003.0021.

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The nature of human being is considered. To be human is to be a part of a network of connected people who bear with one another, and teach and support and receive and give one another. Our life comes both from below and from above: that is to say, both from the physical structures of the world, and from the shaping influence which moulds what those structures can express. This is especially true of the way we see ourselves and each other. Humans both build on existing resources, and also receive creative inspiration. This inspiration is not able to be fully captured in impersonal language. We find our life in full by being willing to admit this.
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Stewart, Edmund. Greek Tragedy on the Move. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198747260.001.0001.

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This work is one of the first full studies of the dissemination of Greek tragedy in the archaic and classical periods. Drawing on recent research in network theory, it seeks to reinterpret classical tragedy as a Panhellenic art form. It thereby offers a radically new perspective on the interpretation of the extant tragic texts, which have often been seen as the product of the fifth-century Athenian democracy. Tragedy grew out of, and became part of, a common Greek (or Panhellenic) culture, which was itself sustained by frequent travel and exchange. This book shows how Athens was a major Panhellenic centre within a wider and, by the fifth century, well-established network of festivals and patrons. The part played by non-Athenians in the festival culture of Attica is fully reassessed and it is estimated that as much as a quarter of all tragic poets who produced plays in Athens during the classical period were non-citizens. In addition, the book re-examines the evidence for tragedies that were probably or certainly performed outside Athens and shows how and why they were calculated to appeal to a broad Panhellenic audience. The stories they contained were themselves tales of travel. Together the works of the tragedians told and reworked the history of the Greek peoples and showed how they were connected through the wanderings of their ancestors. Tragedy, like the poets and their creations, was meant to travel and this is the first full study of tragedy on the move in the archaic and classical periods.
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Balyshev, Marat. Astronomical research in Kharkiv at the end of the 19th century – the first half of the 20th century. “Naukova Dumka”, 2022. http://dx.doi.org/10.15407/978-966-00-1863-1.

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The main milestones of the formation and development of astronomical science in Kharkiv during 1883–1945 are reconstructed on the example of the activities of the astronomical observatory of Kharkiv University. During this period, the outstanding worldview science in Kharkiv has achieved significant success: the works of Kharkiv astronomers have received world recognition; a well-known scientific planetary school has been established at the Observatory; the scientific community highly appreciated the research on the physics and chemistry of the Moon, the giant and small planets of the Solar System. The primary goal of the research is to inscribe the history of the university Observatory into the European and world context. Its purpose is to summarize the results of a comprehensive historical ad scientific study of the development of astronomical research in Kharkiv at the end of the 19th century – the first half of the 20th century and identification of ways of further scientific research. The completed research, which continues the problems of works devoted to the study of the history of astronomical science in Ukraine, focuses on expanding the well-known source base by attracting new retro-information resources. In particular, the monograph used a significant array of archival primary sources from almost twenty archival and library institutions of different countries. Most of them were introduced into scientific circulation for the first time, which allowed to determine and specify the sequence of stages of development of astronomical science in Kharkiv during the research period, to clarify and identify the little-known circumstances of the observatory life. The methodological basis of the study is the principles of historism, objectivity and a systematic approach to studying the problem. To solve specific problematic tasks in the monograph, general scientific and specially historical methods were used which allowed to study, analyze and summarize the presented factual material in a complex manner. The main sections of the monograph represent the dynamics of replenishment of the instrumental base of the university observatory, the chronology of the construction of the observatory complex of buildings at the location of the modern Scientific Research Institute of Astronomy of the V. N. Karazin Kharkiv National University. According to the author’s periodization, the stages of formation of subjects and directions of scientific work of university astronomers have been analyzed, including: seismic observations with the help of horizontal Rebeur-Paschwitz pendulums, research of the activity of the Sun, astrometric observations on the Repsold meridian circle of for the purpose of compiling a catalog of zodiac stars, studying lunar eclipses and meteor showers. The participation of university astronomers in the creation of the plan of the city of Kharkiv and its connection with the general network of precise geometric leveling of the Military Topographic Department of the General Staff; the organization of observations by an expedition of Kharkiv astronomers of the total Solar eclipse of 1914 in Henichesk; the creation of the School-workshop of precision mechanics at the Faculty of Physics and Mathematics of Kharkiv University were considered; information on the participation of Kharkiv astronomers in the events of the civil war during the Ukrainian Revolution was documented. The scientific research activity of Kharkiv astronomers during 1920-1930-s which was devoted to carrying out important astrometric works on meridian observations of star declinations by absolute methods and observations of Kopf-Rentz stars according to the programs of the International Astronomical Union; the initiation of the creation of the Catalog of faint stars; research in astrophysics aimed at studying the physical conditions on the Moon and the Sun, planets and the interstellar environment; performing long series of spectrophotometric observations of the Moon, Jupiter, Mars and Saturn under different conditions of observation; study of the kinematics of stellar systems of different order, the physical parameters and evolution of stars, the morphology of the Galaxy, the nature of the stellar subsurfaces and atmospheres, dust and gas nebulae, new stars and the variability of stars have been considered; the directions of solid works carried out in the field of celestial mechanics, devoted to the dynamics of the minor planets of the Jupiter group, the definition and improvement of the orbits of minor planets have been clarified. The development of amateur astronomy in Kharkiv, in particular, the functioning of circles and societies that directed their activities to the dissemination of astronomical knowledge, was highlighted; the participation of their representatives in astronomical observations at the Kharkiv Astronomical Observatory was emphasized. Reconstructed the development of historical events in the 1930s related to the involvement of Soviet and Western astronomers in the processes of political confrontation between the USSR and the Western world; investigated the course of circumstances that prevented the implementation of the project of creating a new modern astronomical center of national importance – the central Ukrainian observatory in Kharkiv; the participation of an expedition of Kharkiv astronomers in the observation of the «great Soviet eclipse» – the total solar eclipse of 1936 – in the North Caucasus is highlighted; established the facts of political «purges» and repressions by the People’s Commissariat for Internal Affairs ( the NKVD) in the Kharkiv Astronomical Observatory. The activity of the Kharkiv Astronomical Observatory has been documented and authentic biographical information about its representatives during the Nazi occupation of 1941–1943, the period of the German-Soviet war, has been presented; the unpopular facts of the forced collaboration of some scientists are highlighted; the process of recovery and reconstruction of the Kharkiv Astronomical Observatory after the liberation of the city is characterized. With the aim of researching the personal history of Kharkiv astronomy of the studied period, the monograph presents the results of a historical and biographical study of facts of life and scientific heritage of scientists who fully devoted themselves to Science, laid the foundations for the future development of many directions of modern astronomical research, made a significant contribution to the treasury of the national and European astronomical science, whose activities were connected with the Kharkiv Astronomical Observatory, in particular: Grigory Levytsky, Ludwig Struve, Mykola Evdokymov, Otto Struve, Mykola Barabashov, Boris Gerasimovich, Vasil Fesenkov, Oleksiy Razdolsky, Boris Ostashchenko-Kudryavtsev, Nicholas Bobrovnikov, Paraskovia Parkhomenko, Mstislav Savron, Boris Semeykin, Kostyantyn Savchenko and others (25 biographical essays are presented). A significant part of the mentioned factual material was also introduced into scientific circulation for the first time. A separate section of the monograph provides chronologically structured information that reflects the sequence of research work of the Kharkiv Astronomical Observatory employees during the period under study: from astrometric observations of stars and seismic research to spectrohelioscopic and spectroheliographic observations of the Sun and the initiation of the Kharkiv school of planetary science. It is assumed that the materials of the monograph will be used in research work devoted to the study of the process of institutionalization of astronomical research in Kharkiv at the end of the 19th century – the first half of the 20th century.
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Book chapters on the topic "Fully connected Neural Network"

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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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Quasdane, Mohamed, Hassan Ramchoun, and Tawfik Masrour. "Learning Sparse Fully Connected Layers in Convolutional Neural Networks." In Artificial Intelligence and Industrial Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43520-1_16.

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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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Hubert, Christine. "Design of fully and partially connected random neural networks for pattern completion." In New Trends in Neural Computation. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56798-4_137.

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Akbar, Shazia, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, and Anne Martel. "Transitioning Between Convolutional and Fully Connected Layers in Neural Networks." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67558-9_17.

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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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Conference papers on the topic "Fully connected Neural Network"

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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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Meshkinfam, Behdokht, Jovana Samaniego, Vaishnavi Dabhade, and Feruza A. Amirkulova. "Convolutional neural networks and fully connected neural networks for acoustic super-focusing." In Metamaterials, Metadevices, and Metasystems 2024, edited by Nader Engheta, Mikhail A. Noginov, and Nikolay I. Zheludev. SPIE, 2024. http://dx.doi.org/10.1117/12.3037618.

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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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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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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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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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Reports on the topic "Fully connected Neural Network"

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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 attention-enhanced atrous convolution-based efficient network (WACEfNet), a new convolutional neural network designed specifically for real-time visual classification challenges using resource-constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width-wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive over-head. Extensive benchmarking confirms state-of-the-art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high-fidelity real-time analytics across a variety of embedded perception paradigms.
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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, i.e., by how much monetary policy shifts real rates in the ergodic distribution of the model, can be substantial when we combine low inflation targets and high levels of wealth inequality.
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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 ResNet50V2 architecture, our algorithm effectively leverages learned features for precise detection. We hypothesize that controlled feature extraction using a deeper pretrained CNN model can selectively generate the most relevant feature maps adapting to the domain, thereby improving performance. Experimental results demonstrate that SandBoilNet outperforms state-of-the-art semantic segmentation methods in accurately detecting sand boils, achieving a Balanced Accuracy (BA) of 85.52%, Macro F1-score (MaF1) of 73.12%, and an Intersection over Union (IoU) of 57.43% specifically for sand boils. This proposed approach represents a novel and effective solution for accurately detecting and segmenting sand boils from levee images toward automating the monitoring and maintenance of levee infrastructure.
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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 the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems.
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Milek, Karen, and Richard Jones, eds. Science in Scottish Archaeology: ScARF Panel Report. Society of Antiquaries of Scotland, 2012. http://dx.doi.org/10.9750/scarf.06.2012.193.

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The main recommendations of the panel report can be summarised under four key headings:  High quality, high impact research: the importance of archaeological science is reflected in work that explores issues connected to important contemporary topics, including: the demography of, the nature of movement of, and contact between peoples; societal resilience; living on the Atlantic edge of Europe; and coping with environmental and climatic change. A series of large-scale and integrated archaeological science projects are required to stimulate research into these important topics. To engage fully with Science in Scottish Archaeology iv these questions data of sufficient richness is required that is accessible, both within Scotland and internationally. The RCAHMS’ database Canmore provides a model for digital dissemination that should be built on.  Integration: Archaeological science should be involved early in the process of archaeological investigation and as a matter of routine. Resultant data needs to be securely stored, made accessible and the research results widely disseminated. Sources of advice and its communication must be developed and promoted to support work in the commercial, academic, research, governmental and 3rd sectors.  Knowledge exchange and transfer: knowledge, data and skills need to be routinely transferred and embedded across the archaeological sector. This will enable the archaeological science community to better work together, establishing routes of communication and improving infrastructure. Improvements should be made to communication between different groups including peers, press and the wider public. Mechanisms exist to enable the wider community to engage with, and to feed into, the development of the archaeological and scientific database and to engage with current debates. Projects involving the wider community in data generation should be encouraged and opportunities for public engagement should be pursued through, for example, National Science Week and Scottish Archaeology Month.  Networks and forums: A network of specialists should be promoted to aid collaboration, provide access to the best advice, and raise awareness of current work. This would be complemented by creating a series inter-disciplinary working groups, to discuss and articulate archaeological science issues. An online service to match people (i.e. specialist or student) to material (whether e.g. environmental sample, artefactual assemblage, or skeletal assemblage) is also recommended. An annual meeting should also be held at which researchers would be able to promote current and future work, and draw attention to materials available for analysis, and to specialists/students looking to work on particular assemblages or projects. Such meetings could be rolled into a suitable public outreach event.
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