Relevant bibliographies by topics / Convolution Neural Networks (CNN)

Contents

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

Academic literature on the topic 'Convolution Neural Networks (CNN)'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Convolution Neural Networks (CNN)"

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Dai, Yongpeng, Tian Jin, Yongkun Song, Shilong Sun, and Chen Wu. "Convolutional Neural Network with Spatial-Variant Convolution Kernel." Remote Sensing 12, no. 17 (2020): 2811. http://dx.doi.org/10.3390/rs12172811.

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Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local posit
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Akbar, Mutaqin. "Traffic sign recognition using convolutional neural networks." Jurnal Teknologi dan Sistem Komputer 9, no. 2 (2021): 120–25. http://dx.doi.org/10.14710/jtsiskom.2021.13959.

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Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005,
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Kurshid, Madina, and Mansotra Saksham. "Genderpredictions using Convolution Neural Networks." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 537–40. https://doi.org/10.35940/ijrte.C4606.099320.

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Nowadays Deep learning was advanced so much in our daily life. From 2014, there is massive growth in this technology as there is a vast amount of data present. We are even getting better results from whatever we may do. In my work, I have used Convolution Neural Networks as my project depends on image classification. So what I’m trying to do is I’m using two classes in which one class is male and one class is female. I’m classifying both the classes and trying to predict who is male and who is female. For this, I have been using layers like Sequential, Convolution2D, Max-pool
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Wang, Wei, Yanjie Zhu, Zhuoxu Cui, and Dong Liang. "Is Each Layer Non-trivial in CNN? (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15915–16. http://dx.doi.org/10.1609/aaai.v35i18.17954.

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Convolutional neural network (CNN) models have achieved great success in many fields. With the advent of ResNet, networks used in practice are getting deeper and wider. However, is each layer non-trivial in networks? To answer this question, we trained a network on the training set, then we replace the network convolution kernels with zeros and test the result models on the test set. We compared experimental results with baseline and showed that we can reach similar or even the same performances. Although convolution kernels are the cores of networks, we demonstrate that some of them are trivi
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Swasthi, B. S., R. Anagha, S. Arpitha, B. S. Sanjay, and K. Harshitha. "Parking Assist using Convolution Neural Networks." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 248–52. https://doi.org/10.35940/ijeat.F1379.089620.

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Parking vehicles are one of the most frustrating tasks that people face these days. Locating an available parking space is a huge headache especially in urban areas. This paper aims to design one such parking system which, in many ways reduces the hassles of parking. The paper presents a system where a Machine Learning model, Convolution Neural Network(CNN) is used to classify parking slots in a parking space into vacant and filled slots. In order to optimize the task of classification, the method of Transfer Learning is implemented in the paper. The problem of parking stands not only limited
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Lan, Weichao, and Liang Lan. "Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8235–42. http://dx.doi.org/10.1609/aaai.v35i9.17002.

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Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit. However, the compression ratio of existing binary CNN models is upper bounded by ∼ 32. To address this limitation, we propose a novel method
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Purwono, Purwono, Alfian Ma'arif, Wahyu Rahmaniar, Haris Imam Karim Fathurrahman, Aufaclav Zatu Kusuma Frisky, and Qazi Mazhar ul Haq. "Understanding of Convolutional Neural Network (CNN): A Review." International Journal of Robotics and Control Systems 2, no. 4 (2023): 739–48. http://dx.doi.org/10.31763/ijrcs.v2i4.888.

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The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convoluti
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Zhang, Yulin, Feipeng Li, Haoke Xu, Xiaoming Li, and Shan Jiang. "Efficient Convolutional Neural Networks Utilizing Fine-Grained Fast Fourier Transforms." Electronics 13, no. 18 (2024): 3765. http://dx.doi.org/10.3390/electronics13183765.

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Convolutional Neural Networks (CNNs) are among the most prevalent deep learning techniques employed across various domains. The computational complexity of CNNs is largely attributed to the convolution operations. These operations are computationally demanding and significantly impact overall model performance. Traditional CNN implementations convert convolutions into matrix operations via the im2col (image to column) technique, facilitating parallelization through advanced BLAS libraries. This study identifies and investigates a significant yet intricate pattern of data redundancy within the
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Yan, Chenhong, Shefeng Yan, Tianyi Yao, et al. "A Lightweight Network Based on Multi-Scale Asymmetric Convolutional Neural Networks with Attention Mechanism for Ship-Radiated Noise Classification." Journal of Marine Science and Engineering 12, no. 1 (2024): 130. http://dx.doi.org/10.3390/jmse12010130.

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Ship-radiated noise classification is critical in ocean acoustics. Recently, the feature extraction method combined with time–frequency spectrograms and convolutional neural networks (CNNs) has effectively described the differences between various underwater targets. However, many existing CNNs are challenging to apply to embedded devices because of their high computational costs. This paper introduces a lightweight network based on multi-scale asymmetric CNNs with an attention mechanism (MA-CNN-A) for ship-radiated noise classification. Specifically, according to the multi-resolution analysis
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Salman, Hasan Ahmed, and Ali Kalakech. "Image Enhancement using Convolution Neural Networks." Babylonian Journal of Machine Learning 2024 (January 25, 2024): 30–47. http://dx.doi.org/10.58496/bjml/2024/003.

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The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with image
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Dissertations / Theses on the topic "Convolution Neural Networks (CNN)"

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Kapoor, Rishika. "Malaria Detection Using Deep Convolution Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749143868579.

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Hossain, Md Tahmid. "Towards robust convolutional neural networks in challenging environments." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181882.

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Image classification is one of the fundamental tasks in the field of computer vision. Although Artificial Neural Network (ANN) showed a lot of promise in this field, the lack of efficient computer hardware subdued its potential to a great extent. In the early 2000s, advances in hardware coupled with better network design saw the dramatic rise of Convolutional Neural Network (CNN). Deep CNNs pushed the State-of-The-Art (SOTA) in a number of vision tasks, including image classification, object detection, and segmentation. Presently, CNNs dominate these tasks. Although CNNs exhibit impressive cla
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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the
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Ioannou, Yani Andrew. "Structural priors in deep neural networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278976.

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Deep learning has in recent years come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite breakthroughs in training deep networks, there remains a lack of understanding of both the optimization and structure of deep networks. The approach advocated by many researchers in the field has been to train monolithic networks with excess complexity, and strong regularization --- an approach that leaves much to desire in efficiency. Instead we propose that carefully designing networks in considerati
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Martell, Patrick Keith. "Hierarchical Auto-Associative Polynomial Convolutional Neural Networks." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

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Svensson, Göran, and Jonas Westlund. "Intravenous bag monitoring with Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148449.

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Drip bags are used in hospital environments to administerdrugs and nutrition to patients. Ensuring that they are usedcorrectly and are refilled in time are important for the safetyof patients. This study examines the use of a ConvolutionalNeural Network (CNN) to monitor the fluid levels of drip bagsvia image recognition to potentially form the base of an earlywarning system, and assisting in making medical care moreefficient. Videos of drip bags were recorded as they wereemptying their contents in a controlled environment and fromdifferent angles. A CNN was built to analyze the recordeddata in
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Li, Xile. "Real-time Multi-face Tracking with Labels based on Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36707.

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This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face
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Wang, Run Fen. "Semantic Text Matching Using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362134.

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Semantic text matching is a fundamental task for many applications in NaturalLanguage Processing (NLP). Traditional methods using term frequencyinversedocument frequency (TF-IDF) to match exact words in documentshave one strong drawback which is TF-IDF is unable to capture semanticrelations between closely-related words which will lead to a disappointingmatching result. Neural networks have recently been used for various applicationsin NLP, and achieved state-of-the-art performances on many tasks.Recurrent Neural Networks (RNN) have been tested on text classificationand text matching, but it d
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Habrman, David. "Face Recognition with Preprocessing and Neural Networks." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128704.

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Face recognition is the problem of identifying individuals in images. This thesis evaluates two methods used to determine if pairs of face images belong to the same individual or not. The first method is a combination of principal component analysis and a neural network and the second method is based on state-of-the-art convolutional neural networks. They are trained and evaluated using two different data sets. The first set contains many images with large variations in, for example, illumination and facial expression. The second consists of fewer images with small variations. Principal compon
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Ahlin, Björn, and Marcus Gärdin. "Automated Classification of Steel Samples : An investigation using Convolutional Neural Networks." Thesis, KTH, Materialvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209669.

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Automated image recognition software has earlier been used for various analyses in the steel making industry. In this study, the possibility to apply such software to classify Scanning Electron Microscope (SEM) images of two steel samples was investigated. The two steel samples were of the same steel grade but with the difference that they had been treated with calcium for a different length of time.  To enable automated image recognition, a Convolutional Neural Network (CNN) was built. The construction of the software was performed with open source code provided by Keras Documentation, thus e
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Books on the topic "Convolution Neural Networks (CNN)"

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Chen, G., Andrew Adamatzky, and Leon O. Chua. Chaos, CNN, Memristors and Beyond: A Festschrift for Leon Chua. World Scientific Publishing Co Pte Ltd, 2013.

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Cnn: A Paradigm for Complexity (World Scientific Series on Nonlinear Science, Series a , Vol 31). World Scientific Publishing Company, 1998.

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Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing - ebooks Account, 2017.

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Andrew, Adamatzky, and Guan-Rong Chen. Chaos, CNN, Memristors and Beyond: A Festschrift for Leon Chua. World Scientific Publishing Co Pte Ltd, 2013.

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Chaos, CNN, Memristors and Beyond: A Festschrift for Leon Chua. World Scientific Publishing Co Pte Ltd, 2013.

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Yang, Tao. Handbook of CNN Image Processing: All You Need to Know about Cellular Neural Networks (YangSky.com Monographs in Information Sciences). Yang's Scientific Research Institute LLC, 2002.

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Saidane, Zohra. Image and video text recognition using convolutional neural networks: Study of new CNNs architectures for binarization, segmentation and recognition of text images. LAP Lambert Academic Publishing, 2011.

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Book chapters on the topic "Convolution Neural Networks (CNN)"

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Bisong, Ekaba. "Convolutional Neural Networks (CNN)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_35.

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Gharehbaghi, Arash. "Convolutional Neural Networks (CNN)." In Deep Learning in Time Series Analysis. CRC Press, 2023. http://dx.doi.org/10.1201/9780429321252-15.

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Xiao, Cao, and Jimeng Sun. "Convolutional Neural Networks (CNN)." In Introduction to Deep Learning for Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82184-5_6.

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Dai, Qionghai, and Yue Gao. "Neural Networks on Hypergraph." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_7.

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AbstractWith the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods. For the spectral-based methods, the convolution operation is formulated in the spectral domain of graph, and we introduce the typical spectral-based methods, including hypergraph neural networks (HGNN), hypergraph convolution with attention (Hyper-Atten), and hyperbolic hypergraph neural network (HHGNN), which extend hypergraph computation to hyperbolic spaces beyond the Euclidean space. For the spatial-based methods, the convolution operation is defined in groups of spatially close vertices. We then present spatial-based hypergraph neural networks of the general hypergraph neural networks (HGNN+) and the dynamic hypergraph neural networks (DHGNN). Additionally, there are several convolution methods that attempt to reduce the hypergraph structure to the graph structure, so that the existing graph convolution methods can be directly deployed. Lastly, we analyze the association and comparison between hypergraph and graph in the two areas described above (spectral-based, spatial-based), further demonstrating the ability and advantages of hypergraph on constructing and computing higher-order correlations in the data.
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Teoh, Teik Toe. "CNN for Brain Tumor Classification." In Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8814-1_2.

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Teoh, Teik Toe. "CNN for Diabetic Retinopathy Detection." In Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8814-1_6.

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Teoh, Teik Toe. "CNN for Skin Cancer Classification." In Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8814-1_5.

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Teoh, Teik Toe. "CNN for Pneumonia Image Classification." In Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8814-1_3.

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Teoh, Teik Toe. "CNN for White Blood Cell Classification." In Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8814-1_4.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Convolutional Neural Networks." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_13.

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AbstractWe provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation for using CNN that clearly shows the advantages of this topology compared to feedforward networks for processing images. Several practical examples with plant breeding data are provided using CNNs under two scenarios: (a) one-dimensional input data and (b) two-dimensional input data. The examples also illustrate how to tune the hyperparameters to be able to increase the probability of a successful application. Finally, we give comments on the advantages and disadvantages of deep neural networks in general as compared with many other statistical machine learning methodologies.
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Conference papers on the topic "Convolution Neural Networks (CNN)"

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Wahyudi, Wahyudi, and Guruh Fajar Shidik. "Edible and Poisonous Mushroom Classification using Convolution Neural Network (CNN)." In 2024 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2024. https://doi.org/10.1109/isemantic63362.2024.10762192.

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Anjaneyulu, Battula Prasanna, Chadipiralla Pavan Kunar Reddy, Gangireddy Venkata AjayKumar Reddy, Chava Yogitha, Pandiselvam Pandiyarajan, and Baskaran Maheshwaran. "DeepFake Detection using Convolutional Neural Networks (CNN) and Recurrent Neural Network(RNN)." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810970.

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Herdiman, Anggi, Dian Sa’Adillah Maylawati, Diena Rauda Ramdania, Wildan Budiawan Zulfikar, Muhammad Insan Al-Amin, and Muhammad Ali Ramdhani. "Household Waste Classification with Convolutional Neural Networks (CNN)." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10956537.

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Yang, Hangbo, Nicola Peserico, Shurui Li, et al. "Prototyped and Upgraded Programmable On-chip Photonic Joint Transform Correlator-Based CNN." In CLEO: Science and Innovations. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sm4m.4.

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Manansala, Angelica J., and Engr Charmaine C. Paglinawan. "Classification of Coffea Liberica Quality Using Convolution Neural Networks (Slim-CNN, YOLOv5, and VGG-16)." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723931.

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Mettildha Mary, I., T. Ragunthar, M. Priyadharsini, H. Shyam Krishnaa, and M. K. Sujit. "Automatic Fish Species Identification Using Convolutional Neural Networks (CNN)." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10762987.

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Kalaimanivel, S., and K. France. "Crop Disease and Pest Detection using Convolutional Neural Networks (CNN)." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00067.

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Ganesh, VPV Datta, O. Sai Likith Kumar, P. Jithendra, and S. Satheesh Kumar. "CNN LIPNET : Automated Lip Reading Using Deep Convolutional Neural Networks." In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2025. https://doi.org/10.1109/icaect63952.2025.10958857.

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Li, Teng. "Optimization of Algorithm for Network Traffic Anomaly Detection Using Convolutional Neural Networks (CNN)." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721912.

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Lera, Ricardo Di Curzio, and Bruno de Carvalho Albertini. "Hardware-efficient convolution algorithms for CNN accelerators: A brief review." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.233607.

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The Convolutional Neural Network (CNN) is a technology of vast importance in image processing and computer vision applications. The bottleneck of CNNs is the multidimensional convolution, which often demands accelerator hardware. The convolution algorithms these accelerators use directly affect the ratio between speed increase and hardware resource consumption during scaling, a metric known as hardware efficiency. The lower this metric, the more power and area are spent on minor performance improvements. In this review, we analyze the potential for hardware efficiency in the current proven alg
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Reports on the topic "Convolution Neural Networks (CNN)"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental
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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can
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Cerulli, Giovanni. Deep Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/g6nxp3uxsvu3l469.

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This seminar is an introduction to Deep Learning and Artificial Intelligence methods for the social, economic, and health sciences using Python. After introducing the subject, the seminar will cover the following methods: (i) Feedforward Neural Networks (FNNs) (ii) Convolutional Neural Networks (CNNs); and (iii) Recursive Neural Networks (RNNs). The course will offer various instructional examples using real datasets in Python. An Instats certificate of completion is provided at the end of the seminar, and 2 ECTS equivalent points are offered.
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Saha, Roshni. Classification of Parkinson’s Disease Using MRI Data and Deep Learning Convolution Neural Networks. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-362.

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Eka Saputro, Widianto. PENGENALAN ALFABET BAHASA ISYARAT TANGAN PADA CITRA DIGITAL MENGGUNAKAN PENDEKATAN CONVEX HULL DAN CONVOLUTIONAL NEURAL NETWORK (CNN). ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.rwpbjj07.1.

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Eka Saputro, Widianto. PENGENALAN ALFABET BAHASA ISYARAT TANGAN PADA CITRA DIGITAL MENGGUNAKAN PENDEKATAN CONVEX HULL DAN CONVOLUTIONAL NEURAL NETWORK (CNN). ResearchHub Technologies, Inc., 2024. https://doi.org/10.55277/researchhub.rwpbjj07.

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Chua, Leon O. Nonlinear Circuits and Neural Networks: Chip Implementation and Applications of the TeraOPS CNN Dynamic Array Supercomputer. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389212.

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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the interna
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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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