Relevant bibliographies by topics / CNN (Convolutional Neural Networks)

Contents

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

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

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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

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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Convolutional neural networks for leaf image-based plant disease classification." International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 328–41. https://doi.org/10.11591/ijai.v8.i4.pp328-341.

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Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system imp
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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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เชิดสม, พงษ์ศธร, та วนิดา แก่นอากาศ. "การวิเคราะห์การมีส่วนร่วมของนักเรียนในห้องเรียนออนไลน์ โดยใช้ Convolutional Neural Networks (CNN)". วารสารงานวิจัยและพัฒนาเชิงประยุกต์ โดยสมาคม ECTI 3, № 3 (2023): 39–52. http://dx.doi.org/10.37936/ectiard.2023-3-3.250499.

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การระบาดของเชื้อไวรัสโคโรนา (COVID-19) ส่งผลกระทบในภาคการศึกษา เช่น การเรียนจาก ห้องเรียนปกติสู่ห้องเรียนออนไลน์ ทำให้การติดตามการมีส่วนร่วมในห้องเรียนออนไลน์เป็นไปด้วยความ ยากลำบาก นอกจากจะส่งผลต่อประสิทธิภาพของผู้เรียนแล้ว กรณีที่ร้ายแรงที่สุดที่อาจจะเกิดขึ้นคือการ หลุดจากการศึกษาของผู้เรียน เพื่อให้ผู้สอนได้ทราบถึงการมีส่วนร่วมของผู้เรียนและสามารถปรับเปลี่ยน การการเรียนการสอนให้เหมาะสมกับสถาพแวดล้อมในการเรียนออนไลน์ บทความนี้จึงได้นำเสนอการพัฒนาแบบจำลองที่ใช้ในการตรวจสอบการมีส่วนร่วมในชั้นเรียน ออนไลน์โดยใช้โครงข่ายประสาทเทียมแบบคอนโวลูชัน (Convolutional Neural Networks : CNN) ที่ใช้ใบหน้าข
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Aditya, Kakde Nitin Arora Durgansh Sharma. "A COMPARATIVE STUDY OF DIFFERENT TYPES OF CNN AND HIGHWAY CNN TECHNIQUES." Global Journal of Engineering Science and Research Management 6, no. 4 (2019): 18–31. https://doi.org/10.5281/zenodo.2639265.

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In recent years, convolutional networks have shown breakthrough performance in image classification and detection. The main reason behind the performance of convnets is that they are inspired from the mammal’s visual cortex. In this paper, we have investigated the performance of four models that are Alexnet, Highway Convolutional Neural Network, Convolutional Neural Network and an evolutionary approach on highway convolutional neural network on the basis of train loss, test loss, train accuracy and test accuracy. These models are tested on two datasets that are WANG dataset and Simpsons
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R.Thiruvengatanadhan. "Musical Genre Classification using Convolutional Neural Networks." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 1 (2020): 228–30. https://doi.org/10.35940/ijitee.A8172.1110120.

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Music has likewise been separated into Genres and sub sorts on the premise on music. To show that, we contrast the outcomes acquired and a Convolutional Neural Network (CNN). Experiments were conducted on Marsyas databases with distinct characteristics for genre classification. The proposed CNN results in better accuracy in music genre classification.
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Khaydarova, Rezeda, Dmitriy Mouromtsev, Vladislav Fishchenko, Vladislav Shmatkov, Maxim Lapaev, and Ivan Shilin. "ROCK-CNN." International Journal of Embedded and Real-Time Communication Systems 12, no. 3 (2021): 14–31. http://dx.doi.org/10.4018/ijertcs.2021070102.

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The paper is dedicated to distributed convolutional neural networks on a resource constrained devices cluster. The authors focus on requirements that meet the users' needs. Based on this, architecture of the system is proposed. Two use cases of CNN computations on a ROCK-CNN cluster are mentioned, and algorithms for organizing distributed convolutional neural networks are described. Experiments to validate proposed architecture and algorithms for distributed deep learning computations are conducted as well.
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Gaskarov, Rodion Dmitrievich, Alexey Mikhailovich Biryukov, Alexey Fedorovich Nikonov, Daniil Vladislavovich Agniashvili, and Danil Aydarovich Khayrislamov. "Steel Defects Analysis Using CNN (Convolutional Neural Networks)." Russian Digital Libraries Journal 23, no. 6 (2020): 1155–71. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1155-1171.

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Steel is one of the most important bulk materials these days. It is used almost everywhere - from medicine to industry. Detecting this material's defects is one of the most challenging problems for industries worldwide. This process is also manual and time-consuming. Through this study we tried to automate this process. A convolutional neural network model UNet was used for this task for more accurate segmentation with less training image data set for our model. The essence of this NN (neural network) is in step-by-step convolution of every image (encoding) and then stretching them to initial
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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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Jin, Jiani. "Convolutional Neural Networks for Biometrics Applications." SHS Web of Conferences 144 (2022): 03013. http://dx.doi.org/10.1051/shsconf/202214403013.

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A convolutional neural network (CNN) is a feed-forward neural network that can react with other units in a specific range and can handle huge images well as a deep learning algorithm. CNN is a very convenient tool for conveying visual information and can be good for improving recognition accuracy. However, volumetric neural networks also increase the complexity of the networks, making them more challenging to optimize and more prone to overfitting. This paper will focus on the history of CNN development and the current use of the method, and the difficulties encountered. Furthermore, we will a
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Liu, Taoyu. "Application of convolutional neural networks in image classification and applications of improved convolutional neural networks." Applied and Computational Engineering 81, no. 1 (2024): 56–62. http://dx.doi.org/10.54254/2755-2721/81/20241009.

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Abstract. This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised mo
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Dissertations / Theses on the topic "CNN (Convolutional Neural Networks)"

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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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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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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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Nikzad, Dehaji Mohammad. "Structural Improvements of Convolutional Neural Networks." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410448.

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Over the last decade, deep learning has demonstrated outstanding performance in almost every application domain. Among different types of deep frameworks, convolutional neural networks (CNNs), inspired by the biological process of the visual system, can learn to extract discriminative features from raw inputs without any prior manipulation. However, efficient information circulation and the ability to explore effective new features are still two key and challenging factors for a successful deep neural network. In this thesis, we aim at presenting novel structural improvements of the CNN framew
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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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Buratti, Luca. "Visualisation of Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Le Reti Neurali, e in particolare le Reti Neurali Convoluzionali, hanno recentemente dimostrato risultati straordinari in vari campi. Purtroppo, comunque, non vi è ancora una chiara comprensione del perchè queste architetture funzionino così bene e soprattutto è difficile spiegare il comportamento nel caso di fallimenti. Questa mancanza di chiarezza è quello che separa questi modelli dall’essere applicati in scenari concreti e critici della vita reale, come la sanità o le auto a guida autonoma. Per questa ragione, durante gli ultimi anni sono stati portati avanti diversi studi in modo tale d
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LEONARDI, MARCO. "Image Collection Management using Convolutional Neural Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365014.

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Al giorno d’oggi ormai quasi chiunque possiede uno smartphone dotato di una telecamera ad alta risoluzione. Negli ultimi decenni, i contenuti multimediali (immagini e video) stanno sempre più spesso diventando il principale mezzo di comunicazione. Dato il continuo calo dei prezzi dei dispositivi di archiviazione, il numero totale di immagini salvate sta aumentando notevolmente, andando così a creare collezioni di immagini sempre più grandi, a tal punto da essere una problema per chi vuole le vuole esplorare. Data una libreria di immagini, il processo di selezione di un gruppo di foto che rap
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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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Singh, Vineeta. "Understanding convolutional networks and semantic similarity." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593269596368388.

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Books on the topic "CNN (Convolutional Neural Networks)"

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Mou, Lili, and Zhi Jin. Tree-Based Convolutional Neural Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1870-2.

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Milosevic, Nemanja. Introduction to Convolutional Neural Networks. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5648-0.

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Habibi Aghdam, Hamed, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57550-6.

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Venkatesan, Ragav, and Baoxin Li. Convolutional Neural Networks in Visual Computing. CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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Teoh, Teik Toe. Convolutional Neural Networks for Medical Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8814-1.

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Koonce, Brett. Convolutional Neural Networks with Swift for Tensorflow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6168-2.

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Ozturk, Saban. Convolutional Neural Networks for Medical Image Processing Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003215141.

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Naved, Mohd, V. Ajantha Devi, Loveleen Gaur, and Ahmed A. Elngar. IoT-enabled Convolutional Neural Networks: Techniques and Applications. River Publishers, 2023. http://dx.doi.org/10.1201/9781003393030.

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Khan, Salman, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun. A Guide to Convolutional Neural Networks for Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01821-3.

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Lu, Le, Yefeng Zheng, Gustavo Carneiro, and Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42999-1.

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

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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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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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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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Gad, Ahmed Fawzy. "Convolutional Neural Networks." In Practical Computer Vision Applications Using Deep Learning with CNNs. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4167-7_5.

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Conference papers on the topic "CNN (Convolutional Neural Networks)"

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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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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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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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Ngozi Ukamaka, Okonkwo, Raphael Ozighor Enihe, and Abdullahi Monday Jubril. "Cloud Detection in Satellite Imagery Using Deep Convolutional Neural Networks (CNN)." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629738.

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Hu, Jiahan. "Image Discrimination and Parameter Analysis Based on Convolutional Neural Networks (CNN)." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012937000004508.

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Tan, Chaoyi, Xiangtian Li, Xiaobo Wang, Zhen Qi, and Ao Xiang. "Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)." In 2024 4th International Conference on Electronic Information Engineering and Computer (EIECT). IEEE, 2024. https://doi.org/10.1109/eiect64462.2024.10866018.

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Dhyani, Mayank, Sujal Chauhan, Tejas Vats, and Sanjeev Thakur. "Survey on Prediction of Cardiomegaly Disease Using Convolutional Neural Networks (CNN)." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11005115.

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Reports on the topic "CNN (Convolutional Neural Networks)"

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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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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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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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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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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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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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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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Forrest, Robert. Convolutional Neural Networks for Signal Detection. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1813655.

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio
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