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

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

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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Potnuru, Samanvi, Agrawal Shruti, Ranjan Mallick Soubhagya, et al. "Alzheimer's disease diagnosis using convolutional neural networks model." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 206–13. https://doi.org/10.11591/ijict.v13i2.p206-213.

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The global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based mod

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Banupriya., M., Majeed Ahmar Peer Ibrahim. M. Abdul, R. Ashwin., Krishnaraj. R. Haresh, and R. Haridhanush. "Sign Language Recognition Using CNN." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 101–9. https://doi.org/10.5281/zenodo.7933248.

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Sign language is one of the oldest and most natural form of language for communication, but since most people do not know sign language and interpreters are very difficult to come by this project come up with a real time method using convolutional neural networks for fingerspelling based american sign language. Convolutional neural networks (CNNs) have shown great promise in this field due to their ability to automatically learn relevant features from raw input data. In this method, the hand is first passed through a filter and after the filter is applied then it involves pre-processing the in

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Anees, Fatima Khan, P. Bhavya, and Ravinder Reddy R. "Land Classification using Convolutional Neural Networks." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 79–83. https://doi.org/10.35940/ijrte.A3030.079220.

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Identifying the physical aspect of the earth’s surface (Land cover) and also how we exploit the land (Land use) is a challenging problem in environment monitoring and much of other subdomains. One of the most efficient ways to do this is through Remote Sensing (analyzing satellite images). For such classification using satellite images, there exist many algorithms and methods, but they have several problems associated with them, such as improper feature extraction, poor efficiency, etc. Problems associated with established land-use classification methods can be solved by using various op

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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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Roy, Sanjiban Sekhar, Nishant Rodrigues, and Y.-h. Taguchi. "Incremental Dilations Using CNN for Brain Tumor Classification." Applied Sciences 10, no. 14 (2020): 4915. http://dx.doi.org/10.3390/app10144915.

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Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On

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Li, Hao, Xiaorui Xiong, Chaoxian Liu, Yong Ma, Shan Zeng, and Yaqin Li. "SFFNet: Staged Feature Fusion Network of Connecting Convolutional Neural Networks and Graph Convolutional Neural Networks for Hyperspectral Image Classification." Applied Sciences 14, no. 6 (2024): 2327. http://dx.doi.org/10.3390/app14062327.

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The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, w

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Puneet Gupta. "Pneumonia Detection Using Convolutional Neural Networks." January 2021 7, no. 01 (2021): 77–80. http://dx.doi.org/10.46501/ijmtst070117.

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Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolution

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Fadhlan, Hafizhelmi Kamaru Zaman. "Gender classification using custom convolutional neural networks architecture." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5758–71. https://doi.org/10.11591/ijece.v10i6.pp5758-5771.

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Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed convolutional neural network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some pop

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Bredikhin, Arsentiy Igorevich. "Training algorithms for convolutional neural networks." Yugra State University Bulletin 15, no. 1 (2019): 41–54. http://dx.doi.org/10.17816/byusu20190141-54.

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In this article we consider one of the most used classes of neural networks convolutional neural networks (hereinafter CNN). In particular, the areas of their application, algorithms of signal propagation by CNN and CNN training are described and the methods of CNN functioning algorithms implementation in MATLAB programming language are given. The article presents the results of research on the effectiveness of the CNN learning algorithm in solving classification problems with its help. In the course of these studies, such a characteristic of the neural network as the dynamics of the network e

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Pratomo, Awang Hendrianto, Nur Heri Cahyana, and Septi Nur Indrawati. "Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification." Science in Information Technology Letters 4, no. 1 (2023): 54–64. http://dx.doi.org/10.31763/sitech.v4i1.1182.

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Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (

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Chimakurthi, Venkata Naga Satya Surendra. "Application of Convolution Neural Network for Digital Image Processing." Engineering International 8, no. 2 (2020): 149—xxx. http://dx.doi.org/10.18034/ei.v8i2.592.

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In order to train neural network algorithms for multiple machine learning tasks, like the division of distinct categories of objects, various deep learning approaches employ data. Convolutional neural networks deep learning algorithms are quite strong when it comes to image processing. With the recent development of multi-layer convolutional neural networks for high-level tasks like object recognition, object acquisition, and recent semantic classification, the field has seen great success in this approach. The two-phase approach is frequently employed in semantic segregation. In the second st

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M., Sushma Sri, Rajendra Naik B., and Jaya Sankar K. "Object Detection Based on Faster R-Cnn." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 3 (2021): 72–76. https://doi.org/10.35940/ijeat.C2186.0210321.

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In recent years there is rapid improvement in Object detection in areas of video analysis and image processing applications. Determing a desired object became an important aspect, so that there are many numerous of methods are evolved in Object detection. In this regard as there is rapid development in Deep Learning for its high-level processing, extracting deeper features, reliable and flexible compared to conventional techniques. In this article, the author proposes Object detection with deep neural networks and faster region convolutional neural networks methods for providing a simple algor

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Lutsenko, V. S., and A. E. Shukhman. "SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 216 (June 2022): 40–50. http://dx.doi.org/10.14489/vkit.2022.06.pp.040-050.

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Our study briefly discusses the architectures of convolutional neural networks (CNN), their advantages and disadvantages. The features of the architecture of the convolutional neural network U-net are described. An analysis of the CNN U-net was carried out, based on the analysis, a rationale was given for choosing the CNN U-net as the main architecture for using and building subsequent created and analyzed models of cert neural networks to solve the problem of segmentation of medical images. The analysis of architectures of convolutional neural networks, which can be used as convolutional laye

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Avula, Sri Lasya. "Efficient 3D Medical Image Segmentation using CoTr: Bridging CNN and Transformer." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4748–54. http://dx.doi.org/10.22214/ijraset.2023.52686.

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Abstract: Neural networks are a subset of machine learning, and they are at the heart of deep learning algorithms. Before CNNs, identifying objects in images was done manually using time-consuming, manual feature extraction methods. The superior performance of convolutional neural networks, when dealing with images, speech, or audio signals sets them apart from other neural networks. Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. Due to the inductive bias of locality and weight sharing inherent in convolutional operations, these

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Susandri, Susandri, Ahmad Zamsuri, Nurliana Nasution, Yoyon Efendi, and Hiba Basim Alwan. "The Mitigating Overfitting in Sentiment Analysis Insights from CNN-LSTM Hybrid Models." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 24, no. 2 (2025): 297–308. https://doi.org/10.30812/matrik.v24i2.4742.

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This study aims to improve sentiment analysis accuracy and address overfitting challenges in deep learning models by developing a hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks. The research methodology involved multiple stages, starting with preprocessing a dataset of 5,456 rows. This process included removing duplicate data, empty entries, and neutral sentiments, resulting in 2,685 usable rows. To overcome data quantity limitations, data augmentation expanded the training dataset from 2,148 to 10,740 samples. Data transformation was carried out using

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Sun, Wenyan. "Image classification based on CNN with three different networks." Applied and Computational Engineering 15, no. 1 (2023): 181–86. http://dx.doi.org/10.54254/2755-2721/15/20230831.

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Image classification refers to classifying images based on the different features reflected by the information in each image. Image classification is a fundamental issue in computer vision and has important significance. It has a wide range of applications, such as autonomous driving, face recognition, image retrieval, and other fields. This article first gives a birds-eye view of the development of image classification and briefly introduces the factors that affect the accuracy of convolutional neural networks. Experiments and comparative analysis are conducted on the effects of convolutional

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Patro, Surendra, D. C. Jhariya, Mridu Sahu, Pankaj Dewangan, and P. Y. Dhekne. "Igneous rock classification using Convolutional neural networks (CNN)." IOP Conference Series: Earth and Environmental Science 1032, no. 1 (2022): 012045. http://dx.doi.org/10.1088/1755-1315/1032/1/012045.

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Abstract This paper describes how convolutional neural networks are used to identify and classify igneous rocks (CNN). Igneous rocks are formed while still hot, hot magma crystallises and solidifies. Melt originates deep beneath the Earth’s surface, amid active plate borders or hot zones, and then rises to the surface. There are also various kinds of igneous rocks, which are addressed throughout this work, distinguishing each one is a difficult feat in and of itself. Machine learning, which is fundamentally a three-layer neural network, is a subset of deep learning. These neural networks attem

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Kunwar, Suman, and Abayomi Simeon Alade. "Strategies in JPEG Compression using Convolutional Neural Network (CNN)." International Journal of Research Publication and Reviews 4, no. 7 (2023): 1024–32. http://dx.doi.org/10.55248/gengpi.4.723.48514.

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N, Seetayya. "CNN BASED REALTIME AIRCRAFT DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32305.

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Deep learning techniques such as Convolutional Neural Networks (CNN) and Transfer Learning are being used to detect and identify fighter aircraft or jets in a dataset consisting of 21 different aircraft with 20,000 images. The principle of "pooling" in Convolutional Neural Networks (CNN) involves progressively reducing the spatial size of the model to decrease the number of parameters and computations in the network. These techniques have been applied to various aspects of aircraft recognition, including object detection and engine defect detection. Convolutional Neural Networks (CNNs) are wid

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Diah Putri Kartikasari, Fiqri Dian Priyatna Sinaga, Tiara Ayu Triarta Tambak, Zahra Humaira Kudadiri, and M. Khalil Gibran. "Ekstraksi Fitur Citra Grayscale dengan Convolutional Neural Networks." Jurnal Teknik Informatika dan Teknologi Informasi 5, no. 1 (2025): 198–205. https://doi.org/10.55606/jutiti.v5i1.5175.

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This study aims to explore the use of Convolutional Neural Networks (CNN) in feature extraction from grayscale images for avocado object identification. The process begins with taking a grayscale image of the avocado object to be recognized. Convolution is applied using a 3x3 horizontal Sobel kernel filter with a stride of 1 to the right, and a ReLU (Rectified Linear Unit) activation function to improve the network's ability to extract relevant features. After the convolution stage, pooling is carried out using the max pooling method to reduce the image dimension while retaining important info

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Varma, Teena. "Classification of Galaxies using Convolutional Neural Networks (CNN)." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 1359–64. http://dx.doi.org/10.22214/ijraset.2020.5215.

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Alfariza, Elga, Dicksa Ananda Christian Tue, Andi Sofyan Anas, Muhammad Tajuddin, and Ahmat Adil. "Klasifikasi Aksara Sasak Menggunakan Convolutional Neural Networks (CNN)." JTIM : Jurnal Teknologi Informasi dan Multimedia 6, no. 3 (2024): 346–53. https://doi.org/10.35746/jtim.v6i3.623.

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Sasak script is an important cultural heritage for the people of Lombok, but its use is decreasing along with the development of digital technology. This study aims to develop a classification system for Sasak script handwriting using Convolutional Neural Networks (CNN) to improve the accuracy of character recognition. The dataset used consists of handwritten images of 18 basic Sasak script characters collected from 50 volunteers with various writing styles. The methods applied include data preprocessing, augmentation, and training a CNN model with an architecture consisting of several convolu

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Sumargo, Ruly, and Handri Santoso. "Uncovering Malware Families Using Convolutional Neural Networks (CNN)." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 1 (2023): 97. http://dx.doi.org/10.24014/ijaidm.v7i1.27243.

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Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently class

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Raghav, Akash, and Nitin Tewari. "FACIAL EXPRESSION RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS [CNN]." International Journal of Engineering Applied Sciences and Technology 8, no. 3 (2023): 89–93. http://dx.doi.org/10.33564/ijeast.2023.v08i03.012.

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While humans have traditionally excelled at discerning emotions through facial expressions, achieving the same capability with a computer program has proven challenging. However, recent advancements in computer vision and machine learning have made it feasible to recognize emotions accurately. This paper introduces a novel facial emotion identification technique known as Convolutional Neural Networks for Facial Emotion Recognition (FERC). FERC utilizes a twopart convolutional neural network (CNN) architecture: The paper consists of two main sections. The first section is dedicated to removing

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Drönner, Johannes, Nikolaus Korfhage, Sebastian Egli, et al. "Fast Cloud Segmentation Using Convolutional Neural Networks." Remote Sensing 10, no. 11 (2018): 1782. http://dx.doi.org/10.3390/rs10111782.

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Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels instead of using closely integrated spatial information, ignoring the fact that clouds are highly dynamic, spatially continuous entities. This paper proposes a novel cloud classification method based on deep learning. Relying on a Convolutional Neural Network (CNN) architecture for image segmentation, the presented Cloud Segmentation CNN (CS-CNN), cla

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Dr., Rekha Patil, Kumar Katrabad Vidya, Mahantappa, and Kumar Sunil. "Image Classification Using CNN Model Based on Deep Learning." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 60–71. https://doi.org/10.5281/zenodo.7965526.

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In this work, we will use a convolutional neural network to classify images. In the field of visual image analysis, CNNs (a subset of deep neural networks) are the norm. Multilayer perceptron is used to develop CNN; it is based on a hierarchical model that works on network construction and then delivers to a fully linked layer. All the neurons are linked together and their output is processed in this layer. Here, we demonstrate how our system can get the job done in challenging domains like computer vision by using a deep learning approach. Convolutional Neural Networks (CNNs) are a machine le

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Ribli, Dezső, László Dobos, and István Csabai. "Galaxy shape measurement with convolutional neural networks." Monthly Notices of the Royal Astronomical Society 489, no. 4 (2019): 4847–59. http://dx.doi.org/10.1093/mnras/stz2374.

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ABSTRACT We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape measurements as ‘ground truth’ from an overlapping, deeper survey with less sky coverage, the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS). We demonstrate that CNN predictions from single band DES images reproduce the results of CFHTLenS at bright magnitudes and show higher correlation with CFHTLenS at fainter magnitudes than maximum likelihood

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AYENI, Joshua Ayobami. "Convolutional Neural Network (CNN): The architecture and applications." Applied Journal of Physical Science 4, no. 4 (2022): 42–50. http://dx.doi.org/10.31248/ajps2022.085.

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The human brain is made up of several hundreds of billions of interconnected neurons that process information in parallel. Researchers in the field of artificial intelligence have successfully demonstrated a considerable level of intelligence on chips and this has been termed Neural Networks (NNs). Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning (ML) and they are at the heart of deep learning algorithms. These subsets of ML have their names and structures derived from the human brain and the way that biologi

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Ouamna, Hamza, Anass Kharbouche, Noureddine El-Haryqy, Zhour Madini, and Younes Zouine. "Performance Analysis of a Hybrid Complex-Valued CNN-TCN Model for Automatic Modulation Recognition in Wireless Communication Systems." Applied System Innovation 8, no. 4 (2025): 90. https://doi.org/10.3390/asi8040090.

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This paper presents a novel deep learning-based automatic modulation recognition (AMR) model, designed to classify ten modulation types from complex I/Q signal data. The proposed architecture, named CV-CNN-TCN, integrates Complex-Valued Convolutional Neural Networks (CV-CNNs) with Temporal Convolutional Networks (TCNs) to jointly extract spatial and temporal features while preserving the inherent phase information of the signal. An enhanced variant, CV-CNN-TCN-DCC, incorporates dilated causal convolutions to further strengthen temporal representation. The models are trained and evaluated on th

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Pal Chowdhury, Arjun, Pranav Kulkarni, and Mahdi Nazm Bojnordi. "MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices." Journal of Low Power Electronics and Applications 8, no. 4 (2018): 38. http://dx.doi.org/10.3390/jlpea8040038.

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Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in acc

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Sledevič, Tomyslav, and Artūras Serackis. "mNet2FPGA: A Design Flow for Mapping a Fixed-Point CNN to Zynq SoC FPGA." Electronics 9, no. 11 (2020): 1823. http://dx.doi.org/10.3390/electronics9111823.

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The convolutional neural networks (CNNs) are a computation and memory demanding class of deep neural networks. The field-programmable gate arrays (FPGAs) are often used to accelerate the networks deployed in embedded platforms due to the high computational complexity of CNNs. In most cases, the CNNs are trained with existing deep learning frameworks and then mapped to FPGAs with specialized toolflows. In this paper, we propose a CNN core architecture called mNet2FPGA that places a trained CNN on a SoC FPGA. The processing system (PS) is responsible for convolution and fully connected core conf

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Hanafiah, Mastura, Mohd Azraei Adnan, Shuzlina Abdul-Rahman, Sofianita Mutalib, Ariff Md Ab Malik, and Mohd Razif Shamsuddin. "Flower Recognition using Deep Convolutional Neural Networks." IOP Conference Series: Earth and Environmental Science 1019, no. 1 (2022): 012021. http://dx.doi.org/10.1088/1755-1315/1019/1/012021.

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Abstract This study investigates the suitable model for flower recognition based on deep Convolutional Neural Networks (CNN) with transfer learning approach. The dataset used in the study is a benchmark dataset from Kaggle. The performance of CNN for plant identification using images of flower are investigated using two popular image classification models: AlexNet and VGG16. Results show that CNN is proven to produce outstanding results for object recognition, but its achievement can still be influenced by the type of images and the number of layers of the CNN architecture. The models produced

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Abebaw, Zeleke, Andreas Rauber, and Solomon Atnafu. "Design and Implementation of a Multichannel Convolutional Neural Network for Hate Speech Detection in Social Networks." Revue d'Intelligence Artificielle 36, no. 2 (2022): 175–83. http://dx.doi.org/10.18280/ria.360201.

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As the number of social media comments available online grows, the spread of hate speech has grown gradually. When someone uses hate speech as a weapon to injure, degrade, and humiliate others, their freedom, dignity, and personhood can be jeopardized. Deep neural network-based hate speech detection models, such as the conventional single channel convolutional neural network (SC-CNN), have recently demonstrated promising results. The success of the models, however, is dependent on the type of language they are trained on and the training data size. Even with a small amount of training data, th

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Tobji, Rachida, Wu Di, and Naeem Ayoub. "FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security." Applied Sciences 9, no. 10 (2019): 2042. http://dx.doi.org/10.3390/app9102042.

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In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at di

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Suk-Hwan, Jung, and Chung Yong-Joo. "Sound event detection using deep neural networks." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2587~2596. https://doi.org/10.12928/TELKOMNIKA.v18i5.14246.

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We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined r

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Zadrożny, Adam, and Beata Goźlińska. "Background Rejection using Convolutional Neural Networks." Proceedings of the International Astronomical Union 13, S338 (2017): 37–39. http://dx.doi.org/10.1017/s1743921318000492.

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AbstractThe paper presents a proof of concept method of background rejection based on convolutional neural networks (CNN). The method was tested on simulated data and achieved very high accuracy (100%). What is more, method based on CNN is very fast and could be easily applied to wide field surveys. Since early stage results suggest method is very accurate and robust, it could be helpful in creating very low-latency pipelines for EM Follow-up purposes, which will be needed in LIGO-Virgo O3 EM Follow-up.

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Maurya, Mahesh, Varun T, K. Sree latha, and Atul Kumar. "OPTIMIZING VEHICULAR NETWORK MANAGEMENT USING CONVOLUTIONAL NEURAL NETWORKS." ICTACT Journal on Communication Technology 14, no. 2 (2023): 2913–18. http://dx.doi.org/10.21917/ijct.2023.0433.

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CNN have been utilized in many domains and have revolutionized the field of computer vision, natural language processing and vehicular network management. CNNs are loaded with a number of advantages over the current methods of controlling vehicular networks. For instance, they can effectively handle the dynamic behavior of vehicular network due to their ability to learn recognition patterns. Additionally, CNNs are equipped with the capability to perform feature extraction along with its learning and integrating abilities, which can be highly advantageous for vehicular network management. Furth

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Gawali, Prof N. V. "Image Classification Using Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5176–79. http://dx.doi.org/10.22214/ijraset.2023.52435.

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Abstract: Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification tasks due to their ability to automatically learn hierarchical representations from raw pixel data. This paper provides a comprehensive review of CNN-based image classification methods, covering various aspects such as network architectures, training techniques, and evaluation metrics. Additionally, we discuss recent advancements, challenges, and future directions in CNN-based image classification research. The aim of this paper is to provide researchers and practitioners with a thorough unde

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Tripathi, Akash, T. V. Ajay Kumar, Tarun Kanth Dhansetty, and J. Selva Kumar. "Real Time Object Detection using CNN." International Journal of Engineering & Technology 7, no. 2.24 (2018): 33. http://dx.doi.org/10.14419/ijet.v7i2.24.11994.

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Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obta

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Chauhan, Chandrapal. "Plant Leaf Disease Detection Using CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29911.

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The existence of pests and diseases in plants and crops has a substantial impact on agricultural production within a country. Monitoring plants meticulously to detect and identify diseases is a common practice among farmers and experts. However, this approach is often laborious, costly, and not entirely reliable. To mitigate this issue, we propose a Disease Recognition Model based on leaf image classification. Our objective is to detect plant diseases using image processing techniques, specifically leveraging Convolutional Neural Networks (CNNs). CNNs are a category of artificial neural networ

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