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

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

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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Anem, Smt Jayalaxmi, B. Dharani, K. Raveendra, CH Nikhil, and K. Akhil. "Leveraging Convolution Neural Network (CNN) for Skin Cancer Identification." International Journal of Research Publication and Reviews 5, no. 4 (2024): 2150–55. http://dx.doi.org/10.55248/gengpi.5.0424.0955.

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Rifqie, Dary Mochamad, Dewi Fatmarani Surianto, Sudarmanto Jayanegara, Muhammad Fajar B, and M. Miftach Fakhri. "Minimizing Multiplication of Kernel Computation in Convolutional Neural Networks Using Strassen Algorithm." Jurnal MediaTIK 6, no. 2 (2023): 52. http://dx.doi.org/10.26858/jmtik.v6i2.46016.

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Convolution neural networks (CNN) have been widely applied for the computer vision task. However, the success of CNN is limited by the computational complexity of the network, so it is difficult for the model to run the inference process in real time. In this paper, we apply Strassen matrix multiplication to reduce multiplications in convolution operations in CNN, in order to get faster execution for CNN. First, we transform the convolution operation into a matrix multiplication operation using the Toeplitz mapping method, then after that, we apply the Strassen method to these matrices. In the

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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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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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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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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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Saleem, Muhammad Asif, Norhalina Senan, Fazli Wahid, Muhammad Aamir, Ali Samad, and Mukhtaj Khan. "Comparative Analysis of Recent Architecture of Convolutional Neural Network." Mathematical Problems in Engineering 2022 (March 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/7313612.

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Convolutiona neural network (CNN) is one of the best neural networks for classification, segmentation, natural language processing (NLP), and video processing. The CNN consists of multiple layers or structural parameters. The architecture of CNN can be divided into three sections: convolution layers, pooling layers, and fully connected layers. The application of CNN became most demanding due to its ability to learn features from images automatically, involving massive amount of training data and high computational resources like GPUs. Due to the availability of the above-stated resources, mult

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M, Venkata Krishna Reddy, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 6 (2021): 54–60. https://doi.org/10.35940/ijitee.F8804.0410621.

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Profound learning's goes to the achievement of spurs in a large number and understudies to find out about the energizing innovation. At this regular process of novices to venture the multifaceted nature of comprehension and applying profound learning. We present Convolution Neural Network (CNN) EXPLAINER, an intelligent representation instrument intended for non-specialists to learn and inspect (CNN)-Convolution Neural Network a fundamental profound learning model engineering. Our apparatus tends to key difficulties that fledglings face in finding out about Convolution Neural Network, it c

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Park, Sang-Soo, and Ki-Seok Chung. "CENNA: Cost-Effective Neural Network Accelerator." Electronics 9, no. 1 (2020): 134. http://dx.doi.org/10.3390/electronics9010134.

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Convolutional neural networks (CNNs) are widely adopted in various applications. State-of-the-art CNN models deliver excellent classification performance, but they require a large amount of computation and data exchange because they typically employ many processing layers. Among these processing layers, convolution layers, which carry out many multiplications and additions, account for a major portion of computation and memory access. Therefore, reducing the amount of computation and memory access is the key for high-performance CNNs. In this study, we propose a cost-effective neural network a

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Wan, Renzhuo, Shuping Mei, Jun Wang, Min Liu, and Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting." Electronics 8, no. 8 (2019): 876. http://dx.doi.org/10.3390/electronics8080876.

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Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-N

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Ibrahim, Alaa, Mohamed Waleed Fakhr, and Mohamed Farouk. "Secure CNN Computation Using Random Projection and Support Vector Regression." Journal of Advanced Research in Applied Sciences and Engineering Technology 65, no. 1 (2025): 209–25. https://doi.org/10.37934/araset.65.1.209225.

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Convolutional Neural Networks (CNNs) are foundational in numerous machine learning applications, particularly in image processing, where they excel in identifying patterns within visual data. At the core of CNNs lies the 2D convolution operation, which is essential for extracting spatial features from images. However, when applied to sensitive data, such as in medical imaging or surveillance, preserving the privacy of both the input data and the convolutional filters is crucial. This paper introduces a novel approach to secure the 2D convolution operation in CNNs, leveraging random projection

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Dagher, Issam, and Samir Abujamra. "Combined wavelet and Gabor convolution neural networks." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 06 (2019): 1950046. http://dx.doi.org/10.1142/s0219691319500462.

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Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST d

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Archana, D. "Brain Tumor Detection Using Convolution Neural Networks." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46974.

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ABSTRACT Early diagnosis of brain tumors is important for improving patient prognoses; however, traditional diagnostic methods like biopsies require invasive surgical procedures. In this paper, we introduce two deep learning-based methods—a new two-dimensional Convolutional Neural Network (CNN) and a convolutional auto-encoder network—that enable the accurate classification of brain tumors from magnetic resonance imaging (MRI). A data set of 7,000 T1-weighted contrast-enhanced MRI images was utilized, including glioma, meningioma, pituitary gland tumor, and normal brain samples. Preprocessing

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Srinivas, K., B. Kavitha Rani, M. Varaprasad Rao, G. Madhukar, and B. Venkata Ramana. "Convolution Neural Networks for Binary Classification." Journal of Computational and Theoretical Nanoscience 16, no. 11 (2019): 4877–82. http://dx.doi.org/10.1166/jctn.2019.8399.

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Convolutional neural networks (CNNs) are similar to “ordinary” neural networks in the sense that they are made up of hidden layers consisting of neurons with “learnable” parameters. These neurons receive inputs, perform a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies conducted to challenge this norm. Empirical data has shown that the CNN model was able to achieve a test a

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Xie, Shaodong, Jiagang Song, Yuxuan Hu, Chengyuan Zhang, and Shichao Zhang. "Using CNN with Multi-Level Information Fusion for Image Denoising." Electronics 12, no. 9 (2023): 2146. http://dx.doi.org/10.3390/electronics12092146.

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Deep convolutional neural networks (CNN) with hierarchical architectures have obtained good results for image denoising. However, in some cases where the noise level is unknown and the image background is complex, it is challenging to obtain robust information through CNN. In this paper, we present a multi-level information fusion CNN (MLIFCNN) in image denoising containing a fine information extraction block (FIEB), a multi-level information interaction block (MIIB), a coarse information refinement block (CIRB), and a reconstruction block (RB). In order to adapt to more complex image backgrou

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Liu, Mingda, Haiqiang Niu, and Zhenglin Li. "Implementation of Bartlett matched-field processing using interpretable complex convolutional neural network." JASA Express Letters 3, no. 2 (2023): 026003. http://dx.doi.org/10.1121/10.0017320.

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Neural networks have been applied to underwater source localization and achieved better performance than the conventional matched-field processing (MFP). However, compared with MFP, the neural networks lack physical interpretability. In this work, an interpretable complex convolutional neural network based on Bartlett processor (BC-CNN) for underwater source localization is designed, the output and structure of which have clear physical meanings. The relationship between the convolution weights of BC-CNN and replica pressure of MFP is discussed, which effectively presents the interpretability

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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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Varghese, Prathibha, and Arockia Selva Saroja. "Biologically inspired deep residual networks." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1873. http://dx.doi.org/10.11591/ijai.v12.i4.pp1873-1882.

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<p>Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagonal

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Varghese, Prathibha, and Arockia Selva Saroja. "Biologically inspired deep residual networks." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1873–82. https://doi.org/10.11591/ijai.v12.i4.pp1873-1882.

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Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagona

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Sadana, Tushar, Monika Jain, Rahul Saxena, Aashis Kumar, Vidhyanshu Jain, and Saurabh Gupta. "Handwriting Analysis Using Convolutional Neural Networks." International Journal of Engineering & Technology 7, no. 4.41 (2018): 79–82. http://dx.doi.org/10.14419/ijet.v7i4.41.24305.

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Convolution is the technique to blend or overlap two or more functions. This technique when provided to artificial neural networks, works together to learn the features of different categories of objects and detects them based on its features instead of the shape and edges. This helps to detect the objects even in unusual positions. Since, features of an object remains constant, CNN provides high efficiency significantly better than traditional cascade methods. CNN networks follow convolution, max pooling, flattening. These process combines preprocess the image for training and then the image

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Kumar, Dinesh, and Dharmendra Sharma. "Feature Map Augmentation to Improve Scale Invariance in Convolutional Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 13, no. 1 (2022): 51–74. http://dx.doi.org/10.2478/jaiscr-2023-0004.

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Abstract Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any conv

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Wan Khairul Hazim Wan Khairul Amir, Afiqah Bazlla Md Soom, Aisyah Mat Jasin, Juhaida Ismail, Aszila Asmat, and Rozeleenda Abdul Rahm an. "Sales Forecasting Using Convolution Neural Network." Journal of Advanced Research in Applied Sciences and Engineering Technology 30, no. 3 (2023): 290–301. http://dx.doi.org/10.37934/araset.30.3.290301.

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Sales forecasting is an essential component of business management, providing insight into future sales and revenue. It is critical for effective inventory management, cash flow, and business growth planning. While many retailers rely on simple Excel functions or subjective guesses from management, the industry is increasingly turning to machine learning techniques to develop more accurate and reliable prediction models. Among these techniques, Convolutional Neural Networks (CNN) emerged as a suitable option due to their ability to learn and improve accuracy over time. CNN applies several laye

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Fülöp, András, György Csaba, and András Horváth. "A Convolutional Neural Network with a Wave-Based Convolver." Electronics 12, no. 5 (2023): 1126. http://dx.doi.org/10.3390/electronics12051126.

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In this paper, we demonstrate that physical waves can be used to perform convolutions as part of a state-of-the-art neural network architecture. In particular, we show that the damping of waves, which is unavoidable in a physical implementation, does not diminish their usefulness in performing the convolution operations required in a convolutional neural network (CNN), and the damping only slightly decreases the classification accuracy of the network. These results open the door for wave-based hardware accelerators for CNNs.

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Rere, L. M. Rasdi, Mohamad Ivan Fanany, and Aniati Murni Arymurthy. "Metaheuristic Algorithms for Convolution Neural Network." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1537325.

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A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that c

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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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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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Somanatti, Umesha, Basavaraj A. Patil, and Lingaraj Hadimani. "Convolution neural networks for hand gesture recognation." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 525. http://dx.doi.org/10.11591/ijai.v11.i2.pp525-529.

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Hand gestures (not static or fixed positions) are movements of fingers and the arm to communicate messages. Hand gesture recognition is the process of identifying meaningful expressions involving the human hand. Pictorial representation of gestures will enable to understand human computer interaction (HCI). This paper describes a system using convolution neural network (CNN) for recognizing the 26 letters of the English alphabet signaled with hand gestures. A Python program was developed to recognize the gestures made in front of a web camera. The hand gestures obtained are categorized using C

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Umesha, Somanatti, A. Patil Basavaraj, and Hadimani Lingaraj. "Convolution neural networks for hand gesture recognation." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 525–29. https://doi.org/10.11591/ijai.v11.i2.pp525-529.

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Hand gestures (not static or fixed positions) are movements of fingers and the arm to communicate messages. Hand gesture recognition is the process of identifying meaningful expressions involving the human hand. Pictorial representation of gestures will enable to understand human computer interaction (HCI). This paper describes a system using convolution neural network (CNN) for recognizing the 26 letters of the English alphabet signaled with hand gestures. A Python program was developed to recognize the gestures made in front of a web camera. The hand gestures obtained are categorized using C

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Leong, Mei Chee, Dilip K. Prasad, Yong Tsui Lee, and Feng Lin. "Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition." Applied Sciences 10, no. 2 (2020): 557. http://dx.doi.org/10.3390/app10020557.

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This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters than 3D CNNs. We adopt transfer learning from pre-trained 2D CNNs for spatial extraction, followed by temporal encoding, before connecting to 3D convolution layers at the top of the architecture. We

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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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Chidester, Benjamin, Tianming Zhou, Minh N. Do, and Jian Ma. "Rotation equivariant and invariant neural networks for microscopy image analysis." Bioinformatics 35, no. 14 (2019): i530—i537. http://dx.doi.org/10.1093/bioinformatics/btz353.

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Abstract Motivation Neural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying micro

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Thyagaraj, T. "Custom Convolution Neural Network for Breast Cancer Detection." International Journal of Engineering and Advanced Technology (IJEAT) 13, no. 2 (2023): 22–29. https://doi.org/10.35940/ijeat.B4334.1213223.

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<strong>Abstract: </strong>Breast cancer remains a serious global health issue. Leveraging the use of deep learning techniques, this study presents a custom Convolutional Neural Network (CNN) framework for the detection of breast cancer. With the specific objective of accurate classification of breast cancer, a framework is made to analyze high-dimensional medical image information. The CNN's architecture, which consists of specifically developed layers and activation components tailored for the categorization of breast cancer, is described in detail. Utilizing the BreakHis dataset, which comp

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L, Prof Prathima. "CLASSIFICATION OF WBC USING CONVOLUTION NEURAL NETWORKS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32010.

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Leukocytes, developed within the cartilage of the bone, account for barely 1% of the overall blood cell counts. Erratic flourishing of leukocytes induces an outbreak of blood cancer. Amongst three of the diverse sorts of cancer in blood, the suggested ponder provides a vigorous instrument for the sorting of subtypes of leukemia and multiple myeloma, utilizing the related dataset. White blood cells with leukemia are not normal that grow throughout cells present in the red blood. WBCs, and platelets and affect the blood and bone marrow. Whereas, multiple myeloma is a different type of cancer tha

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Xu, Kailin, Fanfan Ye, Qiaoyong Zhong, and Di Xie. "Topology-Aware Convolutional Neural Network for Efficient Skeleton-Based Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2866–74. http://dx.doi.org/10.1609/aaai.v36i3.20191.

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In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received less attention. One reason is that CNNs are considered poor in modeling the irregular skeleton topology. To alleviate this limitation, we propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations. By applying the module to the coordinate level and the joint level subsequ

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Deageon, Kim. "A New Method of Text Classification Based on Recurrent Neural Network." International Journal of Applied Engineering & Technology 5, no. 1 (2023): 13–23. https://doi.org/10.5281/zenodo.7601982.

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<strong>With the development of modern information science and technology, the number of Internet users continues to increase substantially, and the processing of massive data is now a hot spot in data research. Artificial Neural Network (ANN) plays a crucial role in the screening and processing of big data. Artificial neural network has successfully solved many practical problems that have puzzled people for many years in the fields of computer vision, machine translation, automatic driving, etc. Therefore, artificial neural network has been increasingly applied to text classification in Natu

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Sheet, Sinan S. Mohammed, Tian-Swee Tan, Muhammad Amir As'ari, et al. "Convolution neural network model for fundus photograph quality assessment." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 915–23. https://doi.org/10.11591/ijeecs.v26.i2.pp915-923.

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The excellent quality of color fundus photograph is crucial for the ophthalmologist to process the correct diagnosis and for convolutional neural network (CNN) models to optimize output classification. As a result of main causes as acquire devises efficiency and experience of a physician most fundus photographs can have uneven illuminance, blur, and bad contrast, in addition to micro-features of retinal diseases, which need to force their contrast. Fundus photograph quality assessment method is proposed to find out the perfect enhanced color fundus Technique in fundoscopy photographs-based CNN

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T. Blessington, Dr Praveen, and Prof Ravindra Mule. "Image Forgery Detection Based on Parallel Convolutional Neural Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28428.

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Abstract— Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the availability of strong editing tools the tampering of digital content become easy. To detect such scams, we proposed techniques. In our paper, we proposed two important aspects of employing deep convolutional neural networks to image forgery detection. We first explore and examine different preprocessing method along with convolutional neural networks (CNN) architecture. Later we evaluated the different transfer learning fo

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Huang, Feizhen, Jinfang Zeng, Yu Zhang, and Wentao Xu. "Convolutional recurrent neural networks with multi-sized convolution filters for sound-event recognition." Modern Physics Letters B 34, no. 23 (2020): 2050235. http://dx.doi.org/10.1142/s0217984920502358.

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Sound-event recognition often utilizes time-frequency analysis to produce an image-like spectrogram that provides a rich visual representation of original signal in time and frequency. Convolutional Neural Networks (CNN) with the ability of learning discriminative spectrogram patterns are suitable for sound-event recognition. However, there is relatively little effort that CNN makes full use of the important temporal information. In this paper, we propose MCRNN, a Convolutional Recurrent Neural Networks (CRNN) architecture for sound-event recognition, the letter “M” in the name “MCRNN” of our

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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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Mewada, Hiren, Jawad F. Al-Asad, Amit Patel, Jitendra Chaudhari, Keyur Mahant, and Alpesh Vala. "Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification." Open Biomedical Engineering Journal 15, no. 1 (2021): 132–40. http://dx.doi.org/10.2174/1874120702115010132.

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Background: The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates Local Binary Pattern (LBP) based texture information with CNN features. Methods: The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison wit

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