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Journal articles on the topic 'Modified convolutional neural network'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Sapunov, V. V., S. A. Botman, G. V. Kamyshov, and N. N. Shusharina. "Application of Convolution with Periodic Boundary Condition for Processing Data from Cylindrical Electrode Arrays." INFORMACIONNYE TEHNOLOGII 27, no. 3 (2021): 125–31. http://dx.doi.org/10.17587/it.27.125-131.

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In this paper, modification of convolutional neural networks for purposes of processing electromyographic data obtained from cylindrical arrays of electrodes was proposed. Taking into account the spatial symmetry of the array, convolution operation was redefined using periodic boundary conditions, which allowed to construct a neural network that is invariant to rotations of electrodes array around its axis. Applicability of the proposed approach was evaluated by constructing a neural network containing a new type of convolutional layer and training it on the open UC2018 DualMyo dataset in orde

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Wasim Khan. "Image Classification using modified Convolutional Neural Network." Journal of Electrical Systems 20, no. 3 (2024): 3465–72. https://doi.org/10.52783/jes.4982.

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Image classification is the field of research since decades. With evaluation of new technologies, the performance of image classification has been improved and this is evident by it’s us in routine life. However there are scopes to use the deep learning networks to further improve the complex image classification problems. In this paper, the Convolution neural network based(CNN) image classification is evaluated by changing the parameters of CNN like number of layers, number of neurons, block size of convolution operation etc. The parametric analysis in terms of accuracy number of iteration fo

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Iatsenko, D. V., and B. B. Zhmaylov. "IMPROVING THE EFFICIENCY OF THE CONVOLUTIONAL NEURAL NETWORK USING THE METHOD OF INCREASING THE RECEPTIVE FIELD." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 18–24. http://dx.doi.org/10.14489/vkit.2020.09.pp.018-024.

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In many pattern recognition problems solved using convolutional neural networks (CNN), one of the important characteristics of network architecture is the size of the convolution kernel, since it coincides with the size of the maximum element that can act as a recognition sign. However, increasing the size of the convolution kernel greatly increases the number of tunable network parameters. The method of effective receptive field was first applied on AlexNet in 2012. The practical application of the method of increasing the effective receptive field without increasing convolution kernel size i

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Iatsenko, D. V., and B. B. Zhmaylov. "IMPROVING THE EFFICIENCY OF THE CONVOLUTIONAL NEURAL NETWORK USING THE METHOD OF INCREASING THE RECEPTIVE FIELD." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 18–24. http://dx.doi.org/10.14489/vkit.2020.09.pp.018-024.

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In many pattern recognition problems solved using convolutional neural networks (CNN), one of the important characteristics of network architecture is the size of the convolution kernel, since it coincides with the size of the maximum element that can act as a recognition sign. However, increasing the size of the convolution kernel greatly increases the number of tunable network parameters. The method of effective receptive field was first applied on AlexNet in 2012. The practical application of the method of increasing the effective receptive field without increasing convolution kernel size i

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Murinto, Murinto, and Sri Winiarti. "Modified particle swarm optimization (MPSO) optimized CNN’s hyperparameters for classification." International Journal of Advances in Intelligent Informatics 11, no. 1 (2025): 133. https://doi.org/10.26555/ijain.v11i1.1303.

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This paper proposes a convolutional neural network architectural design approach using the modified particle swarm optimization (MPSO) algorithm. Adjusting hyper-parameters and searching for optimal network architecture from convolutional neural networks (CNN) is an interesting challenge. Network performance and increasing the efficiency of learning models on certain problems depend on setting hyperparameter values, resulting in large and complex search spaces in their exploration. The use of heuristic-based searches allows for this type of problem, where the main contribution in this research

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Sun, Kai, Jiangshe Zhang, Junmin Liu, Shuang Xu, Xiangyong Cao, and Rongrong Fei. "Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening." Remote Sensing 15, no. 11 (2023): 2869. http://dx.doi.org/10.3390/rs15112869.

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Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are ge

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Adhari, Firman Maulana, Taufik Fuadi Abidin, and Ridha Ferdhiana. "License Plate Character Recognition using Convolutional Neural Network." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 51–60. http://dx.doi.org/10.20473/jisebi.8.1.51-60.

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Background: In the last decade, the number of registered vehicles has grown exponentially. With more vehicles on the road, traffic jams, accidents, and violations also increase. A license plate plays a key role in solving such problems because it stores a vehicle’s historical information. Therefore, automated license-plate character recognition is needed. Objective: This study proposes a recognition system that uses convolutional neural network (CNN) architectures to recognize characters from a license plate’s images. We called it a modified LeNet-5 architecture. Methods: We used four differen

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Misko, Joshua, Shrikant S. Jadhav, and Youngsoo Kim. "Extensible Embedded Processor for Convolutional Neural Networks." Scientific Programming 2021 (April 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/6630552.

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Convolutional neural networks (CNNs) require significant computing power during inference. Smart phones, for example, may not run a facial recognition system or search algorithm smoothly due to the lack of resources and supporting hardware. Methods for reducing memory size and increasing execution speed have been explored, but choosing effective techniques for an application requires extensive knowledge of the network architecture. This paper proposes a general approach to preparing a compressed deep neural network processor for inference with minimal additions to existing microprocessor hardw

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Prochukhan, Dmytro. "IMPLEMENTATION OF TECHNOLOGY FOR IMPROVING THE QUALITY OF SEGMENTATION OF MEDICAL IMAGES BY SOFTWARE ADJUSTMENT OF CONVOLUTIONAL NEURAL NETWORK HYPERPARAMETERS." Information and Telecommunication Sciences, no. 1 (June 24, 2023): 59–63. http://dx.doi.org/10.20535/2411-2976.12023.59-63.

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Background. The scientists have built effective convolutional neural networks in their research, but the issue of optimal setting of the hyperparameters of these neural networks remains insufficiently researched. Hyperparameters affect model selection. They have the greatest impact on the number and size of hidden layers. Effective selection of hyperparameters improves the speed and quality of the learning algorithm. It is also necessary to pay attention to the fact that the hyperparameters of the convolutional neural network are interconnected. That is why it is very difficult to manually sel

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Luo, Guoliang, Bingqin He, Yanbo Xiong, et al. "An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression." Sensors 23, no. 4 (2023): 2250. http://dx.doi.org/10.3390/s23042250.

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Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compress

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M., Rajeshkumar. "Quad Histogram based Color Feature Extraction and Modified Convolutional Neural Network for Weed Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 85–96. http://dx.doi.org/10.5373/jardcs/v12sp4/20201469.

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Ko, Kyeong-Nam, and Moon-Sik Kang. "High-efficiency ECG Data Analysis Scheme using Modified Residual Convolutional Neural Network Model." Journal of the Institute of Electronics and Information Engineers 58, no. 10 (2021): 42–48. http://dx.doi.org/10.5573/ieie.2021.58.10.42.

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Lukman, Achmad, Wahju Tjahjo Saputro, and Erni Seniwati. "Improving Performance Convolutional Neural Networks Using Modified Pooling Function." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 2 (2024): 343–52. http://dx.doi.org/10.30812/matrik.v23i2.3763.

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The Visual Geometry Group-16 (VGG16) network architecture, as part of the development of convolutional neural networks, has been popular among researchers in solving classification tasks, so in this paper, we investigated the number of layers to find better performance. In addition, we also proposed two pooling function techniques inspired by existing research on mixed pooling functions, namely Qmax and Qavg. The purpose of the research was to see the advantages of our method; we conducted several test scenarios, including comparing several modified network configurations based on VGG16 as a b

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Yang, Lulu, Junjiang Zhu, Tianhong Yan, Zhaoyang Wang, and Shangshi Wu. "A Modified Convolutional Neural Network for ECG Beat Classification." Journal of Medical Imaging and Health Informatics 10, no. 3 (2020): 654–60. http://dx.doi.org/10.1166/jmihi.2020.2913.

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Most convolutional neural networks (CNNs) used to classify electrocardiogram (ECG) beats tend to focus only on the beat, ignoring its relationships with its surrounding beats. This study aimed to propose a hybrid convolutional neural network (HCNN) structure, which classified ECG beats based on the beat's morphology and relationship such as RR intervals. The difference between the HCNN and the traditional CNN lies in the fact that the relationship can be added to any layer in the former. The HCNN was fed with RR intervals at 3 different positions, trained using data from 2170 patients. It was

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Xu, Yang, Yuequan Bao, Jiahui Chen, Wangmeng Zuo, and Hui Li. "Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images." Structural Health Monitoring 18, no. 3 (2018): 653–74. http://dx.doi.org/10.1177/1475921718764873.

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This study conducts crack identification from real-world images containing complicated disturbance information (cracks, handwriting scripts, and background) inside steel box girders of bridges. Considering the multilevel and multi-scale features of the input images, a modified fusion convolutional neural network architecture is proposed. As input, 350 raw images are taken with a consumer-grade camera and divided into sub-images with resolution of 64 × 64 pixels (67,200 in total). A regular convolutional neural network structure is employed as baseline to demonstrate the accuracy benefits from

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Khan, Muhammad Ashfaq. "HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System." Processes 9, no. 5 (2021): 834. http://dx.doi.org/10.3390/pr9050834.

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a

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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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Jiao, Licheng, Sibo Zhang, Lingling Li, Fang Liu, and Wenping Ma. "A modified convolutional neural network for face sketch synthesis." Pattern Recognition 76 (April 2018): 125–36. http://dx.doi.org/10.1016/j.patcog.2017.10.025.

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Zhao, Ying. "Evolutionary Neural Network-Based Online Ecological Governance Monitoring of Industrial Water Pollution." International Journal of Swarm Intelligence Research 16, no. 1 (2025): 1–23. https://doi.org/10.4018/ijsir.370397.

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This paper proposes ENNOEIGS, an evolutionary neural network-based online ecological industrial governance system that integrates advanced neural architectures with evolutionary optimization for robust pollution monitoring. The framework combines convolutional neural networks for dimensional reduction of sensor data, external attention mechanisms for discovering pollution pattern correlations, and convolutional long short-term memory networks for modeling the spatiotemporal evolution of contaminants. A genetic algorithm continuously optimizes the neural network parameters, enabling adaptation

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Wang, Lulu. "Holographic Microwave Image Classification Using a Convolutional Neural Network." Micromachines 13, no. 12 (2022): 2049. http://dx.doi.org/10.3390/mi13122049.

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Holographic microwave imaging (HMI) has been proposed for early breast cancer diagnosis. Automatically classifying benign and malignant tumors in microwave images is challenging. Convolutional neural networks (CNN) have demonstrated excellent image classification and tumor detection performance. This study investigates the feasibility of using the CNN architecture to identify and classify HMI images. A modified AlexNet with transfer learning was investigated to automatically identify, classify, and quantify four and five different HMI breast images. Various pre-trained networks, including ResN

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Chen, Jing, Xiaoxuan Wang, and Yujing Wu. "Internet street view image fusion method using convolutional neural network." Journal of Computational Methods in Sciences and Engineering 24, no. 3 (2024): 1665–78. http://dx.doi.org/10.3233/jcm-247272.

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The use of image fusion technology in the area of information processing is continuing to advance in depth thanks to ongoing hardware advancements and related research. An enhanced convolutional neural network approach is developed to fuse visible and infrared images, and image pre-processing is carried out utilising an image alignment method with edge detection in order to gain more accurate and trustworthy image information. The performance of the fast wavelet decomposition, convolutional neural network, and modified convolutional neural network techniques is compared and examined using four

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Zherebukh, Oleh, and Ihor Farmaha. "Using Neural Networks to Identify Objects in an Image." Computer Design Systems. Theory and Practice 6, no. 1 (2024): 232–40. http://dx.doi.org/10.23939/cds2024.01.232.

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A modified neural network model based on Yolo V5 was developed and the quality metrics of object classification on video images built on the basis of existing known basic neural network architectures were compared. The application of convolutional neural networks for processing images from video surveillance cameras is considered in order to develop an optimized algorithm for detecting and classifying objects on video images. The existing models and architectures of neural networks for image analysis were analyzed and compared. The possibilities of optimizing the process of image analysis usin

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Deng, Guohao, Dang Wang, and Weixin Gao. "Active and Reactive Power Coordination Optimization of the Active Distribution Network." Journal of Physics: Conference Series 2450, no. 1 (2023): 012023. http://dx.doi.org/10.1088/1742-6596/2450/1/012023.

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Abstract The operation and control of the active distribution network are faced with great challenges due to a mass of tunable and controllable devices connected to the network, resulting in large active power loss and voltage deviation. In this paper, a method of active and reactive power coordination optimization for the active distribution network based on a one-dimensional convolutional neural network (1D-CNN) is proposed. This method can mine valuable information from the historical data of distribution networks, and use one-dimensional convolutional neural networks to map the complex non

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Fan, Weiwei, Feng Zhou, Xueru Bai, Mingliang Tao, and Tian Tian. "Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images." Remote Sensing 11, no. 23 (2019): 2862. http://dx.doi.org/10.3390/rs11232862.

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Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neu

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Manabe, Keisuke, Yusuke Asami, Tomonari Yamada, and Hiroyuki Sugimori. "Improvement in the Convolutional Neural Network for Computed Tomography Images." Applied Sciences 11, no. 4 (2021): 1505. http://dx.doi.org/10.3390/app11041505.

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Background and purpose. This study evaluated a modified specialized convolutional neural network (CNN) to improve the accuracy of medical images. Materials and Methods. We defined computed tomography (CT) images as belonging to one of the following 10 classes: head, neck, chest, abdomen, and pelvis with and without contrast media, with 10,000 images per class. We modified the CNN based on the AlexNet with an input size of 512 × 512. We resized the filter sizes of the convolution layer and max pooling. Using these modified CNNs, various models were created and evaluated. The improved CNN was ev

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Sohn, Chanyoung, Heejong Choi, Kangil Kim, Jinwook Park, and Junhyug Noh. "Line Chart Understanding with Convolutional Neural Network." Electronics 10, no. 6 (2021): 749. http://dx.doi.org/10.3390/electronics10060749.

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Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When building a deep neural network, the integrated approach hides the properties of individual subtasks, which can hinder finding the optimal configurations for the understanding task in academia. In this paper, we propose a problem definition for explicitly understanding knowledge in a line chart and provi

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Ali Ramdhani, Muhammad, Dian Sa’adillah Maylawati, and Teddy Mantoro. "Indonesian news classification using convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 2 (2020): 1000. http://dx.doi.org/10.11591/ijeecs.v19.i2.pp1000-1009.

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<span>Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in Natural Language Processing (NLP) research area. In the current NLP research area, Data Mining (DM) or Machine Learning (ML) technique is popular, especially for Deep Learning (DL) method. This research aims to classify text data in the Indonesian language using Convolutional Neural Network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-proce

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Muhammad, Ali Ramdhani, Sa'adillah Maylawati Dian, and Mantoro Teddy. "Indonesian news classification using convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 2 (2020): 1000–1009. https://doi.org/10.11591/ijeecs.v19.i2.pp1000-1009.

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Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in Natural Language Processing (NLP) research area. In the current NLP research area, Data Mining (DM) or Machine Learning (ML) technique is popular, especially for Deep Learning (DL) method. This research aims to classify text data in the Indonesian language using Convolutional Neural Network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-processing phase,

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SHIN, Soo-Yeon, Dong-Myung KIM, and Jae-Won SUH. "Image Denoiser Using Convolutional Neural Network with Deconvolution and Modified Residual Network." IEICE Transactions on Information and Systems E102.D, no. 8 (2019): 1598–601. http://dx.doi.org/10.1587/transinf.2018edl8175.

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Энгель, Е. А., and Н. Е. Энгель Энгель. "INTELLIGENT MODEL FOR MAXIMIZING THE GENERATED POWER OF A RECONFIGURABLE SOLAR POWER PLANT." Proceedings in Cybernetics 22, no. 1 (2023): 52–58. http://dx.doi.org/10.35266/1999-7604-2023-1-52-58.

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The global maximum power point tracking of a solar power plant in partial shading demands a global optimization. Standard algorithms for tracking of maximum power point do not provide for a maximum global power of a solar power plant during real time mode due to low convergence. A model of aximizing the generated power of a reconfigurable solar power plant was developed as a modified fuzzy deep neural network based on the modified quantum-behaved particle swarm optimizer. This neural network consists of the following: convolutional units, recurrent neural networks, and fuzzy units. By processi

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Liu, Ruiqi, Jing Tian, Yuemei Li, et al. "Nailfold Microhemorrhage Segmentation with Modified U-Shape Convolutional Neural Network." Applied Sciences 12, no. 10 (2022): 5068. http://dx.doi.org/10.3390/app12105068.

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Nailfold capillaroscopy is a reliable way to detect and analyze microvascular abnormalities. It is safe, simple, noninvasive, and inexpensive. Among all the capillaroscopic abnormalities, nailfold microhemorrhages are closely associated with early vascular damages and might be present in numerous diseases such as glaucoma, diabetes mellitus, and systemic sclerosis. Segmentation of nailfold microhemorrhages provides valuable pathological information that may lead to further investigations. A novel deep learning architecture named DAFM-Net is proposed for the accurate segmentation in this study.

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Cari, Cari, Mohtar Yunianto, and Aisyah Ajibah Rahmah. "Lung Cancer Detection Using a Modified Convolutional Neural Network (CNN)." INDONESIAN JOURNAL OF APPLIED PHYSICS 14, no. 1 (2024): 52. http://dx.doi.org/10.13057/ijap.v14i1.77032.

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<p><span>Image processing is used to classify lung images with malignant or normal nodules. The Convolutional Neural Network (CNN) method is often used to classify images. This study uses a modified CNN architecture with various layers, filters, batch size, dropout, and epoch values. Variations were made to determine the best accuracy value and reduce the overfitting value of the proposed CNN architecture. This study implements the method using the Keras library with the Python programming language. The data is in the form of CT-Scan images of lung cancer and normal lungs. The resu

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Ma, Liyong, Chengkuan Ma, Yuejun Liu, and Xuguang Wang. "Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization." Computational Intelligence and Neuroscience 2019 (January 15, 2019): 1–11. http://dx.doi.org/10.1155/2019/6212759.

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Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves’ disease, Hashimoto disease, and subacute thyroiditis. A modified DenseNet a

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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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Yang, Jin. "Urban Traffic Flow Prediction with Deep Neural Network." Security and Communication Networks 2022 (June 1, 2022): 1–10. http://dx.doi.org/10.1155/2022/8711873.

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It is critical to realize accurate collecting, visualization, rule mining, and prediction analysis of the traffic flow operating state in order for the intelligent transportation system to achieve exact management and control of traffic flow. Traffic flow prediction is primarily concerned with traffic data on roadways, which has both temporal and spatial correlations. Aiming at the spatiotemporal characteristics, this paper studies two aspects and designs a traffic flow prediction model with a deep neural network. First, this work proposes a traffic flow spatial feature learning algorithm with

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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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Kavitha Rajalakshmi D and P. Bharathisindhu. "A Modified Deep Convolutional Network for Detection of Covid19 from Chest X-Rays Based on Concatenation of Image Preprocessing Techniques and RESnCOV." Metallurgical and Materials Engineering 31, no. 4 (2025): 331–40. https://doi.org/10.63278/1441.

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The fast-spreading coronavirus disease called COVID-19 has impacted millions of people worldwide. It becomes difficult for medical experts to rapidly detect the illness and stop its spread because of its rapid growth and rising numbers. One of the newer areas of study where this issue can be more carefully addressed is medical image analysis. In this study, we implemented an image processing system utilizing deep learning and neural networks to previse the 2019-nCoV using chest roentgen ray images. In order to recognize COVID-19 positive and healthy patients using chest roentgen ray images, th

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Dawud, Awwal Muhammad, Kamil Yurtkan, and Huseyin Oztoprak. "Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning." Computational Intelligence and Neuroscience 2019 (June 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/4629859.

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In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question

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Wagh, Jagruti. "Geographical Area Classification on Satellite Images Using CNN Architecture." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34144.

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This paper presents a novel approach for classi- fying geographical areas in satellite imagery using a modified Convolutional Neural Network (CNN) architecture.The archi- tecture enhances feature extraction and classification accuracy by combining specialized layers like fully connected, pooling, and conventional layers. Our modified CNN performs better at accurately classifying a variety of geographic locations, according to our testing results. By efficiently capturing and analyzing complex spatial patterns, the use of customized layers enhances classification results in satellite-based geog

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Et. al., Siji George C. G,. "Genetic Algorithm Based Hybrid Model Of convolutional Neural Network And Random Forest Classifier For Sentiment Classification." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 3216–23. http://dx.doi.org/10.17762/turcomat.v12i2.2379.

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Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vector

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Firsov, Nikita, Evgeny Myasnikov, Valeriy Lobanov, et al. "HyperKAN: Kolmogorov–Arnold Networks Make Hyperspectral Image Classifiers Smarter." Sensors 24, no. 23 (2024): 7683. https://doi.org/10.3390/s24237683.

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In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov–Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neur

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Lin, Chaojun, Ying Shi, Jian Zhang, Changjun Xie, Wei Chen, and Yue Chen. "An anchor-free detector and R-CNN integrated neural network architecture for environmental perception of urban roads." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 12 (2021): 2964–73. http://dx.doi.org/10.1177/09544070211004466.

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Environmental perception of urban roads is a critical research goal in intelligent transportation technology and autonomous vehicles, and pedestrian location is key to many relevant algorithms. Because anchor-free detectors are faster and region-based convolutional neural networks have a higher accuracy in object detection and classification, we propose an integrated convolutional networking architecture combining an anchor-free detector with a region-based convolutional neural network in the environmental perception task. The proposed network achieves higher precision and increases inference

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Andini, Silfia, Erni Rouza, Luth Fimawahib, et al. "Convolutional Neural Network for object Identification and Detection." Journal of Physics: Conference Series 2394, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2394/1/012018.

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Abstract The goal of this study is to use a Convolutional Neural Network to find the optimum architectural model for classifying cloud images. Cirrus Cumulus Stratus Nimbus uses a source dataset that includes 11 cloud classifications and 2545 cloud photos (CCSN). In this study, the best Convolutional Neural Network is retrained almost fast by transferring education from Google’s basic design. Based on the modified Googlenet architecture, the training and testing phases of the classification process are divided into two. The dataset is separated into three sections during the training phase: 70

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Fouad, Zainab, Marco Alfonse, Mohamed Roushdy, and Abdel-Badeeh M. Salem. "Hyper-parameter optimization of convolutional neural network based on particle swarm optimization algorithm." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3377–84. http://dx.doi.org/10.11591/eei.v10i6.3257.

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Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are dept

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K, Jeyalakshmi, and Rangaraj R. "Accurate liver disease prediction system using convolutional neural network." Indian Journal of Science and Technology 14, no. 17 (2021): 1406–21. https://doi.org/10.17485/IJST/v14i17.451.

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Abstract <strong>Objectives:</strong> To introduce the technique which can ensure the accurate and reliable prediction of liver disease by adapting the deep learning technique. <strong>Methods:</strong> In this work Modified Convolutional Neural Network based Liver Disease Prediction System (MCNN-LDPS) is introduced for the accurate liver disease prediction outcome. In the proposed research work, Dimensionality reduction is carried out using Modified Principal Component Analysis. Optimal feature selection is carried out using Score based Artificial Fish Swarm Algorithm (SAFSA).

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Alsawalqah, Ahmad, and Bakhtiar Rosdi. "Robust Finger Vein Presentation Attack Detection Using XceptionNet-based Modified Depthwise Separable Convolutional Neural Network." Jordan Journal of Electrical Engineering 11, no. 1 (2025): 1. http://dx.doi.org/10.5455/jjee.204-1717325207.

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Finger vein presentation attack detection (FVPAD) biometric systems have seen substantial enhancements through the application of deep learning convolutional neural networks (DCNN). This advancement led to increased complexity, parameters and resource requirements. To address these challenges, a novel modification to the first entry flow of the XceptionNet architecture based on customized depthwise separable convolution (DSC) CNN-based for extracting robust features from FV images to detect spoofing attacks is proposed in this paper. The proposed approach stands out for its simplicity in desig

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Ghafar, Abdul, and Usman Sattar. "Convolutional Autoencoder for Image Denoising." UMT Artificial Intelligence Review 1, no. 2 (2021): 1–11. http://dx.doi.org/10.32350/air.0102.01.

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Image denoising is a process used to remove noise from the image to create a sharp and clear image. It is mainly used in medical imaging, where due to the malfunctioning of machines or due to the precautions taken to protect patients from radiation, medical imaging machines create a lot of noise in the final image. Several techniques can be used in order to avoid such distortions in the image before their final printing. Autoencoders are the most notable software used to denoise images before their final printing. These software are not intelligent so the resultant image is not of good quality

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Dofitas, Cyreneo, Joon-Min Gil, and Yung-Cheol Byun. "Multi-Directional Long-Term Recurrent Convolutional Network for Road Situation Recognition." Sensors 24, no. 14 (2024): 4618. http://dx.doi.org/10.3390/s24144618.

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Understanding road conditions is essential for implementing effective road safety measures and driving solutions. Road situations encompass the day-to-day conditions of roads, including the presence of vehicles and pedestrians. Surveillance cameras strategically placed along streets have been instrumental in monitoring road situations and providing valuable information on pedestrians, moving vehicles, and objects within road environments. However, these video data and information are stored in large volumes, making analysis tedious and time-consuming. Deep learning models are increasingly util

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V, Roseline, and Heren Chellam G. "A Novel Fusion Attention Algorithm for Sentimental Image Analysis." Indian Journal of Science and Technology 15, no. 9 (2022): 386–94. https://doi.org/10.17485/IJST/v15i9.2159.

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Abstract <strong>Objectives:</strong> To implement a novel and hybrid methodology for finding out the positive features when using convolutional neural networks (CNNs) for visual sentiment analysis. To achieve increased accuracy, precision and recall by using this proposed fusion attention methodology. <strong>Methods:</strong> This study proposes a modified methodology encompassing spatial attention, channel attention as well as squeeze excitation modules. An enhanced approach on the basis of convolutional neural networks was used here which utilizes convolution operators by co

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Han, Shuzhen, Pingjuan Niu, Shijie Luo, et al. "A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis." Sensors 23, no. 19 (2023): 8060. http://dx.doi.org/10.3390/s23198060.

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This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activatio

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