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Journal articles on the topic 'Convolutional Neural Networks; cannabinoids'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Negoita, Catalin, Mirela Praisler, and Iulia-Florentina Darie. "Automatic identification of hallucinogenic amphetamines based on their ATR-FTIR spectra processed with Convolutional Neural Networks." MATEC Web of Conferences 342 (2021): 05003. http://dx.doi.org/10.1051/matecconf/202134205003.

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New psychoactive drugs that are leading to severe intoxications are constantly seized on the European black market. Recent studies indicate that most of these new substances are synthetic cannabinoids and hallucinogenic amphetamines. In this study, we are presenting the results obtained with an expert system that was built to identify automatically the class identity of these types of drugs of abuse, based on their Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectra processed with Convolutional Neural Networks (CNNs). CNNs have been applied with great success in recent years in various computer applications, such as image classification, but little work has been done in using this kind of deep learning models for spectral data classification. The aim of this study was to improve the detection accuracy (classification performance) that we have already obtained with other statistical mathematics and artificial intelligence techniques. The performances of the CNN system are discussed in comparison with those of the later models.

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Burlacu, Catalina Mercedes, Adrian Constantin Burlacu, Mirela Praisler, and Cristina Paraschiv. "Harnessing Deep Convolutional Neural Networks Detecting Synthetic Cannabinoids: A Hybrid Learning Strategy for Handling Class Imbalances in Limited Datasets." Inventions 8, no. 5 (2023): 129. http://dx.doi.org/10.3390/inventions8050129.

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The aim of this research was to develop and deploy efficient deep convolutional neural network (DCNN) frameworks for detecting and discriminating between various categories of designer drugs. These are of particular relevance in forensic contexts, aiding efforts to prevent and counter drug use and trafficking and supporting associated legal investigations. Our multinomial classification architectures, based on Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectra, are primarily tailored to accurately identify synthetic cannabinoids. Within the scope of our dataset, they also adeptly detect other forensically significant drugs and misused prescription medications. The artificial intelligence (AI) models we developed use two platforms: our custom-designed, pre-trained Convolutional Autoencoder (CAE) and a structure derived from the Vision Transformer Trained on ImageNet Competition Data (ViT-B/32) model. In order to compare and refine our models, various loss functions (cross-entropy and focal loss) and optimization algorithms (Adaptive Moment Estimation, Stochastic Gradient Descent, Sign Stochastic Gradient Descent, and Root Mean Square Propagation) were tested and evaluated at differing learning rates. This study shows that innovative transfer learning methods, which integrate both unsupervised and supervised techniques with spectroscopic data pre-processing (ATR correction, normalization, smoothing) and present significant benefits. Their effectiveness in training AI systems on limited, imbalanced datasets is particularly notable. The strategic deployment of CAEs, complemented by data augmentation and synthetic sample generation using the Synthetic Minority Oversampling Technique (SMOTE) and class weights, effectively address the challenges posed by such datasets. The robustness and adaptability of our DCNN models are discussed, emphasizing their reliability and portability for real-world applications. Beyond their primary forensic utility, these systems demonstrate versatility, making them suitable for broader computer vision tasks, notably image classification and object detection.

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Burlacu, Catalina Mercedes, Adrian Constantin Burlacu, Mirela Praisler, and Cristina Paraschiv. "Harnessing Deep Convolutional Neural Networks Detecting Synthetic Cannabinoids: A Hybrid Learning Strategy for Handling Class Imbalances in Limited Datasets." Inventions 8, no. 5 (2023): 129. https://doi.org/10.3390/inventions8050129.

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The aim of this research was to develop and deploy efficient deep convolutional neural network (DCNN) frameworks for detecting and discriminating between various categories of designer drugs. These are of particular relevance in forensic contexts, aiding efforts to prevent and counter drug use and trafficking and supporting associated legal investigations. Our multinomial classification architectures, based on Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectra, are primarily tailored to accurately identify synthetic cannabinoids. Within the scope of our dataset, they also adeptly detect other forensically significant drugs and misused prescription medications. The artificial intelligence (AI) models we developed use two platforms: our custom-designed, pre-trained Convolutional Autoencoder (CAE) and a structure derived from the Vision Transformer Trained on ImageNet Competition Data (ViT-B/32) model. In order to compare and refine our models, various loss functions (cross-entropy and focal loss) and optimization algorithms (Adaptive Moment Estimation, Stochastic Gradient Descent, Sign Stochastic Gradient Descent, and Root Mean Square Propagation) were tested and evaluated at differing learning rates. This study shows that innovative transfer learning methods, which integrate both unsupervised and supervised techniques with spectroscopic data pre-processing (ATR correction, normalization, smoothing) and present significant benefits. Their effectiveness in training AI systems on limited, imbalanced datasets is particularly notable. The strategic deployment of CAEs, complemented by data augmentation and synthetic sample generation using the Synthetic Minority Oversampling Technique (SMOTE) and class weights, effectively address the challenges posed by such datasets. The robustness and adaptability of our DCNN models are discussed, emphasizing their reliability and portability for real-world applications. Beyond their primary forensic utility, these systems demonstrate versatility, making them suitable for broader computer vision tasks, notably image classification and object detection.

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4

Prangishvili, Archil, Oleg Namicheishvili, and Mikhael Ramazashvili. "Convolutional Neural Networks." Works of Georgian Technical University, no. 3(517) (September 29, 2020): 33–56. http://dx.doi.org/10.36073/1512-0996-2020-3-33-56.

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5

Cong, Iris, Soonwon Choi, and Mikhail D. Lukin. "Quantum convolutional neural networks." Nature Physics 15, no. 12 (2019): 1273–78. http://dx.doi.org/10.1038/s41567-019-0648-8.

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6

Mirkhan, Mehran, and Mohammad Reza Meybodi. "Restricted Convolutional Neural Networks." Neural Processing Letters 50, no. 2 (2018): 1705–33. http://dx.doi.org/10.1007/s11063-018-9954-x.

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7

Lu, Yao, Guangming Lu, Bob Zhang, Yuanrong Xu, and Jinxing Li. "Super Sparse Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4440–47. http://dx.doi.org/10.1609/aaai.v33i01.33014440.

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To construct small mobile networks without performance loss and address the over-fitting issues caused by the less abundant training datasets, this paper proposes a novel super sparse convolutional (SSC) kernel, and its corresponding network is called SSC-Net. In a SSC kernel, every spatial kernel has only one non-zero parameter and these non-zero spatial positions are all different. The SSC kernel can effectively select the pixels from the feature maps according to its non-zero positions and perform on them. Therefore, SSC can preserve the general characteristics of the geometric and the channels’ differences, resulting in preserving the quality of the retrieved features and meeting the general accuracy requirements. Furthermore, SSC can be entirely implemented by the “shift” and “group point-wise” convolutional operations without any spatial kernels (e.g., “3×3”). Therefore, SSC is the first method to remove the parameters’ redundancy from the both spatial extent and the channel extent, leading to largely decreasing the parameters and Flops as well as further reducing the img2col and col2img operations implemented by the low leveled libraries. Meanwhile, SSC-Net can improve the sparsity and overcome the over-fitting more effectively than the other mobile networks. Comparative experiments were performed on the less abundant CIFAR and low resolution ImageNet datasets. The results showed that the SSC-Nets can significantly decrease the parameters and the computational Flops without any performance losses. Additionally, it can also improve the ability of addressing the over-fitting problem on the more challenging less abundant datasets.

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Lu, Yao, Guangming Lu, Yicong Zhou, Jinxing Li, Yuanrong Xu, and David Zhang. "Highly shared Convolutional Neural Networks." Expert Systems with Applications 175 (August 2021): 114782. http://dx.doi.org/10.1016/j.eswa.2021.114782.

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9

Chen, Yixiong. "Quantum Dilated Convolutional Neural Networks." IEEE Access 10 (2022): 20240–46. http://dx.doi.org/10.1109/access.2022.3152213.

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10

Guo, Yong, Yaofo Chen, Mingkui Tan, Kui Jia, Jian Chen, and Jingdong Wang. "Content-aware convolutional neural networks." Neural Networks 143 (November 2021): 657–68. http://dx.doi.org/10.1016/j.neunet.2021.06.030.

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11

Masi, Giuseppe, Davide Cozzolino, Luisa Verdoliva, and Giuseppe Scarpa. "Pansharpening by Convolutional Neural Networks." Remote Sensing 8, no. 7 (2016): 594. http://dx.doi.org/10.3390/rs8070594.

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12

Wu, Qingxiu, Zhanji Gui, Shuqing Li, and Jun Ou. "Directly Connected Convolutional Neural Networks." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 05 (2018): 1859007. http://dx.doi.org/10.1142/s0218001418590073.

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Convolutional neural networks (CNNs) have better performance in feature extraction and classification. Most of the applications are based on a traditional structure of CNNs. However, due to the fixed structure, it may not be effective for large dataset which will spend much time for training. So, we use a new algorithm to optimize CNNs, called directly connected convolutional neural networks (DCCNNs). In DCCNNs, the down-sampling layer can directly connect the output layer with three-dimensional matrix operation, without full connection (i.e., matrix vectorization). Thus, DCCNNs have less weights and neurons than CNNs. We conduct the comparison experiments on five image databases: MNIST, COIL-20, AR, Extended Yale B, and ORL. The experiments show that the model has better recognition accuracy and faster convergence than CNNs. Furthermore, two applications (i.e., water quality evaluation and image classification) following the proposed concepts further confirm the generality and capability of DCCNNs.

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13

Wang, Hongren, Ce Li, Xiantong Zhen, Wankou Yang, and Baochang Zhang. "Gaussian Transfer Convolutional Neural Networks." IEEE Transactions on Emerging Topics in Computational Intelligence 3, no. 5 (2019): 360–68. http://dx.doi.org/10.1109/tetci.2018.2881225.

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14

Guo, Qingbei, Xiao-Jun Wu, Josef Kittler, and Zhiquan Feng. "Self-grouping convolutional neural networks." Neural Networks 132 (December 2020): 491–505. http://dx.doi.org/10.1016/j.neunet.2020.09.015.

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15

Audhkhasi, Kartik, Osonde Osoba, and Bart Kosko. "Noise-enhanced convolutional neural networks." Neural Networks 78 (June 2016): 15–23. http://dx.doi.org/10.1016/j.neunet.2015.09.014.

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16

Ou, Jun, and Yujian Li. "Vector-kernel convolutional neural networks." Neurocomputing 330 (February 2019): 253–58. http://dx.doi.org/10.1016/j.neucom.2018.11.028.

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17

Krichen, Moez. "Convolutional Neural Networks: A Survey." Computers 12, no. 8 (2023): 151. http://dx.doi.org/10.3390/computers12080151.

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Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

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18

Appleby, Gabriel, Linfeng Liu, and Li-Ping Liu. "Kriging Convolutional Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3187–94. http://dx.doi.org/10.1609/aaai.v34i04.5716.

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Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining advantages of Graph Neural Networks (GNN) and kriging. Compared to standard GNNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the kriging method as a specific configuration. Empirically, we show that this model outperforms GNNs and kriging in several applications.

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19

Jasmin, Praful Bharadiya. "Convolutional Neural Networks for Image Classification." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 673–77. https://doi.org/10.5281/zenodo.8020781.

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Deep learning has recently been applied to scene labelling, object tracking, pose estimation, text detection and recognition, visual saliency detection, and image categorization. Deep learning typically uses models like Auto Encoder, Sparse Coding, Restricted Boltzmann Machine, Deep Belief Networks, and Convolutional Neural Networks. Convolutional neural networks have exhibited good performance in picture categorization when compared to other types of models. A straightforward Convolutional neural network for image categorization was built in this paper. The image classification was finished by this straightforward Convolutional neural network. On the foundation of the Convolutional neural network, we also examined several learning rate setting techniques and different optimisation algorithms for determining the ideal parameters that have the greatest influence on image categorization.

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20

ГРИНЬКО, ІРИНА, ТЕТЯНА СКРИПНИК та ОЛЕКСАНДР БАРМАК. "КВАНТОВІ ЗГОРТКОВІ НЕЙРОННІ МЕРЕЖІ: ОСОБЛИВОСТІ РЕАЛІЗАЦІЇ У ТЕХНІЧНИХ, ПРИРОДНИЧИХ І СОЦІАЛЬНО-ЕКОНОМІЧНИХ СИСТЕМАХ". Herald of Khmelnytskyi National University. Technical sciences 323, № 4 (2023): 87–94. https://doi.org/10.31891/2307-5732-2023-323-4-87-94.

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The paper analyses and investigates the usage of quantum convolutional neural networks in technical, natural, and socio-economic systems. Quantum convolutional neural networks are a novel approach to information processing that is based on the principles of quantum mechanics and artificial intelligence. In technical systems, the potential of using quantum convolutional neural networks for solving complex tasks such as image processing, machine learning, and prediction has been explored. The results have shown that quantum convolutional neural networks can provide more accurate and faster computations compared to classical neural networks. In natural systems, research has been conducted on the use of quantum convolutional neural networks for modeling and predicting complex natural processes. Their effectiveness in understanding genetic data, studying complex molecular structures, and analyzing ecological systems has been investigated. It has been found that quantum convolutional neural networks can deliver more precise and rapid results compared to conventional data processing methods. In socio-economic systems, the possibilities of employing quantum convolutional neural networks for social network analysis, financial market forecasting, and resource management have been studied. The application of quantum convolutional neural networks has the potential to enhance prediction accuracy and facilitate more effective decision-making in socio-economic systems. The research findings confirm that quantum convolutional neural networks have the potential to be utilized in various domains, including technical, natural, and socio-economic systems. They can achieve higher accuracy, processing speed, and predictive capabilities compared to traditional methods.

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21

Liu, Taoyu. "Application of convolutional neural networks in image classification and applications of improved convolutional neural networks." Applied and Computational Engineering 81, no. 1 (2024): 56–62. http://dx.doi.org/10.54254/2755-2721/81/20241009.

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Abstract. This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse convolutional kernels and hybrid sparse convolutional kernels (HDCs) in solving the problem of computational resource consumption is presented. Subsequently, methods for solving the problem of limited training data through transfer learning and network data augmentation techniques are discussed, as well as GAN-generated datasets for solving the overfitting problem. Finally, the effect of degraded images on the classification effectiveness of CNNs is explored. The results show that the improved CNN architecture and algorithms have significant effects in solving the problems of overfitting and computational resource consumption, and can significantly improve the accuracy and efficiency of image classification. And degraded images do adversely affect the accuracy of CNN for image classification.

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22

Zhou, Ding-Xuan. "Deep distributed convolutional neural networks: Universality." Analysis and Applications 16, no. 06 (2018): 895–919. http://dx.doi.org/10.1142/s0219530518500124.

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Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory.

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23

Poudel, Sushan, and Dr R. Anuradha. "Speech Command Recognition using Artificial Neural Networks." JOIV : International Journal on Informatics Visualization 4, no. 2 (2020): 73. http://dx.doi.org/10.30630/joiv.4.2.358.

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Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.

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Ma, Hongli, Fang Xie, Tao Chen, Lei Liang, and Jie Lu. "Image recognition algorithms based on deep learning." Journal of Physics: Conference Series 2137, no. 1 (2021): 012056. http://dx.doi.org/10.1088/1742-6596/2137/1/012056.

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Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

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Hung, Shengyu. "Application of Convolutional Neural Network in Modern Technology Field and Improvement by Time-space Version." Journal of Physics: Conference Series 2386, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2386/1/012026.

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Abstract Based on the basic principle of convolutional neural networks, this paper gradually introduces the development of convolutional neural networks and its related application fields. We also analyze and compare the iterations and developments of convolutional neural networks in three most commonly used areas: medical treatment, face recognition and transportation. A large number of articles have been written to understand the variations and differences of convolutional neural networks in these three areas, such as the use of different training methods or different structures, such as the time-space convolutional neural networks that will be mentioned in the article. And the last part is the summary.

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Pandey, Sunil, Naresh Kumar Nagwani, and Shrish Verma. "Aspects of programming for implementation of convolutional neural networks on multisystem HPC architectures." Journal of Physics: Conference Series 2062, no. 1 (2021): 012016. http://dx.doi.org/10.1088/1742-6596/2062/1/012016.

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Abstract The training of deep learning convolutional neural networks is extremely compute intensive and takes long times for completion, on all except small datasets. This is a major limitation inhibiting the widespread adoption of convolutional neural networks in real world applications despite their better image classification performance in comparison with other techniques. Multidirectional research and development efforts are therefore being pursued with the objective of boosting the computational performance of convolutional neural networks. Development of parallel and scalable deep learning convolutional neural network implementations for multisystem high performance computing architectures is important in this background. Prior analysis based on computational experiments indicates that a combination of pipeline and task parallelism results in significant convolutional neural network performance gains of up to 18 times. This paper discusses the aspects which are important from the perspective of implementation of parallel and scalable convolutional neural networks on central processing unit based multisystem high performance computing architectures including computational pipelines, convolutional neural networks, convolutional neural network pipelines, multisystem high performance computing architectures and parallel programming models.

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Ingole, Vikram S., U. A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, and Roshan Kumar. "A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks." Computation 13, no. 1 (2024): 4. https://doi.org/10.3390/computation13010004.

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Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplistic model architectures. These prevent complex, multifaceted factors influencing crop growth and yield from being captured. In this line, this work fuses multi-source data—satellite imagery, weather data, and soil properties—through the approach of multi-modal fusion using Convolutional Neural Networks and Recurrent Neural Networks. While satellite imagery provides information on spatial data regarding crop health, weather data provides temporal insights, and the soil properties provide important fertility information. Fusing these heterogeneous data sources embeds an overall understanding of yield-determining factors in the model, decreasing the RMSE by 15% and improving R2 by 20% over single-source models. We further push the frontier of feature engineering by using Temporal Convolutional Networks (TCNs) and Graph Convolutional Networks (GCNs) to capture time series trends, geographic and topological information, and pest/disease incidence. TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. Additionally, we introduce other improved model architectures: a custom UNet with attention mechanisms, Heterogeneous Graph Neural Networks (HGNNs), and Variational Auto-encoders. The attention mechanism enables more effective spatial feature encoding by focusing on critical image regions, while the HGNN captures interaction patterns that are complex between diverse data types. Finally, VAEs can generate robust feature representation. Such state-of-the-art architectures could then achieve an MAE improvement of 12%, while R2 for yield prediction improves by 25%. In this paper, the state of the art in yield prediction has been advanced due to the employment of multi-source data fusion, sophisticated feature engineering, and advanced neural network architectures. This provides a more accurate and reliable soybean yield forecast. Thus, the fusion of Convolutional Neural Networks with Recurrent Neural Networks and Graph Networks enhances the efficiency of the detection process.

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Zelenina, Larisa I., Liudmila E. Khaimina, Evgenii S. Khaimin, D. S. Khripunov, and Inga M. Zashikhina. "Convolutional Neural Networks in the Task of Image Classification." Mathematics and Informatics LXV, no. 1 (2022): 19–29. http://dx.doi.org/10.53656/math2022-1-2-con.

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Convolutional neural networks are acquiring general acknowledgement for diverse application areas. The article describes the process of solving the task of images classification using convolutional neural networks. The authors present the examples of using convolutional neural networks for various purposes. The composed set of data is used to implement and train the model of convolutional neural network for the task of classification of medical images.

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29

Maystrenko, K., A. Budilov, and D. Afanasev. "TRANSITION TO CONVOLUTIONAL NEURAL NETWORKS IN RADAR PROBLEMS." Informatization and communication, no. 3 (May 24, 2019): 96–99. http://dx.doi.org/10.34219/2078-8320-2019-10-3-96-99.

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Goal. Identify trends and prospects for the development of radar in terms of the use of convolutional neural networks for target detection. Materials and methods. Analysis of relevant printed materials related to the subject areas of radar and convolutional neural networks. Results. The transition to convolutional neural networks in the field of radar is considered. A review of papers on the use of convolutional neural networks in pattern recognition problems, in particular, in the radar problem, is carried out. Hardware costs for the implementation of convolutional neural networks are analyzed. Conclusion. The conclusion is made about the need to create a methodology for selecting a network topology depending on the parameters of the radar task.

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30

Kalilaev, Dauletiyar Baxtiyarovich. "CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE RECOGNITION." International journal of advanced research in education, technology and management 2, no. 3 (2023): 119–28. https://doi.org/10.5281/zenodo.7734050.

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The purpose of the work, the results of which are presented within the framework of the article, was to study modern architectures of convolutional neural networks for image recognition. The article considers such architectures as AlexNet, ZFnet, VGGNet, GoogleNet, ResNet. A characteristic of the quality of image recognition for a neural network is the top-5 error. Based on the results obtained, it was revealed that at the moment the network with the most accurate result is the ResNet convolutional network with an accuracy rate of 3.57%. The advantage of this study is that this article gives a brief description of the convolutional neural network, and also gives an idea of the modern architectures of convolutional networks, their structure and quality indicators.  

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31

Jiang, Jiamin. "The eye of artificial intelligence - Convolutional Neural Networks." Applied and Computational Engineering 76, no. 1 (2024): 273–79. http://dx.doi.org/10.54254/2755-2721/76/20240613.

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Inspired by the biological visual system, the convolutional neural network has been widely studied and invented in the field of artificial intelligence. As one of the important algorithms in artificial neural networks, convolutional neural networks have shown outstanding application potential in fields such as image recognition, computer vision, and natural language processing. This article will focus on exploring the powerful capabilities of convolutional neural networks in image processing. By delving into the implementation process of a convolutional neural network, readers will gain a deeper understanding of its working principles. In addition, this article will briefly introduce three classic models of convolutional neural networks, providing readers with more background knowledge. Next, this paper will analyze in detail two typical application cases of convolutional neural networks in the field of image processing: intelligent transportation systems and dental imaging technology. These cases demonstrate the successful application of convolutional neural networks in practical scenarios, pointing the way for their future development. In the future, convolutional neural networks will be more widely used in fields such as image and video processing as data scale increases and computing power improves. By using techniques such as model compression and hardware optimization, it is made more suitable for low-power and high-efficiency environments, and its interpretability and applicability are enhanced through data augmentation and model interpretation techniques.

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Jiang, Zheng. "Several Applications of Convolutional Neural Networks in Medical Imaging." Transactions on Computer Science and Intelligent Systems Research 7 (November 25, 2024): 200–205. https://doi.org/10.62051/npafb665.

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With the development of artificial intelligence, convolutional networks have powerful multi-dimensional data processing capabilities and can extract and process features in various images, which has great potential in the field of medical image processing. The development of convolutional neural networks has greatly promoted the development of computer aided diagnosis technology. This paper reviews the principle of four kinds of convolutional neural networks, including AlexNet, GoogleNet, U-Net, R-CNN, and their specific application research, such as diagnosis and analysis of brain tumors, classification of skin lesions, and detection of breast cancer. Compared with traditional convolutional networks, these new models have their own advantages and disadvantages. This paper also summarizes the advantages and disadvantages of these four neural networks. In the end, this paper also puts forward some current challenges in medical image research based on convolutional neural networks and the future prospects of medical image analysis technology combined with convolutional neural networks.

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Haridas, Rahul, and Jyothi R L. "Convolutional Neural Networks: A Comprehensive Survey." International Journal of Applied Engineering Research 14, no. 3 (2019): 780. http://dx.doi.org/10.37622/ijaer/14.3.2019.780-789.

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Tummala, Madhurima. "Image Classification Using Convolutional Neural Networks." International Journal of Scientific and Research Publications (IJSRP) 9, no. 8 (2019): p9261. http://dx.doi.org/10.29322/ijsrp.9.08.2019.p9261.

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Shenoy, Avantika, Bhavin Shewkani, and Tarun Utamchandani. "Melanoma Prediction using Convolutional Neural Networks." International Journal of Computer Applications 182, no. 33 (2018): 17–20. http://dx.doi.org/10.5120/ijca2018918268.

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Lumini, Alessandra, and Loris Nanni. "Convolutional Neural Networks for ATC Classification." Current Pharmaceutical Design 24, no. 34 (2019): 4007–12. http://dx.doi.org/10.2174/1381612824666181112113438.

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Background: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound’s possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. Methods: In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound’s chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Results: Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Conclusion: Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

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Jin, Jiani. "Convolutional Neural Networks for Biometrics Applications." SHS Web of Conferences 144 (2022): 03013. http://dx.doi.org/10.1051/shsconf/202214403013.

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A convolutional neural network (CNN) is a feed-forward neural network that can react with other units in a specific range and can handle huge images well as a deep learning algorithm. CNN is a very convenient tool for conveying visual information and can be good for improving recognition accuracy. However, volumetric neural networks also increase the complexity of the networks, making them more challenging to optimize and more prone to overfitting. This paper will focus on the history of CNN development and the current use of the method, and the difficulties encountered. Furthermore, we will analyze its application in bioinformatics by discussing the papers published in the field about CNN. After the CNN was invented by Leon O. Chua and Lee Yang in 1988, researchers transformed a neural network into a CPU with thousands of cores. Improvements to the CNN in recent years have been made in six main parts: convolutional layer, pooling layer, activation function, loss function, regularization, and optimization, which have reduced the redundancy of the CNN and allowed it to process faster and more accurately processing. Nowadays, it is mainly used for image classification, text processing, video processing, etc. Above all, this paper realizes that CNN has excellent advantages in feature extraction and can play a huge role in dealing with eye biometrics, flower recognition, etc.

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Elingaard, Martin Ohrt, Niels Aage, Jakob Andreas Bærentzen, and Ole Sigmund. "De-homogenization using convolutional neural networks." Computer Methods in Applied Mechanics and Engineering 388 (January 2022): 114197. http://dx.doi.org/10.1016/j.cma.2021.114197.

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Ogawa, Kana, and Pitoyo Hartono. "Collaborative General Purpose Convolutional Neural Networks." Journal of Signal Processing 25, no. 2 (2021): 53–61. http://dx.doi.org/10.2299/jsp.25.53.

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Pakulich, D. V., and S. A. Alyamkin. "SPOOFING DETECTION USING CONVOLUTIONAL NEURAL NETWORKS." Автометрия 57, no. 4 (2021): 91–97. http://dx.doi.org/10.15372/aut20210411.

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Pakulich, D. V., and S. A. Alyamkin. "SPOOFING DETECTION USING CONVOLUTIONAL NEURAL NETWORKS." Автометрия 57, no. 4 (2021): 91–97. http://dx.doi.org/10.15372/aut20210411.

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Xu, Shaoyuan, Yang Cheng, Qian Lin, and Jan Allebach. "Emotion Recognition Using Convolutional Neural Networks." Electronic Imaging 2019, no. 8 (2019): 402–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.8.imawm-402.

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Kumar, Vijay, Saloni Laddha, Aniket, and Nitin Dogra. "Steganography Techniques Using Convolutional Neural Networks." Review of Computer Engineering Studies 7, no. 3 (2020): 66–73. http://dx.doi.org/10.18280/rces.070304.

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

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In this article we consider one of the most used classes of neural networks convolutional neural networks (hereinafter CNN). In particular, the areas of their application, algorithms of signal propagation by CNN and CNN training are described and the methods of CNN functioning algorithms implementation in MATLAB programming language are given. The article presents the results of research on the effectiveness of the CNN learning algorithm in solving classification problems with its help. In the course of these studies, such a characteristic of the neural network as the dynamics of the network error values depending on the learning rate is considered, and the correctness of the algorithm of learning convolutional neural network is checked. In this case, the problem of handwritten digits recognition on the MNIST sample is used as a classification task.

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Dai, Jifeng, Yang Lu, and Ying Nian Wu. "Generative modeling of convolutional neural networks." Statistics and Its Interface 9, no. 4 (2016): 485–96. http://dx.doi.org/10.4310/sii.2016.v9.n4.a8.

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Lutsiv, V. P. "Convolutional deep-learning artificial neural networks." Journal of Optical Technology 82, no. 8 (2015): 499. http://dx.doi.org/10.1364/jot.82.000499.

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Luo, Shuyue, Shangbo Zhou, Yong Feng, and Jiangan Xie. "Pansharpening via Unsupervised Convolutional Neural Networks." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 4295–310. http://dx.doi.org/10.1109/jstars.2020.3008047.

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LU, Liqiang, Size ZHENG, Qingcheng XIAO, Deming CHEN, and Yun LIANG. "Accelerating convolutional neural networks on FPGAs." SCIENTIA SINICA Informationis 49, no. 3 (2019): 277–94. http://dx.doi.org/10.1360/n112018-00291.

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Baldini, Gianmarco, Irene Amerini, and Claudio Gentile. "Microphone Identification Using Convolutional Neural Networks." IEEE Sensors Letters 3, no. 7 (2019): 1–4. http://dx.doi.org/10.1109/lsens.2019.2923590.

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Sharma, Pankaj. "Convolutional Neural Networks for Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 465–69. http://dx.doi.org/10.22214/ijraset.2024.65091.

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Dedicated Identifying aimed at discerning subjective information by analyzing the polarity of opinions conveyed in text. Traditionally, recurrent neural networks (RNNs) have been the dominant approach for this type of analysis because of their ability to process sequential data. However, sentiment analysis has experienced a significant transformation with the introduction of convolutional neural networks (CNNs). Originally developed for image analysis, processing text due to their efficient mechanisms for extracting local features.. This paper explores the role of CNNs in sentiment analysis, evaluating their architecture, methodology, and comparative effectiveness against RNN-based models. We propose a comprehensive CNN-based model for sentiment analysis and examine its potential for sentiment classification tasks across multiple datasets.

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