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Journal articles on the topic 'CNN MODELS'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Aditya, Kakde Nitin Arora Durgansh Sharma. "A COMPARATIVE STUDY OF DIFFERENT TYPES OF CNN AND HIGHWAY CNN TECHNIQUES." Global Journal of Engineering Science and Research Management 6, no. 4 (2019): 18–31. https://doi.org/10.5281/zenodo.2639265.

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In recent years, convolutional networks have shown breakthrough performance in image classification and detection. The main reason behind the performance of convnets is that they are inspired from the mammal’s visual cortex. In this paper, we have investigated the performance of four models that are Alexnet, Highway Convolutional Neural Network, Convolutional Neural Network and an evolutionary approach on highway convolutional neural network on the basis of train loss, test loss, train accuracy and test accuracy. These models are tested on two datasets that are WANG dataset and Simpsons

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Mohammed, Mohammed Ameen, Zheng Han, and Yange Li. "Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models." Advances in Materials Science and Engineering 2021 (September 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/9923704.

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Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up-to-date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open-source CNN models (Model1, M

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Rahul, Singh, Nigam Avnesh, and S. Godfrey Winster Dr. "Insurepp-Machine Learning Webapp." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 154–57. https://doi.org/10.35940/ijeat.D2506.0610521.

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Nowadays, there are many companies which are collecting money in the name of insurance. For them, insurance has become a type of business. To reduce this thing, we have developed INSUREPP which can help in giving less amount and is very easy to use. You just need to click some pictures and upload it in the application. It will use various CNN models. It will check the harm, the seriousness of the harm, the region of the harm and will predict the results. We are making this project so that it takes less time in insurance claiming, as it can predict the cost of damage.

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Hassan, Esraa, Nora El-Rashidy, and fatma M. Talaa. "Review: Mask R-CNN Models." Nile Journal of Communication and Computer Science 3, no. 1 (2022): 17–27. http://dx.doi.org/10.21608/njccs.2022.280047.

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ITOH, MAKOTO, and LEON O. CHUA. "EQUIVALENT CNN CELL MODELS AND PATTERNS." International Journal of Bifurcation and Chaos 13, no. 05 (2003): 1055–161. http://dx.doi.org/10.1142/s0218127403007151.

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In this paper, canonical isolated CNN cell models are proposed by using implicit differential equations. A number of equivalent but distinct CNN cell models are derived from these canonical models. Almost every known CNN cell model can be classified into one or more groups via constrained conditions. This approach is also applied to discrete-time CNN cell models. Pattern formation mechanisms are investigated from the viewpoint of equivalent templates and genetic algorithms. A strange wave propagation phenomenon in nonuniform CNN cells is also presented in this paper. Finally, chaotic associati

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Suresh, Neha, and Dr AnandiGiridharan Dr.AnandiGiridharan. "Predicting Groundnut Disease using CNN Models." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 756–66. http://dx.doi.org/10.51201/jusst/21/05335.

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Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. Groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root, and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rus

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Jing, Juntong. "Denoising Adversarial Examples Using CNN Models." Journal of Physics: Conference Series 2181, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2181/1/012029.

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Abstract It has always been a complicated problem to resolve adversarial attacks because figures with adversarial attacks look similar to the original figures so that models can be fooled. With deceptive data, adversarial attacks can be a threat to neural networks. There are various ways to generate adversarial attacks. For instance, they are using one-step perturbation and using multi-step perturbation. In both methods, noise is added to the images. Therefore, a question pops up: are adversarial attacks similar to normal random noise? This paper aims to find if there is anything in common bet

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Zhan, Zhiwei, Guoliang Liao, Xiang Ren, et al. "RA-CNN." International Journal of Software Science and Computational Intelligence 14, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijssci.311446.

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Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environm

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GÁL, V., J. HÁMORI, T. ROSKA, et al. "RECEPTIVE FIELD ATLAS AND RELATED CNN MODELS." International Journal of Bifurcation and Chaos 14, no. 02 (2004): 551–84. http://dx.doi.org/10.1142/s0218127404009545.

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In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine — CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years

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Wang, Keyi. "Static and Dynamic Hand Gesture Recognition Using CNN Models." International Journal of Bioscience, Biochemistry and Bioinformatics 11, no. 3 (2021): 65–73. http://dx.doi.org/10.17706/ijbbb.2021.11.3.65-73.

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Muhammad, Hussein G., and Zainab A. Khalaf. "Fingerprint Identification System based on VGG, CNN, and ResNet Techniques." Basrah Researches Sciences 50, no. 1 (2024): 14. http://dx.doi.org/10.56714/bjrs.50.1.14.

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This study compares three different pre-trained deep learning models specifically designed for fingerprint identification. The first model uses Convolutional Neural Network (CNN), the second includes Residual Network (ResNet), and the third employs the Visual Geometry Group (VGG) approach. The subsequent comparative assessment reveals the CNN-based model's superior performance, with an impressive F1 score of 96.5%. In contrast, the ResNet and VGG models achieve F1 scores of 94.3% and 92.11%, respectively. These findings highlight the CNN model's ability to accurately identify fingerprints. Fur

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Alofi, Najla, Wafa Alonezi, and Wedad Alawad. "WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 13 (2021): 135–50. http://dx.doi.org/10.3991/ijoe.v17i13.27373.

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Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enh

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Kim, Gun Il, and Beakcheol Jang. "Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection." Mathematics 11, no. 3 (2023): 547. http://dx.doi.org/10.3390/math11030547.

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Crude oil plays an important role in the global economy, as it contributes one-third of the energy consumption worldwide. However, despite its importance in policymaking and economic development, forecasting its price is still challenging due to its complexity and irregular price trends. Although a significant amount of research has been conducted to improve forecasting using external factors as well as machine-learning and deep-learning models, only a few studies have used hybrid models to improve prediction accuracy. In this study, we propose a novel hybrid model that captures the finer deta

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Noh, Seol-Hyun. "Gradient Flow Analysis and Performance Comparison of CNN Models." Journal of KIISE 48, no. 1 (2021): 100–106. http://dx.doi.org/10.5626/jok.2021.48.1.100.

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Ghansham, More Omkar Patil Omkar More Mihir More Samadhan Suryavanshi Manisha Mali. "Comparison of Object Detection Algorithms CNN, YOLO and SSD." International Journal of Scientific Research and Technology 1, no. 11 (2024): 137–44. https://doi.org/10.5281/zenodo.14186397.

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Since 2015, numerous studies have concentrated on object detection, a crucial element of computer vision, using convolutional neural networks (CNN) and their various architectures. Key methods for object detection done by “YOLO (You Only Look Once)”, “CNN”, and “SSD (Single Shot Multibox Detector)”. This paper explores three representative series of methods based on “CNN, YOLO, and SSD”, providing solutions to challenges like bounding box prediction in CNNs. The strength of these algorithms are measured in terms of accuracy, processing speed, and

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Huang, Yefei, Tianlai Xu, Zexu Zhang, Hutao Cui, and Yu Su. "Satellite Segmentation with Pre-trained CNN Models." Journal of Physics: Conference Series 2171, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2171/1/012003.

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Abstract In a generic satellite relative pose estimation pipeline, finding sufficient features in objects is quite essential to build the correct matching relationship and then solve the relative movement. However, for low-earth-orbit (LEO) satellites, since the earth background contains much more texture than objects, an object segmentation process is necessary to provide a prior range for feature extraction. In this work, we address this task with the pre-trained Deeplabv3 and fully convolutional network (FCN). Unlike the fine-tuning or transfer learning processes in other researches, we obt

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Muhammad, Zulqarnain, Ghazali Rozaida, Mazwin Mohmad Hassim Yana, and Rehan Muhammad. "A comparative review on deep learning models for text classification." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 1 (2020): 325–35. https://doi.org/10.11591/ijeecs.v19.i1.pp325-335.

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Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), “Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handle various classification tasks. DBN have excellent learning capabilities to extracts highly distingu

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Su, Zhenyi. "Comparative Analysis of CNN-Based Object Detection Models: Faster R-CNN, SSD, and YOLO." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 147–52. https://doi.org/10.54097/9r6evm71.

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Target detection is widely used in the current environment. With the rapid development of deep learning, innovative models like Convolutional Neural Networks (CNNs) were born. CNNs have been widely used in many practical applications for object detection, since CNNS outperform traditional models in terms of speed and accuracy. This paper first introduces three well-known CNN-based target detection models: Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD) and You Only Look Once (YOLO). Then this paper gives data on speed, accuracy and resource consump

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İni̇k, Özkan, Mustafa Altıok, Erkan Ülker, and Barış Koçer. "MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models." Applied Soft Computing 109 (September 2021): 107582. http://dx.doi.org/10.1016/j.asoc.2021.107582.

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Patil, Priyadarshini, Vipul Deshpande, Vishal Malge, and Abhishek Bevinmanchi. "Fake Face Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 519–22. http://dx.doi.org/10.22214/ijraset.2022.45829.

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Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and tru

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Song, Hyunsun, and Hyunjun Choi. "Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models." Applied Sciences 13, no. 7 (2023): 4644. http://dx.doi.org/10.3390/app13074644.

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Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and mu

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Bhanbhro, Jamsher, Asif Aziz Memon, Bharat Lal, Shahnawaz Talpur, and Madeha Memon. "Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models." Signals 6, no. 2 (2025): 22. https://doi.org/10.3390/signals6020022.

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Speech Emotion Recognition (SER) technology helps computers understand human emotions in speech, which fills a critical niche in advancing human–computer interaction and mental health diagnostics. The primary objective of this study is to enhance SER accuracy and generalization through innovative deep learning models. Despite its importance in various fields like human–computer interaction and mental health diagnosis, accurately identifying emotions from speech can be challenging due to differences in speakers, accents, and background noise. The work proposes two innovative deep learning model

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Dhokane, Mr Rahul. "CAR DAMAGE DETECTION USING CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30508.

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In today's modern society, automobiles play a crucial role, and the automatic classification of car damages holds particular significance for the auto insurance industry. Our proposed solution involves the implementation of two Convolutional Neural Network (CNN) models. Specifically, the VGG16 model is employed to identify and assess the location and severity of car damage, while the Mask R-CNN is utilized to accurately mask the damaged regions. Both models collectively provide valuable insights into the extent. The CNN models effectively filter out images without damages, allowing only those

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

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

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

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

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D, Ms Suseela, Varsha S, Bharaneedharan C, and Lekshana Shivani C. "CLASSIFICATION OF FRESH AND ROTTEN FRUITS USING DIFFERENT CNN MODELS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26057.

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Fruit freshness automated classification is crucial to the agricultural sector. In the traditional procedure, a human being grades the fruit. Additionally, this process is labor-intensive, time-consuming, and ineffective. Additionally, it raises production costs. Therefore, a quick, precise, and automated system that may lessen human effort, enhance production, and decrease manufacturing time and cost is needed for industrial applications. The deep learning- based model for classifying fruit freshness is used in the current work. Various Convolution Neural Network (CNN) models are proposed, an

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Elzayady, Hossam, Khaled M. Badran, and Gouda I. Salama. "Arabic Opinion Mining Using Combined CNN - LSTM Models." International Journal of Intelligent Systems and Applications 12, no. 4 (2020): 25–36. http://dx.doi.org/10.5815/ijisa.2020.04.03.

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Berwo, Michael Abebe, Zhipeng Wang, Yong Fang, Jabar Mahmood, and Nan Yang. "Off-road Quad-Bike Detection Using CNN Models." Journal of Physics: Conference Series 2356, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2356/1/012026.

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Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and asse

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Chen, Nuo, Boyu Han, Zhixin Li, and Haotian Wang. "Breast Cancer Prediction Based on the CNN Models." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 103–9. http://dx.doi.org/10.54097/hset.v34i.5388.

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In modern society, the natural lifespan of an individual increased dramatically benefitting from advanced yet accurate methods of medical treatment. Though many diseases could be treated with a cure, the treatment of cancer has yet to be overcome. Related medical research has proven that the combination of accurate breast cancer diagnoses and treatments at an early stage could prevent the spread of cancer cells as it could increase a person's potential lifespan by a large margin. This research has conducted a comprehensive study on improving the efficiency of autonomous image recognition of br

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A M, Vinod. "Comparative Analysis of CNN Models for ALL Diagnosis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49061.

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Abstract—Acute Lymphoblastic Leukemia (ALL) is a serious pediatric cancer where early and accurate diagnosis is vital for improving treatment outcomes. This project explores the use of Convolutional Neural Networks (CNNs) to develop an effective diagnostic system for identifying ALL from microscopic blood smear images. Leveraging the ALL IDB1 dataset, six pre-trained CNN models—VGG16, VGG19, ResNet101V2, DenseNet201, Mo- bileNetV2, and InceptionV3—were fine-tuned and assessed based on key performance metrics including accuracy, precision, recall, F1-score, and AUC-ROC. To improve model robustn

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Amir, Nour H. Abdul, Aqeel Al-Hilali, and Laith F. Jumma. "Car logo detection using CNN models a review." IET Conference Proceedings 2024, no. 34 (2025): 689–96. https://doi.org/10.1049/icp.2025.0163.

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Prasad, P. R. Krishna, Harshitha Myneni, S. B. S. Sameer Kumar Metra, Balaji Nelakurthi, and Narasimha Naik Meghavath. "Enhanced Sports Image Classification using Deep CNN models." International Journal of Computer Applications 187, no. 2 (2025): 42–49. https://doi.org/10.5120/ijca2025924791.

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Omran musa, Hawraa, and Muhanad Tahrir Younis. "Image-Based Malware Detection Using Deep CNN Models." Iraqi Journal for Computers and Informatics 51, no. 1 (2025): 64–74. https://doi.org/10.25195/ijci.v51i1.542.

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Malware or malicious software represents one of the most remarkable threats to cybersecurity, as it compromises the integrity, confidentiality, and availability of computer systems and networks. Traditional malware detection methodologies frequently prove inadequate in identifying innovative and sophisticated malware variants. Deep learning (DL) presents a promising strategy for malware detection by utilizing advanced algorithms that are capable of discerning intricate patterns from extensive datasets. This study presents a model based on deep learning with Convolutional Neural Network (CNN) f

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Gao, Xue-Yao, Bo-Yu Yang, and Chun-Xiang Zhang. "Combine EfficientNet and CNN for 3D model classification." Mathematical Biosciences and Engineering 20, no. 5 (2023): 9062–79. http://dx.doi.org/10.3934/mbe.2023398.

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<abstract> <p>With the development of multimedia technology, the number of 3D models on the web or in databases is becoming increasingly larger and larger. It becomes more and more important to classify and retrieve 3D models. 3D model classification plays important roles in the mechanical design field, education field, medicine field and so on. Due to the 3D model's complexity and irregularity, it is difficult to classify 3D model correctly. Many methods of 3D model classification pay attention to local features from 2D views and neglect the 3D model's contour information, which c

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Jain, Tanmay, Harshada Mhaske, Sanjay Chilveri, Aniket Chaudhar, and Chinmay Doshi. "Cloudy Weather Prediction Using CNN Models and Satellite Images." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1168–75. http://dx.doi.org/10.22214/ijraset.2024.59342.

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Abstract: Cloudy weather classification is a vital task in meteorology and remote sensing, facilitating various applications such as weather forecasting, climate monitoring, and environmental analysis. In this study, we explore the application of convolutional neural network (CNN) techniques for classifying cloudy weather conditions using the Cloudy Weather Dataset sourced from Kaggle. The primary CNN architectures investigated include AlexNet, LeNet, and ResNet. The dataset undergoes preprocessing steps, including resizing and normalization to floating-point representation. Additionally, for

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Avula, Rohitha, and Hem Charan B.K. "Human Task Recognition using CNN." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 2 (2020): 4–8. https://doi.org/10.35940/ijeat.A1665.1010120.

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In this fast pacing world, computers are also getting better in terms of their performance and speed. It is capable of solving very complex problems like understanding an image, understanding videos and live capturing and processing of data. Due to advancement in technologies like computer vision, machine learning techniques, deep learning methods, artificial intelligence, etc., various models are being made so that prediction of outputs is made simpler and of high accuracy and precision. Our project model is built using a convolutional neural network (CNN). Our dataset consists of 599 videos

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Gunasekaran, Hemalatha, K. Ramalakshmi, A. Rex Macedo Arokiaraj, S. Deepa Kanmani, Chandran Venkatesan, and C. Suresh Gnana Dhas. "Analysis of DNA Sequence Classification Using CNN and Hybrid Models." Computational and Mathematical Methods in Medicine 2021 (July 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/1835056.

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In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days,

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Guang, Jiahe, Xingrui He, Zeng Li, and Shiyu He. "Road Pothole Detection Model Based on Local Attention Resnet18-CNN-LSTM." Theoretical and Natural Science 42, no. 1 (2024): 131–38. http://dx.doi.org/10.54254/2753-8818/42/20240669.

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Abstract. In response to the low detection accuracy and slow speed of existing road pothole detection methods, a road pothole classification detection model based on local attention Resnet18-CNN-LSTM (Long Short-Term Memory network) is proposed. On the basis of Resnet18, a local attention mechanism and a CNN-LSTM combined model are added to propose a road pothole detection model based on local attention Resnet18-CNN-LSTM. The local attention mechanism is used to accurately extract specific target feature values, CNN is used to extract the spatial features of the input data, and LSTM enhances t

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B, S. Smithu, R. Janardhana D, P. Leela C, and Pushpa G. "Forest Fire Risk Assessment and Detection using Deep Learning Models." Indian Journal of Science and Technology 17, no. 46 (2024): 4921–28. https://doi.org/10.17485/IJST/v17i46.2138.

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Abstract <strong>Background:</strong> There is a severe need to detect any kind of fire in a faster and accurate method, especially forest fires to stop huge losses to the human community and the environment losses. The main purpose of the proposal is to identify and evaluate the accuracy of the existing Artificial Intelligence (AI) methods for detecting fire and improve the methods to detect fire in real-world scenarios in faster and accurate methods. <strong>Methods:</strong> The proposal uses a dataset to train a model, and in addition uses a few test images from an existing

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Biswas, Dipto, and Joon-Min Gil. "Design and Implementation for Research Paper Classification Based on CNN and RNN Models." Journal of Internet Technology 25, no. 4 (2024): 637–45. http://dx.doi.org/10.70003/160792642024072504014.

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Deep learning techniques are used as basic essential techniques in natural language processing. They rely on modeling nonlinear relationships within complex data. In this study, “Long Short-Term Memory” (LSTM) and “Gated Recurrent Units” (GRU) deep learning techniques are applied to the classification of research papers. We combine Bidirectional LSTM and GRU with “Convolutional Neural Networks” (CNN) to boost the classification performance for a recommendation system of research papers. In our method, word embedding is also used to classify and recommend research papers. Thus, in this study, w

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Bouchane, Mouna, Wei Guo, and Shuojin Yang. "Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification." Sensors 25, no. 5 (2025): 1399. https://doi.org/10.3390/s25051399.

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Brain–computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. The first model combines a shallow convolutional neural network and a gated recurrent unit (

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Alshirbaji, Tamer Abdulbaki, Nour Aldeen Jalal, Paul D. Docherty, Thomas Neumuth, and Knut Moeller. "Assessing Generalisation Capabilities of CNN Models for Surgical Tool Classification." Current Directions in Biomedical Engineering 7, no. 2 (2021): 476–79. http://dx.doi.org/10.1515/cdbme-2021-2121.

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Abstract Accurate recognition of surgical tools is a crucial component in the development of robust, context-aware systems. Recently, deep learning methods have been increasingly adopted to analyse laparoscopic videos. Existing work mainly leverages the ability of convolutional neural networks (CNNs) to model visual information of laparoscopic images. However, the performance was evaluated only on data belonging to the same dataset used for training. A more comprehensive evaluation of CNN performance on data from other datasets can provide a more rigorous assessment of the approaches. In this

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Denis, Eka Cahyani, Dwi Hariadi Anjar, Farris Setyawan Faisal, Gumilar Langlang, and Setumin Samsul. "COVID-19 classification using CNN-BiLSTM based on chest X-ray images." Bulletin of Electrical Engineering and Informatics 12, no. 3 (2023): 1773~1782. https://doi.org/10.11591/eei.v12i3.4848.

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Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural networkbidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception

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Li, Xiaoan. "CNN-Based Cancer Image Diagnosis: Current Progress and Future Directions." Applied and Computational Engineering 106, no. 1 (2024): 7–12. http://dx.doi.org/10.54254/2755-2721/106/20241262.

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Abstract. With the development of deep learning technology, CNN (Convolutional Neural Network) models have shown great value in medical image analysis, especially in diagnosing early lung cancer, breast cancer, and brain tumors. In this study, we recall and organize the application and progress of CNN models in the field of cancer and tumor diagnosis in the past five years to provide a theoretical basis and reference for related researchers. This article introduces the principles of different CNN cancer diagnostic models and compares and analyzes their results, and ultimately finds that these

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Li, Ying, Xiangrong Wang, and Yanhui Guo. "CNN-Trans-SPP: A small Transformer with CNN for stock price prediction." Electronic Research Archive 32, no. 12 (2024): 6717–32. https://doi.org/10.3934/era.2024314.

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<p>Understanding the patterns of financial activities and predicting their evolution and changes has always been a significant challenge in the field of behavioral finance. Stock price prediction is particularly difficult due to the inherent complexity and stochastic nature of the stock market. Deep learning models offer a more robust solution to nonlinear problems compared to traditional algorithms. In this paper, we propose a simple yet effective fusion model that leverages the strengths of both transformers and convolutional neural networks (CNNs). The CNN component is employed to ext

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GILLI, M., T. ROSKA, L. O. CHUA, and P. P. CIVALLERI. "CNN DYNAMICS REPRESENTS A BROADER CLASS THAN PDEs." International Journal of Bifurcation and Chaos 12, no. 10 (2002): 2051–68. http://dx.doi.org/10.1142/s0218127402005868.

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The relationship between Cellular Nonlinear Networks (CNNs) and Partial Differential Equations (PDEs) is investigated. The equivalence between discrete-space CNN models and continuous-space PDE models is rigorously defined. The key role of space discretization is explained. The problem of the equivalence is split into two subproblems: approximation and topological equivalence, that can be explicitly studied for any CNN model. It is known that each PDE can be approximated by a space difference scheme, i.e. a CNN model, that presents a similar dynamic behavior. It is shown, through several examp

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Kim, Junyoung, Jongho Jeon, Minkwan Kee, and Gi-Ho Park. "The Method Using Reduced Classification Models for Distributed Processing of CNN Models in Multiple Edge Devices." Journal of KIISE 47, no. 8 (2020): 787–92. http://dx.doi.org/10.5626/jok.2020.47.8.787.

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Dai, Ge. "Wind Power Forecasting Based on CNN and LSTM Models." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 32–38. http://dx.doi.org/10.54097/40sedz94.

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Nowadays more and more sustainable and environmentally friendly energy sources are demanded and wind power is playing an increasingly significant role in the energy market. For example, according to the Chinese Academy of Sciences, China plans to generate more than ten times the current scale of wind power in the next decade. Accurate prediction of wind generation is essential for the scheduling, operation, and energy trading of power systems. In this study, a method combining long and short-term memory network (LSTM) and convolutional neural network (CNN) is proposed for wind power generation

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V.Vijayalakshmi, Debbati Pavani, Nannapaneni Sai Srija, and Turupati Saroja. "Arrhythmia on ECG Classification using CNN." international journal of engineering technology and management sciences 9, no. 2 (2025): 740–48. https://doi.org/10.46647/ijetms.2025.v09i02.095.

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Arrhythmia classification from ECG signals is a critical task in cardiac disease diagnosis, demanding high accuracy and robust generalization. This study explores deep learning and ensemble techniques for automated arrhythmia detection using the MIT-BIH dataset. A comparative analysis is conducted on multiple models, including CNN, LSTM, BiLSTM, GRU, SVM, LightGBM, and Random Forest, with an emphasis on hybrid architectures such as CNN + LSTM and CNN + BiLSTM. The performance of these models is evaluated using standard metrics, highlighting the advantages of deep learning over traditional mach

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Radhey, Khandelwal. "Skin Lesion Detection Using CNN." Skin Lesion Detection Using CNN 8, no. 10 (2023): 7. https://doi.org/10.5281/zenodo.10078152.

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Dermatological diseases are highly prevalent and affect individuals of all ages and genders. Accurate prediction of these diseases is crucial for timely diagnosis and effective treatment. Skin lesions, characterized by variations in color, shape, and texture, serve as important indicators of dermatological conditions. In this research, we have conducted a comparative analysis of different models to detect and recognize skin diseases. The objective of our study is to develop a model that can accurately predict various dermatological diseases. The importance of our research lies in addressing th

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