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Journal articles on the topic 'CNN MODEL'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

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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Roopa, Sri Paladugu, Immadisetty Anusha, and Ramesh M. "Skin Cancer Detection using CNN Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 45–49. https://doi.org/10.35940/ijeat.E1079.089620.

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The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is w

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Prasad, G. Shyam Chandra, and K. Adi Narayana Reddy. "Sentiment Analysis Using Multi-Channel CNN-LSTM Model." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (2019): 489–94. http://dx.doi.org/10.5373/jardcs/v11sp12/20193243.

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

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

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Abhirami, A., J. K. Kiran, Ibun Niyas Muaad, Jude Praveena, and Sneha S. Ms. "Multimodal Driver Drowsiness Detection Using Visual and EEG Data with CNN-LSTM and Attention-Based Fusion." Journal of Advance Research in Mobile Computing 7, no. 3 (2025): 8–16. https://doi.org/10.5281/zenodo.15525465.

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<em>Driver’s drowsiness poses a significant yet often unnoticed risk to road safety, as even brief lapses in alertness can lead to accidents. Many existing detection systems struggle with real-time accuracy and effective integration of multiple data sources. To address these limitations, we propose a multimodal driver drowsiness detection system that combines visual and physiological (EEG) data to enhance accuracy and real-time responsiveness. The visual component leverages a Convolutional Neural Network (CNN) for spatial feature extraction, followed by a Long Short-Term Memory (LSTM) ar

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Hasan, Moh Arie, Yan Riyanto, and Dwiza Riana. "Grape leaf image disease classification using CNN-VGG16 model." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (2021): 218–23. http://dx.doi.org/10.14710/jtsiskom.2021.14013.

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This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and the classification uses CNN. The dataset is from Kaggle of 4000 grape leaf images for four classes: leaves with black measles, leaf spot, healthy leaf, and blight. Google images of 100 pieces were also used as test data outside the dataset. The accuracy of the CNN model training is 99.50 %. The classification yields an accuracy of 97.25 % using the test data, while using test

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Prasad Patnayakuni, Siva. "Copy Move Forgery Detection Using an Effective CNN Model." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 758–64. http://dx.doi.org/10.21275/sr22710130316.

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Vyshnavi Ramineni, Vyshnavi Ramineni, and Goo-Rak Kwon Goo-Rak Kwon. "An Implementation of Effective CNN Model for AD Detection." Korean Institute of Smart Media 13, no. 6 (2024): 90–97. http://dx.doi.org/10.30693/smj.2024.13.6.90.

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This paper focuses on detecting Alzheimer’s Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images

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Singh, Manoj Kumar, Ali Sher Khan, Abbas Akbar, Ananya Lamba, and Prakriti Gupta. "Plant Scan: Advanced CNN Model for Leaf Disease Detection." International Journal of Research Publication and Reviews 6, sp5 (2025): 338–45. https://doi.org/10.55248/gengpi.6.sp525.1948.

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Shivarudraiah, Prof. "CNN Model for Smart Agriculture." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47576.

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Abstract— Precision farming is being revolutionized by the integration of innovative machine learning and computer vision methods. Identifying and classifying weeds and crops accurately remains a major challenge in this field, which has a direct effect on optimizing the yield as well as sustainability. In this work, an approach to smart weed detection based on deep learning using Convolutional Neural Networks (CNN) for feature learning followed by comparison of classifiers to select the best-performing model is introduced. In our research, InceptionV3 was utilized to extract features, and four

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Choi, Jiwoo, Sangil Choi, and Taewon Kang. "Personal Identification CNN Model using Gait Cycle." Journal of Korean Institute of Information Technology 20, no. 11 (2022): 127–36. http://dx.doi.org/10.14801/jkiit.2022.20.11.127.

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Li, Yao, Zhongyuan (Jasper) Zhang, Olli Saarela, Divya Sharma, and Wei Xu. "Mediation CNN (Med-CNN) Model for High-Dimensional Mediation Data." International Journal of Molecular Sciences 26, no. 5 (2025): 1819. https://doi.org/10.3390/ijms26051819.

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Complex biological features such as the human microbiome and gene expressions play a crucial role in human health by mediating various biomedical processes that influence disease progression, such as immune responses and metabolic processes. Understanding these mediation roles is essential for gaining insights into disease pathogenesis and improving treatment outcomes. However, analyzing such high-dimensional mediation features presents challenges due to their inherent structural and correlations, such as the hierarchical taxonomic structures in microbial operational taxonomic units (OTUs), ge

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Yue, Wang, and Li Lei. "Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification." Information 26, no. 3 (2023): 117–62. http://dx.doi.org/10.47880/inf2603-02.

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With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in

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Shwetambari Pandurang, Waghmare, Renu Praveen Pathak, and Imtiyaz Ahmad Wani. "BSO-CNN." Tehnički glasnik 19, no. 2 (2025): 203–14. https://doi.org/10.31803/tg-20231116101632.

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Urban water distribution networks must use pressure management to reduce water leakage by modifying storage tank pressure levels in response to variations in water demand. Since each demand node usually restricts the maximum pressure that may be applied, addressing pressure issues at individual nodes is also crucial. To overcome these difficulties, a brand-new Convolutional Neural Network (CNN) Pressure Optimization Model is proposed. This model collects real-time data on water levels and pressure by utilizing level and pressure sensors, and a Backtracking Search Optimization (BSO) model is us

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Tajalsir, Mohammed, Susana Mu˜noz Hern´andez, and Fatima Abdalbagi Mohammed. "ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model." Signal & Image Processing : An International Journal 13, no. 1 (2022): 45–53. http://dx.doi.org/10.5121/sipij.2022.13104.

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When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique

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Sen, Amit Prakash, Nirmal Kumar Rout, Tuhinansu Pradhan, and Amrit Mukherjee. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal Of Science And Technology 15, no. 41 (2022): 2121–28. http://dx.doi.org/10.17485/ijst/v15i41.1421.

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Vyshnavi, Ramineni, and Goo-Rak Kwon. "A Comparative Study of the CNN Model for AD Diagnosis." Korean Institute of Smart Media 12, no. 7 (2023): 52–58. http://dx.doi.org/10.30693/smj.2023.12.7.52.

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Alzheimer’s disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the

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K, Gayathri, and Thangavelu S. "Novel deep learning model for vehicle and pothole detection." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1576–82. https://doi.org/10.11591/ijeecs.v23.i3.pp1576-1582.

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The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural ne

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

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

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Et. al., Ms K. N. Rode,. "Unsupervised CNN model for Sclerosis Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2577–83. http://dx.doi.org/10.17762/turcomat.v12i2.2223.

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Sclerosis detection using brain magnetic resonant imaging (MRI) im-ages is challenging task. With the promising results for variety of ap-plications in terms of classification accuracy using of deep neural net-work models, one can use such models for sclerosis detection. The fea-tures associated with sclerosis is important factor which is highlighted with contrast lesion in brain MRI images. The sclerosis classification initially can be considered as binary task in which the sclerosis seg-mentation can be avoided for reduced complexity of the model. The sclerosis lesion show considerable impac

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Pisal, Shriraj. "Medicinal Herb Identification Using CNN Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5022–27. http://dx.doi.org/10.22214/ijraset.2024.62716.

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Abstract: Automated herb identification plays a crucial role in various industries such as cosmetics, medicine, and food, where the need to accurately identify different plant species is essential. However, existing methods often face challenges when dealing with complex backgrounds and a wide variety of patterns, especially in wild environments. In response to these challenges, we propose an innovative convolutional neural network (CNN) model that incorporates two key components: the Part- Information Perception Module and the Species Classification Module. The Part-Information Perception Mod

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Patil, Prof Kirti. "Sign Language Detection using CNN Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4125–31. http://dx.doi.org/10.22214/ijraset.2024.62528.

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Abstract: Sign Language is mainly used by deaf (hard hearing) and dumb people to exchange information between their own community and with other people. It is a language where people use their hand gestures to communicate as they can’t speak or hear. Sign Language Recognition (SLR) deals with recognizing the hand gestures acquisition and continues till text or speech is generated for corresponding hand gestures. Here hand gestures for sign language can be classified as static and dynamic. Deep Learning Computer Vision is used to recognize the hand gestures by building Deep Neural Network archi

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Sung, Wen-Tsai, Hao-Wei Kang, and Sung-Jung Hsiao. "Speech Recognition via CTC-CNN Model." Computers, Materials & Continua 76, no. 3 (2023): 3833–58. http://dx.doi.org/10.32604/cmc.2023.040024.

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Sai Nikhil, Karlapudi. "Fruit Ripeness Detection Using CNN Model." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47072.

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Abstract—Fruit ripeness detection is crucial in the agricul- ture field to determine the time of harvest and fruit quality. Substantial crop loss might result from timing slips or delays, consequently affecting income rates. Manual detection of ripeness in fruits can be inefficient for large scale implementation and pose issues such as requiring lots of time and intensive labor. Hence, in this project, fruit ripeness is detected utilizing computer vision and machine learning technologies. The method being used will recognize the fruits and categorize their ripeness using the CNN algorithm, on

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Samyuktha S and Sarwath Unnisa. "Emotional Speech Recognition using CNN model." International Journal of Information Technology, Research and Applications 4, no. 1 (2025): 30–38. https://doi.org/10.59461/ijitra.v4i1.164.

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Speech Emotion Recognition (SER) is a new area of artificial intelligence that deals with recognizing human emotions from speech signals. Emotions are an important aspect of communication, affecting social interactions and decision-making processes. This paper introduces a complete SER system that uses state-of-the-art deep learning methods to recognize emotions like Happy, Sad, Angry, Neutral, Surprise, Calm, Fear, and Disgust. The suggested model uses Mel-Spectrograms, MFCCs, and Chroma features for efficient feature extraction. Convolutional layers are utilized to capture complex patterns i

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KEERTHI, MUPPALA NAGA, and PRATHAPARAO KALAHYNDAVI. "Sign Language Detection Using CNN Model." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04756.

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A wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture, are required to develop successful sign language recognition, generation, and translation systems. The speech and hearing- impaired community use sign language as a medium of their communication. Most people who aren't familiar with sign language find it difficult to communicate without an interpreter. Sign language recognition appertains to track and recognize the meaningful emotion of humanmade with head, arms, hands, fingers, etc. The

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

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

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Kamundala, Espoir K., and Chang Hoon Kim. "CNN Model to Classify Malware Using Image Feature." KIISE Transactions on Computing Practices 24, no. 5 (2018): 256–61. http://dx.doi.org/10.5626/ktcp.2018.24.5.256.

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Park, Shin-Woo, and Hyun-Min Joe. "CNN-based Fall Detection Model for Humanoid Robots." JOURNAL OF SENSOR SCIENCE AND TECHNOLOGY 33, no. 1 (2024): 18–23. http://dx.doi.org/10.46670/jsst.2024.33.1.18.

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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Convolutional neural networks for leaf image-based plant disease classification." International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 328–41. https://doi.org/10.11591/ijai.v8.i4.pp328-341.

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Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system imp

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Wang, Jinnan, Weiqin Tong, and Xiaoli Zhi. "Model Parallelism Optimization for CNN FPGA Accelerator." Algorithms 16, no. 2 (2023): 110. http://dx.doi.org/10.3390/a16020110.

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Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. Model parallelism is an effective way to reduce resource usage by distributing CNN inference among several devices. However, parallelizing a CNN model is not easy, because CNN models have an essentially tightly-coupled structure. In this work, we propose a novel model parallelism method to decouple the CNN structure with group convolution and a

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Mr., Anand R., and J. Alamelu Mangai Mr. "A Hybrid Sequence Model for Fake News Detection." ISRAA Journal Scopus, Q4 Indexed 7, no. 12 (2023): 5–14. https://doi.org/10.5281/zenodo.10259536.

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The expansion of the Internet agrees to the rapid extent of facts through public media and social networks. Without the integrity of the information, fake news is spread to thousands of users through social networking. To reach the different people of the society for commercial and political interest. It is a big challenge in society to tackle the spread of such fake news. This research work mainly focuses on the analysis of news articles and its credibility. The proposed work uses "Real or Fake News" data set from Kaggle repository to model three deep learning architectures namely, Bi-LSTM, B

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Dal Cortivo, Davide, Sara Mandelli, Paolo Bestagini, and Stefano Tubaro. "CNN-Based Multi-Modal Camera Model Identification on Video Sequences." Journal of Imaging 7, no. 8 (2021): 135. http://dx.doi.org/10.3390/jimaging7080135.

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Identifying the source camera of images and videos has gained significant importance in multimedia forensics. It allows tracing back data to their creator, thus enabling to solve copyright infringement cases and expose the authors of hideous crimes. In this paper, we focus on the problem of camera model identification for video sequences, that is, given a video under analysis, detecting the camera model used for its acquisition. To this purpose, we develop two different CNN-based camera model identification methods, working in a novel multi-modal scenario. Differently from mono-modal methods,

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Baek, Woon-Young, and Sang-Gil Kang. "Ship Classification Method using Two-Stage CNN Model." Journal of Korean Institute of Information Technology 21, no. 8 (2023): 203–10. http://dx.doi.org/10.14801/jkiit.2023.21.8.203.

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Dinesh, Reddy, and Karthik Abhinav. "Forecasting Stock Price using LSTM-CNN Method." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 1–8. https://doi.org/10.35940/ijeat.A3117.1011121.

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Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information.

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Lee, Seonggu, and Jitae Shin. "Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter." International Journal of Information and Electronics Engineering 9, no. 1 (2019): 34–38. http://dx.doi.org/10.18178/ijiee.2019.9.1.701.

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Srinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.

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Mukkapati, Naveen, and M. S. Anbarasi. "Brain Tumor Classification Based on Enhanced CNN Model." Revue d'Intelligence Artificielle 36, no. 1 (2022): 125–30. http://dx.doi.org/10.18280/ria.360114.

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Brain tumor classification is important process for doctors to plan the treatment for patients based on the stages. Various CNN based architecture is applied for the brain tumor classification to improve the classification performance. Existing methods in brain tumor segmentation have the limitations of overfitting and lower efficiency in handling large dataset. In this research, for brain tumor segmentation purpose the enhanced CNN architecture based on U-Net, for pattern analysis purpose RefineNet and for classifying brain tumor purpose SegNet architecture is proposed. The brain tumor benchm

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Zhang, Jilin, Lishi Ye, and Yongzeng Lai. "Stock Price Prediction Using CNN-BiLSTM-Attention Model." Mathematics 11, no. 9 (2023): 1985. http://dx.doi.org/10.3390/math11091985.

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Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a

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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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Slavova, Angela, and Ronald Tetzlaff. "Edge of chaos in reaction diffusion CNN model." Open Mathematics 15, no. 1 (2017): 21–29. http://dx.doi.org/10.1515/math-2017-0002.

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Abstract In this paper, we study the dynamics of a reaction-diffusion Cellular Nonlinear Network (RD-CNN) nodel in which the reaction term is represented by Brusselator cell. We investigate the RD-CNN dynamics by means of describing function method. Comparison with classical results for Brusselator equation is provided. Then we introduce a new RD-CNN model with memristor coupling, for which the edge of chaos regime in the parameter space is determined. Numerical simulations are presented for obtaining dynamic patterns in the RD-CNN model with memristor coupling.

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Zhao, Xinzhuo, Shouliang Qi, Baihua Zhang, et al. "Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning." Journal of X-Ray Science and Technology 27, no. 4 (2019): 615–29. http://dx.doi.org/10.3233/xst-180490.

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Sheikh, Layba Mahin K., Affan Shaikh, Aniket Sandupatla, Rushikesh Pudale, Aum Bakare, and Prof Mallesh Chavan. "Classification of Simple CNN Model and ResNet50." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4606–10. http://dx.doi.org/10.22214/ijraset.2024.60677.

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Abstract: In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification tasks, achieving state-of-the-art performance in various domains. Among the plethora of CNN architectures, the Simple CNN model and ResNet50 stand out as widely used architectures with distinct characteristics. In this study, we present a comparative analysis of these two architectures in terms of their performance, computational efficiency, and robustness for classification tasks.The Simple CNN model represents a straightforward convolutional neural network architecture with

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Yin, Qiwei, Ruixun Zhang, and XiuLi Shao. "CNN and RNN mixed model for image classification." MATEC Web of Conferences 277 (2019): 02001. http://dx.doi.org/10.1051/matecconf/201927702001.

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In this paper, we propose a CNN(Convolutional neural networks) and RNN(recurrent neural networks) mixed model for image classification, the proposed network, called CNN-RNN model. Image data can be viewed as two-dimensional wave data, and convolution calculation is a filtering process. It can filter non-critical band information in an image, leaving behind important features of image information. The CNN-RNN model can use the RNN to Calculate the Dependency and Continuity Features of the Intermediate Layer Output of the CNN Model, connect the characteristics of these middle tiers to the final

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Zahraa, Najm Abdullah, Abdulridha Abutiheen Zinah, A. Abdulmunem Ashwan, and A. Harjan Zahraa. "Official logo recognition based on multilayer convolutional neural network model." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 5 (2022): 1083–90. https://doi.org/10.12928/telkomnika.v20i5.23464.

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Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recog

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Janarthanan Sekar. "Human and Object Detection Deep Learning Model Using R-CNN." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 748–56. https://doi.org/10.52783/jisem.v10i30s.4897.

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Human and object detection is deep learning model. Which identifies and detects human(people) and object from image. For implementing the human and object detection there many popular algorithms like YOLO (You Only Look Once ), SSD (Single Shot Multi-Box Detector, CNN and R- CNN family. R-CNN family has R-CNN (Region Based Convolution Neural Network), FAST R-CNN, FASTER R- CNN. This time the R-CNN is very popular in market for more accuracy and efficient machine and deep learning object detection model. In this paper we have explain about algorithm and implementation of the human and object de

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Thi, Ha Phan, Chung Tran Duc, and Fadzil Hassan Mohd. "Vietnamese character recognition based on CNN model with reduced character classes." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 962~969. https://doi.org/10.11591/eei.v10i2.2810.

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This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as “c” and “C”, “o” and “O&r

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文, 滋润. "DS-EC-CNN: A Novel Lightweight CNN Model for Indoor Localization of WIFI Fingerprints." Modeling and Simulation 13, no. 03 (2024): 3911–22. http://dx.doi.org/10.12677/mos.2024.133356.

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Ryu, Seongbin, Kyoungseok Lee, and Seungjun Kim. "Merged CNN-Based Finite Element Model Update Methodology." Journal of the Korean Society of Hazard Mitigation 24, no. 6 (2024): 263–72. https://doi.org/10.9798/kosham.2024.24.6.263.

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Finite element analysis requires the construction of a model that accurately reflects the structural characteristics of a target structure. Such models must incorporate precise material and geometric properties. However, discrepancies often arise between the properties assumed during the design phase and those observed in the actual structure. To address this, the models are iteratively updated using real measurement data, minimizing the errors between measured and analytical results. This process, however, can be time-consuming, particularly for complex structures. In this study, we propose a

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Jeong, Jaemin, Ji-Ho Cho, and Jeong-Gun Lee. "Filter combination learning for CNN model compression." ICT Express 7, no. 1 (2021): 5–9. http://dx.doi.org/10.1016/j.icte.2021.01.001.

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