Gotowe bibliografie tematyczne / CNN MODEL

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Gotowa bibliografia na temat „CNN MODEL”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "CNN MODEL"

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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Rozprawy doktorskie na temat "CNN MODEL"

Meng, Zhaoxin. "A deep learning model for scene recognition." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.

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Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene rec

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Hubková, Helena. "Named-entity recognition in Czech historical texts : Using a CNN-BiLSTM neural network model." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385682.

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The thesis presents named-entity recognition in Czech historical newspapers from Modern Access to Historical Sources Project. Our goal was to create a specific corpus and annotation manual for the project and evaluate neural networks methods for named-entity recognition within the task. We created the corpus using scanned Czech historical newspapers. The scanned pages were converted to digitize text by optical character recognition (OCR) method. The data were preprocessed by deleting some OCR errors. We also defined specific named entities types for our task and created an annotation manual wi

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Al-Kadhimi, Staffan, and Paul Löwenström. "Identification of machine-generated reviews : 1D CNN applied on the GPT-2 neural language model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280335.

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With recent advances in machine learning, computers are able to create more convincing text, creating a concern for an increase in fake information on the internet. At the same time, researchers are creating tools for detecting computer-generated text. Researchers have been able to exploit flaws in neural language models and use them against themselves; for example, GLTR provides human users with a visual representation of texts that assists in classification as human-written or machine-generated. By training a convolutional neural network (CNN) on GLTR output data from analysis of machine-gen

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Huss, Anders. "Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200.

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The increasing energy consumption is one of the greatest environmental challenges of our time. Residential buildings account for a considerable part of the total electricity consumption and is further a sector that is shown to have large savings potential. Non Intrusive Load Monitoring (NILM), i.e. the deduction of the electricity consumption of individual home appliances from the total electricity consumption of a household, is a compelling approach to deliver appliance specific consumption feedback to consumers. This enables informed choices and can promote sustainable and cost saving action

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Laine, Emmi. "Desirability, Values and Ideology in CNN Travel -- Discourse Analysis on Travel Stories." Thesis, Stockholms universitet, Institutionen för mediestudier, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-102742.

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Title: Values, Desirability and Ideology in CNN Travel -- a Discourse Analysis on Travel Stories Author: Emmi Laine Course: Journalistikvetenskap, Kandidatkurs, H13 J Kand (Bachelor of Journalism, Fall 2013), JMK, Stockholm University, Sweden Aim: The aim is to examine which values and ideologies CNN Travel fulfills in their stories. Method: Qualitative discourse analysis. Summary: This Bachelor ́s thesis asks what is desirable, which are the values of CNN Travel, the major U.S. news corporation CNN ́s online travel site. The question has been answered through a qualitative discourse analysis

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Appelstål, Michael. "Multimodal Model for Construction Site Aversion Classification." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-421011.

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Aversion on construction sites can be everything from missingmaterial, fire hazards, or insufficient cleaning. These aversionsappear very often on construction sites and the construction companyneeds to report and take care of them in order for the site to runcorrectly. The reports consist of an image of the aversion and atext describing the aversion. Report categorization is currentlydone manually which is both time and cost-ineffective. The task for this thesis was to implement and evaluate an automaticmultimodal machine learning classifier for the reported aversionsthat utilized both the im

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Anam, Md Tahseen. "Evaluate Machine Learning Model to Better Understand Cutting in Wood." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448713.

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Wood cutting properties for the chains of chainsaw is measured in the lab by analyzing the force, torque, consumed power and other aspects of the chain as it cuts through the wood log. One of the essential properties of the chains is the cutting efficiency which is the measured cutting surface per the power used for cutting per the time unit. These data are not available beforehand and therefore, cutting efficiency cannot be measured before performing the cut. Cutting efficiency is related to the relativehardness of the wood which means that it is affected by the existence of knots (hardstruct

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Ghibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.

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The research has the aim to build an autonomous support for traders which in future can be translated in an Active ETF. My thesis work is characterized for a huge focus on problem formulation and an accurate analysis on the impact of the input and the length of the future horizon on the results. I will demonstrate that using financial indicators already used by professional traders every day and considering a correct length of the future horizon, it is possible to reach interesting scores in the forecast of future market states, considering both accuracy, which is around 90% in all the experi

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Rydén, Anna, and Amanda Martinsson. "Evaluation of 3D motion capture data from a deep neural network combined with a biomechanical model." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176543.

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Motion capture has in recent years grown in interest in many fields from both game industry to sport analysis. The need of reflective markers and expensive multi-camera systems limits the business since they are costly and time-consuming. One solution to this could be a deep neural network trained to extract 3D joint estimations from a 2D video captured with a smartphone. This master thesis project has investigated the accuracy of a trained convolutional neural network, MargiPose, that estimates 25 joint positions in 3D from a 2D video, against a gold standard, multi-camera Vicon-system. The p

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Gerima, Kassaye. "Night Setback Identification of District Heating Substations." Thesis, Högskolan Dalarna, Mikrodataanalys, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-36071.

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Energy efficiency of district heating systems is of great interest to energy stakeholders. However, it is not uncommon that district heating systems fail to achieve the expected performance due to inappropriate operations. Night setback is one control strategy, which has been proved to be not a suitable setting for well-insulated modern buildings in terms of both economic and energy efficiency. Therefore, identification of a night setback control is vital to district heating companies to smoothly manage their heat energy distribution to their customers. This study is motivated to automate this

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Książki na temat "CNN MODEL"

Greene, Carol. I can be a model. Childrens Press, 1985.

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Greene, Carol. I can be a model. Childrens Press, 1985.

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Greene, Carol. I can be a model. Childrens Press, 1985.

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Engel, Charles. Can the Markov switching model forecast exchange rates? National Bureau of Economic Research, 1992.

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Danna, Theresa M. Rollover, Mona Lisa!: How anyone can model for artists. Big Guy Pub., 1992.

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Sutherland, H. Constructing a tax-benefit model: What advice can one give? Taxation, Incentives and the Distribution of Income Programme, Suntory-Toyota International Centre for Economics and Related Disciplines, London School of Economics, 1989.

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Penalver, Adrian. How can the IMF catalyse private capital flows? A model. Bank of England, 2004.

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Foundation, Annie E. Casey. Child care you can count on: Model programs and policies. Annie E. Casey Foundation, 1998.

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St-Amour, Luc. Realistic Construction Models You Can Make (Vehicles You Can Make Series). Fox Chapel Publishing Company, 2001.

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Fuhrer, Jeffrey C. An optimizing model for monetary policy analysis: Can habit formation help? Federal Reserve Bank of Boston, 1998.

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Części książek na temat "CNN MODEL"

Beniwal, Rohit, Divyakshi Bhardwaj, Bhanu Pratap Raghav, and Dhananjay Negi. "Text Similarity Identification Based on CNN and CNN-LSTM Model." In Second International Conference on Sustainable Technologies for Computational Intelligence. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4641-6_5.

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Zhang, Shizhou, Yihong Gong, Jinjun Wang, and Nanning Zheng. "A Biologically Inspired Deep CNN Model." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48890-5_53.

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Saadat, Sumaya, and V. Joseph Raymond. "Malware Classification Using CNN-XGBoost Model." In Artificial Intelligence Techniques for Advanced Computing Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5329-5_19.

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Moin, Kashif, Mayank Shrivastava, Amlan Mishra, Lambodar Jena, and Soumen Nayak. "Diabetic Retinopathy Detection Using CNN Model." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6068-0_13.

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Jain, Harshit, Indrajeet Kumar, Isha N. Porwal, et al. "Lung Conditions Prognosis Using CNN Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5080-5_20.

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Chen, Xutong. "CNN Model Optimization Cheme and Applications." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5959-4_216.

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Dhawal, Yesho, Brijesh Kumar Chaurasia, Shruti Bajpai, Subrat Gupta, Shreya Tiwari, and Shubham Tiwari. "Alzheimer Detection Using Optimized CNN Model." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0185-1_28.

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Aryan, Pratyush Nag, Rishikesh B. Trivedi, and Somya R. Goyal. "Cyberbullying Detection Using CNN Prediction Model." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-4892-1_9.

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Goswami, Tilottama, and Shashidhar Reddy Javaji. "CNN Model for American Sign Language Recognition." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_6.

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Ismail, Amr, Ismail Elansary, and Wael A. Awad. "Utilized CNN Model for Lung Diseases Detection." In Proceedings of The First International Conference on Green Sciences. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62672-2_7.

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Streszczenia konferencji na temat "CNN MODEL"

Wang, Yufei. "Stock Price Prediction Based on CNN, LSTM and CNN- LSTM Model." In International Conference on Innovations in Applied Mathematics, Physics and Astronomy. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012982600004601.

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Asha, V., Jincy C. Mathew, Priya Thomas, Kwaza Moinuddin, N. Laxmi, and R. Kiran. "Classification of DNA Using CNN-LSTM with Integrated CNN-RF Hybrid Model." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11035200.

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Hassine, Sarra, Sourour Ammar, and Ilef Ben Slima. "CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013169500003890.

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Chen, Ankang, Zhitao He, and Dan Zhang. "An Anomaly Detection Model for CAN Networks Based on CNN and Transformer." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905930.

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Singh, Vishwank Vikram, and Manjubala Bisi. "Software Fault Localization using CNN-LSTM Model." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725060.

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Satpute, Reena, and Chidozie Peter Onwe. "CNN-LSTM Model for Deepfake Image Detection." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842840.

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Yang, Gan, Guangwei Wang, Yichen Li, and Wenbin Yu. "ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662836.

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Swaroop, Pranshu, Natansh Badolia, Rishav Ranjan, and Manjeet Kumar. "Arrhythmia Classification Using Hybrid CNN-LSTM Model." In 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, 2024. http://dx.doi.org/10.1109/icecsp61809.2024.10698222.

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Maulana, Fahmi Agung, Kaisar Kertarajasa, Yande Satwika Yasa, Sabrina Adinda Sari, and Mahmud Dwi Sulistiyo. "Grapevine Leaves Classification Using Various CNN Model." In 2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE, 2024. https://doi.org/10.1109/icitacee62763.2024.10761961.

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Yang, Menghua, Junkai Yi, Hejun Zhu, and Lingling Tan. "CNN-LSTM-Based Insider Threat Detection Model." In 2025 International Conference on Electrical Automation and Artificial Intelligence (ICEAAI). IEEE, 2025. https://doi.org/10.1109/iceaai64185.2025.10957184.

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Raporty organizacyjne na temat "CNN MODEL"

Slavova, Angela, and Nikolay Kyurkchiev. On CNN Model of Black–Scholes Equation with Leland Correction. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.02.03.

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Mbani, Benson, Valentin Buck, and Jens Greinert. Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast) Short description of the research software. GEOMAR, 2023. http://dx.doi.org/10.3289/sw_1_2023.

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This is an A.I. - based workflow for detecting megabenthic fauna from a sequence of underwater optical images. The workflow (semi) automatically generates weak annotations through the analysis of superpixels, and uses these (refined and semantically labeled) annotations to train a Faster R-CNN model. Currently, the workflow has been tested with images of the Clarion-Clipperton Zone in the Pacific Ocean

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa

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Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analys

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Spilimbergo, Antonio. Growth and Trade: The North can Lose. Inter-American Development Bank, 1997. http://dx.doi.org/10.18235/0011604.

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Models on the composition of trade and growth often assume that the technological content of trade is negatively correlated with the income of the trading partner. First, this paper shows that this assumption is not supported empirically. Second, it presents a Ricardian model with non-homothetic preferences.

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Zhang, Yue. Evaluation of CNN Models with Fashion MNIST Data. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-654.

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Salunkhe, Sanjita Bharat. Intermittent Deployment of Branched CNN Models on Microcontrollers. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-915.

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Andrian, Leandro Gastón, John Leon-Diaz, and Eugenio Rojas. Can Financial Hedging Serve Macroprudential Objectives? Inter-American Development Bank, 2025. https://doi.org/10.18235/0013511.

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We examine hedging as a macroprudential tool in a Sudden Stops model of an economy exposed to commodity price fluctuations. We find that hedging commodity revenues yields significant welfare gains by stabilizing public expenditure, which heavily depends on these revenues. However, this added stability weakens precautionary motives and exacerbates the pecuniary externality that drives overborrowing in such models. As a result, hedging and traditional macroprudential policy act as complements rather than substitutes, with more ag- gressive hedging inducing a stronger macroprudential response. Ou

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