Gotowe bibliografie tematyczne / CNN MODELS

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Gotowa bibliografia na temat „CNN MODELS”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "CNN MODELS"

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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Rozprawy doktorskie na temat "CNN MODELS"

Lind, Johan. "Evaluating CNN-based models for unsupervised image denoising." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176092.

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Images are often corrupted by noise which reduces their visual quality and interferes with analysis. Convolutional Neural Networks (CNNs) have become a popular method for denoising images, but their training typically relies on access to thousands of pairs of noisy and clean versions of the same underlying picture. Unsupervised methods lack this requirement and can instead be trained purely using noisy images. This thesis evaluated two different unsupervised denoising algorithms: Noise2Self (N2S) and Parametric Probabilistic Noise2Void (PPN2V), both of which train an internal CNN to denoise im

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Söderström, Douglas. "Comparing pre-trained CNN models on agricultural machines." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185333.

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Norlund, Tobias. "The Use of Distributional Semantics in Text Classification Models : Comparative performance analysis of popular word embeddings." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-127991.

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In the field of Natural Language Processing, supervised machine learning is commonly used to solve classification tasks such as sentiment analysis and text categorization. The classical way of representing the text has been to use the well known Bag-Of-Words representation. However lately low-dimensional dense word vectors have come to dominate the input to state-of-the-art models. While few studies have made a fair comparison of the models' sensibility to the text representation, this thesis tries to fill that gap. We especially seek insight in the impact various unsupervised pre-trained vect

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Suresh, Sreerag. "An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99287.

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Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy f

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Wang, Zhihao. "Land Cover Classification on Satellite Image Time Series Using Deep Learning Models." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159559249009195.

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Nilsson, Kristian, and Hans-Eric Jönsson. "A comparison of image and object level annotation performance of image recognition cloud services and custom Convolutional Neural Network models." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18074.

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Recent advancements in machine learning has contributed to an explosive growth of the image recognition field. Simultaneously, multiple Information Technology (IT) service providers such as Google and Amazon have embraced cloud solutions and software as a service. These factors have helped mature many computer vision tasks from scientific curiosity to practical applications. As image recognition is now accessible to the general developer community, a need arises for a comparison of its capabilities, and what can be gained from choosing a cloud service over a custom implementation. This thesis

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You, Yantian. "Sparsity Analysis of Deep Learning Models and Corresponding Accelerator Design on FPGA." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204409.

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Machine learning has achieved great success in recent years, especially the deep learning algorithms based on Artificial Neural Network. However, high performance and large memories are needed for these models , which makes them not suitable for IoT device, as IoT devices have limited performance and should be low cost and less energy-consuming. Therefore, it is necessary to optimize the deep learning models to accommodate the resource-constrained IoT devices. This thesis is to seek for a possible solution of optimizing the ANN models to fit into the IoT devices and provide a hardware implemen

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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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Jonsson, Tim, and Isabella Tapper. "Evaluation of two CNN models, VGGNet-16 & VGGNet-19, for classification of Alzheimer’s disease in brain MRI scans." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280141.

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Computer-aided-diagnosis (CAD) emerged in the early 1950s and since then CAD has facilitated the diagnosing of many medical conditions and diseases. In particular, CADfor Alzheimer’s disease (AD) has been immensely researched the last decade thanks to advanced neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Today around 44 million people worldwide have AD and researchers hope to discover accurate ways to detect AD before the symptoms begin. There are currently no validated so-called biological markers (biomarkers) for AD, meaning that th

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Mukhedkar, Dhananjay. "Polyphonic Music Instrument Detection on Weakly Labelled Data using Sequence Learning Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279060.

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Polyphonic or multiple music instrument detection is a difficult problem compared to detecting single or solo instruments in an audio recording. As music is time series data it be can modelled using sequence learning methods within deep learning. Recently, temporal convolutional networks (TCN) have shown to outperform conventional recurrent neural networks (RNN) on various sequence modelling tasks. Though there have been significant improvements in deep learning methods, data scarcity becomes a problem in training large scale models. Weakly labelled data is an alternative where a clip is annot

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Książki na temat "CNN MODELS"

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

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Rudd, Jeremy Bay. Can rational expectations sticky-price models explain inflation dynamics? Federal Reserve Board, 2003.

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Dueker, Michael. Can markov switching models predict excess foreign exchange returns? Federal Reserve Bank of St. Louis, 2001.

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Antonio Diez de los Rios. Can affine term structure models help us predict exchange rates? Bank of Canada, 2006.

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Rios-Zertuche, Daniel. Semantic data models can be implemented efficienty: A performance analysis perspective. Computer Systems Research Institute, University of Toronto, 1988.

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Chari, V. V. Can sticky price models generate volatile and persistent real exchange rates? National Bureau of Economic Research, 2000.

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

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Części książek na temat "CNN MODELS"

Amarir, Safa, Bouchra Nassih, and Aouatif Amine. "Face Recognition Technology Based CNN Models." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-95330-9_12.

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Bisong, Ekaba. "Convolutional Neural Networks (CNN)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_35.

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Patil, Lakshmi, and V. D. Mytri. "Face Recognition with Inception-Based CNN Models." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_48.

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Singh Rajput, Shyam, Deepak Rai, Deeti Hothrik, Sudhanshu Kumar, and Shubhangi Singh. "CNN-Based Models for Image Forgery Detection." In Studies in Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6290-5_10.

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Baaloul, Ali, Nadjia Benblidia, Abdelkader Ouared, and Fatma Zohra Reguieg. "Arabic Lipreading Using YOLO and CNN Models." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-71848-9_2.

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Sanga, Haripriya, Pranuthi Saka, Manoja Nanded, Kousar Nikhath Alpuri, and Sandhya Nadella. "Tilapia Fish Freshness Detection Using CNN Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56703-2_6.

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Yang, Wenli, Guan Huang, Renjie Li, Jiahao Yu, Yanyu Chen, and Quan Bai. "Hybrid CNN-Interpreter: Interprete Local and Global Contexts for CNN-Based Models." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8391-9_16.

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Farhaoui, Othmane, Mohamed Rida Fethi, Ali Omari Alaoui, Imad Zeroual, and Ahmad El Allaoui. "Evaluating CNN and Hybrid CNN-LSTM Models for Arabic Handwritten Character Recognition." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90921-4_90.

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Pacheco-Rodríguez, Hugo Sebastian, Eleazar Aguirre-Anaya, and Ricardo Menchaca-Méndez. "Robustness Evaluation of CNN Models Trained Without Backpropagation." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77293-1_10.

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Sony Priya, S., and R. I. Minu. "Comparison of Various CNN Models for Image Classification." In Inventive Computation and Information Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7402-1_3.

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Streszczenia konferencji na temat "CNN MODELS"

Sharma, Ankita, and Sonam Mittal. "Lung Cancer Prediction Using CNN Models." In 2024 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2024. https://doi.org/10.1109/iccccm61016.2024.11039981.

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Garba, Ahmad Ahmad, Vihsal Jain, and Kanika Singla. "Tomato Leaf Disease Detection Using CNN Models." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840660.

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Johanes, Devin Jonathan, Anderies, and Andry Chowanda. "Portuguese Meals Image Recognition Using CNN Models." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903896.

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Oktovianus, Louis, Jessica Kiyan Tikkhaviro, and Simeon Yuda Prasetyo. "Benchmarking CNN Models for Malaria Cell Detection." In 2024 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). IEEE, 2024. https://doi.org/10.1109/cenim64038.2024.10882645.

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Al Tobi, Maamar Ali Saud, Ramesh Kumar Ganjam Ramaswamy, Rene Sinogaya Pacturan, Ramachandran K. P, and Varghesh Manappallil Joy. "Fault Diagnosis of Wind Turbine Gearbox using CNN-AI." In 2025 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF). IEEE, 2025. https://doi.org/10.1109/aicconf64766.2025.11064280.

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Sharma, Rishabh, and Shikhar Gupta. "Mushroom Classification Using CNN and Gradient Boosting Models." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689875.

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Behal, Rupashi, Pallavi Priya, Yuvraj, Anjali Kapoor, Vivek Kumar Jangra, and Anju Mishra. "Hair Loss Stage Prediction Using different CNN models." In 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, 2024. http://dx.doi.org/10.1109/icecsp61809.2024.10698237.

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Pantelaios, Dimitrios, Paraskevi-Antonia Theofilou, Paraskevi Tzouveli, and Stefanos Kollias. "Hybrid CNN-ViT Models for Medical Image Classification." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635205.

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Kant, Vishnu. "Optimizing Early Skin Cancer Diagnosis through CNN Models." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866883.

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Alrahhal, Maher, Talal Bonny, and Mohammad Alshabi. "Enhancing lung cancer detection with hybrid CNN models." In Real-time Processing of Image, Depth and Video Information 2025, edited by Gian Domenico Licciardo, Matthias F. Carlsohn, and Viktor J. Schneider. SPIE, 2025. https://doi.org/10.1117/12.3061309.

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Raporty organizacyjne na temat "CNN MODELS"

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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Bangalore Vijayakumar, Shreyas. A Comprehensive Pytorch Framework to Benchmark CNN and ViT Models. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-119.

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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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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were test

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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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Dixon, Peter, Michael Jerie, and Maureen Rimmer. Modern Trade Theory for CGE Modelling: the Armington, Krugman and Melitz Models. GTAP Technical Paper, 2015. http://dx.doi.org/10.21642/gtap.tp36.

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This paper is for CGE modelers and others interested in modern trade theory. The Armington specification of trade, assuming country-level product differentiation, has been central to CGE modelling for 40 years. Starting in the 1980s with Krugman and more recently Melitz, trade theorists have preferred specifications with firm-level product differentiation. We draw out the connections between the Armington, Krugman and Melitz models, deriving them as successively less restrictive special cases of an encompassing model. We then investigate optimality properties of the Melitz model, demonstrating

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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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Equihua, M., and O. Perez-Maqueo. Mathematical Modeling and Conservation. American Museum of Natural History, 2010. http://dx.doi.org/10.5531/cbc.ncep.0154.

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Formal models are indispensable tools in natural resource management and in conservation biology. Explicit modeling can be a helpful tool for studying these systems, communicating across disciplines, and integrating varying viewpoints of numerous stakeholders. This module demonstrates how to explicitly construct models as alternative representations to help interpret and understand nature. Through a synthesis and two exercises, it describes the general context of scientific modeling (i.e., use and types of models), and allows students to practice building a model by evaluating the relationship

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Hamill, Daniel D., Jeremy J. Giovando, Chandler S. Engel, Travis A. Dahl, and Michael D. Bartles. Application of a Radiation-Derived Temperature Index Model to the Willow Creek Watershed in Idaho, USA. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41360.

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The ability to simulate snow accumulation and melting processes is fundamental to developing real-time hydrological models in watersheds with a snowmelt-dominated flow regime. A primary source of uncertainty with this model development approach is the subjectivity related to which historical periods to use and how to combine parameters from multiple calibration events. The Hydrologic Engineering Center, Hydrological Modeling System, has recently implemented a hybrid temperature index (TI) snow module that has not been extensively tested. This study evaluates a radiatative temperature index (RT

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