Relevant bibliographies by topics / CNN MODELS

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Academic literature on the topic 'CNN MODELS'

Author: Grafiati
Published: 11 September 2023
Last updated: 16 April 2024

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Journal articles on the topic "CNN MODELS"

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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, Model2, and Model3) which have been well-illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227-pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.
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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 (May 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 (May 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 associative memories are proposed.
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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 (June 18, 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, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of convolutional neural network (CNN) because it automatically detects the important features without any human supervision. The proposed methodology can deeply detect plant disease by using a deep learning process. Ultimately, the groundnut disease classification with its overall performance of the proposed methodology provides 96% accuracy.
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Jing, Juntong. "Denoising Adversarial Examples Using CNN Models." Journal of Physics: Conference Series 2181, no. 1 (January 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 between random noise and adversarial attacks. A normal denoising CNN model is trained with random noise. Then groups of adversarial examples are collected by training on LeNet. Next, the denoising CNN model has been used to denoise those adversarial examples. Finally, after denoising the adversarial examples with the CNN model trained on normal random noise, the classification accuracy increases. Thus, it is reasonable to conclude that normal random noise and adversarial tracks have some common patterns.
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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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Zhan, Zhiwei, Guoliang Liao, Xiang Ren, Guangsi Xiong, Weilin Zhou, Wenchao Jiang, and Hong Xiao. "RA-CNN." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 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 environment. It integrates CNN for local feature extraction, RNN for global feature extraction, and attention mechanism for feature scaling. As a result, it can acquire the correct meaning of sentences. After experimenting with the hotel review dataset, it has an improvement in positive feeling classification compared with the baseline model (3%~13%), and it showed a competitive performance compared with ordinary deep learning models (~1%). On negative feeling classification, it also performed well close to other models.
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GÁL, V., J. HÁMORI, T. ROSKA, D. BÁLYA, ZS BOROSTYÁNKŐI, M. BRENDEL, K. LOTZ, et al. "RECEPTIVE FIELD ATLAS AND RELATED CNN MODELS." International Journal of Bifurcation and Chaos 14, no. 02 (February 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 of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the flexibility of the CNN computing: visual, tactile and auditory modalities are concerned.
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Alofi, Najla, Wafa Alonezi, and Wedad Alawad. "WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 13 (December 6, 2021): 135–50. http://dx.doi.org/10.3991/ijoe.v17i13.27373.

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Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.
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Noh, Seol-Hyun. "Gradient Flow Analysis and Performance Comparison of CNN Models." Journal of KIISE 48, no. 1 (January 31, 2021): 100–106. http://dx.doi.org/10.5626/jok.2021.48.1.100.

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Dissertations / Theses on the topic "CNN MODELS"

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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 images. Four different CNNs were tested in order to investigate how the performance of these algorithms would be affected by different network architectures. The testing used two different datasets: one containing clean images corrupted by synthetic noise, and one containing images damaged by real noise originating from the camera used to capture them. Two of the networks, UNet and a CBAM-augmented UNet resulted in high performance competitive with the strong classical denoisers BM3D and NLM. The other two networks - GRDN and MultiResUNet - on the other hand generally caused poor performance.
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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 vectors have on the performance. In addition, we take a closer look at the Random Indexing representation and try to optimize it jointly with the classification task. The results show that while low-dimensional pre-trained representations often have computational benefits and have also reported state-of-the-art performance, they do not necessarily outperform the classical representations in all cases.
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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 forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model.
Master of Science
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 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. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at only a single home or an aggregate load of a collection of homes. This study aims to address this gap and serve as an analysis on short term load forecasting on 3 communities of residential buildings. Detailed analysis on the model performances across all homes have been studied. Deep learning models have been used in this study and their efficacy is measured compared to a simple ANN model.
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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 empirically studies the performance of five general image recognition services (Google Cloud Vision, Microsoft Computer Vision, IBM Watson, Clarifai and Amazon Rekognition) and image recognition models of the Convolutional Neural Network (CNN) architecture that we ourselves have configured and trained. Image and object level annotations of images extracted from different datasets were tested, both in their original state and after being subjected to one of the following six types of distortions: brightness, color, compression, contrast, blurriness and rotation. The output labels and confidence scores were compared to the ground truth of multiple levels of concepts, such as food, soup and clam chowder. The results show that out of the services tested, there is currently no clear top performer over all categories and they all have some variations and similarities in their output, but on average Google Cloud Vision performs the best by a small margin. The services are all adept at identifying high level concepts such as food and most mid-level ones such as soup. However, in terms of further specifics, such as clam chowder, they start to vary, some performing better than others in different categories. Amazon was found to be the most capable at identifying multiple unique objects within the same image, on the chosen dataset. Additionally, it was found that by using synonyms of the ground truth labels, performance increased as the semantic gap between our expectations and the actual output from the services was narrowed. The services all showed vulnerability to image distortions, especially compression, blurriness and rotation. The custom models all performed noticeably worse, around half as well compared to the cloud services, possibly due to the difference in training data standards. The best model, configured with three convolutional layers, 128 nodes and a layer density of two, reached an average performance of almost 0.2 or 20%. In conclusion, if one is limited by a lack of experience with machine learning, computational resources and time, it is recommended to make use of one of the cloud services to reach a more acceptable performance level. Which to choose depends on the intended application, as the services perform differently in certain categories. The services are all vulnerable to multiple image distortions, potentially allowing adversarial attacks. Finally, there is definitely room for improvement in regards to the performance of these services and the computer vision field as a whole.
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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 implementation of the ANN accelerator on FPGA. The contribution of this thesis mainly lies in two aspects: 1). analyze the sparsity in the two mainstream deep learning models – DBN and CNN. The DBN model consists of two hidden layers with Restricted Boltzmann Machines while the CNN model consists of 2 convolutional layers and 2 sub-sampling layer. Experiments have been done on the MNIST data set with the sparsity of 75%. The ratio of the multiplications resulting in near-zero values has been tested. 2). FPGA implementation of an ANN accelerator. This thesis designed a hardware accelerator for the inference process in ANN models on FPGA (Stratix IV: EP4SGX530KH40C2). The main part of hardware design is the processing array consists of 256 Multiply-Accumulators array, which can conduct multiply-accumulate operations of 256 synaptic connections simultaneously. 16-bit fixed point computation is used to reduce the hardware complexity, thus saving power and area. Based on the evaluation results, it is found that the ratio of the multiplications under the threshold of 2-5 is 75% for CNN with ReLU activation function, and is 83% for DBN with sigmoid activation function, respectively. Therefore, there still exists large space for complex ANN models to be optimized if the sparsity of data is fully utilized. Meanwhile, the implemented hardware accelerator is verified to provide correct results through 16-bit fixed point computation, which can be used as a hardware testing platform for evaluating the ANN models.
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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 actions. To achieve this, accurate and reliable appliance load disaggregation algorithms must be developed. This Master's thesis proposes a novel approach to tackle the disaggregation problem inspired by state of the art algorithms in the field of speech recognition. Previous approaches, for sampling frequencies 1 Hz, have primarily focused on different types of hidden Markov models (HMMs) and occasionally the use of artificial neural networks (ANNs). HMMs are a natural representation of electric appliances, however with a purely generative approach to disaggregation, basically all appliances have to be modelled simultaneously. Due to the large number of possible appliances and variations between households, this is a major challenge. It imposes strong restrictions on the complexity, and thus the expressiveness, of the respective appliance model to make inference algorithms feasible. In this thesis, disaggregation is treated as a factorisation problem where the respective appliance signal has to be extracted from its background. A hybrid model is proposed, where a convolutional neural network (CNN) extracts features that correlate with the state of a single appliance and the features are used as observations for a hidden semi Markov model (HSMM) of the appliance. Since this allows for modelling of a single appliance, it becomes computationally feasible to use a more expressive Markov model. As proof of concept, the hybrid model is evaluated on 238 days of 1 Hz power data, collected from six households, to predict the power usage of the households' washing machine. The hybrid model is shown to perform considerably better than a CNN alone and it is further demonstrated how a significant increase in performance is achieved by including transitional features in the HSMM.
Den ökande energikonsumtionen är en stor utmaning för en hållbar utveckling. Bostäder står för en stor del av vår totala elförbrukning och är en sektor där det påvisats stor potential för besparingar. Non Intrusive Load Monitoring (NILM), dvs. härledning av hushållsapparaters individuella elförbrukning utifrån ett hushålls totala elförbrukning, är en tilltalande metod för att fortlöpande ge detaljerad information om elförbrukningen till hushåll. Detta utgör ett underlag för medvetna beslut och kan bidraga med incitament för hushåll att minska sin miljöpåverakan och sina elkostnader. För att åstadkomma detta måste precisa och tillförlitliga algoritmer för el-disaggregering utvecklas. Denna masteruppsats föreslår ett nytt angreppssätt till el-disaggregeringsproblemet, inspirerat av ledande metoder inom taligenkänning. Tidigare angreppsätt inom NILM (i frekvensområdet 1 Hz) har huvudsakligen fokuserat på olika typer av Markovmodeller (HMM) och enstaka förekomster av artificiella neurala nätverk. En HMM är en naturlig representation av en elapparat, men med uteslutande generativ modellering måste alla apparater modelleras samtidigt. Det stora antalet möjliga apparater och den stora variationen i sammansättningen av dessa mellan olika hushåll utgör en stor utmaning för sådana metoder. Det medför en stark begränsning av komplexiteten och detaljnivån i modellen av respektive apparat, för att de algoritmer som används vid prediktion ska vara beräkningsmässigt möjliga. I denna uppsats behandlas el-disaggregering som ett faktoriseringsproblem, där respektive apparat ska separeras från bakgrunden av andra apparater. För att göra detta föreslås en hybridmodell där ett neuralt nätverk extraherar information som korrelerar med sannolikheten för att den avsedda apparaten är i olika tillstånd. Denna information används som obervationssekvens för en semi-Markovmodell (HSMM). Då detta utförs för en enskild apparat blir det beräkningsmässigt möjligt att använda en mer detaljerad modell av apparaten. Den föreslagna Hybridmodellen utvärderas för uppgiften att avgöra när tvättmaskinen används för totalt 238 dagar av elförbrukningsmätningar från sex olika hushåll. Hybridmodellen presterar betydligt bättre än enbart ett neuralt nätverk, vidare påvisas att prestandan förbättras ytterligare genom att introducera tillstånds-övergång-observationer i HSMM:en.
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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 there are no reliable indicators that can accurately diagnose AD. However, according to experts, machine learning and neuroimaging is among the most promising areas of research focused on biomarkers and early diagnosis of AD. The state-of-the-art machine learning method for image classification are convolutional neural networks (CNNs). At a recent study at Bharati Vidyapeeth’s College of Engineering and Karunya University, a convolutional neural network VGGNet-16 was used in an experiment in order to correctly classify AD using MRI scans. Experimentation was performed on data collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The accuracy using the described method was 95.73% for the validation set. The purpose of this bachelor thesis was to compare two different convolution neural network models: VGGNet-16 and VGGNet-19, comparing their results and performances for classifying AD using MRI scans from ADNI database. Sets of images were elected, some include and some exclude the hippocampus, since AD starts spreading in the hippocampus. Using transfer learning, the CNN models were trained with (a) random validation split, (b) cross validation and (c) different slice range not including the hippocampus. The results of this study show that the models were good at classifying true-negative, which is diagnosing a healthy patient as healthy. Hippocampus seems to be a promising biomarker for AD because experiment (c) achieved a lower accuracy than (a) and (b). In conclusion there is no real statistically proven difference between VGGNet-16 and VGGNet-19. Even then, this thesis showed that simpler CNN architectures can be utilized to classify AD with equally mild success rate on a very limited dataset. The two CNN models’ accuracy were between 66.6- 74.8% for classifying AD depending on the training approach.
Datorstödd diagnostisk (CAD) uppkom under tidigt 50-tal och har sedan dess använts för att diagnostisera många medicinska tillstånd och sjukdomar. Specifikt CAD för Alzheimers sjukdom (AD) har undersökts kraftigt det senaste decenniet till följd av uppkomsten av avancerade hjärnavbildningstekniker såsom Magnetic Resonanse Imaging (MRI) och Positron Emission Tomography (PET). I dagsläget lider 44 miljoner människor av AD. Forskare hoppas i framtiden kunna upptäcka sjukdomen i ett tidigt stadie, men i dagsläget finns ingen pålitlig indikator som med god säkerhet kan klassificera AD. Enligt experter är dock maskininlärning och hjärnavbildningstekniker de mest lovande områdena för tidig diagnostik av AD. Den masknikinlärningsmodell som ligger i framkant för bildigenkänning är faltningsnätverk (CNN). Vid en ny studie av Bharati Vidyapeeth’s College of Engineering och Karunya University användes ett CNN, VGG-16, för att klassificera AD med hjälp av MRI-bilder. Experimentet utfördes på data från Alzheimer’s Disease Neuroimaging Initiative (ADNI) och uppnådde en träffsäkerhet på 95.73%. Syftet med vår studie var att utvärdera två CNN-modeller, VGGNet-16 och VGGNet-19, för att jämföra deras resultat och prestanda vid klassificering av AD med bilder från ADNI-databasen. Uppsättningar av bilder valdes varav hippocampus inkluderades i vissa och exkluderades i andra, detta då AD tros börja i hippocampus. Med överförningsinlärning tränades CNN modellerna på (a) slumpmässigt utvalt valideringsdata, (b) korsvalidering, och (c) bilder utan hippocampus. Resultatet visade att modellerna var bra på att klassificera sanna-negativa, d.v.s. friska patienter klassas som friska. Därefter visade även resultatet att modellerna uppnådde en högre träffsäkerhet i experiment (a) och (b) än i (c). Detta medför att hippocampus kan ses som en användbar biomarkör. Slutligen visade resultatet att modellerna statistiskt sett inte kan urskiljas från varandra, vilket kan tyda på att de presterar lika. Dock visade denna studie att simpla CNN-modeller kan användas för att klassificera AD på väldigt begränsad mängd data. De två modellerna uppnådde en träffsäkerhet på mellan 66,6% – 74,8% vid klassificering av AD beroende på hur modellerna tränats.
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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 annotated for presence or absence of instruments without specifying the times at which an instrument is sounding. This study investigates how TCN model compares to a Long Short-Term Memory (LSTM) model while trained on weakly labelled dataset. The results showed successful training of both models along with generalisation on a separate dataset. The comparison showed that TCN performed better than LSTM, but only marginally. Therefore, from the experiments carried out it could not be explicitly concluded if TCN is convincingly a better choice over LSTM in the context of instrument detection, but definitely a strong alternative.
Polyfonisk eller multipel musikinstrumentdetektering är ett svårt problem jämfört med att detektera enstaka eller soloinstrument i en ljudinspelning. Eftersom musik är tidsseriedata kan den modelleras med hjälp av sekvensinlärningsmetoder inom djup inlärning. Nyligen har ’Temporal Convolutional Network’ (TCN) visat sig överträffa konventionella ’Recurrent Neural Network’ (RNN) på flertalet sekvensmodelleringsuppgifter. Även om det har skett betydande förbättringar i metoder för djup inlärning, blir dataknapphet ett problem vid utbildning av storskaliga modeller. Svagt märkta data är ett alternativ där ett klipp kommenteras för närvaro av frånvaro av instrument utan att ange de tidpunkter då ett instrument låter. Denna studie undersöker hur TCN-modellen jämförs med en ’Long Short-Term Memory’ (LSTM) -modell medan den tränas i svagt märkta datasätt. Resultaten visade framgångsrik utbildning av båda modellerna tillsammans med generalisering i en separat datasats. Jämförelsen visade att TCN presterade bättre än LSTM, men endast marginellt. Därför kan man från de genomförda experimenten inte uttryckligen dra slutsatsen om TCN övertygande är ett bättre val jämfört med LSTM i samband med instrumentdetektering, men definitivt ett starkt alternativ.
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Books on the topic "CNN MODELS"

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Gestures Can Create Models that Help Thinking. [New York, N.Y.?]: [publisher not identified], 2019.

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

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

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Trackside scenes you can model. Waukesha, WI: Kalmbach Books, 2003.

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

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

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

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

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1968-, Johnson Kent J., ed. Small railroads you can build. 2nd ed. Waukesha, WI: Kalmbach Pub. Co., 1996.

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

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Book chapters on the topic "CNN MODELS"

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Bisong, Ekaba. "Convolutional Neural Networks (CNN)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 423–41. Berkeley, CA: 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, 489–504. Singapore: 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, 185–97. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6290-5_10.

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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, 67–80. Cham: 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, 197–208. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8391-9_16.

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Repala, Vamshi Krishna, and Shiv Ram Dubey. "Dual CNN Models for Unsupervised Monocular Depth Estimation." In Lecture Notes in Computer Science, 209–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34869-4_23.

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Leo, Marco, Pierluigi Cacagnì, Luca Signore, Giulio Benincasa, Mikko O. Laukkanen, and Cosimo Distante. "Improving Colon Carcinoma Grading by Advanced CNN Models." In Image Analysis and Processing – ICIAP 2022, 233–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06427-2_20.

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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, 31–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7402-1_3.

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Ravikumaran, P., K. Vimala Devi, and K. Valarmathi. "Smart Diabetes System Using CNN in Health Data Analytics." In Object Detection with Deep Learning Models, 137–63. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736-8.

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Hussain, Abrar, Golriz Hosseinimanesh, Samaneh Naeimabadi, Nayem Al Kayed, and Romana Alam. "WearMask in COVID-19: Identification of Wearing Facemask Based on Using CNN Model and Pre-trained CNN Models." In Lecture Notes in Networks and Systems, 588–601. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82199-9_40.

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Conference papers on the topic "CNN MODELS"

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Chen, Hesen, Jingyu Wang, Qi Qi, Yujian Li, and Haifeng Sun. "Bilinear CNN Models for Food Recognition." In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2017. http://dx.doi.org/10.1109/dicta.2017.8227411.

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Zhang, Feiyang, Shanglong Yang, Shuaiwei Guo, and Xu Xia. "Lymphoma recognition based on CNN models." In 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, edited by Wei Qin. SPIE, 2021. http://dx.doi.org/10.1117/12.2623096.

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Tian, Cuihua, Yiping Zhang, Jingmin Gao, and Zhigang Hu. "Arrhythmia Classification Using 2D-CNN Models." In CCEAI 2022: The 6th International Conference on Control Engineering and Artificial Intelligence. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3522749.3523080.

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Slavova, Angela. "Local activity in reaction-diffusion CNN models." In RENEWABLE ENERGY SOURCES AND TECHNOLOGIES. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5127495.

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Bulus, Ercan. "Gender Determination from Pictures with CNN Models." In 2021 6th International Conference on Computer Science and Engineering (UBMK). IEEE, 2021. http://dx.doi.org/10.1109/ubmk52708.2021.9558915.

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Chuanjie, Zhang, and Zhu Changming. "Facial Expression Recognition Integrating Multiple CNN Models." In 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc51575.2020.9345285.

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Askar, Mariam M., Amgad A. Salama, Hassan M. Elkamchouchi, and Adel M. Al-Fahar. "Breast Cancer Classification Using Various CNN Models." In 2023 International Telecommunications Conference (ITC-Egypt). IEEE, 2023. http://dx.doi.org/10.1109/itc-egypt58155.2023.10206336.

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Rathore, Hemant, Taeeb Bandwala, Sanjay K. Sahay, and Mohit Sewak. "Are CNN based Malware Detection Models Robust?" In SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3485730.3492867.

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Bogar, Shruti Manojkumar, Pranav Deshmukh, Ch Venkata Rami Reddy, and Suneetha Muvva. "Monkeypox Detection using CNN-Based Pretrained Models." In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2023. http://dx.doi.org/10.1109/icaiss58487.2023.10250644.

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Kavitha, S., K. Prakash Kumar, M. Dharshini, and S. Sathyavathi. "Medical Mask Detection Using Various CNN Models." In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2021. http://dx.doi.org/10.1109/icaeca52838.2021.9675506.

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Reports on the topic "CNN MODELS"

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Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 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 analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
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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, February 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 that a Melitz general equilibrium is the solution to a global, cost-minimizing problem. This suggests that envelope theorems can be used in interpreting results from a Melitz model. Next we explain the Balistreri-Rutherford decomposition in which a Melitz general equilibrium model is broken into Melitz sectoral models combined with an Armington general equilibrium model. Balistreri and Rutherford see their decomposition as a basis of an iterative approach for solving Melitz general equilibrium models. We see it as a means for interpreting Melitz results as the outcome of an Armington simulation with additional shocks to productivity and preferences variables. With CGE modelers in mind, we report computational experience in solving a Melitz general equilibrium model using GEMPACK. Key words: Armington, Krugman and Melitz; CGE modelling; international trade. JEL codes: F12; D40; D58; C6
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Spilimbergo, Antonio. Growth and Trade: The North can Lose. Inter-American Development Bank, January 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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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, August 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 (RTI) model’s performance relative to the traditional air TI model. The TI model for Willow Creek performed reasonably well in both the calibration and validation years. The results of the RTI calibration and validation simulations resulted in additional questions related to how best to parameterize this snow model. An RTI parameter sensitivity analysis indicates that the choice of calibration years will have a substantial impact on the parameters and thus the streamflow results. Based on the analysis completed in this study, further refinement and verification of the RTI model calculations are required before an objective comparison with the TI model can be completed.
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Cochrane, John. Can Learnability Save New-Keynesian Models? Cambridge, MA: National Bureau of Economic Research, October 2009. http://dx.doi.org/10.3386/w15459.

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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, January 2018. http://dx.doi.org/10.7546/crabs.2018.02.03.

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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, February 2018. http://dx.doi.org/10.7546/grabs2018.2.03.

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Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, May 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.

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The COVID-19 pandemic has accelerated research worldwide and resulted in a large number of computational models and initiatives. Models were mostly aimed at forecast and resulted in different predictions partially since models were based on different assumptions. In fact the idea that a computational model is just an assumption attempting to explain a phenomenon has not been sufficiently explored. Moreover, the ability to combine models has not been fully realized. The Reference Model for disease progression was performing this task for years for diabetes models and recently started modeling COVID-19. The Reference Model is an ensemble of models that is optimized to fit observed disease phenomenon. The ensemble has the ability to include model components from different sources that compete and cooperate. The recent advance in this model is the ability to include models calculated in different scales, making the model the first known multi-scale ensemble model. This manuscript will review these capabilities and show how multiple models can improve our ability to comprehend the COVID-19 pandemic.
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Francois, Joseph. Scale Economies and Imperfect Competition in the GTAP Model. GTAP Technical Paper, September 2000. http://dx.doi.org/10.21642/gtap.tp14.

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The universe of existing CGE models can be divided into 3 broad categories. The first class of models (of which the standard GTAP model is a classic example) emphasizes the static effects of policy related to general equilibrium resource reallocation. The second involves scale economics and imperfect competition and the third involves dynamic accumulation effects. Development of the second class of models has followed a long period during which many of the basic tenants of modern industrial organization theory were integrated into the core of mainstream trade theory. The resulting class of applied models emphasizes procompetitive effects. This paper presents techniques for the incorporation of several stylized representations of scale economies and imperfect competition into the GTAP modeling framework. A numerical example is also provided. Technical Paper Number 14 can be downloaded in PDF format. To print this you will need the Adobe Acrobat Reader. For those interested in replicating the results in this technical paper, an associated zip file [249K] can be downloaded. The zip file includes readme files with detailed instructions.
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Matar, Walid, and Rami Shabaneh. Can Oil Refiners Adjust to a Greater Supply of Shale Oil? King Abdullah Petroleum Studies and Research Center, January 2021. http://dx.doi.org/10.30573/ks--2020-dp27.

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The advent of American shale oil and its prospects for continued production growth have raised concerns about whether oil refineries can handle the increasingly lighter crude oil supply. To provide a perspective on this issue, we run a global oil refining model for the years from 2017 to 2030. The model’s objective is to maximize refining industry profits in eight global regions, taking into account around 100 grades of crude oil.
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