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Academic literature on the topic 'CNN MODEL'

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Published: 11 September 2023

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Journal articles on the topic "CNN MODEL"

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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 (December 31, 2019): 489–94. http://dx.doi.org/10.5373/jardcs/v11sp12/20193243.

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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 (July 5, 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 image data outside the dataset obtains an accuracy of 95 %. The designed image processing method can be applied to identify and classify disease images on grape leaves.
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Choi, Jiwoo, Sangil Choi, and Taewon Kang. "Personal Identification CNN Model using Gait Cycle." Journal of Korean Institute of Information Technology 20, no. 11 (November 30, 2022): 127–36. http://dx.doi.org/10.14801/jkiit.2022.20.11.127.

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Sen, Amit Prakash, Nirmal Kumar Rout, Tuhinansu Pradhan, and Amrit Mukherjee. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal Of Science And Technology 15, no. 41 (November 5, 2022): 2121–28. http://dx.doi.org/10.17485/ijst/v15i41.1421.

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Vyshnavi, Ramineni, and Goo-Rak Kwon. "A Comparative Study of the CNN Model for AD Diagnosis." Korean Institute of Smart Media 12, no. 7 (August 31, 2023): 52–58. http://dx.doi.org/10.30693/smj.2023.12.7.52.

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Alzheimer’s disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.
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Tajalsir, Mohammed, Susana Mu˜noz Hern´andez, and Fatima Abdalbagi Mohammed. "ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model." Signal & Image Processing : An International Journal 13, no. 1 (February 28, 2022): 45–53. http://dx.doi.org/10.5121/sipij.2022.13104.

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When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy.
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Et. al., Ms K. N. Rode,. "Unsupervised CNN model for Sclerosis Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2577–83. http://dx.doi.org/10.17762/turcomat.v12i2.2223.

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Sclerosis detection using brain magnetic resonant imaging (MRI) im-ages is challenging task. With the promising results for variety of ap-plications in terms of classification accuracy using of deep neural net-work models, one can use such models for sclerosis detection. The fea-tures associated with sclerosis is important factor which is highlighted with contrast lesion in brain MRI images. The sclerosis classification initially can be considered as binary task in which the sclerosis seg-mentation can be avoided for reduced complexity of the model. The sclerosis lesion show considerable impact on the features extracted us-ing convolution process in convolution neural network models. The images are used to train the convolutional neural network composed of 35 layers for the classification of sclerosis and normal images of brain MRI. The 35 layers are composed of combination of convolutional lay-ers, Maxpooling layers and Upscaling layers. The results are com-pared with VGG16 model and results are found satisfactory and about 92% accuracy is seen for validation set.
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Kamundala, Espoir K., and Chang Hoon Kim. "CNN Model to Classify Malware Using Image Feature." KIISE Transactions on Computing Practices 24, no. 5 (May 31, 2018): 256–61. http://dx.doi.org/10.5626/ktcp.2018.24.5.256.

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

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

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Dissertations / Theses on the topic "CNN MODEL"

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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 recognition tasks. Because scene images often contain multiple objects, there may be more useful local semantic information in the convolutional layers of the network, which may be lost in the full connected layers. Therefore, this paper improved the traditional architecture of CNN, adopted the existing improvement which enhanced the convolution layer information, and extracted it using Fisher Vector. Then this thesis introduced the idea of transfer learning, and tried to introduce the knowledge of two different fields, which are scene and object. We combined the output of these two networks to achieve better results. Finally, this thesis implemented the method using Python and PyTorch. This thesis applied the method to two famous scene datasets. the UIUC-Sports and Scene-15 datasets. Compared with traditional CNN AlexNet architecture, we improve the result from 81% to 93% in UIUC-Sports, and from 79% to 91% in Scene- 15. It shows that our method has good performance on scene recognition tasks.
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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 with examples for the project. Based on that, we annotated the final corpus. To find the most suitable neural networks model for our task, we experimented with different neural networks architectures, namely long short-term memory (LSTM), bidirectional LSTM and CNN-BiLSTM models. Moreover, we experimented with randomly initialized word embeddings that were trained during the training process and pretrained word embeddings for contemporary Czech published as open source by fastText. We achieved the best result F1 score 0.444 using CNN-BiLSTM model and the pretrained word embeddings by fastText. We found out that we do not need to normalize spelling of our historical texts to get closer to contemporary language if we use the neural network model. We provided a qualitative analysis of observed linguistics phenomena as well. We found out that some word forms and pair of words which were not frequent in our training data set were miss-tagged or not tagged at all. Based on that, we can say that larger data sets could improve the results.
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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-generated and human-written movie reviews, we are able to take GLTR a step further and use it to automatically perform this classification. However, using a CNN with GLTR as the main source of data for classification does not appear to be enough to be on par with the best existing approaches.
I och med de senaste framstegen inom maskininlärning kan datorer skapa mer och mer övertygande text, vilket skapar en oro för ökad falsk information på internet. Samtidigt vägs detta upp genom att forskare skapar verktyg för att identifiera datorgenererad text. Forskare har kunnat utnyttja svagheter i neurala språkmodeller och använda dessa mot dem. Till exempel tillhandahåller GLTR användare en visuell representation av texter, som hjälp för att klassificera dessa som människo- skrivna eller maskingenererade. Genom att träna ett faltningsnätverk (convolutional neural network, eller CNN) på utdata från GLTR-analys av maskingenererade och människoskrivna filmrecensioner, tar vi GLTR ett steg längre och använder det för att genomföra klassifikationen automatiskt. Emellertid tycks det ej vara tillräckligt att använda en CNN med GLTR som huvuddatakälla för att klassificera på en nivå som är jämförbar med de bästa existerande metoderna.
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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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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 on 20 chosen travel stories, picked by their relevancy, diversity, and their expressive tone. Due to the limited space and the specific textual method, the analysis was restricted to the editorial texts of these stories. The chosen method was discourse analyst Norman Fairclough ́s model of evaluation, which revealed the explicit and implicit ways the media texts suggest desired characteristics. These linguistic devices took the readers ́ agreement for granted, as they imposed a shared cultural ground with common values, which is a base for a mutual understanding. After identifying the explicit and implicit evaluations, they were organized according to some major discursive themes found in the texts, and finally analyzed in order to expose their underlying values. The results showed how these certain values brought forth certain ideologies, to some extent in keeping with recent research of tourism and travel journalism. As the study has been put into a larger context of related research, the following pages will first explain some larger concepts of discourse analysis, such as representation, cultural stereotypes, ideology and power. A cross-section from older to more contemporary theories in culture studies has been utilized; moving from Edward Said ́s postcolonial classic Orientalism, an example of cultural stereotyping, to the more recent topics of ‘promotion culture’ and consumerism, and tourism researcher John Urry ́s ideas about the consumption of places and the ‘tourist gaze.’ In the end, the study considers what kind of power does travel journalism possess over the represented tourism destinations. Finally, when questioning the travel journalists ́ legitimacy and power to represent the travel destinations, poststructuralist Michel Foucault ́s theory about the ‘regime of truth,’ as well as Antonio Gramsci ́s ideas of ‘hegemony,’ theory of dominance through consent, were discussed and confirmed.
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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 image and text data from the reports. Themodel presented is a late-fusion model consisting of a Swedish BERTtext classifier and a VGG16 for image classification. The results showed that an automated classifier is feasible for thistask and could be used in real life to make the classification taskmore time and cost-efficient. The model scored a 66.2% accuracy and89.7% top-5 accuracy on the task and the experiments revealed someareas of improvement on the data and model that could be furtherexplored to potentially improve the performance.
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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 (hardstructure areas) and cracks (no material areas). The actual situation is that all the cuts with knots and cracks are eliminated and just the clean cuts are used, therefore estimating the relative wood hardness by identifying the knots and cracks beforehand can significantly help to automate the process of testing the chain properties, saving time and material and give a better understanding of cutting wood logs to improve chains quality.Many studies have been done to develop methods to analyze and measure different features of an end face. This thesis work is carried out to evaluate a machinelearning model to detect knots and cracks on end faces and to understand their impact on the average cutting efficiency. Mask R-CNN is widely used for instance segmentation and in this thesis work, Mask R-CNN is evaluated to detect and segment knots and cracks on an end face. Methods are also developed to estimatepith’s vertical position from the wood image and generate average cutting efficiency graph based on knot’s and crack’s percentage at each vertical position of wood image.
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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 experiments, and confusion matrices which confirm the good accuracy scores, without an expensive Deep Learning approach. In particular, I used a 1D CNN. I also emphasize that classification appears to be the best approach to address this type of prediction in combination with proper management of unbalanced class weights. In fact, it is standard having a problem of unbalanced class weights, otherwise the model will react for inconsistent trend movements. Finally I proposed a Framework which can be used also for other fields which allows to exploit the presence of the Experts of the sector and combining this information with ML/DL approaches.
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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 project has also investigated if the data from the deep neural network can be connected to a biomechanical modelling software, AnyBody, for further analysis. The final intention of this project was to analyze how accurate such a combination could be in golf swing analysis. The accuracy of the deep neural network has been evaluated with three parameters: marker position, angular velocity and kinetic energy for different segments of the human body. MargiPose delivers results with high accuracy (Mean Per Joint Position Error (MPJPE) = 1.52 cm) for a simpler movement but for a more advanced motion such as a golf swing, MargiPose achieves less accuracy in marker distance (MPJPE = 3.47 cm). The mean difference in angular velocity shows that MargiPose has difficulties following segments that are occluded or has a greater motion, such as the wrists in a golf swing where they both move fast and are occluded by other body segments. The conclusion of this research is that it is possible to connect data from a trained CNN with a biomechanical modelling software. The accuracy of the network is highly dependent on the intention of the data. For the purpose of golf swing analysis, this could be a great and cost-effective solution which could enable motion analysis for professionals but also for interested beginners. MargiPose shows a high accuracy when evaluating simple movements. However, when using it with the intention of analyzing a golf swing in i biomechanical modelling software, the outcome might be beyond the bounds of reliable results.
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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 identification process. The method used in this thesis is a Convolutional Neural Network(CNN) approach using the concept of transfer learning. 133 substations in Oslo are used in this case study to design a machine learning model that can identify a substation as night setback or non-night setback series. The results show that the proposed method can classify the substations with approximately 97% accuracy and 91% F1-score. This shows that the proposed method has a high potential to be deployed and used in practice to identify a night setback control in district heating substations.
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Books on the topic "CNN MODEL"

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

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

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

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The can do workplace: A strength-based model for nonprofits. Melbourne, Florida: Motivational Press, 2015.

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Sutherland, H. Constructing a tax-benefit model: What advice can one give? London: 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. London: Bank of England, 2004.

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Book chapters on the topic "CNN MODEL"

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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, 47–58. Singapore: 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, 540–49. Cham: 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, 191–202. Singapore: 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, 133–43. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6068-0_13.

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Chen, Xutong. "CNN Model Optimization Cheme and Applications." In Lecture Notes in Electrical Engineering, 1771–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5959-4_216.

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Goswami, Tilottama, and Shashidhar Reddy Javaji. "CNN Model for American Sign Language Recognition." In Lecture Notes in Electrical Engineering, 55–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_6.

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Zhang, Ru, Hao Dong, Zhen Yang, Wenbo Ying, and Jianyi Liu. "A CNN Based Visual Audio Steganography Model." In Lecture Notes in Computer Science, 431–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06794-5_35.

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Sakshi, Chetan Sharma, and Vinay Kukreja. "CNN-Based Handwritten Mathematical Symbol Recognition Model." In Cyber Intelligence and Information Retrieval, 407–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4284-5_35.

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Das, Parimita, Dipak Kumar Sahoo, and Biswa Mohan Acharya. "Environmental Pollution Detection Mechanism Using CNN Model." In Lecture Notes in Networks and Systems, 476–82. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6_45.

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Kolla, Morarjee, and T. Venugopal. "Diabetic Retinopathy Classification Using Lightweight CNN Model." In Lecture Notes in Electrical Engineering, 1263–69. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7985-8_131.

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Conference papers on the topic "CNN MODEL"

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Ben Alaya, Karim, and Laszlo Czuni. "CNN-based Tree Model Extraction." In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2021. http://dx.doi.org/10.1109/idaacs53288.2021.9660841.

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Tambi, Ritiz, Paul Li, and Jun Yang. "An efficient CNN model for transportation mode sensing." In SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274783.3275160.

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Nagy, Zoltan, Laszlo Kek, Zoltan Kincses, and Peter Szolgay. "CNN model on cell multiprocessor array." In 2007 European Conference on Circuit Theory and Design (ECCTD 2007). IEEE, 2007. http://dx.doi.org/10.1109/ecctd.2007.4529590.

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Furedi, Laszlo, and Peter Szolgay. "CNN model on stream processing platform." In 2009 European Conference on Circuit Theory and Design (ECCTD 2009). IEEE, 2009. http://dx.doi.org/10.1109/ecctd.2009.5275115.

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Sun, Yuxuan, Jining Xie, Pujie Li, and Bowei Sun. "BLSTM-CNN Relationship Classification Network Model." In 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, 2021. http://dx.doi.org/10.1109/iceiec51955.2021.9463812.

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Diana, Mery, Juntaro Chikama, Motoki Amagasaki, Masahiro Iida, and Morihiro Kuga. "Characteristic Similarity Using Classical CNN Model." In 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2019. http://dx.doi.org/10.1109/itc-cscc.2019.8793442.

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Szolgay, Peter, and Zoltan Nagy. "A CNN motivated array computing model." In 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010). IEEE, 2010. http://dx.doi.org/10.1109/cnna.2010.5430341.

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Slavova, Angela. "Memristor CNN Model for Image Denoising." In 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2019. http://dx.doi.org/10.1109/icecs46596.2019.8964780.

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Tan, Jiaxing, Yongfeng Gao, Weiguo Cao, Marc Pomeroy, Shu Zhang, Yumei Huo, Lihong Li, and Zhengrong Liang. "GLCM-CNN: Gray Level Co-occurrence Matrix based CNN Model for Polyp Diagnosis." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834585.

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Zhang, Chenkai, Yuki Okafuji, and Takahiro Wada. "Evaluation of visualization performance of CNN models using driver model." In 2021 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf49454.2021.9382776.

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Reports on the topic "CNN MODEL"

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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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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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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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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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Novy-Marx, Robert. How Can a Q-Theoretic Model Price Momentum? Cambridge, MA: National Bureau of Economic Research, February 2015. http://dx.doi.org/10.3386/w20985.

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Engel, Charles. Can the Markov Switching Model Forecast Exchange Rates? Cambridge, MA: National Bureau of Economic Research, November 1992. http://dx.doi.org/10.3386/w4210.

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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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de Miguel Beriain, Iñigo, Aliuska Duardo Sánchez, and José Antonio Castillo Parrilla. What Can We Do with the Data of Deceased People? A Normative Proposal. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64580.

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The health and genetic data of deceased people are a particularly important asset in the field of biomedical research. However, in practice, using them is compli- cated, as the legal framework that should regulate their use has not been fully developed yet. The General Data Protection Regulation (GDPR) is not applicable to such data and the Member States have not been able to agree on an alternative regulation. Recently, normative models have been proposed in an attempt to face this issue. The most well- known of these is posthumous medical data donation (PMDD). This proposal supports an opt-in donation system of health data for research purposes. In this article, we argue that PMDD is not a useful model for addressing the issue at hand, as it does not consider that some of these data (the genetic data) may be the personal data of the living relatives of the deceased. Furthermore, we find the reasons supporting an opt-in model less convincing than those that vouch for alternative systems. Indeed, we propose a normative framework that is based on the opt-out system for non-personal data combined with the application of the GDPR to the relatives’ personal data.
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Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40185.

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Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolution elevation models of canopy. The identification and mapping of canopy cover and micro-terrain features in areas of sparse vegetation is demonstrated with an elevation model for a region of western Montana, using algorithms for breaklines, elevation differencing, slope, terrain ruggedness, and breakline gradient direction. These algorithms were created at the U.S. Army Engineer Research Center – Geospatial Research Laboratory (ERDC-GRL) and can be accessed through an in-house tool constructed in the ENVI/IDL environment. After breakline processing, products from these algorithms are brought into a Geographic Information System as analytical layers and applied to a mobility routing model, demonstrating the effect of breaklines as obstacles in the calculation of optimal, off-road routes. Elevation model breakline analysis can serve as significant added value to micro-terrain feature and canopy mapping, obstacle identification, and route planning.
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