Relevant bibliographies by topics / CNN AND LSTM NETWORKS

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Academic literature on the topic 'CNN AND LSTM NETWORKS'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "CNN AND LSTM NETWORKS"

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Garcia, Carlos Iturrino, Francesco Grasso, Antonio Luchetta, Maria Cristina Piccirilli, Libero Paolucci, and Giacomo Talluri. "A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM." Applied Sciences 10, no. 19 (September 27, 2020): 6755. http://dx.doi.org/10.3390/app10196755.

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The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.
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Xu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM." 電腦學刊 34, no. 3 (June 2023): 221–35. http://dx.doi.org/10.53106/199115992023063403016.

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<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject&rsquo;s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p>&nbsp;</p>
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Liu, Tianyuan, Jinsong Bao, Junliang Wang, and Yiming Zhang. "A Hybrid CNN–LSTM Algorithm for Online Defect Recognition of CO2 Welding." Sensors 18, no. 12 (December 10, 2018): 4369. http://dx.doi.org/10.3390/s18124369.

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At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks.
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Geng, Yue, Lingling Su, Yunhong Jia, and Ce Han. "Seismic Events Prediction Using Deep Temporal Convolution Networks." Journal of Electrical and Computer Engineering 2019 (April 2, 2019): 1–14. http://dx.doi.org/10.1155/2019/7343784.

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Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.
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Banda, Anish. "Image Captioning using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2666–69. http://dx.doi.org/10.22214/ijraset.2021.37846.

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Abstract: In the model we proposed, we examine the deep neural networks-based image caption generation technique. We give image as input to the model, the technique give output in three different forms i.e., sentence in three different languages describing the image, mp3 audio file and an image file is also generated. In this model, we use the techniques of both computer vision and natural language processing. We are aiming to develop a model using the techniques of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to build a model to generate a Caption. Target image is compared with the training images, we have a large dataset containing the training images, this is done by convolutional neural network. This model generates a decent description utilizing the trained data. To extract features from images we need encoder, we use CNN as encoder. To decode the description of image generated we use LSTM. To evaluate the accuracy of generated caption we use BLEU metric algorithm. It grades the quality of content generated. Performance is calculated by the standard calculation matrices. Keywords: CNN, RNN, LSTM, BLEU score, encoder, decoder, captions, image description.
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Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.

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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long short term memory) which is a type of RNN (Recurrent Neural Network) together so that using computer vision computer can recognize the context of an image and display it in natural language like English. Gray Scale Image captioning can give captions for both monochrome and color images. Keywords: Image, Caption, Convolutional Neural Networks, Long Short Term Memory, Recurrent Neural Network
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Zhang, Jilin, Lishi Ye, and Yongzeng Lai. "Stock Price Prediction Using CNN-BiLSTM-Attention Model." Mathematics 11, no. 9 (April 23, 2023): 1985. http://dx.doi.org/10.3390/math11091985.

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Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a CNN-BiLSTM-Attention-based model is proposed to boost the accuracy of predicting stock prices and indices. First, the temporal features of sequence data are extracted using a convolutional neural network (CNN) and bi-directional long and short-term memory (BiLSTM) network. Then, an attention mechanism is introduced to fit weight assignments to the information features automatically; and finally, the final prediction results are output through the dense layer. The proposed method was first used to predict the price of the Chinese stock index—the CSI300 index and was found to be more accurate than any of the other three methods—LSTM, CNN-LSTM, CNN-LSTM-Attention. In order to investigate whether the proposed model is robustly effective in predicting stock indices, three other stock indices in China and eight international stock indices were selected to test, and the robust effectiveness of the CNN-BiLSTM-Attention model in predicting stock prices was confirmed. Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases.
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Yang, Xingyu, and Zhongrong Zhang. "A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China." Water 14, no. 15 (July 31, 2022): 2377. http://dx.doi.org/10.3390/w14152377.

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In this study, a deep learning model is proposed to predict groundwater levels. The model is able to accurately complete the prediction task even when the data utilized are insufficient. The hybrid model that we have developed, CNN-LSTM-ML, uses a combined network structure of convolutional neural networks (CNN) and long short-term memory (LSTM) network to extract the time dependence of groundwater level on meteorological factors, and uses a meta-learning algorithm framework to ensure the network’s performance under sample conditions. The study predicts groundwater levels from 66 observation wells in the middle and lower reaches of the Heihe River in arid regions and compares them with other data-driven models. Experiments show that the CNN-LSTM-ML model outperforms other models in terms of prediction accuracy in both the short term (1 month) and long term (12 months). Under the condition that the training data are reduced by 50%, the MAE of the proposed model is 33.6% lower than that of LSTM. The results of ablation experiments show that CNN-LSTM-ML is 26.5% better than the RMSE of the original CNN-LSTM structure. The model provides an effective method for groundwater level prediction and contributes to the sustainable management of water resources in arid regions.
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Sridhar, C., and Aniruddha Kanhe. "Performance Comparison of Various Neural Networks for Speech Recognition." Journal of Physics: Conference Series 2466, no. 1 (March 1, 2023): 012008. http://dx.doi.org/10.1088/1742-6596/2466/1/012008.

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Abstract Speech recognition is a method where an audio signal is translated into text, words, or commands and also tells how the speech is recognized. Recently, many deep learning models have been adopted for automatic speech recognition and proved more effective than traditional machine learning methods like Artificial Neural Networks(ANN). This work examines the efficient learning architectures of features by different deep neural networks. In this paper, five neural network models, namely, CNN, LSTM, Bi-LSTM, GRU, and CONV-LSTM, for the comparative study. We trained the networks using Audio MNIST dataset for three different iterations and evaluated them based on performance metrics. Experimentally, CNN and Conv-LSTM network model consistently offers the best performance based on MFCC Features.
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Xu, Lingfeng, Xiang Chen, Shuai Cao, Xu Zhang, and Xun Chen. "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation." Sensors 18, no. 10 (September 25, 2018): 3226. http://dx.doi.org/10.3390/s18103226.

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To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.
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Dissertations / Theses on the topic "CNN AND LSTM NETWORKS"

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Graffi, Giacomo. "A novel approach for Credit Scoring using Deep Neural Networks with bank transaction data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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With the PSD2 open banking revolution FinTechs obtained a key role in the financial industry. This role implies the inquiry and development of new techniques, products and solutions to compete with other players in this area. The aim of this thesis is to investigate the applicability of the state-of-the-art Deep Learning techniques for Credit Risk Modeling. In order to accomplish it, a PSD2-related synthetic and anonymized dataset has been used to simulate an application process with only one account per user. Firstly, a machine-readable representation of the bank accounts has been created, starting from the raw transactions’ data and scaling the variables using the quantile function. Afterwards, a Deep Neural Network has been created in order to capture the complex relations between the input variables and to extract information from the accounts’ representations. The proposed architecture accomplished the assigned tasks with a Gini index of 0.55, exploiting a Convolutional encoder to extract features from the inputs and a Recurrent decoder to analyze them.
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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.
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Lin, Alvin. "Video Based Automatic Speech Recognition Using Neural Networks." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2343.

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Neural network approaches have become popular in the field of automatic speech recognition (ASR). Most ASR methods use audio data to classify words. Lip reading ASR techniques utilize only video data, which compensates for noisy environments where audio may be compromised. A comprehensive approach, including the vetting of datasets and development of a preprocessing chain, to video-based ASR is developed. This approach will be based on neural networks, namely 3D convolutional neural networks (3D-CNN) and Long short-term memory (LSTM). These types of neural networks are designed to take in temporal data such as videos. Various combinations of different neural network architecture and preprocessing techniques are explored. The best performing neural network architecture, a CNN with bidirectional LSTM, compares favorably against recent works on video-based ASR.
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BHATT, HARSHIT. "SPEAKER IDENTIFICATION FROM VOICE SIGNALS USING HYBRID NEURAL NETWORK." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18865.

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Identifying the speaker in audio visual environment is a crucial task which is now surfacing in the research domain researchers nowadays are moving towards utilizing deep neural networks to match people with their respective voices the applications of deep learning are many-fold that include the ability to process huge volume of data robust training of algorithms feasibility of optimization and reduced computation time. Previous studies have explored recurrent and convolutional neural network incorporating GRUs, Bi-GRUs, LSTM, Bi-LSTM and many more[1]. This work proposes a hybrid mechanism which consist of an CNN and LSTM network fused using an early fusion method. We accumulated a dataset of 1,330 voices by recording through a python script of length of 3 seconds in .wav format. The dataset consists of 14 categories and we used 80% for training and 20% for testing. We optimized and fine-tuned the neural networks and modified them to yield optimum results. For the early fusion approach, we used the concatenation operation that fuses neural networks prior to the training phase. The proposed method achieves 97.72% accuracy on our dataset and outperforms all existing baseline mechanisms like MLP, LSTM, CNN, and RNN. This research serves as a contribution to the ongoing research in speaker identification domain and paves way to future directions using deep learning.
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Lagerhjelm, Linus. "Extracting Information from Encrypted Data using Deep Neural Networks." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155904.

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In this paper we explore various approaches to using deep neural networks to per- form cryptanalysis, with the ultimate goal of having a deep neural network deci- pher encrypted data. We use long short-term memory networks to try to decipher encrypted text and we use a convolutional neural network to perform classification tasks on encrypted MNIST images. We find that although the network is unable to decipher encrypted data, it is able to perform classification on encrypted data. We also find that the networks performance is depending on what key were used to en- crypt the data. These findings could be valuable for further research into the topic of cryptanalysis using deep neural networks.
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Näslund, Per. "Artificial Neural Networks in Swedish Speech Synthesis." Thesis, KTH, Tal-kommunikation, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239350.

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Text-to-speech (TTS) systems have entered our daily lives in the form of smart assistants and many other applications. Contemporary re- search applies machine learning and artificial neural networks (ANNs) to synthesize speech. It has been shown that these systems outperform the older concatenative and parametric methods. In this paper, ANN-based methods for speech synthesis are ex- plored and one of the methods is implemented for the Swedish lan- guage. The implemented method is dubbed “Tacotron” and is a first step towards end-to-end ANN-based TTS which puts many differ- ent ANN-techniques to work. The resulting system is compared to a parametric TTS through a strength-of-preference test that is carried out with 20 Swedish speaking subjects. A statistically significant pref- erence for the ANN-based TTS is found. Test subjects indicate that the ANN-based TTS performs better than the parametric TTS when it comes to audio quality and naturalness but sometimes lacks in intelli- gibility.
Talsynteser, också kallat TTS (text-to-speech) används i stor utsträckning inom smarta assistenter och många andra applikationer. Samtida forskning applicerar maskininlärning och artificiella neurala nätverk (ANN) för att utföra talsyntes. Det har visats i studier att dessa system presterar bättre än de äldre konkatenativa och parametriska metoderna. I den här rapporten utforskas ANN-baserade TTS-metoder och en av metoderna implementeras för det svenska språket. Den använda metoden kallas “Tacotron” och är ett första steg mot end-to-end TTS baserat på neurala nätverk. Metoden binder samman flertalet olika ANN-tekniker. Det resulterande systemet jämförs med en parametriskt TTS genom ett graderat preferens-test som innefattar 20 svensktalande försökspersoner. En statistiskt säkerställd preferens för det ANN- baserade TTS-systemet fastställs. Försökspersonerna indikerar att det ANN-baserade TTS-systemet presterar bättre än det parametriska när det kommer till ljudkvalitet och naturlighet men visar brister inom tydlighet.
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Evholt, David, and Oscar Larsson. "Generative Adversarial Networks and Natural Language Processing for Macroeconomic Forecasting." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273422.

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Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data.
Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.
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Volný, Miloš. "Využití umělé inteligence jako podpory pro rozhodování v podniku." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2019. http://www.nusl.cz/ntk/nusl-399447.

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This thesis is concerned with future trend prediction on capital markets on the basis of neural networks. Usage of convolutional and recurrent neural networks, Elliott wave theory and scalograms for capital market's future trend prediction is discussed. The aim of this thesis is to propose a novel approach to future trend prediction based on Elliott's wave theory. The proposed approach will be based on the principle of classification of chosen patterns from Elliott's theory by the way of convolutional neural network. To this end scalograms of the chosen Elliott patterns will be created through application of continuous wavelet transform on parts of historical time series of price for chosen stocks.
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Broomé, Sofia. "Objectively recognizing human activity in body-worn sensor data with (more or less) deep neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210243.

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This thesis concerns the application of different artificial neural network architectures on the classification of multivariate accelerometer time series data into activity classes such as sitting, lying down, running, or walking. There is a strong correlation between increased health risks in children and their amount of daily screen time (as reported in questionnaires). The dependency is not clearly understood, as there are no such dependencies reported when the sedentary (idle) time is measured objectively. Consequently, there is an interest from the medical side to be able to perform such objective measurements. To enable large studies the measurement equipment should ideally be low-cost and non-intrusive. The report investigates how well these movement patterns can be distinguished given a certain measurement setup and a certain network structure, and how well the networks generalise to noisier data. Recurrent neural networks are given extra attention among the different networks, since they are considered well suited for data of sequential nature. Close to state-of-the-art results (95% weighted F1-score) are obtained for the tasks with 4 and 5 classes, which is notable since a considerably smaller number of sensors is used than in the previously published results. Another contribution of this thesis is that a new labeled dataset with 12 activity categories is provided, consisting of around 6 hours of recordings, comparable in number of samples to benchmarking datasets. The data collection was made in collaboration with the Department of Public Health at Karolinska Institutet.
Inom ramen för uppsatsen testas hur väl rörelsemönster kan urskiljas ur accelerometerdatamed hjälp av den gren av maskininlärning som kallas djupinlärning; där djupa artificiellaneurala nätverk av noder funktionsapproximerar mappandes från domänen av sensordatatill olika fördefinerade kategorier av aktiviteter så som gång, stående, sittande eller liggande.Det finns ett intresse från den medicinska sidan att kunna mäta fysisk aktivitet objektivt,bland annat eftersom det visats att det finns en korrelation mellan ökade hälsorisker hosbarn och deras mängd daglig skärmtid. Denna typ av mätningar ska helst kunna göras medicke-invasiv utrustning till låg kostnad för att kunna göra större studier.Enklare nätverksarkitekturer samt återimplementeringar av bästa möjliga teknik inomområdet Mänsklig aktivitetsigenkänning (HAR) testas både på ett benchmarkingdataset ochpå egeninhämtad data i samarbete med Institutet för Folkhälsovetenskap på Karolinska Institutetoch resultat redovisas för olika val av möjliga klassificeringar och olika antal dimensionerper mätpunkt. De uppnådda resultaten (95% F1-score) på ett 4- och 5-klass-problem ärjämförbara med de bästa tidigare publicerade resultaten för aktivitetsigenkänning, vilket äranmärkningsvärt då då betydligt färre accelerometrar har använts här än i de åsyftade studierna.Förutom klassificeringsresultaten som redovisas bidrar det här arbetet med ett nyttinhämtat och kategorimärkt dataset; KTH-KI-AA. Det är jämförbart i antal datapunkter medspridda benchmarkingdataset inom HAR-området.
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Chowdhury, Muhammad Iqbal Hasan. "Question-answering on image/video content." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205096/1/Muhammad%20Iqbal%20Hasan_Chowdhury_Thesis.pdf.

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This thesis explores a computer's ability to understand multimodal data where the correspondence between image/video content and natural language text are utilised to answer open-ended natural language questions through question-answering tasks. Static image data consisting of both indoor and outdoor scenes, where complex textual questions are arbitrarily posed to a machine to generate correct answers, was examined. Dynamic videos consisting of both single-camera and multi-camera settings for the exploration of more challenging and unconstrained question-answering tasks were also considered. In exploring these challenges, new deep learning processes were developed to improve a computer's ability to understand and consider multimodal data.
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Books on the topic "CNN AND LSTM NETWORKS"

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Chen, G., Andrew Adamatzky, and Leon O. Chua. Chaos, CNN, Memristors and Beyond: A Festschrift for Leon Chua. World Scientific Publishing Co Pte Ltd, 2013.

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Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing - ebooks Account, 2017.

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Gilbert, Sara. Built for Success: The Story of CNN. Creative Company, The, 2013.

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Yang, Tao. Handbook of CNN Image Processing: All You Need to Know about Cellular Neural Networks (YangSky.com Monographs in Information Sciences). Yang's Scientific Research Institute LLC, 2002.

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Book chapters on the topic "CNN AND LSTM NETWORKS"

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Lamba, Puneet Singh, and Deepali Virmani. "CNN-LSTM-Based Facial Expression Recognition." In Lecture Notes in Networks and Systems, 379–89. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9712-1_32.

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Bhogal, Rosepreet Kaur, and V. Devendran. "Human Activity Recognition Using LSTM with Feature Extraction Through CNN." In Lecture Notes in Networks and Systems, 245–55. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9967-2_24.

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Pravanya, P., K. Lakshmi Priya, S. K. Khamarjaha, K. Buela Likhitha, P. M. Ashok Kumar, and R. Shankar. "Human Activity Recognition Using CNN-Attention-Based LSTM Neural Network." In Intelligent Communication Technologies and Virtual Mobile Networks, 593–605. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1767-9_43.

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Mahalakshmi, G. S., Gokul Sunilkumar, Steven Fredrick Gilbert, and S. Sendhilkumar. "Classification of Family Domain of Amino Acid Sequences Using CNN-LSTM." In Lecture Notes in Networks and Systems, 645–53. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9228-5_55.

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Kim, Tae-Young, and Sung-Bae Cho. "Predicting the Household Power Consumption Using CNN-LSTM Hybrid Networks." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 481–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_50.

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Vakitbilir, Nuray, Adnan Hilal, and Cem Direkoğlu. "Prediction of Daily Solar Irradiation Using CNN and LSTM Networks." In Advances in Intelligent Systems and Computing, 230–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64058-3_28.

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Alam, Jahangir, Abderrahim Fathan, and Woo Hyun Kang. "Text-Independent Speaker Verification Employing CNN-LSTM-TDNN Hybrid Networks." In Speech and Computer, 1–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87802-3_1.

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Shaila, S. G., V. R. Gurudas, K. Hithyshi, M. Mahima, and H. R. PoojaShree. "CNN-LSTM-Based Deep Learning Model for Early Detection of Breast Cancer." In Lecture Notes in Networks and Systems, 83–91. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1559-8_9.

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Bhogal, Rosepreet Kaur, and V. Devendran. "Correction to: Human Activity Recognition Using LSTM with Feature Extraction Through CNN." In Lecture Notes in Networks and Systems, C1. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9967-2_76.

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Gusmanov, Kamill. "CNN LSTM Network Architecture for Modeling Software Reliability." In Software Technology: Methods and Tools, 210–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29852-4_17.

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Conference papers on the topic "CNN AND LSTM NETWORKS"

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Prakash, Satya, Anand Singh Jalal, and Pooja Pathak. "Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India." In 2023 6th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2023. http://dx.doi.org/10.1109/iscon57294.2023.10112065.

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Sejwal, Sahil, Neetu Faujdar, and Shipra Saraswat. "Sentiment Analysis Using Hybrid CNN-LSTM Approach." In 2021 5th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2021. http://dx.doi.org/10.1109/iscon52037.2021.9702449.

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Liu, Han, Donghang Cheng, Xiaojun Sun, and Feng Wang. "Radar emitter recognition based on CNN and LSTM." In 2021 International Conference on Neural Networks, Information and Communication Engineering, edited by Zhiyong Zhang. SPIE, 2021. http://dx.doi.org/10.1117/12.2615142.

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Gupta, Smridhi, Arushi Garg, Vidhi Bishnoi, and Nidhi Goel. "Pulmonary Nodules Binary Classification using CNN and LSTM." In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2023. http://dx.doi.org/10.1109/spin57001.2023.10116430.

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Saroha, Nakul, Mihir Aryan, Mayank Singh, and Anurag Goel. "CNN-LSTM Based Approach for Sleep Apnea Detection." In 2023 6th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2023. http://dx.doi.org/10.1109/iscon57294.2023.10112203.

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Lente, Caio, Roberto Hirata Jr., and Daniel Macêdo Batista. "An Improved Tool for Detection of XSS Attacks by Combining CNN with LSTM." In Anais Estendidos do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbseg_estendido.2021.17333.

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Cross-Site Scripting (XSS) is still a significant threat to web applications. By combining Convolutional Neural Networks (CNN) with Long ShortTerm Memory (LSTM) techniques, researchers have developed a deep learning system called 3C-LSTM that achieves upwards of 99.4% accuracy when predicting whether a new URL corresponds to a benign locator or an XSS attack. This paper improves on 3C-LSTM by proposing different network architectures and validation strategies and identifying the optimal structure for a more efficient, yet similarly accurate, version of 3C-LSTM. The authors identify larger batch sizes, smaller inputs, and cross-validation removal as modifications to achieve a speedup of around 3.9 times in the training step.
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Lente, Caio, Roberto Hirata Jr., and Daniel Macêdo Batista. "An Improved Tool for Detection of XSS Attacks by Combining CNN with LSTM." In Anais Estendidos do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbseg_estendido.2021.17333.

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Cross-Site Scripting (XSS) is still a significant threat to web applications. By combining Convolutional Neural Networks (CNN) with Long ShortTerm Memory (LSTM) techniques, researchers have developed a deep learning system called 3C-LSTM that achieves upwards of 99.4% accuracy when predicting whether a new URL corresponds to a benign locator or an XSS attack. This paper improves on 3C-LSTM by proposing different network architectures and validation strategies and identifying the optimal structure for a more efficient, yet similarly accurate, version of 3C-LSTM. The authors identify larger batch sizes, smaller inputs, and cross-validation removal as modifications to achieve a speedup of around 3.9 times in the training step.
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Guo, Qiutong, Shun Lei, Qing Ye, and Zhiyang Fang. "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534453.

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Singla, Bhavik, Anuj Kumar Jain, Raj Gaurang Tiwari, Vinay Kukreja, and Vikrant Sharma. "Classification Model Using CNN and LSTM for Cow Pregnancy." In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2023. http://dx.doi.org/10.1109/spin57001.2023.10117172.

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Liu, Fan, Xingshe Zhou, Tianben Wang, Jinli Cao, Zhu Wang, Hua Wang, and Yanchun Zhang. "An Attention-based Hybrid LSTM-CNN Model for Arrhythmias Classification." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852037.

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Reports on the topic "CNN AND LSTM NETWORKS"

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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers, and Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1760289.

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Chua, Leon O. Nonlinear Circuits and Neural Networks: Chip Implementation and Applications of the TeraOPS CNN Dynamic Array Supercomputer. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada389212.

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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, June 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards.
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental repairs. Dental arches in detachable prosthodontics may be categorized using Convolutional neural networks (CNN). By properly positioning the teeth, machine learning in CAD/CAM software can reestablish healthy inter-maxillary connections. AI may assist with accurate color matching in challenging cosmetic scenarios that include a single central incisor or many front teeth. Intraoral detectors can identify implant placements in implant prosthodontics and instantly input them into CAD software. The design and execution of dental implants could potentially be improved by utilizing AI.
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