scholarly journals Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7716
Author(s):  
Krzysztof K. Cwalina ◽  
Piotr Rajchowski ◽  
Alicja Olejniczak ◽  
Olga Błaszkiewicz ◽  
Robert Burczyk
Keyword(s):  
Deep Learning ◽  
Interface Design ◽  
Mean Squared Error ◽  
Radio Channel ◽  
Computational Cost ◽  
Urban Environments ◽  
Data Allocation ◽  
Urban Networks ◽  
Radio Interface ◽  
Channel State Estimation

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

scholarly journals StressNet - Deep learning to predict stress with fracture propagation in brittle materials

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Yinan Wang ◽  
Diane Oyen ◽  
Weihong (Grace) Guo ◽  
Anishi Mehta ◽  
Cory Braker Scott ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internal Stress ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Computational Cost ◽  
Brittle Materials ◽  
Fracture Propagation ◽  
Percentage Error ◽  
Internal Stresses ◽  
The Mean

AbstractCatastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2258
Author(s):  
Madhab Raj Joshi ◽  
Lewis Nkenyereye ◽  
Gyanendra Prasad Joshi ◽  
S. M. Riazul Islam ◽  
Mohammad Abdullah-Al-Wadud ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
User Study ◽  
Mean Squared Error ◽  
Color Image ◽  
Machine Learning Techniques ◽  
Global Features ◽  
Black And White ◽  
Historical Images ◽  
Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

Using Deep Learning to Support the User Interface Design of Mobile Applications with App Inventor

2021 ◽  
Author(s):  
Daniel de Souza Baulé ◽  
Christiane Gresse von Wangenheim ◽  
Aldo von Wangenheim ◽  
Jean C. R. Hauck ◽  
Edson C. Vargas Júnior
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Mobile Applications ◽  
Interface Design ◽  
User Interface Design ◽  
App Inventor

Lightweight deep residual network for alzheimer’s disease classification using sMRI slices

2021 ◽  
pp. 1-9
Author(s):  
Yanteng Zhang ◽  
Qizhi Teng ◽  
Linbo Qing ◽  
Yan Liu ◽  
Xiaohai He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Computational Cost ◽  
Disease Classification ◽  
Learning Networks ◽  
Feature Maps ◽  
Medical Field ◽  
Widespread Application ◽  
Healthy Control

Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly.

scholarly journals Accelerating Super-Resolution and Visual Task Analysis in Medical Images

2020 ◽  
Vol 10 (12) ◽  
pp. 4282
Author(s):  
Ghada Zamzmi ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Task Analysis ◽  
Multiple Scales ◽  
Medical Images ◽  
Computational Cost ◽  
Super Resolution ◽  
Visual Task ◽  
Learning Networks ◽  
High Resolution Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

scholarly journals Deep learning-based waste detection in natural and urban environments

2022 ◽  
Vol 138 ◽  
pp. 274-284
Author(s):  
Sylwia Majchrowska ◽  
Agnieszka Mikołajczyk ◽  
Maria Ferlin ◽  
Zuzanna Klawikowska ◽  
Marta A. Plantykow ◽  
...  
Keyword(s):  
Deep Learning ◽  
Urban Environments

scholarly journals Linear Regression Algorithm in Machine Learning through MATLAB

2021 ◽  
Vol 9 (12) ◽  
pp. 989-995
Author(s):  
Kalva Sindhu Priya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Critical Part ◽  
Squared Error ◽  
Master Level ◽  
Predicted Model ◽  
The Given

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

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