Testing of a Deep Learning Model Providing Monocular Depth Estimation on Mobile Devices via Web Service

2021 ◽  
Author(s):  
Alper Tunga Akin ◽  
Cetin Comert
Keyword(s):  
Deep Learning ◽  
Web Service ◽  
Mobile Devices ◽  
Depth Estimation ◽  
Learning Model ◽  
Monocular Depth ◽  
Deep Learning Model

scholarly journals Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1227
Author(s):  
Seung-Taek Oh ◽  
Deog-Hyeon Ga ◽  
Jae-Hyun Lim
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Mobile Device ◽  
Local Level ◽  
Learning Model ◽  
Learning System ◽  
Ultraviolet Rays ◽  
Optimum Number ◽  
Data Set ◽  
Deep Learning Model

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

Personalized web service recommendation through mishmash technique and deep learning model

2022 ◽  
Author(s):  
S. Ganesh Kumar ◽  
S. S. Sridhar ◽  
Azham Hussain ◽  
S. V. Manikanthan ◽  
T. Padmapriya
Keyword(s):  
Deep Learning ◽  
Web Service ◽  
Learning Model ◽  
Service Recommendation ◽  
Deep Learning Model

scholarly journals Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2364
Author(s):  
Donghee Ha ◽  
Mooseop Kim ◽  
KyeongDeok Moon ◽  
Chi Yoon Jeong
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Network Model ◽  
Mobile Device ◽  
Learning Model ◽  
Selection Method ◽  
Superior Performance ◽  
Processing Unit ◽  
Average Power Consumption ◽  
Deep Learning Model

Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device. Therefore, herein, we propose an acceleration method for on-device learning to mitigate the device heterogeneity. The proposed method efficiently utilizes unified memory for reducing the latency of data transfer during network model training. In addition, we propose the layer-wise processor selection method to consider the latency generated by the difference in the processor performing the forward propagation step and the backpropagation step in the same layer. The experiments were performed on an ODROID-XU4 with the ResNet-18 model, and the experimental results indicate that the proposed method reduces the latency by at most 28.4% compared to the central processing unit (CPU) and at most 21.8% compared to the graphics processing unit (GPU). Through experiments using various batch sizes to measure the average power consumption, we confirmed that device heterogeneity is alleviated by performing on-device learning using the proposed method.

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.

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