Assessing the Quality of Real-Time Hybrid Simulation Tests with Deep Learning Models

2021 ◽  
pp. 13-22
Author(s):  
Elif Ecem Bas ◽  
Mohamed A. Moustafa
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Hybrid Simulation ◽  
Learning Models

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

2018 ◽  
Author(s):  
Noé Sturm ◽  
Jiangming Sun ◽  
Yves Vandriessche ◽  
Andreas Mayr ◽  
Günter Klambauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Scaffold Hopping ◽  
Learning Models ◽  
Industrial Data ◽  
Structural Descriptors ◽  
Bioactivity Profile ◽  
Machine Learning Models

<div>This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. </div><div>The fingerprint was used to build machine learning models (multi-task deep learning + SVM) for compound activity predictions towards a panel of 131 targets. </div><div>Quality of the predictions and the scaffold hopping potential of the HTSFP were systematically compared to traditional structural descriptors ECFP. </div><div><br></div>

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

scholarly journals Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

2019 ◽  
Vol 1 (1) ◽  
pp. 450-465 ◽  
Author(s):  
Abhishek Sehgal ◽  
Nasser Kehtarnavaz
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Open Source Software ◽  
Mobile Applications ◽  
Network Models ◽  
Software Tools ◽  
Learning Tools ◽  
Smartphone Apps ◽  
Learning Models ◽  
Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

scholarly journals Sa2032 PIXEL VS PATCH-BASED DEEP LEARNING MODELS, PAVING THE WAY TOWARDS REAL-TIME COMPUTER-ASSISSTED DETECTION OF BARRETT’S NEOPLASIA

2020 ◽  
Vol 91 (6) ◽  
pp. AB251
Author(s):  
Mohamed Abdelrahim ◽  
Masahiro Saiko ◽  
Yukiko Masaike ◽  
E.J.A.Z. HOSSAIN ◽  
Sophie Arndtz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Models ◽  
The Way

Deep Learning Models for Real-time Human Activity Recognition with Smartphones

2019 ◽  
Vol 25 (2) ◽  
pp. 743-755 ◽  
Author(s):  
Shaohua Wan ◽  
Lianyong Qi ◽  
Xiaolong Xu ◽  
Chao Tong ◽  
Zonghua Gu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Models

scholarly journals Deep Learning Models Compression for Agricultural Plants

2020 ◽  
Vol 10 (19) ◽  
pp. 6866
Author(s):  
Arnauld Nzegha Fountsop ◽  
Jean Louis Ebongue Kedieng Fendji ◽  
Marcellin Atemkeng
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Resource Limitation ◽  
Learning Models ◽  
Speed Up ◽  
Agricultural Plants ◽  
Remote Computation ◽  
Plant Seedlings ◽  
Considerable Loss

Deep learning has been successfully showing promising results in plant disease detection, fruit counting, yield estimation, and gaining an increasing interest in agriculture. Deep learning models are generally based on several millions of parameters that generate exceptionally large weight matrices. The latter requires large memory and computational power for training, testing, and deploying. Unfortunately, these requirements make it difficult to deploy on low-cost devices with limited resources that are present at the fieldwork. In addition, the lack or the bad quality of connectivity in farms does not allow remote computation. An approach that has been used to save memory and speed up the processing is to compress the models. In this work, we tackle the challenges related to the resource limitation by compressing some state-of-the-art models very often used in image classification. For this we apply model pruning and quantization to LeNet5, VGG16, and AlexNet. Original and compressed models were applied to the benchmark of plant seedling classification (V2 Plant Seedlings Dataset) and Flavia database. Results reveal that it is possible to compress the size of these models by a factor of 38 and to reduce the FLOPs of VGG16 by a factor of 99 without considerable loss of accuracy.

scholarly journals Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

2021 ◽  
Vol 38 (6) ◽  
pp. 1875-1885
Author(s):  
Ruchi Jayaswal ◽  
Manish Dixit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Preventive Measure ◽  
Face Mask ◽  
Histogram Equalization ◽  
Test Accuracy ◽  
The Face ◽  
Novel Coronavirus ◽  
Face Detector

A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

scholarly journals Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

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