The Development and Application of Real-Time Deep Learning Models to Drive Directional Drilling Efficiency

2020 ◽  
Author(s):  
Dingzhou Cao ◽  
Don Hender ◽  
Sam Ariabod ◽  
Chris James ◽  
Yuxing Ben ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Directional Drilling ◽  
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

Ransomware Traffic Classification Using Deep Learning Models

2020 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Arivudainambi D. ◽  
Varun Kumar K.A. ◽  
Vinoth Kumar R. ◽  
Visu P.
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Classification Model ◽  
Traffic Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Novel Method

Ransomware is a malware which affects the systems data with modern encryption techniques, and the data is recovered once a ransom amount is paid. In this research, the authors show how ransomware propagates and infects devices. Live traffic classifications of ransomware have been meticulously analyzed. Further, a novel method for the classification of ransomware traffic by using deep learning methods is presented. Based on classification, the detection of ransomware is approached with the characteristics of the network traffic and its communications. In more detail, the behavior of popular ransomware, Crypto Wall, is analyzed and based on this knowledge, a real-time ransomware live traffic classification model is proposed.

Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter

2021 ◽  
pp. 396-408
Author(s):  
Marc-André Kaufhold ◽  
Markus Bayer ◽  
Daniel Hartung ◽  
Christian Reuter
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Credibility Assessment ◽  
Learning Models

scholarly journals Monitoring global trends in Covid-19 vaccination intention and confidence: a social media-based deep learning study

2021 ◽  
Author(s):  
Xinyu Zhou ◽  
Alex de Figueiredo ◽  
Qin Xu ◽  
Leesa Lin ◽  
Per E Kummervold ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Eastern Mediterranean ◽  
Learning Models ◽  
Social Media Data ◽  
Emerging Trends ◽  
The Us ◽  
Media Monitoring ◽  
Media Data

AbstractBackgroundThis study developed deep learning models to monitor global intention and confidence of Covid-19 vaccination in real time.MethodsWe collected 6.73 million English tweets regarding Covid-19 vaccination globally from January 2020 to February 2021. Fine-tuned Transformer-based deep learning models were used to classify tweets in real time as they relate to Covid-19 vaccination intention and confidence. Temporal and spatial trends were performed to map the global prevalence of Covid-19 vaccination intention and confidence, and public engagement on social media was analyzed.FindingsGlobally, the proportion of tweets indicating intent to accept Covid-19 vaccination declined from 64.49% on March to 39.54% on September 2020, and then began to recover, reaching 52.56% in early 2021. This recovery in vaccine acceptance was largely driven by the US and European region, whereas other regions experienced the declining trends in 2020. Intent to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia, Eastern Mediterranean, and Western Pacific regions, but low in American, European, and African regions. 12.71% tweets expressed misinformation or rumors in South Korea, 14.04% expressed distrust in government in the US, and 16.16% expressed Covid-19 vaccine being unsafe in Greece, ranking first globally. Negative tweets, especially misinformation or rumors, were more engaged by twitters with fewer followers than positive tweets.InterpretationThis global real-time surveillance study highlights the importance of deep learning based social media monitoring to detect emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions.FundingNational Natural Science Foundation of China.Research in contextEvidence before this studyWith COVID-19 vaccine rollout, each country should investigate its vaccination intention in local contexts to ensure massive vaccination. We searched PubMed for all articles/preprints until April 9, 2021 with the keywords “(“Covid-19 vaccines”[Mesh] OR Covid-19 vaccin*[TI]) AND (confidence[TI] OR hesitancy[TI] OR acceptance[TI] OR intention[TI])”. We identified more than 100 studies, most of which are country-level cross-sectional surveys, and the largest global survey of Covid-19 vaccine acceptance only covered 32 countries to date. However, how Covid-19 vaccination intention changes over time remain unknown, and many countries are not covered in previous surveys yet. A few studies assessed public sentiments towards Covid-19 vaccination using social media data, but only targeting limited geographical areas. There is a lack of real-time surveillance, and no study to date has globally monitored Covid-19 vaccination intention in real time.Added value of this studyTo our knowledge, this is the largest global monitoring study of Covid-19 vaccination intention and confidence with social media data in over 100 countries from the beginning of the pandemic to February 2021. This study developed deep learning models by fine-tuning a Bidirectional Encoder Representation from Transformer (BERT)-based model with 8000 manually-classified tweets, which can be used to monitor Covid-19 vaccination beliefs using social media data in real time. It achieves temporal and spatial analyses of the evolving beliefs to Covid-19 vaccines across the world, and also an insight for many countries not yet covered in previous surveys. This study highlights that the intention to accept Covid-19 vaccination have experienced a declining trend since the beginning of the pandemic in all world regions, with some regions recovering recently, though not to their original levels. This recovery was largely driven by the US and European region (EUR), whereas other regions experienced the declining trends in 2020. Intention to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia region (SEAR), Eastern Mediterranean region (EMR), and Western Pacific region (WPR), but low in American region (AMR), EUR, and African region (AFR). Many AFR countries worried more about vaccine effectiveness, while EUR, AMR, and WPR concerned more about vaccine safety (the most concerns with 16.16% in Greece). Online misinformation or rumors were widespread in AMR, EUR, and South Korea (12.71%, ranks first globally), and distrust in government was more prevalent in AMR (14.04% in the US, ranks first globally). Our findings can be used as a reference point for survey data on a single country in the future, and inform timely and specific interventions for each country to address Covid-19 vaccine hesitancy.Implications of all the available evidenceThis global real-time surveillance study highlights the importance of deep learning based social media monitoring as a quick and effective method for detecting emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions, especially in settings with limited sources and urgent timelines. Future research should build multilingual deep learning models and monitor Covid-19 vaccination intention and confidence in real time with data from multiple social media platforms.

scholarly journals Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

2021 ◽  
Author(s):  
Roberto Bentivoglio ◽  
Elvin Isufi ◽  
Sebastian Nicolaas Jonkman ◽  
Riccardo Taormina
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Case Studies ◽  
Flood Risk ◽  
Numerical Models ◽  
Flood Mapping ◽  
Future Research ◽  
Learning Models ◽  
Research Directions ◽  
Future Research Directions

Abstract. Deep Learning techniques have been increasingly used in flood risk management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 45 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Traditional models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies and sites. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Neural operators can also speed up numerical models while preserving the underlying physical equations and could thus be used for reliable real-time warning. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes.

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