scholarly journals When 5G Meets Deep Learning: A Systematic Review

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 208
Author(s):  
Guto Leoni Santos ◽  
Patricia Takako Endo ◽  
Djamel Sadok ◽  
Judith Kelner
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
High Speed ◽  
Vehicular Networks ◽  
Smart Cities ◽  
The Internet ◽  
Battery Life ◽  
Learning Models ◽  
Broadband Internet ◽  
Main Research

This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.

scholarly journals When 5G Meets Deep Learning: A Systematic Review

2020 ◽  
Author(s):  
Guto Leoni Santos ◽  
Patricia Takako Endo ◽  
Djamel Sadok ◽  
Judith Kelner
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
The Internet ◽  
Battery Life ◽  
Correct Operation ◽  
Broadband Internet ◽  
Main Research

This last decade, the amount of data exchanged in the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. But for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include: high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements. Among these we list ultra-densification, millimeter Wave usage, antennas with massive multiple-input multiple-output (MIMO), antenna beamforming to increase spacial diversity, edge/fog computing, among others. Each of these technologies brings its own design issues and challenges. For instance, ultra-densification and MIMO will increase the complexity to estimate channel condition and traditional channel state information (CSI) estimation techniques are no longer suitable due to the complexity of the new scenarios. As a result, new approaches to evaluate network condition such as by continuously collecting and monitoring key performance indicators become necessary. Timely decisions are needed to ensure the correct operation of such network. In this context, deep learning (DL) models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contributions, this paper presents a systematic review about how DL is being applied to solve some 5G issues. We examine data from the last decade and the works that addressed diverse 5G problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using DL models in 5G scenarios and identify several issues that deserve further consideration.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

scholarly journals Automatic image annotation based on deep learning models: a systematic review and future challenges

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Myasar Mundher Adnan ◽  
Mohd Shafry Mohd Rahim ◽  
Amjad Rehman ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Image Annotation ◽  
Automatic Image Annotation ◽  
Learning Models ◽  
Future Challenges

scholarly journals Blockchain and Internet of Things in Higher Education.pdf

2020 ◽  
Author(s):  
Tanweer Alam ◽  
Mohamed Benaida
Keyword(s):  
Internet Of Things ◽  
Mobile Communications ◽  
High Speed ◽  
Wireless Networking ◽  
Research Area ◽  
The Internet ◽  
Web Pages ◽  
Main Research ◽  
Iot Platform ◽  
The Internet Of Things

Building the innovative blockchain-based architecture across the Internet of Things (IoT) platform for the education system could be an enticing mechanism to boost communication efficiency within the 5 G network. Wireless networking would have been the main research area allowing people to communicate without using the wires. It was established at the start of the Internet by retrieving the web pages to connect from one computer to another computer Moreover, high-speed, intelligent, powerful networks with numerous contemporary technologies, such as low power consumption, and so on, appear to be available in today's world to connect among each other. The extension of fog features on physical things under IoT is allowed in this situation. One of the complex tasks throughout the area of mobile communications would be to design a new virtualization framework based on blockchain across the Internet of Things architecture. The goal of this research is to connect a new study for an educational system that contains Blockchain to the internet of things or keeping things cryptographically secure on the internet. This research combines with its improved blockchain and IoT to create an efficient interaction system between students, teachers, employers, developers, facilitators and accreditors on the Internet. This specified framework is detailed research's great estimation.

scholarly journals Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 73
Author(s):  
Kuldoshbay Avazov ◽  
Mukhriddin Mukhiddinov ◽  
Fazliddin Makhmudov ◽  
Young Im Cho
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
High Speed ◽  
Data Augmentation ◽  
Smart Cities ◽  
Fire Detection ◽  
Training Dataset ◽  
Digital Cameras ◽  
Augmentation Techniques ◽  
Fire Accidents

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

scholarly journals A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
G. Kothai ◽  
E. Poovammal ◽  
Gaurav Dhiman ◽  
Kadiyala Ramana ◽  
Ashutosh Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dynamic Behavior ◽  
Traffic Congestion ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Experimental Result ◽  
Learning Models ◽  
Essential Information ◽  
Proposed Model

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

scholarly journals Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sergei Bedrikovetski ◽  
Nagendra N. Dudi-Venkata ◽  
Hidde M. Kroon ◽  
Warren Seow ◽  
Ryash Vather ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Learning Models ◽  
Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

scholarly journals Deep Learning-Based Models for Pain Recognition: A Systematic Review

2020 ◽  
Author(s):  
Rasha M. Al-Eidan ◽  
Hend Al-Khalifa ◽  
AbdulMalik Alsalman
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Data Sets ◽  
Observer Variability ◽  
Learning Models ◽  
Learning Methods ◽  
Inter Observer Variability ◽  
Recognition Systems ◽  
Pain Recognition ◽  
Open Issues

The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.

scholarly journals An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021 ◽  
Vol 11 (5) ◽  
pp. 2196
Author(s):  
Andrea Tundis ◽  
Gaurav Mukherjee ◽  
Max Mühlhäuser
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Communication Systems ◽  
The Internet ◽  
Learning Models ◽  
Specific Content ◽  
Character Identification ◽  
Code Language ◽  
Mixed Code

Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

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