TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles

Alam, Furqan; Mehmood, Rashid; Katib, Iyad; Altowaijri, Saleh M.; Albeshri, Aiiad

doi:10.1007/s11036-019-01319-2

Cited by 35 publications

(28 citation statements)

References 72 publications

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“…A range of technologies is contributing to the development of these smart systems. These include the Internet of Things (IoT) [32][33][34][35][36], social media [21][22][23]37,38], big data [39][40][41][42][43][44], high performance computing (HPC) [45][46][47][48], cloud, fog, and edge computing [34,[49][50][51][52], and machine learning [36,53]. The applications include healthcare [34,39,[54][55][56], transportation [57,58], and others [59,60].…”

Section: Literature Reviewmentioning

confidence: 99%

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

et al. 2020

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Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

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Section: Literature Reviewmentioning

confidence: 99%

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

et al. 2020

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“…The devices and sensors generate big data [ 20 , 21 , 22 ] that must be continuously processed and analyzed to make smart decisions and communicated to the IoE devices for actuation and other purposes. A sensor’s data may also be aggregated with other sensors for context-awareness, enhanced decision making, exploratory analyses, cross-sectoral and global optimizations, and other reasons (e.g., see [ 23 ]). The Fog Layer consists of fog nodes placed in various 6G connection providers (e.g., base stations) closer to the edge devices in the IoE Layer.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Deep learning (DL), machine learning (ML), neural network (NN), pattern recognition, computer vision, natural language processing, clustering, etc. are tools that can be used to train computers to accomplish specific tasks such as computer vision and natural language processing (NLP) [ 23 , 24 , 25 ]. AI models usually rely on data to build their knowledge therefore big data and data collected from a huge number of devices and sensors, as in IoE, has provided the fuel for the AI models [ 4 , 20 , 21 , 22 ].…”

Section: Background and Related Workmentioning

confidence: 99%

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Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

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“…Systems for self-driving gain new abilities each year. This involves fusing data from multiple sources as well as forces researchers to come up with new and innovative frameworks for data sharing and analysis among connected vehicles, like for example approach presented by Alam et al in [3]. On the other hand, a key subject in this ecosystem constitute millions of drivers whose state and behavior play an essential role.…”

Section: Introductionmentioning

confidence: 99%

Fast eyes detection in thermal images

Knapik

Cyganek

2020

Multimed Tools Appl

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In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.

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TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles

Cited by 35 publications

References 72 publications

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Fast eyes detection in thermal images

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