Automatic Event Detection in Smart Cities Using Big Data Analytics

Suma, Sugimiyanto; Mehmood, Rashid; Albeshri, Aiiad

doi:10.1007/978-3-319-94180-6_13

Cited by 29 publications

(21 citation statements)

References 21 publications

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“…The challenges in this respect include management, integration, and distributed computation of data including the difficulties related to managing the 4V characteristics of big data, i.e., volume, velocity, variety, and veracity. There have been some works on the use of big data platforms in Twitter data analytics in various languages but in different application domains [18][19][20][21][22][23]37,78,79]. To the best of our knowledge, no work has been reported that uses big data technologies for data analytics in healthcare using tweets in the Arabic language.…”

Section: Research Gapmentioning

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: Research Gapmentioning

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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“…Recently, smart city is a prospective research field to solve various urban problems [1,2] including city sustainability issues [3,4]. Context-awareness for cities is a basis of smart city applications and intelligent urban services [1,5,6]. Most of the smart city applications have been focused on urban ecology issues [3,4] and infrastructure management [1,7].…”

Section: Introductionmentioning

confidence: 99%

“…To solve the human-centric urban problems, we need contextual information intimately connected to citizens' lives. To extract the information, various studies have been conducted on discovering social issues mainly from social media [1,5,6,9]. The social issues vary from citizens' mutual interest (e.g., iconic movie directors or big sports games) to serious social problems (e.g., housing shortage and real estate bubble).…”

Section: Introductionmentioning

confidence: 99%

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

et al. 2020

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This study aims at discovering social desires and conflicts from subculture narrative multimedia. Since one of the primary purposes in the subculture consumption is vicarious satisfaction, the subculture works straightforwardly describe what their readers want to achieve and break down. The latent desires and conflicts are useful for understanding our society and realizing smart governance. To discover the social issues, we concentrate on that each subculture genre has a unique imaginary world that consists of inventive subjects. We suppose that the subjects correspond to individual social issues. For example, game fiction, one of the popular genres, describes a world like video games. Under game systems, everyone gets the same results for the same efforts, and it can be interpreted as critics for the social inequality issue. Therefore, we first extract subjects of genres and measure the membership degrees of subculture works for each genre. Using the subjects and membership degrees, we build a genealogy tree of subculture genres by tracing their evolution and differentiation. Then, we extract social issues by searching for the subjects that come from the real world, not imaginary. If a subculture work criticizes authoritarianism, it might include subjects such as government officials and bureaucrats. A combination of the social issues and genre genealogy tree will show diachronic changes in our society. We have evaluated the proposed methods by extracting social issues reflected in Korean web novels.

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“…The penetration of these technologies to all spheres of everyday life has given rise to the smart infrastructure developments; smart transportation infrastructure is at the forefront of these developments [1,32,33,34,35,36]. The use of GPS devices and mobile signals to collect vehicle location and congestion data [37]; the use of big data [38,39,40] and high performance computing (HPC) [38,40,41,42] technologies; mobile, cloud and fog computing [37,43,44,45,46]; image processing and artificial intelligence (AI) for traffic analysis [47]; urban logistics prototyping [48]; vehicular ad hoc networks [44,49,50,51,52]; autonomous driving [47]; autonomic transportation systems [53,54,55]; and the use of social media for traffic event detection [56,57,58] are a few examples. There is a need for innovative uses of the cutting-edge technologies in transportation.…”

Section: Introductionmentioning

confidence: 99%

Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs

Aqib

Mehmood

Alzahrani

et al. 2019

Sensors

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Road transportation is the backbone of modern economies, albeit it annually costs 1.25 million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.

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Automatic Event Detection in Smart Cities Using Big Data Analytics

Cited by 29 publications

References 21 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

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs

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