A Survey on Big Multimedia Data Processing and Management in Smart Cities

Usman, Muhammad; Jan, Mian Ahmad; He, Xiangjian; Chen, Jinjun

doi:10.1145/3323334

Cited by 37 publications

(16 citation statements)

References 88 publications

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“…Compared with the high research cost of cognitive neuroscience technology, the cost of the questionnaire survey is relatively low, and the sample coverage is relatively large. However, the questionnaire survey method has certain defects in subjective bias, sample size, and timeliness [13]. Network big data analysis technology has a relatively large advantage in these aspects.…”

Section: Traditional Research Techniquesmentioning

confidence: 99%

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Zhao¹,

Tang

2021

Mobile Information Systems

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Big data is a large-scale rapidly growing database of information. Big data has a huge data size and complexity that cannot be easily stored or processed by conventional data processing tools. Big data research methods have been widely used in many disciplines as research methods based on massively big data analysis have aroused great interest in scientific methodology. In this paper, we proposed a deep computational model to analyze the factors that affect social and mental health. The proposed model utilizes a large number of microblog manual annotation datasets. This huge amount of dataset is divided into six main factors that affect social and mental health, that is, economic market correlation, the political democracy, the management law, the cultural trend, the expansion of the information level, and the fast correlation of the rhythm of life. The proposed model compares the review data of different influencing factors to get the correlation degree between social mental health and these factors.

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Section: Traditional Research Techniquesmentioning

confidence: 99%

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Zhao¹,

Tang

2021

Mobile Information Systems

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“…Upon the correct identification of such dependencies at the data processing layer, associated medical staff or software agents can rapidly respond to the situation. Although efforts are visible to offer various data processing methods and platforms suitable for big data management and extracting meaningful information [199], further researches are required to investigate whether these existing techniques are necessarily resource efficient in the context of ML/DLbased brain disorder identification.…”

Section: ) Resource Efficient Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…Usman et al 68 focused on different machine learning (ML) systems that process and manipulated big multi‐media data produced by various systems in smart cities. They also worked on different problems and constraints while processing massive multi‐media data in real‐time.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Leveraging big data in smart cities: A systematic review

Karimi

Kashani

Akbari

et al. 2021

Concurrency and Computation

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Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

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A Survey on Big Multimedia Data Processing and Management in Smart Cities

Cited by 37 publications

References 88 publications

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Leveraging big data in smart cities: A systematic review

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