Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion

Khan, Sulaiman; Nazir, Shah; García‐Magariño, Iván; Hussain, Anwar

doi:10.1016/j.compeleceng.2020.106906

Cited by 73 publications

(30 citation statements)

References 13 publications

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“…For the first time in history, digital urban design has dramatically improved our ability to study and determine the causes and uncertainties behind the appearance of urban form with a high probability of convergence to correctness. Based on multi-source big data, machine learning and artificial intelligence [25][26][27], urban design can already begin to explain the basic composition of urban space form elements, the holistic interpretation of social and population relations, the integration of urban vitality elements and their dynamic distribution and patterns. In particular, it is possible to make more and more realistic and regular descriptions and generalizations of urban design objects that are interdisciplinary, crossdisciplinary, multi-scale, and beyond individual cognition and recognition abilities.…”

Section: Paradigm Of the Fourth Generation Urban Design: Human-computer Interactionmentioning

confidence: 99%

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Wang

2021

Front. Struct. Civ. Eng.

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The wave of “digital age” featuring digital information is coming. Digital technology is profoundly changing the societal development direction and evolution paths. It also has significant bearing on production modes, social interactions and lifestyles. With regard to urban design, a system of knowledge about the creation and adaptation of material space forms that integrate humanities, art, technology and materials, digital technology has provided it with a brand-new and revolutionary scientific impetus for its evolution. The result of this evolution is “digital urban design paradigm based on human-computer interaction”, i.e., the urban development is moving toward “pan-dimensionality” and “individual ubiquity”. The future of urban design will construct a new approach to urban research and engineering, which is more complex, capable of accommodating and compatible with multiple goals of “instrumental rationality” and “value rationality”. Such a new approach shall be led by the probabilistic theory of “gray scale thinking”, reflecting quaternary synergetic view of “scientific rationality, ecological rationality, cultural rationality and technical rationality” to realize the cognitive progress of “engineering for the benefit of mankind”.

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Section: Paradigm Of the Fourth Generation Urban Design: Human-computer Interactionmentioning

confidence: 99%

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Wang

2021

Front. Struct. Civ. Eng.

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“…8 [52] Another methodology has the objective of developing the fusion-basis congestion-control system by utilizing the Deep-learning model.…”

Section: Sno Author Descriptionmentioning

confidence: 99%

Survey of Deep-learning Techniques in Big-Data analytics

Goswami¹,

Kumar²

2021

Preprint

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The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

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“…In this modern digital age of ever-growing computer technology, the machine learning algorithms play a key role in all fields of life, especially in the areas of text recognition [1], network security [2,3], privacy [4], traffic flow predictions [5], object detection [6], and may others. One of the major applications of machine learning algorithm is Optical Character Recognition (OCR) system development.…”

Section: Introductionmentioning

confidence: 99%

Isolated Handwritten Pashto Character Recognition Using a K-NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

et al. 2021

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Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of offline Pashto characters. This database consists of 11352 character images (258 samples for each 44 characters in a Pashto script). Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation.

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Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion

Cited by 73 publications

References 13 publications

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Survey of Deep-learning Techniques in Big-Data analytics

Isolated Handwritten Pashto Character Recognition Using a K-NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

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