A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management

Adeel, Ahsan; Gogate, Mandar; Farooq, Saadullah; Ieracitano, Cosimo; Dashtipour, Kia; Larijani, Hadi; Hussain, Amir

doi:10.1007/978-981-13-0992-2_5

Cited by 72 publications

(37 citation statements)

References 26 publications

(21 reference statements)

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“…Topic discovery, text categorization, or text classification can be defined as the process of labeling unstructured data (e.g., in webpages or documents) by using pre-defined categories in order to define such data and organize them [21][22][23][24][25][26]. A lot of work has been done to study the different aspects of text classification techniques and their usage in the knowledge discovery.…”

Section: Previous Workmentioning

confidence: 99%

Semantic Ontology-Based Approach to Enhance Arabic Text Classification

Hawalah

2019

BDCC

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Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.

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Section: Previous Workmentioning

confidence: 99%

Semantic Ontology-Based Approach to Enhance Arabic Text Classification

Hawalah

2019

BDCC

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“…The posture is about how the people hold their body and position the limbs. Within the advancement of the technology, the human has chosen the sedentary lifestyle which leading to less physical activity and movement [14,15,16,17,18,19]. The long time sitting during the work or study leads to decrease in muscle strength.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

et al. 2021

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The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%.

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“…Disaster management has played a significant role in mitigating and minimising loss of life and damage to properties and infrastructure. Effective disaster management demands intelligent infrastructure for the collection, integration, management, and analysis of a variety of distributed data sources, including ground-based sensors, video streaming, and satellite imagery [1]. The emergence of social networks and crowd-sourcing has enabled the application of human-centric approaches that allow the public to provide essential disaster-related information that can be used to enhance the effectiveness of disaster management in reducing the impact of natural disasters.…”

Section: Introductionmentioning

confidence: 99%

Use of Social Media Data in Disaster Management: A Survey

et al. 2021

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Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.

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A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management

Cited by 72 publications

References 26 publications

Semantic Ontology-Based Approach to Enhance Arabic Text Classification

Semantic Ontology-Based Approach to Enhance Arabic Text Classification

A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

Use of Social Media Data in Disaster Management: A Survey

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