The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social media have been crawled over the years to collect and analyze disasterrelated multimedia content. Satellite imagery has also been widely explored for disasters analysis. In this paper, we survey the existing literature on disaster detection and analysis of the retrieved information from social media and satellites. Literature on disaster detection and analysis of related multimedia content on the basis of the nature of the content can be categorized into three groups, namely (i) disaster detection in text; (ii) analysis of disaster-related visual content from social media; and (iii) disaster detection in satellite imagery. We extensively review different approaches proposed in these three domains. Furthermore, we also review benchmarking datasets available for the evaluation of disaster detection frameworks. Moreover, we provide a detailed discussion on the insights obtained from the literature review, and identify future trends and challenges, which will provide an important starting point for the researchers in the field.
Summary
Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures.
In the modern era of computer and technology, images and videos play a vital role. Therefore, there is always a need for robust skin detection system in order to cope with the intolerable and objectionable contents. In this paper, an efficient method has been implemented for skin detection, which detects the skin in different images under different environmental conditions. We have used the two machine learning approaches i.e. Random Forests and Multilayer perceptron for skin detection. We have also then combined the results of these two approaches used. We have used total of 554 images in our experiments.
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