Search

Leveraging multi-modal information sources has attracted the attention of researchers and practitioners for developing resources and technologies in the broad areas of applied Artificial Intelligence (AI). During the occurrence of a natural disasters, people heavily use social media for communication by posting multimedia information in the form of texts and images. In such a critical situations, it becomes imperative to use all modalities of information sources to better capture vital knowledge related to the crisis. In this paper, we propose an effective deep learning model to leverage multi-modal information sources in the form of both texts and images, and then disseminate useful information at the time of natural disasters. Our proposed model classifies the tweets into seven critical and potentially actionable categories, such as the reports of 'injured or dead people', 'infrastructure damage' etc. Experiments on a benchmark dataset show that fusion of multi-modal information sources, viz. text and image both, are more effective compared to uni-modal (i.e. either text or image) source in extracting meaningful information generated during disaster situations. By using information from both the modalities (text and image), we obtain a macro F1-Score of 0.51, which is a significant improvement over the baseline models that make use of only text or image for the classification. We supplement our results with a thorough analysis exploring the reasons for this phenomenon, thus, further demonstrating the utility of exploiting multiple modalities. The primary contribution of this paper lies in developing an attentive deep learning model that uses social media text and image to help classify images into crucial classes for disaster domain. The major findings of our research are that using textual features while classifying the corresponding images, improves the classification performance. We also explore different methods of fusion of multi-modal features and conclude that fusion through attention mechanism works best for image classification in disaster domain. • A multi-modal deep learning model for image classification in disaster domain. • Devising an attention mechanism for combination of the two modalities. • Showing how adding text can help in image classification. [ABSTRACT FROM AUTHOR]

• Presented an extensive overview on different stages of electrocardiogram analysis. • Summarized the most notable developments of smart health monitoring systems. • Discussed the latest hardware implementations of real-time ECG monitoring systems. • Identified challenges and limitations for various automatic ECG diagnosis systems. • Outlines the future vision of next generation ECG monitoring systems for healthcare. Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death globally. Early prediction of CVD's can help in reducing the complications of high-risk patients. The electrocardiogram (ECG) is an efficient aiding tool, provides accurate information about various cardiac conditions of the human heart. Evaluation and interpretation of ECG signal has become the major goal in current research to identify and mitigate risky cardiovascular conditions. The ECG signal is efficiently analysed and classified using signal processing techniques, ranging from traditional to machine learning approaches and its subbranches, such as deep learning are used for the early detection and diagnosis of cardiac conditions and arrhythmias. The development of novel types of body sensors increases the need for automated, low-cost, real-time, and efficient ECG monitoring systems that can be used at home or in ambulatory settings. IoT in healthcare industry enables the doctors and specialists to diagnose the patient status remotely in a smart and efficient manner. Motivated with all the research done in this area for early diagnosis of cardiac abnormalities and save the life of high-risk patients, we surveyed numerous research works reported in the literature and provide a glance on several aspects of ECG signal monitoring systems. We first introduce the step-by-step framework of ECG signal analysis starting data acquisition to classification, describes each stage analysis of both traditional and advanced machine learning models that have been reported in the literature. We provide a deep discussion on ECG signal acquisition, pre-recorded clinical ECG data taken from the databases, signal processing and denoising, detection of feature based on feature engineering, and signal classification along with the comparative assessment amongst reviewed studies. This work also presents the detailed analysis of smart health care system that employs portable and wearable devices, as well as the Internet of Things (IoT) and wireless technologies, as an innovative medium for data transmission that allows for remote access to a patient's health status at lower costs and with greater efficiency. Moreover, this work provides an extensive knowledge on various hardware platforms that have been adopted for the development of biomedical processors including microcontrollers, FPGA and ASIC. Additionally, the challenges and limitations are discussed in this research field and directions for future work are suggested. This is the first review paper of its kind to present a comprehensive discussion on all aspects of cardiovascular disease monitoring systems in one place. [ABSTRACT FROM AUTHOR]