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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]

Statistical methods enable rational and quantitative studies of systems or processes, and support management decisions. Statistical Process Control (SPC) is a standard methodology for measuring, controlling and improving the quality of processes and products through statistics. Presented by Walter A. Shewhart in the 1920's, control charts are among the most popular SPC tools for monitoring processes. However, despite their age, their application in Artificial Intelligence (AI) has only recently begun appearing in the literature. In this paper, we use SPC and AI techniques to present a new process monitoring tool. The proposed classification control chart, which we call class-chart, offers a more robust and flexible alternative to traditional SPC tools. In addition to the ability to recognize patterns and diagnose problems, regardless of the sample scenario, this new approach is capable of performing its monitoring functions on a large scale, predicting market scenarios and processes on large volumes of data. We evaluate the performance of the class-chart by the average run length in extensive numerical simulations. Finally, two real data sets are used to illustrate the applicability of the proposed control chart for classification data. • New monitoring tool based on Statistical Process Control and Artificial Intelligence. • Quality characteristic of interest is multiple categorical and latent. • So-called class-chart is more robust and flexible than usual process monitoring tools. • Ability to take control of future predictions. [ABSTRACT FROM AUTHOR]

• Development of a fuzzy classification system to ascertain an overall health status. • Determination of human BPM, RR and rhythm analysis via robotic tactile sensing. • Determination of human CRT and health status via use of tactile sensing and vision. • Pilot study to evaluate accuracy of vital sign measurement and fuzzy triage on human subjects. The ability to quickly and accurately triage a person's medical condition in an emergency situation or other critical scenarios could mean the difference between life and death. Endowing a robotic system with vision and tactile capabilities, similar to those of medical professionals, and thus enabling robots to assess a patient's status in an emergency is a highly sought after characteristic in healthcare robotics. This paper presents a novel fuzzy triage system exploiting visual and tactile sensing, to equip a robot with the skills to accurately determine key vital signs in humans. There are three key signs of human health: respiratory rate, pulse rate (Beats Per Minute (BPM)) and capillary refill time. Using ground truth from a medical professional, the fuzzy triage system is trained and validated initially with informed synthetic data and then further evaluated using vital signs data collected from subjects in a pilot study. Results from this pilot study indicate that the fuzzy triage system is capable of classifying a patient's health using the novel approaches for collecting BPM, Respiratory Rate (RR) and Capillary Refill Time (CRT) which replicate, to some extent, the approaches used by medical professionals for measuring vital signs. Furthermore, the intelligent system proved capable of determining whether a pulse was regular or arrhythmic, whether respiratory rate was regular or irregular, and determining the subject's capillary refill time. Such results imply that this system could ultimately be used, for example, in a home assistance robot for elderly or disabled persons, or as a first responder robot. Ultimately the aim would be that these methods could be utilised by robotic systems in emergency scenarios or disaster zones. [ABSTRACT FROM AUTHOR]