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As information technology has evolved, digital media has become increasingly fragmented and has started to proliferate multiple information channels. In order to optimize on the various digital channels that are available, organizations are increasingly recognizing the importance of gaining solid insights into consumer behavior and preferences that can be translated into marketing strategies. Specifically, they are keen to identify which information channels they can use to effectively reach and communicate with their target market. In this regard, this paper describes how multi criteria decision making can be used to develop a new method of decision making that will enable an effective and systematic decision process of fuzzy AHP and TOPSIS. Further, these techniques can be used for the developing framework for identifying consumer preferences. This paper provides a demonstration of the underpinning working methodology of the proposed model by examining an real case that is based on the decision process Internet users employ during their online search for information.

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Purpose Electrocardiogram (ECG) is one of the most essential tools for detecting heart problems. Till today most of the ECG records are available in paper form. It can be challenging and time-consuming to manually assess the ECG paper records. Hence, automated diagnosis and analysis are possible if we digitize such paper ECG records. Methods The proposed work aims to convert ECG paper records into a 1-D signal and generate an accurate diagnosis of heart-related problems using deep learning. Camera-captured ECG images or scanned ECG paper records are used for the proposed work. Effective pre-processing techniques are used for the removal of shadow from the images. A deep learning model is used to get a threshold value that separates ECG signal from its background and after applying various image processing techniques threshold ECG image gets converted into digital ECG. These digitized 1-D ECG signals are then passed to another deep learning model for the automated diagnosis of heart diseases into different classes such as ST-segment elevation myocardial infarction (STEMI), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and T-wave abnormality. Results The accuracy of deep learning-based binarization is 97%. Further deep learning-based diagnosis approach of such digitized paper ECG records was having an accuracy of 94.4%. Conclusions The digitized ECG signals can be useful to various research organizations because the trends in heart problems can be determined and diagnosed from preserved paper ECG records. This approach can be easily implemented in areas where such expertise is not available. Supplementary Information The online version contains supplementary material available at 10.1007/s40846-021-00632-0.

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