# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 4 Applications in Technological, Financial, and Other Successful Areas
YouTube, with automatic captioning, and the rise of virtual voice assistants like Siri, Alexa, Cortana, or Google Assistant, have made it an expanding field in these days [80]. DL is also helping Facebook to draw value from a larger portion of its unstructured datasets. Most of its DL technology is built on PyTorch platform and created by its Facebook AI Research [81] division. Twitter processes lots of data through DNN to learn over time which are the user preferences, and the group working in DL technologies within the company is called Twitter Cortex [82]. This type of approach still lacks the ability to learn the context of a conversation or the concepts that are being treated, which increases the number of misinterpretations of the text, and, subsequently, the user's distrust. To avoid this effect, further research on learning the concepts themselves would be desirable. Some attempts have been initially applied in visual concepts, which mention the possibility of applying them to different applications, such as textual or auditory scenarios [83]. _(3) Finance._ DL and BD play a main role in many phases and processes of the financial ecosystem, including the following ones: stock price prediction based on market sentiment, news, and old track record of the stock; fraud detection in credit cards and transactions; contextual search for research documents used by investment banks; customer segmentation to support sales, promotion, and marketing campaigns by collecting and analyzing all available data; or risk management, using a powerful risk prediction model and offering a global vision of different sectors and areas where financial risk may appear. For example, American Express relies heavily on data and DL to help detect fraud in near-real time, therefore saving millions in losses [84]. In [85], it is examined the behavior of borrowers to predict the mortgage risk, in [86] the fraud in credit card transactions is detected, and in [87] an algorithm for trading is proposed. The black-box characteristics (lack of interpretability) that these solutions present when taking decisions should be taken into account, since they can cause millionaire losses and subsequent demands. _(4) Remote Sensing._ The power of DL has also been exploited by remote sensing scientists to tackle different challenges such as monitoring global changes or evaluating strategies for the reduction of resources consumption [88]. In the agriculture sector, the goal is around improving profitability and efficiency by reducing the cost of an operation or by increasing product value. BD and DL solutions are implemented to help farmers to make better decisions and to develop efficient ways of crops management, livestock management, water management and soil management [89]. Data sources like satellites, unmanned aerial vehicle (UAVs), mobile applications, and remote sensors are continuously monitoring farms, enabling the farmers to use prediction models. Disease recognition in plants based on leaf images captured by mobile devices [90], identification of stage of fruit for automated harvesting [91], and automated monitoring and management of the agricultural lands in large scales using remote sensing [92] are additional examples of agricultural applications which are currently expanding. In the energy sector, gas and oil companies are driving new levels of performance and improving the use of resources by using DL. For example, BP's oil and gas wells have sensors that generate data to train DL architectures [93], with the aim of understanding the realities of conditions at each site, where optimizing the performance of the equipment and the monitoring maintenance are needed to prevent breakdowns. DL is also being applied to detect anomalies (that conventional methods would not detect it), to reduce carbon emissions, or to explore deep sea gas, to name just a few.