The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

In the Internet of Things (IoT) technologies, wireless sensor networks (WSNs) are one essential part. The IoT network commonly consists of WSNs, where hundreds or even thousands of small sensors are capable of sensing, processing, and sending environ...

Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the hidden state at time t − 2 to be directly transferred to time t without the nonlinear transformation at time t − 1. This is effective in improving the long-term dependence of RNNs. Then, a novel cell structure named Single Gate Recurrent Unit (SGRU) was presented. This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity. Next, applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties. Finally, the performance of our proposed TFC-SGRU was verified through several experiments in terms of long-term memory and anti-interference capabilities. Experimental results demonstrated that our proposed TFC-SGRU model can capture helpful information with time step 1500 and effectively filter out the noise. The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.

WSNs which are the major component in the IoT mainly use interconnected intelligent wireless sensors. These wireless sensors sense monitor and gather data from their surroundings and then deliver them to users or access connected IoT devices remotely...

As it enters into a hyper connected society, we are requesting a new technological convergence. Also, these days are a convergent society based on IoT, AI in earnest. We need to build up ‘digital literacy’, that is the ability to critically watch digital information to choice the correct and right information in the flood of information in these days.This paper is for sharing our research process and conclusion about the convergence subject for cultivating a digital literacy. The subject “Comprehension of digital literacy” is made for fostering students who to be great talents can be critical, integrating, problem-solving, creative. This subject was managed by three professors who are consist of a humanities major, a social science major, an engineering major. For this, Three teachers shared AI knowledge based on expert knowledges per major, and based on this, this subject was developed for cultivating students who get a critical comprehension about digital information and a creative solve-problems with digital tools. This subject consists of four parts; in the first part, students have recognized the necessity of digital literacy and main conception of digital literacy. In the second part, students have recognized ethical manner and ethical mind as digital literacy. Next, they have learned various tools of digital and could use those tools. Lastly, students have fulfilled a critical project with digital tools.The survey at the end of this class shows positive effects of our subject to students getting abilities in many domains: solving problem, thinking integrated, creating knowledge, digital ethics, team teaching, self-efficacy. This integrating subject is judged that cultivating student of various major to get basic knowledge of digital literacy by changing of existing cognition and this is can be leading case jumping over academic boundary.