Smart tourism is an important component of smart cities. However, in many cities, insecurity, safety, fraud, and the lack of availability of proper information about resources are the biggest hurdles toward independent mobility. These issues can be overcome via proactive participation of local citizens to help tourists as well as by cooperation between citizens, city administration, and tourists. As a result, a number of components of smart cities can be strengthened, from sustainable mobility in tourism development to economic development. This is very challenging, as it requires a combination of societal inputs and advanced smart and effective tools. This article presents a viable Internet of Things (IoT)-based solution called iTour that is a framework for independent tourist mobility. In the process, we analyze the difficulties in initiatives and lessons, exploring possible roles of the IoT.

In the last few years, with increased population the most critical component of human life is healthcare. Compare to other deadly diseases, heart disease is one of the most lethal diseases, affecting the lives of millions of people worldwide. It is very important to detect heart disease must early so the loss of lives can be prevented. The availability of enormous amounts of data for medical diagnostics has aided in the development of complex learning-based models for automated early detection of cardiac problems. The classical machine learning approaches unable to generalize the new data sets which have not been seen in the training set. Therefore, the trained model has less accuracy in prediction stage. To minimize this issue, need to balance between training and testing datasets. This paper proposes a novel deep learning architecture using a 1D convolutional neural network for classification between healthy and non-healthy persons with balanced datasets to reduce the limitations of classical machine learning approach. Several clinical parameters are used for evaluating the risk contour in the patients which supports in early diagnosis. Various regularization methods are used to avoid overfitting in the proposed model. The proposed model achieves over 97% training accuracy and 96% test accuracy on the dataset. This is compared in detail with other machine learning algorithms using various performance parameters which proves the effectiveness of the proposed model.

Now we are in an era of technology transformation in our everyday life, where data play a key role in the decision making and bringing the action into reality. These data are collected from many distributed sources. Another important concept in this process is machine learning (ML) and data analytics. Federated learning (FL) is the term coined by Google. It facilitated the distributed learning process and shared the results to the outcomes to the central entity instead of conducting the complete learning process at the centre. In the traditional machine learning approach, data are brought to the model. Whereas FL brings ML techniques to the data used at end devices. In a nutshell, FL, which is also known as distributed learning deals with both centralized and distributed devices. The central entity(server) selects the statistical model which is to be trained. Then, it sends the trained model to multiple decentralized devices or servers. These distributed nodes train the model locally with their own data. Finally, the central entity pools the results from the distributed nodes and prepares a common model without accessing the data from the other nodes.1,2