IoT-Deep Learning Based Activity Recommendation System

The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical settings.The Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health care.Healthcare Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have increased.The existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports.Patients’health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies.This research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it accordingly.The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures.Through a healthcare DSS,this framework is clever enough to detect and analyze a patient’s data.Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care.The data reported in this study demonstrate the notion.This device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.

Joint model of user check-in activities for point-of-interest recommendation

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.