Review of Mobile Cloud Computing in Healthcare for Diabetics Patients Using Machine Learning Techniques

The number of chronic diseases such as diabetes, cancer, heart disease, and others is fast increasing in our daily lives. The disadvantages of the traditional healthcare system are becoming more prevalent. One of the most important is that healthcare is only offered in hospitals. No one has access to it and no one is monitoring it. Patients' information is securely acquired from the hospital, with their consent, and monitored on a regular basis using their smart phones in mobile cloud computing. On a daily basis, a real-time mobile cloud health monitoring system is used. The patient's specifics concerning various metrics for data collection, such as blood glucose level, high/low blood pressure, high cholesterol, oxygen level, and so on, are being monitored. Diabetic patients are tracked via mobile cloud-IoT and certain wearable health tracking devices and sensors. Doctors will review the individuals' medical records and make recommendations for improving their health. In the future, it will aid in the control or recovery of diabetics. To provide improved security and performance, the proposed system can leverage advanced encryption techniques in conjunction with a machine learning classifier. Keyword: Predictive analytics; Prediction models; Machine learning; Classifications, Healthcare, Diabetes, Blood Glucose and privacy module.

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

The Teaching Optimization Algorithm Mode of Integrating Mobile Cloud Teaching into Ideological and Political Courses under the Internet Thinking Mode

The purposes are to use the Internet technology to innovate the ideological and political (IAP) classroom teaching mode, take full advantage of the mobile platform under big data (BD), improve the effectiveness of IAP education, and deeply grasp the internal needs of students for the IAP classroom. First, the specific meaning and implementation process of mobile cloud teaching (MCT) under the Internet are deeply studied, as well as the connotation of the IAP course. Second, some suggestions are proposed. A theoretical analysis of the synergy between MCT and IAP classroom is conducted, and the teaching situation of cloud teaching classroom from the basic construction and operation mode of MCT platform is discussed. Finally, a case study on blue-ink cloud class is carried out. The results show that to realize the innovation of the IAP classroom teaching mode, it is considered that the IAP classroom has a strong dependence on the information. The MCT platform has wide access to information, which can well connect with the IAP classroom. The teaching methods of the cloud teaching platform tend to be diversified and interesting, and the overall teaching pattern is different from the traditional classroom teaching mode. The teaching effect is greatly improved based on the MCT platform. To achieve the synergy of the Internet and the MCT platform, the advantages of the cloud teaching platform should be expanded, the online communication between teachers and students should be strengthened, and the channels for information acquisition should be opened up. Blue-ink cloud class fully utilizes the advantages of the cloud teaching platform, which provides a great reference for IAP classroom teaching. Its flexible teaching method and intelligent management are helpful to the reform of IAP classroom teaching in the future.