Online food delivery applications have gained significant attention in the metropolitan cities by diminishing the burden of traveling and waiting time by offering online food delivery options for... Show More

Online food delivery applications have gained significant attention in the metropolitan cities by diminishing the burden of traveling and waiting time by offering online food delivery options for various dishes from many such restaurants. Users enjoy these services and share their experiences and opinions on social media platforms that impact the trust of customers and change their purchasing habits. This drastic revolution of user activities is an opportunity for targeted social marketing. This research is based on Twitter's data and aimed to identify the influence of social media in food delivery e-commerce businesses including decision making, marketing strategy, consumer behavior analysis, and improving brand reputation. In this article, the authors proposed an Apache Spark-based social media analytics framework to process the tweets in real time to identify the influences of generated insights on e-commerce decision making. The experimental analysis highlighted the exponentially grown influence of social media in food delivery e-commerce portals in past years.

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a... Show More

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

With the popularization of network application and the rapid development of electronic science, big data technology is constantly maturing and widely used in various fields. For the continuous... Show More

With the popularization of network application and the rapid development of electronic science, big data technology is constantly maturing and widely used in various fields. For the continuous development of regional medical informatization in China, based on the concept of big data technology and regional medical information data sharing, this paper adopts big data to improve the quality and efficiency of medical information service so as to realize the reasonable distribution and sharing of medical resources and provide references for medical service innovation in China.

Cloud computing is a high computational distributed environment with high reliability and quality of service. It is playing an important role in the next generation of computing with pay per use... Show More

Cloud computing is a high computational distributed environment with high reliability and quality of service. It is playing an important role in the next generation of computing with pay per use model and high elasticity. With increased requirement for cloud resources, load over the cloud servers has increased, which makes cloud use a more efficient algorithm to maintain its performance and quality of service to users. The performance metrics that define the performance of task scheduling include execution time, finish time, scheduling time, task completion cost, and load balancing on each computing resources. So, to overcome existing solutions and provide better QoS performance, a neural-network-based GA-ANN scheduling algorithm is proposed in this paper, which outperforms the existing solutions. To simulate the proposed GA-ANN model, cloudsim3.0 toolkit is used, and the performance is evaluated by comparing simulation time, average start time, average finish time, execution time, and utilization percentage of computing resources (VMs).

Fault tolerance is the most imperious issue in the cloud to provide reliable services. Inherent vulnerability to failure hampers the performance and reliability of cloud services. Hence, to achieve... Show More

Fault tolerance is the most imperious issue in the cloud to provide reliable services. Inherent vulnerability to failure hampers the performance and reliability of cloud services. Hence, to achieve reliability, fault tolerance becomes a mandatory feature which is hard to implement due to the dynamic infrastructure and complex interdependencies. Numerous fault tolerance techniques have been developed in the literature to address the challenges of cloud reliability. A recent research survey presented in this paper attempts to integrate the different fault tolerance architecture. This study presents a critical research review on various existing fault tolerance techniques to improve services reliability, availability, and applications execution in the cloud. A comparative analysis, based on different critical metrics like failure prediction, detection strategy, failure history, VM placement, and limitations, of the reviewed framework systems is also included in the paper. This review intends to facilitate the development of the new fault tolerance technique for the cloud environment.

To reduce the large data experiment platform construction cost and reduce the learning difficulty big data, this article is based on virtualization technology through the Docker software installed... Show More

To reduce the large data experiment platform construction cost and reduce the learning difficulty big data, this article is based on virtualization technology through the Docker software installed on the Linux system, using the Open VpN routing forwarding, using Java web technology, realizing the big data within the local area network (LAN) cluster environment fleetly, and constructed of lightweight data experiment platform. Through this platform, we can create a big data cluster with one key, provide a variety of experimental environments matching the courses, focus on the technology itself, and greatly improve learning efficiency. Experimental analysis shows that the proposed construction method has a host occupancy rate of around 10% and a memory occupancy rate of around 10%, and the system runs stably.