International Journal of Advanced Networking and Applications

4616-4620

Authors: Dr. Jehan Murugadhas, Ms. Al-Ghaliya Mohammed Al-Aamri, Ms. Marya Sulaiman Al-Sabahi

Smart Home Automation System for Elderly, and Handicapped People using Mobile Phone is a mobile web based application. The aim of this project is to propose a mobile device based remote control that permits elderly with physical challenges, in particular, aged and handicapped people, to command their desired devices without moving to the nearest control point [1]. At the same time, the local control is not excluded but alternative additional controls are achieved using a remote control supported by Raspberry pi 3 processor. The smart home automation system is a recent Internet of Things (IoT) based terminology but it is far away from people’s usage. In fact, the maximum of home electronic devices are automated but the interconnection of these technologies, the inter-corporation of automated various appliances in a reasonable design, and the ease of deployment due to different communication provides comfortable and convenient. These systems are manageable, low-power consumption, secured, efficient, flexible, scalable and cost saving. In addition, it is showcase from other project by comfortable accessibility and supported by easy-to-use with familiar interfaces. This research work is a dwelling IoT that connects the home electrical devices and services allowing them to be remotely managed, monitored or accessed. The scope of this research work is to helps, the elderly and handicapped peoples to live with peaceful and individual confidentiality.

4627-4632

Authors: Aviral Kumar Singhal, Niraj Singhal

Fog computing (or fogging) is extending cloud computing to the edge of the network. It describes a decentralized computing structure located between the cloud and devices that produce data. Operations like computing, storing and networking between end devices and data centers of cloud computing are facilitated by Fog computing. Decentralization and flexibility are the main difference between Fog computing and Cloud computing. Advantages of Fog computing include, minimize latency, conserve network bandwidth, reduce operating costs, enhance security, improve reliability, deepen insights and boost business agility. Various applications of Fog computing are, smart cities and smart electric grids, smart transportation networks, connected cars, real-time analytics etc. Here, this paper presents a comparative study of Cloud computing and Fog computing.

4639-4643

Authors: Wang Kai, He Hong

Questionnaire is one of the most important means to collect data and make information decision. In this paper, the micro expression recognition technology of artificial intelligence is applied in the field of print advertising satisfaction collection and information decision-making. Starting wi th the analysis of micro expression, a new concept of satisfaction factor of print advertising is proposed and then various optimization functions in neural network are analyzed and discussed. In the following part, the Adabound optimization function is applied to the network model in the example of clothing print advertisement and the convergence s peed of the loss function is accelerated in order to improve the detection accuracy of the model. Experimental result shows the satisfaction of the viewers to the clothing print advertisements, allowing researchers to judge their purchase desire at the same time.

4650-4655

Authors: Maaz Rasheed Malik, Liu Yining, Salahuddin Shaikh

Many researchers have worked on the Software fault forecast model because the software fault forecast is very important in software development projects. In terms of the Software fault forecast model, earlier researchers have examined defective datasets models with the help of metrics and classification methods. Classification is assuming an exceptionally major job in the software fault forecast model, which is an important issue in data mining. The machine learning system as a finding way for the information securing or information extraction issue has examined it widely. The contribution to a classifier is a training data set of precedents, every one of which is labeled with a class name. Classification separates data tests into target classes. Software modules are categorized as defected models or not defected models by classification draws near. In Classification, class categories are known thus it is a supervised learning approach. In our research, software fault forecast datasets models are examined with the help of tree vector machine classification. Our proposed model is a tree vector machine, which is used for increasing the positive accuracy and efficiency of the software fault forecast model. We have used multiple tree classifiers for getting more accurate results and compare them with each other. During the analysis of the experiments, j48, random forest and random tree have increased their performance in accuracy as well as efficiency. However, the performance of REP Tree, Hoeffding Tree and Decision Stump is not so good at all measure rates. The experiment's analysis results showed that not every tree classifier could be good in all in measure unit.