An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality

In all dimensions of education and all subjects, Smartphones have turned out to be broadly acknowledged technology. It plays an essential task in advanced online education systems. Because of smart devices� effortlessness and extension property, it is getting to be mandatory for portable applications. This paper analyses the research on Smart Devices (SD) to incorporate visual simulation into e-learning. The researchers created an Augmented Reality (AR) platform for e-learners to expand the coursebook with graphics and virtual multimedia applications. This paper recommends a Mobile e-Learning (MeL) application termed �MeL app. The advanced MeL app methods have been tested using Mann-Whitney �U� Test in the lecture hall using real-time learners. The proposed MeL app planned to create the learning practice easier, focusing on e-learner�s requirements by encouraging e-learners and instructor relationships to maintain communicative development-based e-learning for Technical Higher Education (THE). Software engineering learners assess this proposed framework in THE. Future work in this investigation incorporates new highlights, testing the device in extreme situations, evaluating the instructive perspectives utilizing more significant and increasingly various understudy and beginner inhabitants, and at last, extending the application space

Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant networks SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue