1/22/2024 0 Comments Multiple contexts have a path ofThis study aims to generate a deep neural-network learning model with a knowledge base for ambient context-awareness. ( 2018) proposes ambient context modeling for the health-risk assessment using a knowledge base and deep neural networks. It guarantees high quality of service as well as consistent network connections by improving quality streaming and reducing network latency. This study applies an MLP deep learning algorithm and the multilayer perceptron classifier in the intermediate roles between a core server and a client application. The developed algorithm aims to select the optimal streaming segment, and reduces network latency in adaptive MEC streaming. ( 2018) develops a context-aware adaptive algorithm using ambient-intelligence, dynamic adaptive streaming over HTTP (DASH) in mobile edge computing (MEC). It monitors the progress of context data, analyzing, planning, and executing contextual dynamic reconfigurations. In addition, it develops a smart home implementation based on ambient devices and a home gateway using the autonomic computing MAPE/K loop and the cisco packet tracer simulator. This study focuses on reconfiguring IoT systems using an evolution manager’s structure, and focuses on reconfiguration steps using processing-context data and a decision-making process. They propose contextual dynamic reconstruction using an ambient architecture-level IoT framework with more flexibility and comfort of use. ( 2018) suggests a non-intrusive contextual dynamic reconfiguration process of an ambient-intelligence IoT system. The central issue is to introduce selected research papers which includes trends in topics like context computing for the internet of things, context computing for networks, ambient embedded systems, context software, ambient context computing, adaptive knowledge base systems, advancement in wireless technologies, ambient IoT contexts, hybrid networking systems, artificial intelligence, innovative applications of semantic computing, knowledge mining, big data analysis, and ambient intelligence. To address these issues, various studies have been conducted in the computer science area. The main issues of context-aware computing are the integration of the data collected from multiple data sources and the protection of personal information about the end-users. Along with the information collected in real-time, from the underlying data such as user preferences or behavior pattern data are analyzed and learned to achieve adaptive decision-making in consideration of personal situations. The information from a real situation makes it possible to realize human-oriented decision making through the application of a variety of machine learning techniques, such as feature extraction, learning, and inference. Ambient intelligence turns real situations into information, and provides a user-friendly intelligence service using the information. A variety of IoT devices, such as smartphones, tablet PCs, wearable devices, smart bands, smart sensors, cameras, microphones, and GPS devices, can be connected with each other to collect context-aware data of the user’s surroundings in real time. Through IoT-based sensing, context-aware computing connects a variety of information found in the real world to ambient intelligence. Context-aware computing is an ambient-intelligence environment for adapting to the situations around humans, to their surroundings, and their use of software and hardware.
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