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Название: Green Internet of Things and Machine Learning

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Программы

Серия:

isbn: 9781119793120

isbn:

СКАЧАТЬ mobile apps are available which are based on various ML algorithms. Such type of learning is similar to where network bandwidth is consumed to operate [17].

       1.5.3.2 Computational Advertisement

       1.5.3.3 Sentiment Analysis

      Sentiment analysis is different from the text analysis. Text analysis is focused to retrieve the facts and information but not be able to find the customer’s sentiments which lead to misunderstand the customers need. This misunderstanding may be loss of the valuable information. Hence, sentiment analysis is important to find the product’s review either a positive or negative. Sentiment categorization used in movie reviews, recommendation systems, and business intelligence applications [18].

      1.5.4 Reinforcement Learning–Based Applications

       1.5.4.1 Traffic Forecasting Service

      Traffic forecasting system is the real-time prediction of the traffic on the road. Day by day, numbers of vehicles are increasing on the road, which leads to increase the road accident. So, it is very necessary for traffic management. Using ML method, we can predict the real-time traffic and easily solve this problem. Such types of the systems find the digital traffic flow using satellite map and routing-based information [19].

       1.5.4.2 Computer Games

      The gaming industry has grown-up extremely in the recent time. AI-driven applications are widely used to create interactive gaming experience for the users. Such agents can take a multiple roles such as teammates, player’s opponents, or other non-player characters [19]. Different fields of ML help the programmers to develop games that are well suited to the present market demands.

       1.5.4.3 Machinery Applications

       1.5.4.4 Stock Market Analysis

      To make profit in financial market, it is necessary to analyze and predict the stock market trends. For this proper understanding and prediction, skills are required. This is possible by using ML algorithms. Reinforcement learning and SVM [19] are used to predict such types of market trends, which help us to maximize the stock profit with low risk.

      Due to cheap and high speed internet connection, the internet is growing very rapidly with various internet devices, and these internet devices are connected with the help of IoT (Internet of Things). IoT includes some physical devices with internet connection to provide smart or intelligence applications in real world. Such types of physical devices are capable to analyze, process, and store the sensor data. These devices are some types of embedded machines which can be controlled from around the world using some processing elements and software.

Year Summary of the research Reference
2008 Discussed decomposable RFID devices for healthcare [21]
2010 Discussed various protocols to increase energy savings at the reader by decreasing collisions between tag responses [22]
2011 Discussed RFID inventory technique called automatic power stepping (APS) based on tag response and variable slot sizes [23]
2012 Discussed energy-efficient probabilistic estimation techniques to minimize the energy disbursed by active devices [24]
2013 Discussed a cost-effective RFID devices with printing facilities in order to attain ecofriendly tag antennas [25]
2014 Discussed Reservation Aloha for No Overhearing (RANO) for effective communication intervals to removing problem in active RFID [26]
2017 Discussed RFID size reduction of non-decomposable substantial in their industrial [27]