## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.2 Artificial Intelligence and Indoor Systems  
Figure 10 shows a typical plan of a smart home, including three layouts and a number of sensors for enhancing the living environment, which is relevant to the energy management. For example, a system for monitor indoor areas [79], a system using users' breathing acoustics [80], and a smart system for controlling heating, ventilation, and air conditioning [44]. Each paper separately is reviewed in this section. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [79] conducted a study on smart home 2.0 that is a novel smart system of botanical IoT framework to monitor indoor plants and gain automatic control strategies considering the user's mood and emotion in a real-time manner. Smart home 2.0 is the second generation of smart homes that unifies smart home and botanical IoT to make a better suited to the indoor environment and enhance the quality of life, resulting in better physical and mental health for home users. The system includes three parts of smart home 2.0 infrastructure (sensors and construction of the greenhouse), cloud platform, and users. The system collects data on ambient  
Figure 10: A typical plan of a smart home, including three layouts and a number of sensors for enhancing the living environment. Note: (i) a system able to monitor indoor plants and gain automatic control strategies [79] that are visualized in layout number 8 by authors, (ii) a system using users’ breathing acoustics for getting permission and identification in a smart home [80] that is visualized in layout number 9 by authors, and (iii) a smart system for controlling heating, ventilation, and air conditioning by a novel random neural network model [44] that is visualized in layout number 10 by authors. The location of sensors is schematically shown in the figure, and they can be relocated. temperature, humidity, soil humidity, illumination intensity, CO\({}_{2}\), O\({}_{3}\), O\({}_{2}\), and NO\({}_{2}\). By analyzing the data, the system runs intelligent monitoring and automatic planning control strategy. AI technology and machine learning techniques are used for better interactions with the user. The implementation of smart home 2.0 consists of six stages: (1) equipment implementation, (2) equipment registration, (3) user registration, (4) authority allocation, (5) equipment configuration, (6) configuration of automatic planning strategy. To test the platform, an experiment in a prototype smart home 2.0 system is designed and implemented to investigate the growth of the Korean Ginseng plant. An intelligent cultivating strategy related to the plant of the experiment is designed to manage the growth of Korean Ginseng by controlling the main environmental factors. The results of the growth of the experiment satisfy the expectation. The privacy and security issues of smart home 2.0 and improvement assessment in the user's quality of life are suggested to be addressed in the future. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [80] presented a breathing-based authentication, which is a novel framework for home-automation control, which is based on voice or audio used in the entry point of smart devices for identification and permission. It can be used to unlock, activate, and deactivate constrained smart devices, such as smartphones, smart glasses, wearables, sensor-enabled doors, or coffee makers. This framework consists of the target user, feature extractor (using Mel-frequency cepstral coefficients -MFCC), machine learning techniques (long short-term memory (LSTM), support vector machine (SVM)), and user prediction. First, a target user breathes on the microphone of a device (e.g., to unlock it), from which pre-processing of breathing acoustics and items extracting using MFCC (Mel-frequency cepstral coefficients) is done. Then, the items are inserted into a classifier to get the prediction output using long short-term memory (LSTM) and support vector machine (SVM).