## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.2 Artificial Intelligence and Indoor Systems
If the predicted user is the same as the target user, then the identification is successful. The SVM model is used for a comparative baseline. For the experiment, a dataset consists of acoustic samples of three breathing gestures--deep breathing, normal breathing, and sniffing (two quick inhales)--of 10 users collected during three different sessions. The first 50 samples are selected and enhanced to 550 samples by two common data-augmentation techniques of frequency wrapping and amplitude scaling for model training. The remaining 10 original samples (from sessions 2 and 3) are kept for testing. Four Android-based OS devices of three types (two smartphones, a wearable smartwatch, and a Raspberry Pi (IoT)) are applied for the experiment. The results showed that the accuracy of a suitable quantized LSTM-based (long short-term memory-a variant of RNN) deep-learning model is higher than 90%. The delay for user authentication is very small. Besides, RNNs are persuasive lightweight choices for convolutional neural networks (CNNs) for many sensor devices, especially if the application uses temporal items of the sensor. Table 5 shows a summary of the 'artificial intelligence' theme. Detailed descriptions of the relevant practices identified in the AISH literature are presented following the table. A major part of the article reports the utilization of AI algorithms for smart home applications in recent years. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [48] presented a new IoT-based system for monitoring nutrition called smart log, which is a consumer electronics product with Wi-Fi enabled sensors for nutrition quantification and a smartphone application that gathers nutritional facts in food and ingredients. The smart log contains a smart sensor board and a smartphone application. A timestamp for recording the meal type, i.e., breakfast, lunch, dinner, and food weighing sensor to measure the nutrition consumed, which is paired with load cells and a microcontroller, is placed on the sensor board. The load cell chosen has the capability of weighing things within the range of 0 to 5 kg. The analog to digital converter (ADC) used for weighing applications has an on-chip programmable gain amplifier (PGA), analog power supply regulator, and internal oscillator. The proposed system of this paper includes the following parts: (1) a perceptron neural network, which is five-layered along with a network-based model called Bayesian that manages meal estimation, is for better understanding of the smart-log, and the prototype uses an open IoT platform in order to analyze and store data. (2) Optical character recognition (OCR) is used for nutrition data obtaining. (3) A belief network (BN) or Bayesian is used for food classification. (4) The stochastic gradian descent algorithm is used in algorithm 1. (5) A JAVA application for the nutritional value obtained from the Internet (United States Department of Agriculture (USDA) database). (6) The Waikato environment for knowledge analysis (WEKA) is used for data analysis in the experimental case study. The proposed system includes four methods as follows: (1) Nutritional value measurement method, by which the food weight is calculated with load cells, nutrition data is obtained by using smart log, future meals are suggested based on remained food, and the food is classified with the machine learning method. A BN structure is used based on the constraint method, in which the nutritional deficiency is decided based on conditional dependencies. The proposed system is evaluated by conducting an experimental case study, and the proposed smart-log system consists of the following two interlinked stages: The first stage is nutrient value and weight data acquisition, where the implementation of the hardware is done with commercially available off-the-shelf (COTS) components. The load cell used for data acquisition is connected to a 24 bit-ADC), which does the weighing process in applications.