## 4 Content Analysis and Data Mining
### Group 3: Energy Management and Artificial Intelligence
#### 4.3.2 Artificial Intelligence and Indoor Systems
The ADC output is the serial data output (DOUT) and serial clock (SCK), and the digital output is finished whenever DOUT is low. The amount of pulse changes at SCK controls the inputs and outputs. The minor size, low-power waste, and high-operational speed of smart-log prototype used in the experiment have resulted in designing the prototype with two microcontrollers--the first one, a wireless module placed on the board, and the second one, which is considered as the final prototype, which are conducted without the wireless module in order to transfer data wirelessly to the cloud environment. Model 2 can be used as a \"thing\" due to the smaller form component in the IoT-based answer in the proposed system. The second stage is nutrition gaining, where a JAVA application for the nutritional value obtained from the USDA database. Calculating the food item's nutritional value before consumption is obtained from the weight of food acquired by the sensor board and the nutritional data recovered from the cloud. Any remained food will be going under some precise calculation. The third stage is obtaining data analysis, where food item classification into different classes is done by using WEKA, an arff (attribute-relation file format) produced by the application will be passed on to the WEKA, and the results of the classifier is shown by a JAVA application on a web page. The results of evaluation experiment showed that the nutrition prediction accuracy of smart-log results in 98.6% accuracy, and the proposed smart-log system in this paper could be used for adults as well as infants, and the system along with analyzing the meal content can have suggestions in order to decrease unbalance diet. It is suggested to track user activities for precise automated diet prediction for adults integrating smart-log with the physiological observing mechanism. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [49] presented a new independent home deep neural network object-tracking mechanism for non-GPS environments using pattern recognition and machine learning techniques for home-based human-robot interaction (HRI) system. The system consists of an assistive robot (Kobuki Turtlebot 2, modified with ASUS Xtion Pro Live RGB-D camera, powered with ODROID XU4 microcontroller, and BOSCH BND055 IMU installed camera), Amazon Echo device, Amazon service for Alexa, Amazon web services, Alexa custom skill software, machine learning techniques, and AI methods (different algorithms like deep convolutional neural networks (CNN)). When an elderly user requests for a service or task by voice, the task is divided into smaller elements of action, location, and object. A deep neural network object detector inspects several possible matching objects and displays them on the screen of the robot. When a certain object is found, it is asked from the user to confirm it. The robot then completes the task using its physical control abilities. For the system's vision-based object detection and mapping, the issues of multi-object detection, object ownership clustering, object tracking, object mapping, object database creation, determination of optimal action sequence, and environment obstacle avoidance are addressed. The system is evaluated, with an experiment, (1) to process the rate for object detection and tracking, and (2) to track the confidence gradient. First, tests are performed using a direct USB 2.0 connection from the desktop computer to the camera onboard the robot. The second tests use images transfers over Transmission Control Protocol for Robot Operating System (TCPROS) on a Wi-Fi connection from the robot to the desktop computer. A resulting plot of the raw and filtered confidence level of a chair detected, while the camera frame moves closer, showed that raw confidence level of a special object that is tracked by the multi-object detector is noisy; while the filtered data showed a general boosting trend of confidence, providing a true positive detection of the object.