By Yong Soo Kim, Young J. Ryoo, Moon-soo Jang, Young-Chul Bae
Intelligent platforms were initiated with the try and imitate the human mind. humans desire to allow machines practice clever works. Many recommendations of clever structures are in line with synthetic intelligence. based on altering and novel standards, the complex clever structures disguise a large spectrum: great info processing, clever keep an eye on, complicated robotics, man made intelligence and laptop studying. This publication specializes in coordinating clever platforms with hugely built-in and foundationally practical parts. The publication involves 19 contributions that includes social network-based recommender structures, software of fuzzy enforcement, strength visualization, ultrasonic muscular thickness size, local research and predictive modeling, research of 3D polygon facts, blood strain estimation approach, fuzzy human version, fuzzy ultrasonic imaging technique, ultrasonic cellular clever expertise, pseudo-normal picture synthesis, subspace classifier, cellular item monitoring, standing-up movement tips approach, attractiveness constitution, multi-CAM and multi-viewer, strong Gaussian Kernel, multi human circulation trajectory extraction and model coordination. This version is released in unique, peer reviewed contributions masking from preliminary layout to ultimate prototypes and authorization.
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Extra info for Advanced Intelligent Systems
1 Similarity-Margin Based Feature Selection Algorithm Hedjazi  proposes a feature selection method for symbolic interval data based on similarity margin. In this method, classes are parameterized by an interval prototype based on an appropriate learning process. A similarity measure is defined in order to estimate the similarity between the interval feature value and each class prototype. Then, a similarity margin concept has been introduced. The heuristic search is avoided by optimizing an objective function to evaluate the importance of each interval feature in a similarity margin framework.
1. Outline of our system constitution CSV file Personal computer An Energy Visualization by Camera Monitoring 53 Numeral regions Counter region Frame Fig. 2. An example of gas meter image 3 Proposed Method Figure 3 shows a flowchart of the proposed method. The proposed method consists of counter region extraction, numeral region extraction, numeral recognition and interpolation. Firstly, the system extracts counter region from a gas meter image from edge information. Secondly, the system extracts numeral regions by binarization processing and connected-component labeling.
Thirdly, the system extracts human distributions from thermal difference distributions by fuzzy inferences and O-F map . Here, O-F (ObjectFloor) map is calculated in preprocess. Fourthly, human positions are calculated by the Connected Component Labeling. Finally, human movement trajectories (HMTs) are extracted from human positions by associating the positions and past HMTs with minimizing their distance. 38 M. Kuki et al. Learning(Preprocess) Nl max max – 5% Subtract 5% Thermal distribution 5% min Fuzzy inference Object 0 Thermal difference distribution O-F Map Merge Measurement max – 5% max Subtract 5% Thermal distribution Floor Fuzzy inference 5% min 0 Extract HMTs Human distribution Thermal difference distribution Extracted HMTs Fig.