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The operating of the anthropometric identification supported byConvolution Neural Network system is to locate precious anthropology spots and certaindistances between each feature area on a person's face. Identifies the anthropometricpoints from 2D captured pictures by 3 perspective views promised an implementation between medical diagnostics to solve the problem of data retrieval time and efficiency compared to other manual measures.

A massive growth in the modernize population, because of disabled people, patients after surgery those whom cannot have ability to move and even wash themselves. Tied on their own bed, has created many problems including the need for massage service and convenient healthcare to help patients body relax and shampoo themselves which reduce medical care staff, otherwise, most of them feel inferior when someone touches their head when they are sick or having dermatological problem. In this paper, on-going projects related to essential technology for reducing work force and overcrowding in Vietnamese hospital are discussed. We propose robotic autonomous scalp massage and shampoo combine with traditional Vietnamese’s acupuncture massage to aid in resolving problems that people suffering from bed sick are experiencing, reports on design, service personalization and deployment of an assistive robot to support elderly people, disabled and patients with scalp care. Levenberg-Marquardt algorithm in artificial neural network are applied to train from collected acupuncture points to give the best trajectories following patient’s symptoms, analyzed for diagnosing and evaluating hairy scalp issues for the neural network input variables associated with body values such as height, weight, age or race which plays an important role in the composition of the multi-layer perceptron. The experimented and analyzed from multi-modal data collection of the first ever longitudinal field trials from patients throughout each experiment, indoor environments demonstrate by people with unmovable needs care. From the experiments, it indicates the ability of combining the mechanical system with 2-DOFs, human scalp massages by water jets with trajectories predicted by machine learning based on physical factors to meet the desired position on human scalp, providing enhanced resilience to being treated patients.

Service robots have recently drawn a lot of attention from the public. Integrating with the artificial intelligence of computer science, modern service robots have great potential because they are capable of performing many sophisticated human tasks. In this paper, the service robot named MiABot as receptionist robot is described, it is a mobile robot with autonomous platform being used with a differential drive and controlled by mini PC. The MiABot could sense its surroundings with the aid of various electronic sensors while mechanical actuators were used to move it around. Robot's behaviour was determined by the program, which was loaded to the microcontrollers and PC with Artificial Intelligence. The experiment results demonstrated the feasibility and advantages of this predictive control on the trajectory tracking of a mobile robot. Service robots are designed to assist humans in reception tasks. Robots will interact closely with a group of people in their daily environment. This means that it is essential to create models for natural and intuitive communication between humans and robots.The theoretical basis of artificial intelligence and its application in the field of natural language processing. Besides, robot software architecture is designed and developed. Robot operation modes and implementation are addressed and discussed, they contain information on algorithm for human – robot interacting in natural language, thus a simple approach for generating robot response in arm gesture and emotion. Finally, system evaluation and testing is addressed..

Some effective techniques for identifying defects and estimating defect areas are the main requirements for computer vision and visual processing. This study provides an image processing algorithm to identify and calculate areas of defects on banana leaves. The algorithm consists of the main steps of processing images, segmenting images, labeling, size filtering, determining the boundaries for candidate areas such as chalks, spider webs, pus banana, soils, torn leave. Extracting colour characteristics to identify defects and estimate ultimately areas. Defect of banana has a lot of leak so we use number of defects to classify in good or reject leaf. Extracting boundary features and estimating boundary lengths to determine torn leaves. Experiments were conducted on 200 leaves to be identified. The accuracy of the proposed method is 89.8% for the method of color identification of disability defects and 94.7% for the method of identifying torn leaves.