Physical disability has affected many people’s lives across the world. One of these disabilities that strongly affected some large category of people is visual lose. Blind people often face difficulties in moving around freely such as: in crossing the street, in reading, driving or socializing. They often rely on using certain aid devices to reach certain places or perform any other daily activities such as walking sticks. There are ongoing scientific researches in the area of rectifying blindness, but it has to go long way to achieve the solution. Also, there are research unleashes the ideas of assisting the blind people deficiency but lacks in technological aspects of implementation. This research project aims at helping blind people of all categories to achieve their day to day tasks easier through the use of a smart device. By using artificial intelligent and image processing, this smart device is able to detect faces, colors and deferent objects. The detection process is manifested by notifying the visually impaired person through either a sound alert or vibration. Additionally, this study presents a palpable survey that entails visually impaired people from the local community. Subsequently, the project uses both Open CV and Python for programming and implementation. The exertion of this project prototype investigates the algorithms which are used for detecting the objects. Also, it demonstrates how this smart device could detects certain physical object and how it could send a warning signal when faced by any obstacles. Overall, this research will be a positive addition in the world of health care sector by supporting blind people with the use of smart technology.
Keywords: Artificial Intelligent | Open CV | Python | Face Recognition | Object Detection | Health Care Introduction

Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.
Keywords: Big data | Artificial intelligent | Data mining | Image quality assessment

With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including highend steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (Vf) and driving force (Df) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.
Keywords: Alloy design | Machine learning | Physical metallurgy | Small sample problem | Stainless steel