A deep learning-based home safety perception system for household service robot
In 2016, the population of people over the age of 65 in Macau was 11.2%. This means that Macau has already become an aging society. As such, more younger generations are needed to look after the elderly. According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly population, it is urgent to find a fast and effective way to ensure the safety of the elderly. As there is a lot more danger besides falling in an elderly life, we aim to build a robot collocated with its danger detection system to ensure the safety of the elderly at home. The reason we decided to use robots is that we want to have larger flexibility and mobility, for example, we can give elderly rescue materials when they need help. Moreover, more home robots will be used in the future, they can just apply our system to theirs and ensure the safety of elderlies. In this research, we mainly used cameras with the Openpose model to detect dangers such as falling, potential human action danger, and environmental danger. Innovative ways are used to detect fall action, collocated with our home robot, it is a foreseeing project that could ensure the safety of the elderly in a home environment.
DetectTimely
This research project focuses on developing a web-based multi-platform solution for augmenting prognostic strategies to diagnose breast cancer (BC), from a variety of different tests, including histology, mammography, cytopathology, and fine-needle aspiration cytology, all in an automated fashion. The respective application utilizes tensor-based data representations and deep learning architectural algorithms, to produce optimized models for the prediction of novel instances against each of these medical tests. This system has been designed in a way that all of its computation can be integrated seamlessly into a clinical setting, without posing any disruption to a clinician’s productivity or workflow, but rather an enhancement of their capabilities. This software can make the diagnostic process automated, standardized, faster, and even more accurate than current benchmarks achieved by both pathologists, and radiologists, which makes it invaluable from a clinical standpoint to make well-informed diagnostic decisions with nominal resources.
Utilizing Computer Vision And Machine Learning Algorithms To Control Smart Systems Helping Physically Disabled People.
About 15% of the world's population lives with some form of disability, of whom 2-4% experience significant difficulties in functioning. The global disability prevalence is higher than previous WHO estimates, which date from the 1970s and suggested a figure of around 10%. This global estimate for disability is on the rise due to population ageing and the rapid spread of chronic diseases, as well as improvements in the methodologies used to measure disability. This research deals specifically with the physically disabled and often people with physical disabilities feel frustrated because they cannot do activities such as: playing sports and doing exercise. Having a physical disability also changes the way a person lives their life. They may find their life changes and activities they had previously included as part of their daily routine such as brushing their teeth, washing and doing household chores suddenly become a huge effort and many people require another person's help to carry out these activities. Also, they suffer from three basic challenges like; education, economic and, communication. Firstly, Education: The results of the investigation revealed that the physically handicapped. They face a lot of problems while studying they can't learn as the normal ones and they needs someone to help in learning. Secondly, Economic: they can't work and achieve income to help in his practical life. And finally Communication: they can't communicate with others because of his disability.
Enhancement of Online Stochastic Gradient Descent using Backward Queried Images
Stochastic gradient descent (SGD) is one of the preferred online optimization algorithms. However, one of its major drawbacks is its predisposition to forgetting previous data when optimizing through a data stream, also known as catastrophic interference. In this project, we attempt to mitigate this drawback by proposing a new low-cost approach which incorporates backward queried images with SGD during online training. Under this new approach, we propose that for every new training sample through the data stream, the neural network is optimized using the corresponding backward queried image from the initial dataset. After compiling the accuracy of the proposed method and SGD under a data-stream of 50,000 training cases with 10,000 test cases and comparing our algorithm to SGD, we see substantial improvements in the performance of the neural network with two different MNIST datasets (Fashion and Kuzushiji), classifying the MNIST datasets at a high accuracy for the mean, minimum, lower quartile, median, and upper quartile, while maintaining lower standard deviation in performance, demonstrating that our proposed algorithm can be a potential alternative to online SGD.
Mentor Hunt App
The Information Technology (IT) area has shown great growth in recent years, even with the economic recession that 巴西 has been through and the impact of the coronavirus pandemic. It is estimated that by 2024 the area will have a deficit of more than 290 thousand professionals. However, companies still face other difficulties in hiring, especially people who are looking for their first job in the Information Technology area. Most part of these difficulties are lack of qualified manpower and high prerequisites to fill internship or junior positions. As a result, the objective of this project is: to develop a platform that connects people who seek guidance, improvement or professional relocation in the Information Technology area with professionals that already have the experience they are seeking. The first step was a research and analysis of similar platforms in the market, whose proposal involves mentoring or professional connections, and it concluded that there are no services that fully meet the project’s proposal. In the second step, a research was done about mobile development, highlighting Flutter and Firebase platform. The third step defined the application’s features, such as suggestion of users and mentors, search for users, become a mentor, private chat, video calls, Portuguese and English languages, light and dark themes and profile customization. The suggestion of users and mentors is done by a match with the registered users, relating their areas of work (where the user has experience) and the areas of interest of each one. For the coding of the project, Flutter and Firebase technologies were used. To design the app, it followed Material Design specifications. For testing and distribution, the app was published on Play Store, Google’s Android application platform. The tests were performed by both the researcher and a selected group of users to verify if the functionalities were in accordance to what was defined in the beginning of the project. Perceiving the correct functioning of the application, the project achieved the proposed objective. In addition, it expanded its reach area, because it is possible to find users and mentors from any other area of the market.