A New Two-step Ensemble Learning Model for Improving Stress Prediction of Automobile Drivers

Commuting when there is a significant volume of traffic congestion has been acknowledged as one of the key factors causing stress. Significant levels of stress whilst driving are seen to have a profoundly negative effect on the actions and ability of a driver; this has the capacity to result in risks, hazards and accidents. As such, there is a recognized need to determine drivers’ levels of stress and accordingly predict the key causes responsible for high levels of stress. In this work, the objective is centred on providing an ensemble machine learning framework in order to determine the stress levels of drivers. Moreover, the study also provides a fresh set of data, as gathered from 14 different drivers, with data collection having taken place during driving in Amman, Jordan. Data was gathered via the implementation of a wearable biomedical instrument that was attached to the driver on a continuous basis in order to gather physiological data. The data gathered was accordingly categorised into two different groups: ‘Yes’, which represents the presence of stress, whilst ‘No’ represents the absence of stress. Importantly, in an effort to circumvent the negative impact of driver instances with a minority class on stress predictions, oversampling technique was applied. A two-step ensemble classifier was developed through bringing together the findings from random forest, decision tree, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers, which was then inputted into a Multi-Layer Perceptron neural network. The experimental findings highlight that the suggested framework is far more precise and has a more scalable capacity when compared with all classifiers in relation to accuracy, g-mean measures and sensitivity.

Embryoscope - An Advanced Embryo Monitoring Biomedical Instrument

Embryoscope is a machine which is used to monitor embryo from the time of conception. It allows fertility specialist to select the most viable embryo during in-vitro fertilization. This instrument reduces the risk of taking embryo from the incubator for observation. It can monitor 12 embryos at once and take photos of each embryo every 5 to 10 minutes during the entire incubation period. With the help of this instrument, fertility expert can also monitor the abnormality in embryo. The embryoscope is an incubator with an integrated camera. The detection of abnormality can be done without harming the embryo.

Mathematical Modeling and Computational Simulation of a New Biomedical Instrument Design

Endo surgiclip instrument is the biomedical instrument that can be applied for endoscopic surgery to assist surgeons in homeostasis and secure mucosal gap surfaces during surgical operations. Since some clinic feedbacks show the surgiclip drop-off incidents which can potentially sever organ and tissue, the improvement of endo surgiclip instrument has been made in these years. Since few research papers were involved in the study of endo surgiclip instrument performance via mathematical modeling and computational simulation, currently some instrumental modifications are mainly based on clinic lab tests which prolong the improvement cycle and increase additional manufacturing cost. This paper introduces a new biomedical surgiclip instrument based on mathematical modeling, computer-aided simulation, and prototype testing. The analytic methodology proposed in this paper can help engineers in biomedical industry develop and improve biomedical instrument. Compared to the current conventional surgiclip instruments, this new surgiclip instrument can properly assist surgeon in surgical procedure with less operational force and no surgiclip drop-off incident. The prototype has also been built and tested. Both computational simulation and prototype testing show close results which validate the feasibility of this newly developed endo surgiclip instrument and the methodologies of mathematical modeling based computational simulation proposed in this paper.

EDUCATION SYSTEM INTEGRATION IN CROSS-DISCIPLINARY WITH COLLEGES — VIRTUAL BIOMEDICAL INSTRUMENT (VBI) FOR EXAMPLE

Due to the rapid development of computer science, software, and biomedical engineering, many clinical diagnosis instruments and control system improved accordingly. The minimization and practicality of sensors, as well as the efficiency and stability of computers make simulation control system in medical instruments become the most important part of developing items. The relevant subject of course, virtual biomedical instrumentations (VBI), which combined with computer science, electrical engineering, and medical science, has become a major developing course in biomedical education. Cross-disciplinary research needs experts in different areas and most cross-disciplinary schools cannot afford it. So it requires integration and cooperation between schools. To address this issue, a graduated course has been designed to provide an opportunity for all professions in each field to discuss and share their resources on an e-learning platform. With the progress of the project, VBI course integrates biomedical education resources from north to south colleges to reduce the request of manpower for cross-disciplinary course of biomedical. In addition, VBI courses initiate the cooperation between academic community and industry.