Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach.

The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field.

Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant devices for CVA patients. We reviewed a total of 383 CVA patients’ (1623 observations) prescription data for eight different walking assistant devices from five hospitals. Among the classification models, the advanced tree-based classification models (LightGBM and tree models in AutoGluon) achieved classification results of over $90\%$ accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost $92\%$ in accuracy, recall, precision, and F1-score, and $86.8\%$ in balanced accuracy) but also demonstrated that the classification performances of the tree-based models were higher than that of the other models on its leaderboard. Therefore, we believe that tree-based classification models have potential as practical diagnosis tools for medical rehabilitation.

Along with recent achievements in deep learning empowered by enormous amounts of training data, preserving privacy of an individual related to the gathered data is becoming an essential part of public data collection and publication. Advancements in deep learning threaten traditional image anonymization techniques with model inversion attacks that try to reconstruct the original image from the anonymized image. In this paper, we propose a privacy-preserving adversarial protector network (PPAPNet) as an image anonymization tool to convert an image into another synthetic image that is both realistic and immune to model inversion attacks. Our experiments on various datasets show that PPAPNet can effectively convert a sensitive image into a high-quality and attack-immune synthetic image.

Suggesting more efficient driving routes generate benefits not only for individuals by saving commute time, but also for society as a whole by reducing accident rates and social costs ...

There is a strong positive correlation between the development of deep learning and the amount of public data available. Not all data can be released in their raw form because of the risk to the privacy of the related individuals. The main objective of privacy-preserving data publication is to anonymize the data while maintaining their utility. In this paper, we propose a privacy-preserving semi-generative adversarial network (PPSGAN) that selectively adds noise to class-independent features of each image to enable the processed image to maintain its original class label. Our experiments on training classifiers with synthetic datasets anonymized with various methods confirm that PPSGAN shows better utility than other conventional methods, including blurring, noise-adding, filtering, and generation using GANs.