COVID19 coronavirus has widely infected more than 10 million people and killed more than 500,000 globally till July 1, 2020. In this paper, we describe a potential methodology, integration of image preprocess, Guided Grad-CAM, machine learning and risk management based on chest radiography images, as one of workable alarm and analysis systems to support clinicians against COVID-19 outbreak threat. We leverage pre-trained CNN models as backbone with further transfer learning to analyze public open datasets composed of 5851 chest radiography images for 4 classes classification, and 15478 images from COVIDx dataset for 3 classes classification, facilitated with steps of ROI and mask, and CNN layer visualization of guided grad-CAM to help CNN focused on critical infection focus in qualitative perspective. In quantitative perspective of 4 classes classification result, accuracy, average sensitivity, average precision, and COVID19 sensitivity of single ResNet50 and our second bagging ensemble model are (77.2%/78.8%/81.9%/100%) and (81.5%/81.4%,86.8%/100%) respectively. Ensemble way of several CNNs and other machine learning methods used here is to contribute about 4% accuracy improvement on top of best single CNN (ResNet50). In our 3 classes classification, those metrics of ensemble model and benchmark are (93.1%/90.1%/89.7%/83%) and (90%/85.9%, 82.4%/77%). We conclude ensemble approach would facilitate weaker classifier more. Beside to accuracy-oriented analysis, a cost minimization approach is suggested here to provide clinicians options of different risk consideration flexibility by trade off among different categories and performance rates.

The incidence of both non-melanoma and melanoma skin cancers has been increasing over the past decades. Currently, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year. In this study, we proposed an ensemble CNNs for multi-class classification study based on integration of image preprocess, deep learning and risk management on skin cancer dermoscopy images with Meta-data. We basically demonstrate general stacking ensemble flow by stages from data preprocessing, CNNs grouping, Meta-data concatenating and 1st CNNs ensemble and 2nd meta-classifiers ensemble. Our result showed good performance, especially the contribution from 1st ensemble while we check break-down details. With our 5% holdout validation dataset, we saw accuracy maximum 91% in our ensemble model, compared with best single CNN (DenseNet121) 87.3%. Furthermore, we also introduced cost adjustment analysis in ensemble phase to manipulate different risk management trade-off strategy for specific categories or metrics, rather than in deep learning phase, so that no need to retrain CNN via loss function adjustment.

Kinmen, the famous Cold War island also known as Quemoy, is a typical island with isolated power grids. It considers the promotion of renewable energy and electric charging vehicles to be two essential strategies to achieve the goal of a low-carbon island and smart grid. With this motivation in mind, the main objective of this study is to design and deploy an energy management system for hundreds of current PV sites distributed on the island, energy storage systems, and charging stations on the island. In addition, the real-time acquisition of the data for power generation, power storage, and power consumption systems will be used for future demand and response analysis. Moreover, the accumulated dataset will also be utilized for the forecast or prediction of renewable energy generated by the PV systems or power consumed by the battery units or charging stations. The results of this study are promising since a practical, robust, and workable system and database are developed and implemented with a variety of Internet of Things (IoT), data transmission technologies, and the hybrid of on-premises and cloud servers. Users of the proposed system can remotely access the visualized data through the user-friendly web-based and Line bot interfaces seamlessly.

In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep learning algorithm from the standpoint of feature space and similarity analysis. Firstly, we utilized Local Interpretable Model-agnostic Explanations (LIME) to justify the necessity of the region of interest (ROI) process and further prepared ROI via U-Net segmentation that masked out non-lung areas of images to prevent the classifier from being distracted by irrelevant features. The experimental results were promising, with detection performance reaching an overall accuracy of 95.5%, a sensitivity of 98.4%, a precision of 94.7%, and an F1 score of 96.5% on the COVID-19 category. Secondly, we applied similarity analysis to identify outliers and further provided an objective confidence reference specific to the similarity distance to centers or boundaries of clusters while inferring. Finally, the experimental results suggested putting more effort into enhancing the low-accuracy subspace locally, which is identified by the similarity distance to the centers. The experimental results were promising, and based on those perspectives, our approach could be more flexible to deploy dedicated classifiers specific to different subspaces instead of one rigid end-to-end black box model for all feature space.

This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness for physicians screening high-risk patients among skin cancer survivors. Using data from the 1248 skin cancer survivors extracted from five cancer registries, we benchmarked a random forest algorithm against MLP, C4.5, AdaBoost, and bagging algorithms for several metrics. Additionally, in this study, we leveraged the synthetic minority over-sampling technique (SMOTE) for the issue of the imbalanced dataset, cost-sensitive learning for risk assessment, and SHAP for the analysis of feature importance. The proposed random forest outperformed the other models, with an accuracy of 90.2%, a recall rate of 95.2%, a precision rate of 86.6%, and an F1 value of 90.7% in the SPSC category based on 10-fold cross-validation on a balanced dataset. Our results suggest that the four features, i.e., age, stage, gender, and involvement of regional lymph nodes, which significantly affect the output of the prediction model, need to be considered in the analysis of the next causal effect. In addition to causal analysis of specific primary sites, these clinical features allow further investigation of secondary cancers among skin cancer survivors.

This study used artificial intelligence techniques to identify clinical cancer biomarkers for recurrent gastric cancer survivors. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random Forest using MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged the synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive exPlanations (SHAPs) for feature importance analysis in this study. Our proposed Random Forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86%, and an F1 of 88.2% on the recurrent category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI (body mass index), and gender; these features significantly affect the prediction model’s output and are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.