Despite recent advances in precision medicine, lung cancer remains the leading cause of cancer-related mortality worldwide. To determine the prognosis of non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer, comprehensive analysis of various clinical factors are necessary. Artificial intelligence can help physician quickly identify key information from the vast amount of medical information including positron emission tomography (PET) scan. In this study, we compared image feature-extraction models and survival estimation models to determine an optimal model that effectively extracts features related to survival time. We collected PET image data of 2,685 patients who were diagnosed with NSCLC and received treatment at the Chonnam National University Hwasun Hospital in South Korea over a period of seven years. We compared four convolution neural network models, DenseNet, NFNet, EfficientNet, and ResNet, and two survival estimation models, CoxPH and CoxCC. The best model was determined based on criteria such as C-index, mean absolute error (MAE), classification accuracy for survival status, and learning time. The results show that DenseNet combined with CoxPH delivers superior performance for most of the criteria. In particular, the MAE for this combination was very low (391.50 days), and the model predicted survival days well; the five-year classification accuracy, which can indicate a cure for cancer, was high (95%). Extracted features were visualized using Score-CAM; thus, the learning process of the model could be understood without requiring expert knowledge of PET. In addition, the learning time for this model was short.

Verifying pollen germination using microscopic images is a difficult task. It is usually time-consuming and may entail reduced accuracy and reproducibility. Therefore, in this study, we used random forest (RF) and convolutional neural network (CNN) models to perform image classification on raw data corresponding to pollens with different germination rates; the data were obtained via flow cytometry. A heat map, which was based on the RF analysis results, showed that the variables that significantly influenced the classification decision between NG and 60G categories were mainly located in the center and top-right regions of the 30×30 pixel image. Additionally, a variable importance plot showed that among the 900 input variables, pixel_316 was the variable that contributed the most toward prediction. Gradient-weighted class activation mapping was used to visualize the class activation maps of the CNN model. The bottom-left region of the activation map was activated in the NG image. However, the 60G image showed that not only the bottom-left region but also the top-right region was activated. Both the models classified the input images into NG and 60G categories with high accuracy. However, considering that the RF model does not reflect the characteristics of adjacent variables, the CNN model is more appropriate for classifying pollen germination images corresponding to pollen with various germination rates into distinct classes. Taken together, these results suggest that the CNN model can provide a reliable method for verifying the pollen performance.

To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore associations with sleep quality (SQ). We propose a deep learning model composed of a structure that decomposes and restores the signal in epoch units and a structure that predicts the SOL distribution. We used the Sleep Heart Health Study public dataset, which includes a large number of study subjects, to estimate and evaluate the proposed model. The proposed model estimated the SOL distribution and divided it into four clusters. The advantage of the proposed model is that it shows the process of falling asleep for individual participants as a probability graph over time. Furthermore, we compared the baseline of good SQ and SOL and showed that less than 10 minutes SOL correlated better with good SQ. Moreover, it was the most suitable sleep feature that could be predicted using early EEG, compared with the total sleep time, sleep efficiency, and actual sleep time. Our study showed the feasibility of estimating SOL distribution using deep learning with an early EEG and showed that SOL distribution within 10 minutes was associated with good SQ.

Sleep onset latency (SOL) is an important factor relating to the sleep quality of a subject. Therefore, accurate prediction of SOL is useful to identify individuals at risk of sleep disorders and to improve sleep quality. In this study, we estimate SOL distribution and falling asleep function using an electroencephalogram (EEG), which can measure the electric field of brain activity. We proposed a Multi Ensemble Distribution model for estimating Sleep Onset Latency (MEDi-SOL), consisting of a temporal encoder and a time distribution decoder. We evaluated the performance of the proposed model using a public dataset from the Sleep Heart Health Study. We considered four distributions, Normal, log-Normal, Weibull, and log-Logistic, and compared them with a survival model and a regression model. The temporal encoder with the ensemble log-Logistic and log-Normal distribution showed the best and second-best scores in the concordance index (C-index) and mean absolute error (MAE). Our MEDi-SOL, multi ensemble distribution with combining log-Logistic and log-Normal distribution, shows the best score in C-index and MAE, with a fast training time. Furthermore, our model can visualize the process of falling asleep for individual subjects. As a result, a distribution-based ensemble approach with appropriate distribution is more useful than point estimation.

Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autoregressive Neural Network (PaSTANet), which is a hybrid model that includes both a multi-kernel residual convolution neural network model and a Gaussian seasonality-trend model. To compare the performance of the PaSTANet, we used daily data from the Garak market for four crops: onion, radish, Chinese cabbage, and green onion, and performed long-term price forecasts for one year in 2023. The PaSTANet shows good performance on all four crops compared to other conventional statistical and deep learning-based models. In particular, for onion, the (mean absolute error (MAE) for the long-term forecast of 2023 is 107, outperforming the second-best Prophet (152) by 29.6%. Chinese cabbage, radish, and green onion all outperform the existing models with MAE of 2008, 3703, and 557, respectively. Moreover, using the confidence interval, the predicted price was categorized into three intervals: probability, caution, and warning. Comparing the percentage of classified intervals about the true prices in our test set, we found that they accurately detect the large price volatility.

Sleep is associated with mental and physical health; therefore, it is important to pre-assess sleep quality for daily life. However, previous sleep studies, such as self-reporting and sleep stage (SS) classification, have limitations in their ability to assess sleep quality at an early stage. Therefore, a new approach based on the pre-estimation system for sleep quality using a sensor-based model is required. In this study, we propose a distribution-based deep learning model to pre-assess sleep quality using the estimation of the falling asleep process (FAP) at the early SS. Sleep onset latency (SOL) is considered to pre-estimate the FAP because SOL is associated with insomnia and could be observable at the beginning of sleep. Moreover, multimodal polysomnography (PSG) signals, including two-channel electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG), are used to consider the complexity of sleep dynamics. The advantage of the proposed method is combining each distribution from PSG with the importance score to compare influence and show the probability of wake over time. To evaluate the model performance, the feature extraction and temporal encoder with the previous SS module are compared, respectively, and the proposed model achieved the best mean absolute error (MAE) (8.65) and concordance index (C-index) (0.708) among baseline models, including AttnSleep, TinySleepNet, and MEDI-SOL, while maintaining a competitive Brier score (BS) (0.041) and negative binomial log-likelihood (NBLL) (0.145). As a result, the main contribution of this study is that it can pre-assess sleep quality by estimating a probability over time of when a subject will fall asleep.