Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA)

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer

We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.

Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos

Various studies have been conducted for detecting humans in images. However, there are the cases where a part of human body disappears in the input image and leaves the camera field of view (FOV). Moreover, there are the cases where a pedestrian comes into the FOV as a part of the body slowly appears. In these cases, human detection and tracking fail by existing methods. Therefore, we propose the method for predicting a wider region than the FOV of a thermal camera based on the image prediction generative adversarial network version 2 (IPGAN-2). When an experiment was conducted using the marathon subdataset of the Boston University-thermal infrared video benchmark open dataset, the proposed method showed higher image prediction (structural similarity index measure (SSIM) of 0.9437) and object detection (F1 score of 0.866, accuracy of 0.914, and intersection over union (IoU) of 0.730) accuracies than state-of-the-art methods.

Image Region Prediction from Thermal Videos Based on Image Prediction Generative Adversarial Network

Various studies have been conducted on object detection, tracking, and action recognition based on thermal images. However, errors occur during object detection, tracking, and action recognition when a moving object leaves the field of view (FOV) of a camera and part of the object becomes invisible. However, no studies have examined this issue so far. Therefore, this article proposes a method for widening the FOV of the current image by predicting images outside the FOV of the camera using the current image and previous sequential images. In the proposed method, the original one-channel thermal image is converted into a three-channel thermal image to perform image prediction using an image prediction generative adversarial network. When image prediction and object detection experiments were conducted using the marathon sub-dataset of the Boston University-thermal infrared video (BU-TIV) benchmark open dataset, we confirmed that the proposed method showed the higher accuracies of image prediction (structural similarity index measure (SSIM) of 0.9839) and object detection (F1 score (F1) of 0.882, accuracy (ACC) of 0.983, and intersection over union (IoU) of 0.791) than the state-of-the-art methods.

Enhancement of damaged-image prediction through Cahn–Hilliard image inpainting

We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn–Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn–Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction by applying the Cahn–Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage.