Artificial and convolutional neural networks applications to predict heating and cooling loads for residential buildings

Gas tungsten arc (GTA) welding is characterized by the use of non-consumable electrodes and shielding gas for effective welding of various metals. Welding quality can be evaluated by a visual test, but it has the disadvantage that accuracy is limited by the experience of the inspector. The quality of GTA welding can be predicted better through deep learning models that use machine vision, image processing, and welding data such as voltage, current, and feeding speed. In this study, artificial neural network (ANN) models and convolutional neural network (CNN) models are developed for estimating welding quality using v-groove GTA welding experiments. The GTA welding system comprises welding equipment and a vision system. After completing the welding experiments, 21 features and 1 label are collected from the welding data. The observations are then normalized, shuffled, and divided into training data and test data. ANN and CNN models are developed with varied architectures, then verified by calculating root mean squared error, mean absolute error, and accuracy. ed ANN models are developed by applying the relative weight and product measure methods to the original ANN models. The abstract ANN models show similar or better accuracy with fewer features.

Comparative analysis of deep learning models for predicting biocompatibility in tissue scaffold images.

Improving the accuracy of predicting the performance of solar collectors through clustering analysis with artificial neural network models

This study aimed to diagnose healthy and misfire conditions in vehicle engines using vibration data recorded by a low-cost ADXL1002 accelerometer interfaced with a BeagleBone Black controller. Vibration signals were acquired from a vehicle engine using a cost-effective microelectromechanical system (MEMS) accelerometer. The data was analyzed using artificial neural network (ANN) and convolutional neural networks (CNN) models. The experimental results demonstrated that the two-dimensional (2D) CNN and 2D DCNN models significantly outperformed the ANN and one-dimensional (1D) CNN models in terms of prediction accuracy. Specifically, the validation accuracies were 90.12%, 92.42%, 96.52%, and 98.21% for ANN, 1D CNN, 2D CNN, and 2D DCNN, respectively. Furthermore, detailed accuracy analysis revealed that the 2D DCNN model achieved the highest prediction accuracy of 99% with healthy and 97% with misfire conditions. The findings indicate that transforming 1D vibration signals into 2D grayscale images enhances the model’s ability to distinguish between different engine conditions. Thus, employing 2D neural network architectures in conjunction with low-cost ADXL1002 accelerometer proves to be a highly effective approach for diagnosing complex systems such as vehicle engines.

Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer

In this chapter, a methodology for the utilization of edge-detector based hybrid artificial neural network (ANN) models for urinary bladder cancer is presented. The diagnosis of the bladder cancer can often be complex and it requires invasive diagnostic methods such as biopsy and histopatological evaluation. ANN utilization can provide faster and less invasive diagnosis. The methodology of ANN utilization for urinary bladder cancer diagnosis is based on images obtained with confocal laser endomicroscope during cystoscopy. Such approach can be challenging from the standpoint of computational resources, due to ANN model complexity. Higher computational resources are often inaccessible, especially in clinical practice. Here lies a motive for simplification of ANN models for urinary bladder cancer diagnosis. For these reasons, edge detector-based hybrid models are introduced due to their simpler architectures. From obtained results, it can be noticed that the highest performances are achieved with Laplacian-based convolutional neural network (CNN) model. On the other hand, such approach requires more complex CNN architectures in comparison to gradient-based hybrid CNN models. If Sobel edge detector is utilized, similar classification performances are achieved with less complex CNN model.

Based on studies using high-medium resolution images, convolutional neural networks (CNNs) and semantic segmentation have shown superiority over classical machine learning (ML), particularly in small-scale mapping. However, few/no studies have assessed the techniques on coarse resolution image classification for extensive area land cover mapping. In this study, we evaluated the performance and feasibility of three CNN models (1-D CNN, 2-D CNN, and 3-D CNN), and U-net for coarse-resolution satellite image classification and compared them to a random forest (RF) classifier. We utilized time-series, coarse resolution (1 km) composite imageries acquired by FengYun-3C visible and infrared radiometer. Labeled datasets were collected as shapefiles and split into three independent datasets: training, validation, and test datasets, and preprocessed to meet each model's input format requirements. We conducted several experiments to optimize models and select the best models. Then, the best models were evaluated on an unseen dataset. Among the DL models, one-dimensional (1-D) CNN achieved the highest overall accuracy (OA) 0. 87 and kappa (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i>) 0.84, 2% higher than the best results attained by 2-D CNN, 3-D CNN, and U-net models. However, 1-D CNN is outperformed by RF which achieved 0.89 (OA) and 0.87 (k). Achieving the best and the second-best results using RF and 1-D CNN models, respectively, indicates the superiority of the pixel-based method and the insignificance of spatial information in coarse-resolution image classification. Furthermore, although the DL models can yield high accuracy, especially 1-D CNN, they are less feasible than RF classifiers for coarse-resolution satellite image classification in extensive area land cover mapping.

INTRODUCTION: When Diabetic Retinopathy (DR) is not identified promptly; it frequently results in sight impairment. To properly diagnose and treat DR, preprocessing of picture methods and precise prediction models are essential. With the help of numerous well-liked filters and a Deep CNN (Convolutional Neural Network) model, the comprehensive method for DR image preparation and prognosis presented in this research is described. Using the filters that focus boundaries and contours in the ocular pictures is the first step in the initial processing stage. This procedure tries to find anomalies linked to DR. By the usage of filters, the excellence of pictures can be developed and minimize disturbances, preserving critical information. The Deep CNN algorithm has been trained to generate forecasts on the cleaned retinal pictures following the phase of preprocessing. The filters efficiently eliminate interference without sacrificing vital data. Convolutional type layers, pooling type layers, and fully associated layers are used in the CNN framework, which was created especially for image categorization tasks, to acquire data and understand the relationships associated with DR. OBJECTIVES: Using image preprocessing techniques such as the Sobel, Wiener, Gaussian, and non-local mean filters is a promising approach for DR analysis. Then, predicting using a CNN completes the approach. These preprocessing filters enhance the images and prepare them for further examination. The pre-processed images are fed into a CNN model. The model extracts significant information from the images by identifying complex patterns. DR or classification may be predicted by the CNN model through training on a labeled dataset. METHODS: The Method Preprocessing is employed for enhancing the clarity and difference of retina fundus picture by removing noise and fluctuation. The preprocessing stage is utilized for the normalization of the pictures and non-uniform brightness adjustment in addition to contrast augmentation and noise mitigation to remove noises and improve the rate of precision of the subsequent processing stages. RESULTS: To improve image quality and reduce noise, preprocessing techniques including Sobel, Wiener, Gaussian, and non-local mean filters are frequently employed in image processing jobs. For a particular task, the non-local mean filter produces superior results; for enhanced performance, it may be advantageous to combine it with a CNN. Before supplying the processed images to the CNN for prediction, the non-local mean filter can assist reduce noise and improve image details. CONCLUSION: A promising method for DR analysis entails the use of image preprocessing methods such as the Sobel, Wiener, Gaussian, and non-local mean filters, followed by prediction using a CNN. These preprocessing filters improve the photos and get them ready for analysis. After being pre-processed, the photos are sent into a CNN model, which uses its capacity to discover intricate patterns to draw out important elements from the images. The CNN model may predict DR or classification by training it on a labeled dataset. The development of computer-aided diagnosis systems for DR is facilitated by the integration of CNN prediction with image preprocessing filters. This strategy may increase the effectiveness of healthcare workers, boost patient outcomes, and lessen the burden of DR.

To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30 min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction.

This study proposes a method to extract the signature bands from the deep learning models of multispectral data converted from the hyperspectral data. The signature bands with two deep-learning models were further used to predict the sugar content of the Syzygium samarangense. Firstly, the hyperspectral data with the bandwidths lower than 2.5 nm were converted to the spectral data with multiple bandwidths higher than 2.5 nm to simulate the multispectral data. The convolution neural network (CNN) and the feedforward neural network (FNN) used these spectral data to predict the sugar content of the Syzygium samarangense and obtained the lowest mean absolute error (MAE) of 0.400° Brix and 0.408° Brix, respectively. Secondly, the absolute mean of the integrated gradient method was used to extract multiple signature bands from the CNN and FNN models for sugariness prediction. A total of thirty sets of six signature bands were selected from the CNN and FNN models, which were trained by using the spectral data with five bandwidths in the visible (VIS), visible to near-infrared (VISNIR), and visible to short-waved infrared (VISWIR) wavelengths ranging from 400 to 700 nm, 400 to 1000 nm, and 400 to 1700 nm. Lastly, these signature-band data were used to train the CNN and FNN models for sugar content prediction. The FNN model using VISWIR signature bands with a bandwidth of ± 12.5 nm had a minimum MAE of 0.390°Brix compared to the others. The CNN model using VISWIR signature bands with a bandwidth of ± 10 nm had the lowest MAE of 0.549° Brix compared to the other CNN models. The MAEs of the models with only six spectral bands were even better than those with tens or hundreds of spectral bands. These results reveal that six signature bands have the potential to be used in a small and compact multispectral device to predict the sugar content of the Syzygium samarangense.

This study used a retrospective data analysis approach combined with model development and validation. The present study introduces a 2.5D convolutional neural network (CNN) model leveraging CT imaging to facilitate the early detection of malignant vertebral compression fractures (MVCFs), potentially reducing reliance on invasive biopsies. Vertebral histopathologic biopsy is recognized as the gold standard for differentiating between osteoporotic and malignant vertebral compression fractures (VCFs). Nevertheless, its application is restricted due to its invasive nature and high cost, highlighting the necessity for alternative methods to identify MVCFs. The clinical, imaging, and pathologic data of patients who underwent vertebral augmentation and biopsy at institution 1 and institution 2 were collected and analyzed. On the basis of the vertebral CT images of these patients, 2D, 2.5D, and 3D CNN models were developed to identify the patients with osteoporotic vertebral compression fractures (OVCF) and MVCF. To verify the clinical application value of the CNN model, two rounds of reader studies were performed. The 2.5D CNN model performed well, and its performance in identifying MVCF patients was significantly superior to that of the 2D and 3D CNN models. In the training data set, the area under the receiver operating characteristic curve (AUC) of the 2.5D CNN model was 0.996 and an F1 score of 0.915. In the external cohort test, the AUC was 0.815 and an F1 score of 0.714. And clinicians' ability to identify MVCF patients has been enhanced by the 2.5D CNN model. With the assistance of the 2.5D CNN model, the AUC of senior clinicians was 0.882, and the F1 score was 0.774. For junior clinicians, the 2.5D CNN model-assisted AUC was 0.784 and the F1 score was 0.667. The development of our 2.5D CNN model marks a significant step toward noninvasive identification of MVCF patients. The 2.5D CNN model may be a potential model to assist clinicians in better identifying MVCF patients.

Accurate prediction of heating load (HL) and cooling load (CL) in residential buildings is essential for efficient building energy management. In this study, a hybrid machine learning (ML) model is developed to estimate the HL and CL of the energy efficient residential buildings from eight input parameters: the relative compactness, the roof area, wall area, surface area, glazing area, the overall height, the orientation, and the glazing area distribution. This is a novel model based on the multi-output radial basis function neural networks (RBFNNs) optimized using mountain gazelle optimizer (MGO). The performance of MGO-RBFNN is then compared with two metaheuristics based RBFNN models, namely particle swarm optimization (PSO) and Genetic algorithm (GA), and benchmarked against generalized regression neural network, extreme learning machine, and a gradient based RBFNN. The results show that the proposed multi-output optimized RBFNN models considerably improved both the training and testing accuracy performance of the original RBFNN. The MGO-RBFNN model (testing performance: determination coefficient (R2) of 0.99 and 0.97; root mean squared (RMSE) error of 0.84, and 3.09; and mean absolute percentage error (MAPE) of 3.90, and 13.02, respectively, for HL and CL) outperformed both the PSO and GA-optimized RBFNN, and other compared ML models. The relevancy factor analysis demonstrated that building energy consumption is positively correlated with relative compactness, roof area, glazing area, overall height, and glazing area distribution, whereas orientation, wall area, and surface area have a negative effect on it. Moreover, overall height and relative compactness are the most influential variables affecting building energy consumption.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). In this retrospective study, CT images and clinical information (age, sex and medical history) from 1020 participants were collected and divided into a single-centre training set (n = 760; age: 55.8 ± 13.4years; men: 500), a single-centre testing set (n = 134; age: 53.1 ± 14.3years; men: 90), and two independent multicentre testing sets from two different hospitals (n = 62, age: 57.97 ± 11.88, men: 41; n = 64, age: 57.40 ± 13.36, men: 35). A Faster Region-based CNN (Faster R-CNN) model was applied to integrate CT images and clinical information. Then, a result merging technique was used to convert 2D inferences into 3D lesion results. The diagnostic performance was assessed on the basis of the receiver operating characteristic (ROC) curve, free-response ROC (fROC) curve, precision, recall (sensitivity), F1-score, and diagnosis time. The classification performance was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity. The CNN model showed improved performance on fresh, healing, and old fractures and yielded good classification performance for all three categories when both clinical information and CT images were used compared to the use of CT images alone. Compared with experienced radiologists, the CNN model achieved higher sensitivity (mean sensitivity: 0.95 > 0.77, 0.89 > 0.61 and 0.80 > 0.55), comparable precision (mean precision: 0.91 > 0.87, 0.84 > 0.77, and 0.95 > 0.70), and a shorter diagnosis time (average reduction of 126.15s). A CNN model combining CT images and clinical information can automatically detect and classify rib fractures with good performance and feasibility in actual clinical practice. • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categoriesthan experienced radiologists with a shorter diagnosis time in actual clinical practice.

BackgroundTimely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response.ObjectiveThe aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set.MethodsWe first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word “measles” posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings.ResultsCohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642).ConclusionsThe proposed scheme can successfully classify the public’s opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola.