Explainable optimization of deep learning model for COVID-19 detection using chest images

Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations.

Heart rate modeling and prediction of construction workers based on physical activity using deep learning

Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin

BackgroundMore than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures.ObjectiveWe aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries.MethodsThe COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set.ResultsA total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea.ConclusionsThe CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Simultaneous attenuation and scatter correction of PET data in the image: quantitative and clinical assessment of image-to-image deep learning models.

The extent of renewable power generation usage has increased in recent times, and it will become inevitable soon to generate clean and green power. Microgrid units use renewable energy for power generation. The inclusion of Distributed energy resources (DER) will lead to intermittent power generation because of the continuously changing weather and seasonal conditions. The electricity consumption of the end-user also changes according to the time and the seasons. The source forecasting and the load forecasting becomes very important to schedule the energy storage device operations. In this paper, we use Solar energy as the source,solar irradiance changes with respect to place and time. In this article, Solar forecasting is performed for one month. If in case there are occurrences of an event like days of autonomy, effective load management can be performed provided a prior possession of knowledge about the source availability. We have different Artificial intelligence techniques like Fuzzy Logic, Machine learning (ML), and Deep learning (DL) mechanisms for source forecasting. When the microgrid is set up at a particular place, the irradiance, data and other parameters are considered for building the dataset. This article consists of a brief introduction about the microgrid and different source forecasting techniques. Machine learning algorithms and Deep learning algorithms are discussed and the efficiency of the various algorithms are compared using the Root Mean Square Error (RMSE) values.

Aim:This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses.Settings and Design:This was an in vitro study.Materials and Methods:A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the L*, a*, and b* values were recorded. The relationship between the L*, a*, and b* values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. L*, a*, and b* values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated.Statistical Analysis Used:Data were analyzed with the Student t-test (α=0.05).Results:The mean RMSE values and MAE values for the ANN algorithm (0.029 ± 0.0152 and 0.045 ± 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 ± 0.0005 and 0.002 ± 0.0008, respectively) (P < 0.001).Conclusions:Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.

The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers.

Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation.

Recommendation system is a subclass of information filtering system to help users find relevant items of interest from a large set of possible selections. Model-based collaborative filtering utilized the ratings of the user–item matrix dataset to generate a prediction. Essentially, this type of intelligent system plays a critical role in e-commerce, social network, and popular domains increasingly. In this research work, we present the comparison of the two widely used efficient techniques such as Biased Matrix Factorization and a regular Matrix Factorization, both using Stochastic Gradient Descent (SGD). We have conducted experiments on two real-world public datasets: Book Crossing and Movie Lens 100 K and evaluated by two metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Our experiments demonstrated that Biased Matrix Factorization used SGD technique results in a substantial increase in recommendation accuracy for rating prediction in experimental both datasets. Compute with a regular Matrix Factorization technique, Biased Matrix Factorization produced the reduction of the RMSE by 25.78% and MAE by 19.69% for Book Crossing dataset and RMSE by 19.69% and MAE by 14.08% for Movie Lens 100 K dataset. As expected when comparing the results of different datasets, Biased Matrix Factorization using SGD materialize less prediction error.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

A novel featurization methodology using JaGen algorithm for time series forecasting with deep learning techniques

Accurately predicting aerosol optical depth (AOD), a key parameter for characterizing atmospheric aerosols, is essential due to the increasing prevalence of air pollution and its detrimental effects. From existing literature on AOD time-series prediction, linear models like seasonal autoregressive integrated moving average (SARIMA) are commonly used, while nonlinear models such as machine learning (ML) and deep learning (DL) have gained popularity recently for their ability to handle nonlinear patterns. This study introduces a hybrid model, particle swarm optimization-seasonal autoregressive integrated moving average-support vector regression (PSOSARIMA-PSOSVR), which integrates linear and nonlinear modeling through residual modeling to significantly enhance AOD prediction accuracy. By combining these approaches, we aim to better handle the nonlinearities and overall variability in AOD data, leading to more accurate predictions. To address hyperparameter tuning challenges, including the risks of model misspecification and overfitting or underfitting, PSO is utilized for optimization. PSO’s evolutionary optimization ensures efficient tuning for optimal model performance. Monthly AOD data sourced from the moderate resolution imaging spectroradiometer (MODIS) satellite covering the northern Indian region from 2001 to 2019 is used for experiments. Performance metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and root mean square error ratio (RSR) are employed to evaluate model accuracy and dependability. The results of this study illustrate that the proposed PSOSARIMA-PSOSVR model outperforms both standalone PSOSARIMA and PSOSVR models. Moreover, it consistently surpasses SARIMA, SVR, and long short-term memory (LSTM) in AOD prediction, effectively addressing non-stationarity and variability challenges in AOD data. This study suggests the hybrid model as a promising approach for improving the accuracy of AOD prediction.

There is growing interest in imputing missing data in tabular datasets using deep learning. A commonly used metric in evaluating the performance of a deep learning-based imputation model is root mean square error (RMSE), which is a prediction evaluation metric. In this paper, we demonstrate the limitations of RMSE for evaluating deep learning-based imputation performance by conducting a comparative analysis between RMSE and alternative metrics in the statistical literature including qualitative, predictive accuracy, and statistical distance. To minimize model and dataset biases, we use two different deep learning imputation models (denoising autoencoders and generative adversarial nets) and a regression imputation model. We also use two tabular datasets with growing amounts of missing data from different industry sectors: healthcare and financial. Our results show that contrary to the commonly used RMSE metric, the statistical metric of Jensen Shannon distance best assessed the imputation models’ performance. The regression model also ranked higher than deep learning when evaluated using the Jensen Shannon metric.KeywordsImputationMissing dataDeep learningModel checkingMultiple imputationEvaluation metrics