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Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling. [ABSTRACT FROM AUTHOR]

Summary: Many electricity consumers around the world are billed electric peak load charges. In the United States, these demand charges can contribute up to 70% of a consumer's electricity bill. Research has shown that the financial impact of these charges to the consumers can be reduced by acting on the intelligence provided by peak electric load day (PELD) forecasts. Unfortunately, published research detailing PELDs forecasting methodologies for facilities adopting behind the meter renewable electricity generation (BTMREG) is non‐existent. This paper contributes: (a) a PELD forecasting methodology applicable to consumers with BTMREG. A case study demonstrates the methodology's ability to achieve 93% of the potential savings in kW, translating to demand charge reductions of approximately US$ 142 129 per year. (b) The first side‐by‐side performance comparisons of electric load and PELD forecasting models for scenarios with and without BTMREG. (c) The first PELD forecasting model savings comparison for scenarios with and without BTMREG. Experimental results suggest that counterintuitively, BTMREG adoption can translate into higher peak load savings for electricity consumers. [ABSTRACT FROM AUTHOR]

This paper presents a sensitivity analysis of similar days (SD) parameters to increase the accuracy of artificial neural network (ANN) and SD-based short-term price forecasting. Work presented in this paper is an extended version of previous works done by the authors to integrate ANN and SD method for predicting electricity price. The focus here is on sensitivity analysis of SD parameters while keeping the parameters same for ANN to forecast hourly electricity prices in the Pennsylvania--New Jersey--Maryland (PJM) (regional transmission organization in northeast America) electricity market. Several cases are simulated by choosing (a) two, (b) three, (c) four, and (d) five SD parameters to calculate the norm. In addition, sensitivity analysis has been carried out by changing the time framework of SD (d = 15,30, 45, 60) and the number of selected similar price days (N = 5, 10). From sensitivity analysis, it is identified that the optimized mean absolute percentage error (MAPE) is obtained using case-c with d = 30 and N = 10. MAPE of reasonably small value, along with forecast mean square error and mean absolute error of around 2$/MWh and 1$/MWh, is obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by the developed ANN model based on the optimized case are accurate and efficient. [ABSTRACT FROM AUTHOR]

Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons. Author summary: Modelling the prevalence of an infectious disease enables public health organisations to prepare for and minimise its impact. However, traditional disease indicators are often quite restrictive as they provide information with a significant delay. Recent research efforts have provided evidence of the value of alternative information sources such as Web search activity trends. Our work incorporates this information into influenza forecasting models to achieve state-of-the-art accuracy. In addition, the proposed Bayesian neural network architectures also provide associated uncertainty estimates for the forecasts, positioning our methodology as a practical complementary tool for disease surveillance and policy making. [ABSTRACT FROM AUTHOR]

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set. [ABSTRACT FROM AUTHOR]

The debate about the efficacy of mergers and acquisitions as a growth strategy in terms of ex-post value creation has been developing for decades. This paper aims to create an artificial neural network that examines trends in the financials and marks the potential sources of value creation in the high-tech industry mergers between 2011 and 2021. The findings demonstrate that ANN can be implemented as a highly efficient model for analyzing complex financial events due to its flexibility and lack of prior assumptions about the data. [ABSTRACT FROM AUTHOR]

Floods are significant global hazards that generally lead to loss of lives and billions worth of properties, especially in flood-prone regions. Various excellent artificial intelligence algorithms that are aimed at predicting streamflow to minimize the effects of floods are proposed by researchers. Most of these models depend on the assumption that both training and testing data set are similar and sufficient. However, in reality, many of these data set varies along with time and are insufficient in some new basins. Motivated by the success of transfer learning in natural language processing, image processing and time series forecasting. In this paper, we proposed two hybrid transfer learning models for streamflow forecasting. The proposed models, which integrate Gated Recurrent Unit (GRU) with transfer learning and integration of Long Short Term Memory (LSTM) with transfer learning, are compared to our reference model. The proposed coupled Transfer Learning with the GRU (TL+GRU) model outperforms the baseline models, i.e., the transfer learning model and the coupled Transfer Learning with LSTM model (TL+LSTM) for most of the basins when streamflow and precipitation data set from Model Parameter Estimation Experiment (MOPEX) basins in the United States of America is used. As a result, we can finally conclude that, with Artificial Neural Networks' (ANN) integration to transfer learning, more enhanced performance are obtained. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

The Ogallala Aquifer, located in the Central Plains of the United States, is essential for agricultural irrigation and public water supply. Indiscriminate pumping from the aquifer has caused several negative impacts, such as deterioration of water quality and depletion of groundwater levels, which urgently demand better management. This paper applies hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) for predicting annual groundwater levels in 403 wells of the Ogallala Aquifer. First, the methodology employed HCA to cluster homogeneous wells based on the time series of groundwater levels. Then, the study calibrated an ANN model for each cluster (composed of one or more wells) using previous annual values of groundwater levels as input. The HCA results showed a particular pattern in the spatial distribution of the 30 found clusters, revealing that the Ogallala Aquifer holds higher groundwater levels in the western part, which gradually decrease, advancing to the east. The ANN models provided proper predictions even for wells outside of the calibration data set. This investigation concludes that the integration of HCA and ANN enabled single models to accurately forecast annual groundwater levels for sets of wells in the Ogallala Aquifer. [ABSTRACT FROM AUTHOR]

Water distribution network (WDN) leakage is estimated to account for more than 20% of potable water in the United States. Because existing leakage detection practices are mainly laborious or require excessive budget allocation, novel model-based techniques have become prevalent by employing smart meters. Specifically, incorporating machine learning and metaheuristic optimization methods into such model-based approaches to predict water pipeline leakage is one of the state-of-the-art approaches. This paper presents a predictive leakage detection approach by leveraging advanced metering infrastructure (AMI) and cyber-monitoring data. Primarily, the synchronous collection of consumption and monitoring data (i.e., flow and pressure) constitutes the proposed predictive tool in a combinative framework of genetic algorithms (GA) and particle swarm optimization (PSO). The time-consuming EPANET 2.2 simulation toolkit in MATLAB is circumvented by a set of trained artificial neural networks (ANNs). Sensitivity analyses are conducted to investigate the sensitivity of the proposed leakage prediction approach to multiple parameters. Ultimately, the number and location of the flow and pressure monitoring stations are optimized using maximum sensor availability thresholds. Mean absolute percentage error (MAPE) is employed to measure the model's accuracy. The proposed predictive tool offers insightful horizons on how considerable background leakage can be detected and prioritized without using labor-intensive, costly methods. [ABSTRACT FROM AUTHOR]

Technology forecasting is an important and critical issue that determines the starting point of planning and is considered as a management tool directly related to the future. In the previous research items, the development of renewable energy technologies was of concern. Moreover, due to the increasing need of countries to produce electricity and facing the lack of resources, this research focuses on forecasting photovoltaic technology. Accordingly, in this paper, for technological research in the field of solar energy, the patents extracted from one of the most famous renewable energy databases in the United States (US patent database) between 200 and 2020 were examined. Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. The results show that in the future, photovoltaic technology research will move towards the third generation of technology (organic materials) as well as focus on environmental parameters and their effects on the performance of photovoltaic systems. [ABSTRACT FROM AUTHOR]

The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neural network (MIDAS-BP model) to forecast carbon dioxide emissions. Such analysis uses mixed frequency data to study the effects of quarterly economic growth on annual carbon dioxide emissions. The forecasting ability of MIDAS-BP is remarkably better than MIDAS, ordinary least square (OLS), polynomial distributed lags (PDL), autoregressive distributed lags (ADL), and auto-regressive moving average (ARMA) models. The MIDAS-BP model is suitable for forecasting carbon dioxide emissions for both the short and longer term. This research is expected to influence the methodology for forecasting carbon dioxide emissions by improving the forecast accuracy. Empirical results show that economic growth has both negative and positive effects on carbon dioxide emissions that last 15 quarters. Carbon dioxide emissions are also affected by their own change within 3 years. Therefore, there is a need for policy makers to explore an alternative way to develop the economy, especially applying new energy policies to establish a low carbon society. [ABSTRACT FROM AUTHOR]

Conventional statistical postprocessing techniques offer limited ability to improve the skills of probabilistic guidance for heavy precipitation. This paper introduces two artificial neural network (ANN)-based, geographically aware, and computationally efficient postprocessing schemes, namely, the ANN-multiclass (ANN-Mclass) and the ANN–censored, shifted gamma distribution (ANN-CSGD). Both schemes are implemented to postprocess Global Ensemble Forecast System (GEFS) forecasts to produce probabilistic quantitative precipitation forecasts (PQPFs) over the contiguous United States (CONUS) using a short (60 days), rolling training window. The performances of these schemes are assessed through a set of hindcast experiments, wherein postprocessed 24-h PQPFs from the two ANN schemes were compared against those produced using the benchmark quantile mapping algorithm for lead times ranging from 1 to 8 days. Outcomes of the hindcast experiments show that ANN schemes overall outperform the benchmark as well as the raw forecast over the CONUS in predicting probability of precipitation over a range of thresholds. The relative performance varies among geographic regions, with the two ANN schemes broadly improving upon quantile mapping over the central, south, and southeast, and slightly underperforming along the Pacific coast where skills of raw forecasts are the highest. Between the two schemes, the hybrid ANN-CSGD outperforms at higher rainfall thresholds (i.e., >50 mm day−1), though the outperformance comes at a slight expense of sharpness and spatial specificity. Collectively, these results confirm the ability of the ANN algorithms to produce skillful PQPFs with a limited training window and point to the prowess of the hybrid scheme for calibrating PQPFs for rare-to-extreme rainfall events. [ABSTRACT FROM AUTHOR]

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC). [ABSTRACT FROM AUTHOR]

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The Infant Mortality Rate (IMR) is defined as the number of infants for every thousand infants that do not survive until their first birthday. IMR is an important metric not only because it provides information about infant births in an area, but it also measures the general societal health status. In the United States of America, the IMR is higher than many other developed countries, despite the high level of prosperity. It is important to note here that the U.S.A. exhibits strong and persistent inequalities in the IMR across different racial and ethnic groups (Kochanek et al. in Natl Vital Stat Rep 65(4):1–122, 2006). In this paper, we study predictive models in the problem of infant mortality. We implement traditional machine learning models and state-of-the-art neural network models with various combinations of features extracted from birth certificates. Those combinations include features that can be summed as socio-economic and ethical features related to the mother and the father of the infant and medical measurements during the pregnancy and the delivery. We approach the classification problem of infant mortality, whether an infant will survive until her first birthday or not, both as binary and multi-class based on the time of death. We focus on understanding and exploring the importance of features extracted from the birth certificates. For example, we test the performance of models trained on the general population to models trained in subsets of the population, e.g., for individual races. We show in our experimental evaluation comparisons between different predictive models (including those used by epidemiology researchers), various combinations of features, different distributions in the training set and features' importance. [ABSTRACT FROM AUTHOR]

Liu, C., 2019. Attribution of marine crop production in the United States of America based on artificial neural network learning. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research Journal of Coastal Research, Special Issue No. 98, pp. 277–283. Coconut Creek (Florida), ISSN 0749-0208. Marine crops play an important role in the food chain and in agriculture. Marine crop products are used as livestock feed and to meet people's food requirements. This paper examines what factors cause differences in marine crop production and attribution to marine crop yield. The paper builds a marine crop production model based on an artificial neural network. Then it analyzes the sensitivity of all factors to understand their contributions to marine crop production. [ABSTRACT FROM AUTHOR]

In this paper we present a method for the recognition of hand-written digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented. The classification task is performed using a Convolutional Neural Network (CNN). CNN is a special type of multi-layer neural network, being trained with an optimized version of the back-propagation learning algorithm. CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters), and with robustness to distortions and simple geometric transformations. The main contributions of this paper are related to the original methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background. By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology) database. [ABSTRACT FROM AUTHOR]

• This paper proposed a novel wind speed forecasting algorithm. • A new dataset division mechanism based on the moving window is used in this paper. • In order to improve the accuracy and stability simultaneously, the multi-objective particle swarm optimization is applied. • We compared the proposed MOPSO-Combined method with the combined model that published before in the paper. Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. [ABSTRACT FROM AUTHOR]

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to demonstrate how the use of data mining (DM) analysis can be used to evaluate how well cameras that monitor red-light-signal controlled intersections improve traffic safety by reducing fatalities. Design/methodology/approach - The paper demonstrates several different data modeling techniques - decision trees, neural networks, market-basket analysis and K-means models. Decision trees build rule sets that can abet future decision making. Neural networks try to predict future outcomes by looking at the effects of historical inputs. Market-basket analysis shows the strength of the relationships between variables. K-means models weigh the impact of homogenous clusters on target variables. All of these models are demonstrated using real data gathered by the Department of Transportation from fatal accidents at red-light-signal controlled intersections in Maryland and Washington, IX: from the year 2000 through 2003. Findings - The results of the DM analysis will show predictable relationships between the demographic data of drivers and fatal accidents; the type of collision and fatal accidents and between the time of day and fatal accidents. Research limitations/implications - The limitations of missing or incomplete data sets are addressed in this paper. Practical implications - This paper can act as a guide to follow for red light camera program managers or local municipalities to conduct their own analysis. Originality/value - This paper builds upon prior research in DM and also extends the body of research that examines the effectiveness of red camera programs as they mature. [ABSTRACT FROM AUTHOR]

This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A prediction module estimates two quality metrics of slip cast pieces through the simultaneous execution of two neural networks. A process improvement algorithm optimizes controllable process settings using the neural network prediction module in the objective function. An expert system module contains a hierarchy of two fuzzy logic rule bases. The rule bases prescribe processing times customized to individual production lines given ambient conditions, mold characteristics and the neural network predictions. This paper demonstrates the applicability of newer computational techniques to a very traditional manufacturing process and the system has been implemented at a major US plant. [ABSTRACT FROM AUTHOR]

Bank failure prediction is an important study for regulators in the banking industry because the failure of a bank leads to devastating consequences. If bank failures are correctly predicted, early warnings can be sent to the responsible authorities for precaution purposes. Therefore, a reliable bank failure prediction or early warning system is invaluable to avoid adverse repercussion effects on other banks and to prevent drastic confidence losses in the society. In this paper, we propose a novel self-organizing neural fuzzy inference system, which functions as an early warning system of bank failures. The system performs accurately based on the auto-generated fuzzy inference rule base. More importantly, the simplified rule base possesses a high level of interpretability, which makes it much easier for human users to comprehend. Three sets of experiments are conducted on a publicly available database, which consists of 3635 United States banks observed over a 21-year period. The experimental results of our proposed model are encouraging in terms of both accuracy and interpretability when benchmarked against other prediction models. [ABSTRACT FROM AUTHOR]

Ordinary differential equations (ODEs) are widely used to model complex dynamics that arise in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally challenging. In this paper, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with rectified linear unit (ReLU) activation function. Our method is able to recover the ODE system without being subject to the curse of dimensionality and the complexity of the ODE structure. We have shown that our method is consistent if the ODE possesses a general modular structure with each modular component involving only a few input variables, and the network architecture is properly chosen. Theoretical properties are corroborated by an extensive simulation study that also demonstrates the effectiveness of the proposed method in finite samples. Finally, we use our method to simultaneously characterize the growth rate of COVID-19 cases from the 50 states of the United States. • ODEs are used for modeling complex dynamics in various fields. • Calibrating complex ODE models with noisy data is difficult. • A two-stage nonparametric approach is proposed based on deep learning. • The approach is proven to be consistent. [ABSTRACT FROM AUTHOR]

In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model. [ABSTRACT FROM AUTHOR]

Several reservoirs across the United States are filling with sediment, which jeopardizes their functionality and increases maintenance costs. USACE developed the Reservoir Sedimentation Information (RSI) system to assess reservoir aggradation and track dam operation suitability for water resource management and dam safety. The RSI data set contains historical elevation-capacity data for approximately 400 dams (excluding navigation structures), which correspond to less than 1% of dams across the United States. Thus, there is a critical need to develop methods for estimating reservoir sedimentation for unmonitored sites. The goal of this project was to create a generalized method for estimating reservoir sedimentation rates using reservoir design information and watershed data. To meet this objective, geospatial tools were used to build a refined composite data set to complement the RSI system's data with precipitation and watershed characteristics. Nine deep learning models were then used on the benchmark data set to determine its accuracy at predicting capacity loss for the RSI reservoirs: four supervised machine learning models, four deep neural network (DNN) models, and a multilinear power regression model. A DNN model, containing a progressively increasing node and layer construction, was deemed the most accurate, with R2 values from its calibration and validation data sets being 0.83 and 0.70, respectively. The best model was recalibrated over the entire data set, which showed greater accuracy on the prediction of the RSI reservoir's capacity loss, with an R2 of 0.81. This predictive model could be used to evaluate the capacity loss of unmonitored reservoirs, forecast sedimentation rates under future climate conditions, and identify reservoirs with the highest risk of losing functionality. Practical Applications: Many communities depend on reservoirs for a variety of socioeconomic benefits, such as providing reliable water sources and flood mitigation. Rivers entering reservoirs are a constant source of silt, sand, and gravel particles (i.e., sediments) that deposit slowly, filling the reservoirs over time, thus reducing their volume capacity and effectiveness. Surveys to measure reservoir capacities are labor intensive and expensive and many sites go unmonitored. This study provides prediction tools to estimate capacity loss over time using data derived from publicly available data sources (e.g., digital elevation models, monthly precipitation data, and the National Inventory of Dams). A key role of this tool is to forecast reservoir capacity loss over time under varying climate change conditions and relate the associated sedimentation processes to local and regional conditions. The tool can also be applied broadly to hindcast capacity loss for reservoirs with or without prior surveys for identifying high-risk sites that should be investigated further. USACE plans to use these prediction tools with the RSI database to conduct a national assessment of reservoir impacts, which will inform distributions of federal resources to address water security concerns related to reservoir sedimentation. [ABSTRACT FROM AUTHOR]

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. Detailed maps of tree species can be used for forest analysis, biogeographic research and ecosystem monitoring. This study provides individual tree species maps for 24 sites in the United States National Ecological Observatory Network (NEON), covering 80 tree species and 100 million individuals. [ABSTRACT FROM AUTHOR]

BACKGROUND: The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. OBJECTIVE: The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. METHOD: A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. RESULTS: 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. CONCLUSIONS: There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT Key Points: Artificial neural networks can accurately forecast semiseasonal groundwater level changesMuMoC improved the groundwater well‐level forecasting skill for 1‐ to 4‐month lead times with respect to a single ANN model by 25% in NSEThe implementation of MuMoC is recommended in case of densely gauged areas [ABSTRACT FROM AUTHOR]

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power. [ABSTRACT FROM AUTHOR]

Applications of artificial neural networks (ANNs) in forecasting flow rates at multiple sections in a river system are presented. Model formulations are based on learning characteristics of the actual and fractional storage variations in river reaches during unsteady flow. Multilayer perceptrons (MLP), MLPs with memory, time delay neural networks (TDNNs), and multiple gamma memory neural networks in three model forms are used to forecast flow rates in Tar River Basin, United States. Model performances are evaluated in terms of statistical criteria, RMS error, and coefficient of efficiency. Maximum RMS error resulted for the models are less than 6.50% of the respective observed mean value. A coefficient of efficiency value of more than 0.95 for the models indicates satisfactory performances. Results presented in this paper depict flow variations corresponding to implicitly specified storage variations and demonstrate applicability of the ANNs in real time flow forecasting for multiple sections in a basin obeying continuity principle. [ABSTRACT FROM AUTHOR]

We could get many helpful information and results from satellite remote sensing images and aerial images, including disaster monitoring, grid hidden danger identification, and electricity consumption management. In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible. With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources and improving analysing efficiency flexibly, we propose an assessing and verification framework based on bio-inspired perception and understanding, which summarizes the most appropriate image scale in the electric facilities place recognition. The proposed framework consists of different scenes aerial images datasets, several electric facilities place recognition methods, and credible evaluating methods mimicking mammals. Firstly, we gather satellite remote images and aerial images of sufficient electric power facilities in the United States via Google Earth and other public datasets. Then, several typical place recognition methods are adopted to testing recognition ability of multi-scale perception results, like SAD, NetVLAD, and GIST descriptor. To get more reliable result, multi-units and multi-scenes experiments are implemented roundly. After all experiments and evaluations, we could get that the most appropriate image scale is 1000 m size and the highest recognition accuracy of electric power facilities location is 500 m. Conclusion in our article shows the recommended perception form and scale closest to human cognition in the power grid management and maintenance. [ABSTRACT FROM AUTHOR]

The prediction of wave height and flux of energy is essential for most ocean engineering applications. To simultaneously predict both wave parameters, this paper presents a novel approach using short-term time prediction horizons (6h and 12h). Specifically, the methodology proposed presents a twofold simultaneity: 1) both parameters are predicted by a single model, applying the multi-task learning paradigm, and 2) the prediction tasks are tackled for several neighbouring ocean buoys with such single model by the development of a zonal strategy. Multi-Task Evolutionary Artificial Neural Network (MTEANN) models are applied to two different zones located in the United States, considering measurements collected by three buoys in each zone. Zonal MTEANN models have been compared in a two-phased procedure: 1) against the three individual MTEANN models specifically trained for each buoy of the zone, and 2) against some state-of-the-art regression techniques. Results achieved show that the proposed zonal methodology obtains not only better performance than the individual MTEANN models, but it also requires a lower number of connections. Besides, the zonal MTEANN methodology outperforms state-of-the-art regression techniques. Hence, the proposed approach results in an excellent method for predicting both significant wave height and flux of energy at short-term prediction time horizons. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. Methods: The Web of Science database was searched for titles of English‐language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R‐based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Results: Thirty‐seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors." Conclusion: Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed. [ABSTRACT FROM AUTHOR]

High temperatures within a data center can cause a number of problems, such as increased cooling costs and increased hardware failure rates. To overcome this problem, researchers have shown that workload management, focused on a data center's thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload's thermal effect on a data center, which will be suitable for real-time scenarios. We use machine learning techniques, such as artificial neural networks (ANN) as our prediction methodology. We use real data taken from a data center's normal operation to conduct our experiments. To reduce the data's complexity, we introduce a thermal impact matrix to capture the spacial relationship between the data center's heat sources, such as the compute nodes. Our results show that machine learning techniques can predict the workload's thermal effects in a timely manner, thus making them well suited for real-time scenarios. Based on the temperature prediction techniques, we developed a thermal-aware workload scheduling algorithm for data centers, which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing an endurable decline in performance. [ABSTRACT FROM AUTHOR]

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia-based models provide more accurate forecasts than Simple Sum-based models provided they are constructed within a nonlinear framework. [ABSTRACT FROM AUTHOR]

This paper describes a case study of the development and testing of a prototype system to support condition-based maintenance of the door systems of airport transportation vehicles. Every door open/close cycle produces a "signature" that can indicate the current degradation level of the door system. A combined statistical and neural network approach was used. Time, electrical current and voltage signals from the open/close cycles are processed in real-time to estimate, using the neural network, the condition of the door set relative to maintenance needs. Data collection hardware for the vehicle was designed, developed and tested to monitor door characteristics to quickly predict degraded performance, and to anticipate failures. The prototype system was installed on vehicle door sets at the Pittsburgh International Airport and tested for several months under actual operating conditions. Note to Practitioners-This paper describes the development of an automated system that consists of hardware and software to monitor the condition of a mechanical door set on board airport ground transportation vehicles. The aim is to use the current and past conditions to estimate (using neural network predictions) when the door set is in need of scheduled maintenance. This will allow airports to achieve very high availability rates (near 100%) for these vehicles, while also achieving cost effective maintenance policies. Currently, vehicles tend to be over-maintained because of the availability levels mandated by airport authorities. This system, although in prototype form, shows the viability of an automated condition-based maintenance approach. The drawback proved to be the variability among door sets from vehicle to vehicle, which means the predictive software of the system would need to be customized for each vehicle. Several patents, both U.S. and foreign; have been granted to this automated system. [ABSTRACT FROM AUTHOR]

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time. [ABSTRACT FROM AUTHOR]

The concern about water quality in inland water bodies such as lakes and reservoirs has been increasing. Owing to the complexity associated with field collection of water quality samples and subsequent laboratory analyses, scientists and researchers have employed remote sensing techniques for water quality information retrieval. Due to the limitations of linear regression methods, many researchers have employed the artificial neural network (ANN) technique to decorrelate satellite data in order to assess water quality. In this paper, we propose a method that establishes the output sensitivity toward changes in the individual input reflectance channels while modeling water quality from remote sensing data collected by Landsat thematic mapper (TM). From the sensitivity, a hypothesis about the importance of each band can be made and used as a guideline to select appropriate input variables (band combination) for ANN models based on the principle of parsimony for water quality retrieval. The approach is illustrated through a case study of Beaver Reservoir in Arkansas, USA. The results of the case study are highly promising and validate the input selection procedure outlined in this paper. The results indicate that this approach could significantly reduce the effort and computational time required to develop an ANN water quality model. [ABSTRACT FROM AUTHOR]

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired. [ABSTRACT FROM AUTHOR]

The COVID-19 health and economic crisis has affected all areas of social life globally, including the economy. The world economy has declined due to purchasing power for individuals who have been forced to stay at home and cannot perform work. These restrictions to prevent the spread of SARS-Cov-2 have led to an increase in electronic commerce and mobile commerce as tools for procuring goods and services. In this paper, we conducted a longitudinal analysis of mobile commerce as an essential electronic commerce component, establishing the main drivers of mobile commerce and the intensity of their influences. The research focuses on mobile commerce in the United States (U.S.). It covers the period 2010-2020, the last year of this period capturing the context of the COVID-19 pandemic and its impact on electronic commerce (e-commerce) and mobile commerce (m-commerce). In the macroeconomic analysis of competitiveness, we established the main drivers of m-commerce, using artificial neural networks and the mediation effects found between the variables that describe m-commerce, e-commerce, and total sales, using structural equation modeling. The research results indicate an increase in the share of e-commerce in total sales and a predominance of the m-commerce share in e-commerce on the background of traffic restrictions and social distance rules imposed due to the COVID-19 pandemic. Stakeholders in the m-commerce area should consider the following enhancing drivers: increasing internet speed, expanding 5G and Wi-Fi networks, and increasing accessibility and trust in mobile devices and applications. [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

Overall performance of energy infrastructure in the United States has been assessed as D+. More than 65% of America's energy is transported through the oil and gas pipelines, which have experienced more than 10,000 failures during the last three decades. There is a critical need for a failure prediction tool that can forecast the consequences of the hazardous failures. Failure of gas pipelines has become the subject of interest for some studies in the past. Previous studies mainly focused on physical models that need inspection data or developed subjective models. This paper aims at developing a model to forecast the consequences of the potential failures of such pipes using the historical data of the U.S. gas pipes network. The model applies a neurofuzzy technique in order to recognize the existing pattern among the input and output variables. It estimates the financial consequences of various failure scenarios for specific pipes in terms of size and specified minimum yield strength. For this purpose, a bowtie model is developed, and all possible scenarios of failure are identified. Various combinations of the identified factors and different number and types of membership functions, are applied in order to optimize the model's efficiency. The developed model is validated with an approximate accuracy of 80%. This study assists practitioners and academics who are working on the risk assessment of gas pipelines to plan for their lifecycle inspection. [ABSTRACT FROM AUTHOR]

Editorial. Examines the role of artificial neural networks (ANN) in elucidating patterns and extracting relationships from hydrology in the U.S. Description of an ANN; Uses; Goals of the Task Committee on Application of Artificial Neural Networks in Hydrology of the American Society of Civil Engineers.