Business-to-Business E-Commerce

Turban, Efraim; King, David B.; Lee, Jae Kyu; Liang, Ting‐Peng; Turban, Deborrah C.

doi:10.1007/978-3-319-10091-3_4

Cited by 62 publications

(77 citation statements)

References 2 publications

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“…Neural network studies have also illustrated the method's ability to simulate complex hydrological characteristics across various regions and time periods [54,55]. In addition, artificial neural networks are highly capable of processing different types of data efficiently [56]. This allows the proposed model to use input data (GRACE, meteorological forcings, and soil types) similar to previous, well-established data assimilation studies, yet depart from these physically based methods conceptually and offer a quicker computational time.…”

Section: Downscaling Grace Datamentioning

confidence: 97%

“…ANNs are particularly useful for this task as they are often employed in spatial data analysis and offer the ability to efficiently and comprehensively handle large, diverse, and noisy spatial datasets [56]. Because they are not yet widely used in statistical downscaling of remote sensing data, this research also represents a novel application of ANNs.…”

Section: The Neural Network Approach and Input Datamentioning

confidence: 99%

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Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

2018

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NASA's Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km 2 ). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km 2 ). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km 2 ) dataset of groundwater storage changes over a portion of California's Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model's simulated maps have the potential for application to local groundwater management initiatives in the region.

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Section: Downscaling Grace Datamentioning

confidence: 97%

Section: The Neural Network Approach and Input Datamentioning

confidence: 99%

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

2018

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“…In extracting, manipulating, and analyzing data, managers adopt various techniques; this is called Data Analysis. In the meantime, Artificial Neural Networks (ANN) [15][16][17][18] is a field of Artificial Intelligence (AI) [15][16][17][18] The Ascott Limited is a Singapore company that has grown to be the world's largest international serviced residence owner-operator. It has over 23,000 operating serviced residence units in key cities in the Asia Pacific, European, and Gulf regions, as well as over 10,000 units that are under development, making a total of more than 33,000 units in over 200 properties.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Development of Hotel Bankruptcy Prediction Model on Artificial Neural Network

Choi¹,

Lee²

2014

Journal of the Korea Society of Computer and Information

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This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which

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“…Turban et al (2005) suggests that measuring KM success is important from three aspects; first, it provides the organization's valuation; second, it helps justifying investment in knowledge management activities and third, it helps man-agement in concentrating on what is important for organizational performance. Jennex and Olfman (2004) have proposed a KM success model.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge Management Practice at a Bulgarian Bank

Shah

Rahneva

Ahmed

2014

International Journal of Knowledge Management

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This paper reports on knowledge management (KM) practices in the customer service and lending departments of one of Bulgaria's top retail banks and investigates how KM processes can be further improved. The Bank's KM activities have been studied using observations, interviews and informal discussions for data collection. Findings were compared and contrasted with existing literature in similar contexts. Although rudiments of knowledge sharing are evident from the KM activities in different departments of the bank, the limitations such as resistance to change of the implemented KM systems are impeding the effectiveness of the knowledge management process. More training and incentives are needed to increase knowledge creation and sharing. Moreover, a clearly articulated KM strategy along with success criteria and commitment and support from senior management is needed. There is a severe lack of knowledge management studies in Bulgarian context in general and Bulgarian banking sector in particular. The authors' findings will potentially help in improving knowledge sharing practice as well as provide a valuable insight into knowledge management related issues in the Bulgarian context. The findings from this research can be useful to companies from Eastern Europe and other regions in improving their knowledge sharing practice.

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Business-to-Business E-Commerce

Cited by 62 publications

References 2 publications

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

A Development of Hotel Bankruptcy Prediction Model on Artificial Neural Network

Knowledge Management Practice at a Bulgarian Bank

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