Spatio-Temporal Neural Network Approach for Location Prediction State-of-the-Art, Challenges and Future Directions

2019 ◽  
Vol 7 (5) ◽  
pp. 1057-1067
Author(s):  
N. Venkata Subba Reddy ◽  
D. S. R. Murthy
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Location Prediction ◽  
Network Approach ◽  
Neural Network Approach ◽  
Future Directions ◽  
Spatio Temporal

scholarly journals Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach

2017 ◽  
Author(s):  
Takuo Hamaguchi ◽  
Hidekazu Oiwa ◽  
Masashi Shimbo ◽  
Yuji Matsumoto
Keyword(s):  
Neural Network ◽  
Knowledge Base ◽  
State Of The Art ◽  
Test Time ◽  
Network Approach ◽  
Missing Information ◽  
Neural Network Approach ◽  
Training Time ◽  
Proposed Model ◽  
Graph Neural Networks

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC: how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time. The experimental results show the effectiveness of our proposed model in the OOKB setting. Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset.

scholarly journals Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

2021 ◽  
Author(s):  
Toly Chen ◽  
Yu-Cheng Lin
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Feedforward Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Forecasting Accuracy ◽  
Hit Rate ◽  
The Past ◽  
Acceptable Range ◽  
Accuracy And Precision

AbstractMost existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.

scholarly journals Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3537
Author(s):  
Jean-Marie Vient ◽  
Frederic Jourdin ◽  
Ronan Fablet ◽  
Baptiste Mengual ◽  
Ludivine Lafosse ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
State Of The Art ◽  
Satellite Observations ◽  
Data Driven ◽  
Network Approach ◽  
Satellite Measurements ◽  
Neural Network Approach ◽  
Reconstruction Performance

Due to complex natural and anthropogenic interconnected forcings, the dynamics of suspended sediments within the ocean water column remains difficult to understand and monitor. Numerical models still lack capabilities to account for the variabilities depicted by in situ and satellite-derived datasets. Besides, the irregular space-time sampling associated with satellite sensors make crucial the development of efficient interpolation methods. Optimal Interpolation (OI) remains the state-of-the-art approach for most operational products. Due to the large increase of both in situ and satellite measurements more and more available information is coming from in situ and satellite measurements, as well as from simulation models. The emergence of data-driven schemes as possibly relevant alternatives with increased capabilities to recover finer-scale processes. In this study, we investigate and benchmark three state-of-the-art data-driven schemes, namely an EOF-based technique, an analog data assimilation scheme, and a neural network approach, with an OI scheme. We rely on an Observing System Simulation Experiment based on high-resolution numerical simulations and simulated satellite observations using real satellite sampling patterns. The neural network approach, which relies on variational data assimilation formulation for the interpolation problem, clearly outperforms both the OI and the other data-driven schemes, both in terms of reconstruction performance and of a greater ability to recover high-frequency events. We further discuss how these results could transfer to real data, as well as to other problems beyond interpolation issues, especially short-term forecasting problems from partial satellite observations.

Extending Learning Feasibility Through Feedforward Sequential Learning

1998 ◽  
Vol 2 (6) ◽  
pp. 228-233
Author(s):  
Michael K. Weir ◽  
◽  
Li Hui Chen ◽  
Keyword(s):  
Neural Network ◽  
Sequential Learning ◽  
Network Approach ◽  
Feedforward Networks ◽  
Neural Network Approach ◽  
Indexing Scheme ◽  
State Sequence ◽  
Final Weight ◽  
Single State ◽  
Spatio Temporal

In this paper, a sequence-based neural network approach called feedforward sequential learning (FSL) is proposed for extending the range of feasibility for feedforward networks in the three areas of architecture, training, and generalization. The extension is enabled through a spatio-temporal indexing scheme that decomposes the task into a sequence of simpler subproblems. Each subproblem is then solved by a separate weight state. The separate trained weight states are then combined into a continuous final weight state sequence to enable smooth generalization. FSL can be used to train mappings of analog or discrete I/O with underlying continuity for pattern association or classification. Implementation of FSL is illustrated and tested by learning the 2-spirals problem and an extended 4-spiral version. Training is found to be faster and more robust than its single-state counterpart. The generalization obtained indicates that the underlying patterns are classified more smoothly with FSL. Overall, the results suggest FSL to be a feasible approach to consider for complex and decomposable tasks.

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