A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems

2010 ◽  
pp. 255-272
Author(s):  
Dimosthenis Kyriazis ◽  
Anastasios Doulamis ◽  
Theodora Varvarigou
Keyword(s):  
Neural Network ◽  
Relevance Feedback ◽  
Medical Information ◽  
Linear Feedback ◽  
Neural Network Approach ◽  
Content Selection ◽  
Retrieval Systems ◽  
Non Linear ◽  
Information Retrieval Systems ◽  
Trained Neural Network

In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical content) retrieval systems. In greater detail, the user who searches for information is considered to be part of the retrieval process in an interactive framework, who evaluates the results provided by the system so that the user automatically updates its performance based on the users’ feedback. In order to achieve the latter, we propose an adaptively trained neural network (NN) architecture that is able to implement the non- linear feedback. The term “adaptively” refers to the functionality of the neural network to update its weights based on the user’s content selection and optimize its performance.

A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems

2011 ◽  
pp. 1857-1873
Author(s):  
Dimosthenis Kyriazis ◽  
Anastasios Doulamis ◽  
Theodora Varvarigou
Keyword(s):  
Neural Network ◽  
Relevance Feedback ◽  
Medical Information ◽  
Linear Feedback ◽  
Neural Network Approach ◽  
Content Selection ◽  
Retrieval Systems ◽  
Non Linear ◽  
Information Retrieval Systems ◽  
Trained Neural Network

In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical content) retrieval systems. In greater detail, the user who searches for information is considered to be part of the retrieval process in an interactive framework, who evaluates the results provided by the system so that the user automatically updates its performance based on the users’ feedback. In order to achieve the latter, we propose an adaptively trained neural network (NN) architecture that is able to implement the non- linear feedback. The term “adaptively” refers to the functionality of the neural network to update its weights based on the user’s content selection and optimize its performance.

scholarly journals Excitation Forces Estimation for Non-linear Wave Energy Converters: A Neural Network Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12334-12339
Author(s):  
M. Bonfanti ◽  
F. Carapellese ◽  
S.A. Sirigu ◽  
G. Bracco ◽  
G. Mattiazzo
Keyword(s):  
Neural Network ◽  
Wave Energy ◽  
Linear Wave ◽  
Network Approach ◽  
Neural Network Approach ◽  
Wave Energy Converters ◽  
Non Linear ◽  
Energy Converters

scholarly journals Neural Networks for Postprocessing Ensemble Weather Forecasts

2018 ◽  
Vol 146 (11) ◽  
pp. 3885-3900 ◽  
Author(s):  
Stephan Rasp ◽  
Sebastian Lerch
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Distribution ◽  
Predictor Variables ◽  
Specific Information ◽  
Observation Data ◽  
Neural Network Approach ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Trained Neural Network

Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.

scholarly journals Forward Kinematics for Suspended Under-Actuated Cable-Driven Parallel Robots: A Neural Network Approach

2021 ◽  
Author(s):  
Utkarsh A. Mishra ◽  
Stéphane Caro
Keyword(s):  
Neural Network ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Least Square ◽  
Equilibrium Equations ◽  
Neural Network Approach ◽  
Comparison Results ◽  
Unsupervised Neural Network ◽  
Non Linear ◽  
Neural Network Algorithm

Abstract Kinematic analysis of under-constrained Cable-Driven Parallel Robots has been a topic of interest because of the inherent coupling between the loop-closure and static equilibrium equations. The paper proposes an unsupervised neural network algorithm to perform real-time forward geometrico-static analysis of such robots in a suspended configuration under the action of gravity. The formulation determines a non-linear function approximation to model the problem and proves to be efficient in solving for consecutive and close waypoints in a path. The methodology is applied on a six-degree-of-freedom (6-DOF) spatial under-constrained suspended cable-driven parallel robot. Specific comparison results to show the effectiveness of the proposed method in tracking a given path and degree of constraint satisfaction are presented against the results obtained from non-linear least-square optimization.

scholarly journals A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm

2020 ◽  
Vol 02 (03) ◽  
pp. 151-160 ◽  
Author(s):  
Dr. V. Suma
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Deep Neural Network ◽  
Research Work ◽  
Support Vector ◽  
Retrieval Performance ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Hashing Algorithm ◽  
Distributed Cloud

The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.

A User-Centered Approach for Information Retrieval

2011 ◽  
pp. 165-178 ◽  
Author(s):  
Antonio Picariello
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Statistical Information ◽  
Web Pages ◽  
General Knowledge ◽  
Test Set ◽  
Retrieval Technique ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Relevant Component

Information retrieval can take great advantages and improvements considering users’ feedbacks. Therefore, the user dimension is a relevant component that must be taken into account while planning and implementing real information retrieval systems. In this chapter, we first describe several concepts related to relevance feedback methods, and then propose a novel information retrieval technique which uses the relevance feedback concepts in order to improve accuracy in an ontology-based system. In particular, we combine the Semantic information from a general knowledge base with statistical information using relevance feedback. Several experiments and results are presented using a test set constituted of Web pages.

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