Application of Neural Networks in Assessing Changes around Implant after Total Hip Arthroplasty

Szarek, A.; Korytkowski, Marcin; Rutkowski, Leszek; Scherer, Rafał; Szyprowski, J.

doi:10.1007/978-3-642-29350-4_40

Cited by 24 publications

(5 citation statements)

References 6 publications

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“…Neural networks are extensively applied to various classification, pattern recognition, and forecasting real-world problems in engineering, medical, financial and other fields [5], [6], [7]. What often makes real-life applications challenging is the inherent high dimensionality of NNs.…”

Section: Neural Networkmentioning

confidence: 99%

Training high-dimensional neural networks with cooperative particle swarm optimiser

Rakitianskaia

Engelbrecht

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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This paper analyses the behaviour of particle swarm optimisation applied to training high-dimensional neural networks. Despite being an established neural network training algorithm, particle swarm optimisation falls short at training high-dimensional neural networks. Reasons for poor performance of PSO are investigated in this paper, and hidden unit saturation is hypothesised to be a cause of the failure of PSO in training highdimensional neural networks. An analysis of various activation functions and search space boundaries leads to the conclusion that hidden unit saturation can be slowed down by combining activation function choice with appropriate search space boundaries. Bounded search is shown to significantly outperform unbounded search in high-dimensional neural network error search spaces.

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Section: Neural Networkmentioning

confidence: 99%

Training high-dimensional neural networks with cooperative particle swarm optimiser

Rakitianskaia

Engelbrecht

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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“…To assess the changes around the hip implant after the surgery, a neural network was used. NN has predicted the variations around the hip implant that shows the plan for the surgery and to find the risky regions where the decalcification of bone and the stability loss can be possible near the contact between the bone and implant (Szarek et al, 2012). Similarly the detecting the loosening of the hip implant through the radiographs was predicted using CNN (Borjali et al, 2019).…”

Section: Design Of Biomedical Implantsmentioning

confidence: 99%

Computational intelligence based design of biomaterials

Arulraj¹,

Datta²

2022

cmms

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This paper presents an overview of the applications of computational intelligence techniques, viz. artificial neural networks, fuzzy inference systems, and genetic algorithms, for the design of biomaterials with improved performance. These techniques are basically used for developing data-driven models and for optimization. The paper introduces the domain of biomaterials and how they can be designed using computational intelligence techniques. Then a brief description of the tools is made, followed by the applications of the tools in various domains of biomaterials. The applications range in all classes of materials ranging from alloys to composites. There are examples of applications for the surface treatment of biomaterials, materials for drug delivery systems, materials for scaffolds and even in implant design. It is found the tools can be effectively used for designing new and improved biomaterials.

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“…[2,15,32,46]) and the learning properties of neural networks (see e.g. [34,35,50,51,69,73]). For the purposes of the proposed method, we have developed a new neuro-fuzzy one-class classifier, proposed by us earlier (see e.g.…”

Section: Introductionmentioning

confidence: 99%

New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values

Zalasiński

Cpałka

Hayashi

2015

Artificial Intelligence and Soft Computing

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Abstract. Identity verification based on the dynamic signature is an important issue of biometrics. There are many effective methods to the signature verification which take into account the dynamic characteristics of the signature (e.g. velocity of the pen, the pen's pressure on the surface of the graphic tablet, etc.). Among these methods, the ones based on the so-called global features are very important. In our previous paper we have proposed new algorithm for evolutionary selection of the dynamic signature global features, which selects a subset of features individually for each user. Algorithm proposed in this paper is a faster version of the method proposed earlier. During development of the algorithm we resigned from using evolutionary selection of global features and standardized working of the classifier in the context of all users. The paper contains the simulation results for the BioSecure database of the dynamic signatures.

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Application of Neural Networks in Assessing Changes around Implant after Total Hip Arthroplasty

Cited by 24 publications

References 6 publications

Training high-dimensional neural networks with cooperative particle swarm optimiser

Training high-dimensional neural networks with cooperative particle swarm optimiser

Computational intelligence based design of biomaterials

New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values

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