Information in full and reduced data sets of electrical impedance spectra from various skin conditions, compared using a holographic neural network

1997 ◽  
Vol 35 (4) ◽  
pp. 415-419 ◽  
Author(s):  
S. Ollmar ◽  
I. Nicander ◽  
J. Ollmar ◽  
L. Emtestam
2017 ◽  
Vol 26 (2) ◽  
pp. 335-358 ◽  
Author(s):  
Piyabute Fuangkhon

AbstractInstance selection endeavors to decide which instances from the data set should be maintained for further use during the learning process. It can result in increased generalization of the learning model, shorter time of the learning process, or scaling up to large data sources. This paper presents a parallel distance-based instance selection approach for a feed-forward neural network (FFNN), which can utilize all available processing power to reduce the data set while obtaining similar levels of classification accuracy as when the original data set is used. The algorithm identifies the instances at the decision boundary between consecutive classes of data, which are essential for placing hyperplane decision surfaces, and retains these instances in the reduced data set (subset). Each identified instance, called a prototype, is one of the representatives of the decision boundary of its class that constitutes the shape or distribution model of the data set. No feature or dimension is sacrificed in the reduction process. Regarding reduction capability, the algorithm obtains approximately 85% reduction power on non-overlapping two-class synthetic data sets, 70% reduction power on highly overlapping two-class synthetic data sets, and 77% reduction power on multiclass real-world data sets. Regarding generalization, the reduced data sets obtain similar levels of classification accuracy as when the original data set is used on both FFNN and support vector machine. Regarding execution time requirement, the speedup of the parallel algorithm over the serial algorithm is proportional to the number of threads the processor can run concurrently.


Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format


Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.


2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.


Author(s):  
Yanzou Duan ◽  
Jinpeng Tian ◽  
Jiahuan Lu ◽  
Chenxu Wang ◽  
Weixiang Shen ◽  
...  

2021 ◽  
Vol 1757 (1) ◽  
pp. 012013
Author(s):  
Xinyu Zhang ◽  
Xiaoyan Chen ◽  
Zichen Wang ◽  
Miao Zhang

Author(s):  
Thomas Blaschke ◽  
Jürgen Bajorath

AbstractExploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.


2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.


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