2,104 research outputs found
Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI
Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor.
Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery.
Deep-learning techniques are becoming popular in medical applications and image-based diagnosis.
Convolutional Neural Networks are the preferred architecture for object detection and classification in images.
In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma
detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing
the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using
Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-
A Two-stage Classification Method for High-dimensional Data and Point Clouds
High-dimensional data classification is a fundamental task in machine
learning and imaging science. In this paper, we propose a two-stage multiphase
semi-supervised classification method for classifying high-dimensional data and
unstructured point clouds. To begin with, a fuzzy classification method such as
the standard support vector machine is used to generate a warm initialization.
We then apply a two-stage approach named SaT (smoothing and thresholding) to
improve the classification. In the first stage, an unconstraint convex
variational model is implemented to purify and smooth the initialization,
followed by the second stage which is to project the smoothed partition
obtained at stage one to a binary partition. These two stages can be repeated,
with the latest result as a new initialization, to keep improving the
classification quality. We show that the convex model of the smoothing stage
has a unique solution and can be solved by a specifically designed primal-dual
algorithm whose convergence is guaranteed. We test our method and compare it
with the state-of-the-art methods on several benchmark data sets. The
experimental results demonstrate clearly that our method is superior in both
the classification accuracy and computation speed for high-dimensional data and
point clouds.Comment: 21 pages, 4 figure
Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms
Evolutionary artificial neural networks (EANNs) refer to a special class of
artificial neural networks (ANNs) in which evolution is another fundamental
form of adaptation in addition to learning. Evolutionary algorithms are used to
adapt the connection weights, network architecture and learning algorithms
according to the problem environment. Even though evolutionary algorithms are
well known as efficient global search algorithms, very often they miss the best
local solutions in the complex solution space. In this paper, we propose a
hybrid meta-heuristic learning approach combining evolutionary learning and
local search methods (using 1st and 2nd order error information) to improve the
learning and faster convergence obtained using a direct evolutionary approach.
The proposed technique is tested on three different chaotic time series and the
test results are compared with some popular neuro-fuzzy systems and a recently
developed cutting angle method of global optimization. Empirical results reveal
that the proposed technique is efficient in spite of the computational
complexity
Prediction and performance evaluation of BDI forecasting models : Cross efficiency, the directional distance function and the AVS utility function
Workshop 2015 -Advances in DEA Theory and Applications (December 1-2, 2015)In the study, we propose a nonparametric efficiency measurement approach for the forecasting model selection problem. Three autoregressive models and three fuzzy time series approaches are employed for the calibration of data structure to depict the trend. The directional distance function and portfolio theory are further used to evaluate the performance of BDI predictions. A directional distance function is defined that looks for possible increases in accuracy and skewness, and decreases in variance obtained by cross efficiencies of those forecasting models. We also establish a link to proper indirect accuracy- variance -skewness (AVS) utility function for various users in various utilities. An empirical section on a set of forecasting Baltic Dry Index (BDI) forecasting models serves as an illustration.The workshop is supported by JSPS (Japan Society for the Promotion of Science), Grant-in-Aid for Scientific Research (B), #25282090, titled “Studies in Theory and Applications of DEA for Forecasting Purpose.本研究はJSPS科研費 基盤研究(B) 25282090の助成を受けたものです
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