Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Synthesising the trait information of European Chironomidae (Insecta: Diptera): Towards a new database

Chironomidae are among the most conspicuous and ecological diverse group of freshwater invertebrates. They may dominate unimpacted communities in abundance and biomass, accounting for more than 50% of macroinvertebrate species in standing and flowing waters. In deep zones of eutrophic lakes and highly human-impacted streams, they are often the only family of aquatic insects remaining. In bioassessment programmes, Chironomids are often identified at the family and subfamily levels, due to difficulties in the taxonomic identification of larvae resulting from a high intrinsic morphological similarity. This may potentially result in bias as, similarly to Ephemeroptera, Trichoptera or Plecoptera, Chironomidae species, which are replaced along natural and human-impacted gradients due to differences in their ecological requirements. Recently, multiple trait-based approaches have been proposed to complement taxonomic-based assessment of streams and rivers using macroinvertebrates. However, the lack of specific trait information for Chironomidae prevents their use in the functional assessment of communities. Therefore, here, we aimed to: (1) develop a trait database for European Chironomidae genera that can be used in future bioassessment and ecological studies; (2) evaluate, by multivariate analyses, whether our new database provides additional information on Chironomidae compared to the trait information provided in the commonly used European trait database (Tachet et al., 2010); and (3) determine whether the new information on Eltonian traits (proxy to biological traits) translates the most accepted phylogenetic relationships among Chironomidae subfamilies. We gathered information on 744 species and 178 genera, for 37 traits covering 186 trait categories, and found substantial differences between our database and the commonly used European trait database. In addition, available information on traits was not always in agreement with phylogenetic relationships among subfamilies. Orthocladiinae and Chironominae which are considered sister groups in evolutionary terms actually showed confident trait relatedness based on Eltonian traits tree while the remaining relationships between subfamilies are questionable. In addition, different traits can occur in closely related taxa depending on the environmental drivers operating on their habitats. Our study reveals that the usually accepted redundancy within the Chironomidae family and subfamilies must be a product of averaging the information from finer taxonomic resolution added to the substantial lack of information for this insect group.     

三维肺部图像中疑似肺结节结构的自动提取

本文提出了一种肺部CT图像三维数据中自动提取疑似结节区域的方法。首先结合阈值分割、种子填充等方法,在三维体数据上分割出肺实质。进而利用改进的模糊C均值聚类,提取出结节及具有结节特征的血管、支气管等感兴趣区域。该工作对感兴趣区域的特征提取有重要意义,是早期肺癌计算机辅助诊断重要的一步。     

人工神经网络的局域性对网络训练速度的影响

从网络结构入手,提出了网络局域性的概念。作为网络结构的一种定量描述,探讨了网络结构与训练速度、预测精度间的对应关系。结果表明网络的训练速度随局域性的增加而增加,网络的预测精神在局域性０．５５附近达到最高,任何偏离都会导致网络预测精度的下降。为在生化过程具体应用中选择合理的神经网络类型提供了理论依据。     

Computerized classification of Mediterranean vegetation using panchromatic aerial photographs

Abstract. Historical aerial photographs are an important source for data on medium- to long-term (10 - 50 yr) vegetation changes. Older photographs are panchromatic, and manual interpretation has traditionally been used to derive vegetation data from such photographs. We present a method for computerized analysis of panchromatic aerial photographs, which enables one to create high resolution, accurate vegetation maps. Our approach is exemplified using two aerial photographs (from 1964 and 1992) of a test area on Mt. Meron, Israel. Spatial resolution (pixel size) of the geo-rectified photos was 0.30 m and spatial accuracy (RMS error) ca. 1 m. An illumination adjustment prior to classification was found to be essential in reducing misclassification error rates. Two classification approaches were employed: a standard maximum-likelihood supervised classifier, and a modification of a supervised classification, which takes into account spectral properties of individual pixels as well as their neighbourhood characteristics. Accuracy of the maximum likelihood classification was 81 % in the 1992 image and 54 % in the 1964 image. The neighbour classifier increased accuracy to 89 % and 82 % respectively. The overall results suggest that computerized analysis of sequences of panchromatic aerial photographs may serve as a valuable tool for the quantification of medium-term vegetation changes.     

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real‐time. This work presents a system for on‐line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on‐line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on‐line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on‐line biomass estimation. The root mean squared error of prediction is 4 × 10⁶ cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy‐based expert system allows to provide the desired yeast cell concentration of 100–120 × 10⁶ cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.     

The management of vegetation classifications with fuzzy clustering

Questions: Does fuzzy clustering provide an appropriate numerical framework to manage vegetation classifications? What is the best fuzzy clustering method to achieve this? Material: We used 531 relevés from Catalonia (Spain), belonging to two syntaxonomic alliances of mesophytic and xerophytic montane pastures, and originally classified by experts into nine and 13 associations, respectively. Methods: We compared the performance of fuzzy C‐means (FCM), noise clustering (NC) and possibilistic C‐means (PCM) on four different management tasks: (1) assigning new relevé data to existing types; (2) updating types incorporating new data; (3) defining new types with unclassified relevés; and (4) reviewing traditional vegetation classifications. Results: As fuzzy classifiers, FCM fails to indicate when a given relevé does not belong to any of the existing types; NC might leave too many relevés unclassified; and PCM membership values cannot be compared. As unsupervised clustering methods, FCM is more sensitive than NC to transitional relevés and therefore produces fuzzier classifications. PCM looks for dense regions in the space of species composition, but these are scarce when vegetation data contain many transitional relevés. Conclusions: All three models have advantages and disadvantages, although the NC model may be a good compromise between the restricted FCM model and the robust but impractical PCM model. In our opinion, fuzzy clustering might provide a suitable framework to manage vegetation classifications using a consistent operational definition of vegetation type. Regardless of the framework chosen, national/regional vegetation classification panels should promote methodological standards for classification practices with numerical tools.     

A lead dependent ischemic episodes detection strategy using Hermite functions

In this work a new strategy for automatic detection of ischemic episodes is proposed. A new measure for ST deviation based on the time–frequency analysis of the ECG and the use of a reduced set of Hermite basis functions for T wave and QRS complex morphology characterization, are the key points of the proposed methodology.Usually, ischemia manifests itself in the ECG signal by ST segment deviation or by QRS complex and T wave changes in morphology. These effects might occur simultaneously. Time–frequency methods are especially adequate for the detection of small transient characteristics hidden in the ECG, such as ST segment alterations. A Wigner–Ville transform-based approach is proposed to estimate the ST shift. To characterize the alterations in the T wave and the QRS morphologies, each cardiac beat is described by expansions in Hermite functions. These demonstrated to be suitable to capture the most relevant morphologic characteristics of the signal. A lead dependent neural network classifier considers, as inputs, the ST segment deviation and the Hermite expansion coefficients. The ability of the proposed method in ischemia episodes detection is evaluated using the European Society of Cardiology ST–T database. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.     

Application of fuzzy VIKOR for evaluation of green supply chain management practices

Environmentally sustainable activities have received an increasing interest among the firms to improve their practices in the supply chain. Although environmental regulations force firms consider these issues, but, green issues are new, evolving every day, and requires a continuous study in the field to gain a complete understanding of the problems. In this study, we illustrate the case of a laptop manufacturer in Malaysia that pursues to evaluate green supply chain management (GSCM) indicators among its practitioners. This paper develops a quantitative evaluation model to measure the uncertainty of GSCM activities and applies an approach based on Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method which is an extension of intuitionistic fuzzy environment aiming to solve the green multi-criteria decision making (GMCDM) problem. The triangular fuzzy numbers (TFNs) were used to handle imprecise numerical quantities. Then, a hierarchical multiple criteria decision making (MCDM) model was proposed based on fuzzy sets theory and VIKOR method to deal with the problem. The results show the alternative ranks of the four evaluated companies which was based on their performance in GSCM initiatives. The results also indicated that the main criteria of the research ranked as follows respectively: eco-design, green production, green purchasing, green recycling, green transportation and green warehousing. Finally, a comparative analysis of results by fuzzy VIKOR is presented. Additionally the scope for future studies is provided at the end of the paper.