Environmental niche modeling (ENM) has emerged as a promising tool for identifying grass species with potential for rangeland restoration. This approach can detect suitable areas and environments ...where these species can be planted. In this study, we employed ENM to estimate the potential distribution range of 50 grass species of the grasslands and shrublands of northern Mexico. The outcome of the ENM served to identify grass species with potential for restoration in Mexico, especially those not commonly used for that purpose in the past. Results suggested the possibility of selecting seven grass species with the potential for revegetating degraded grasslands, nine for shrublands, and six for alkaline soils. This research provides insights into the environmental adaptations of different grass species distributed in the rangelands of northern Mexico. Ecologists, conservation planners, researchers, and range managers could use these outcomes and the maps of the potential distribution ranges as supportive information to conduct effective restoration efforts. In turn, this can assist in increasing the probability of success of future rangelands restoration programs, which are often costly in terms of financial investments and labor.
•A new contrast pattern-based classifier for class imbalance problems is introduced.•PBC4cip is based on the support of the patterns and the class imbalance level.•PBC4cip outperforms several ...classifiers designed for class imbalance problems.
Contrast pattern-based classifiers are an important family of both understandable and accurate classifiers. Nevertheless, these classifiers do not achieve good performance on class imbalance problems. In this paper, we introduce a new contrast pattern-based classifier for class imbalance problems. Our proposal for solving the class imbalance problem combines the support of the patterns with the class imbalance level at the classification stage of the classifier. From our experimental results, using highly imbalanced databases, we can conclude that our proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems. Additionally, we show that our classifier significantly outperforms other state-of-the-art classifiers not directly based on contrast patterns, which are also designed to deal with class imbalance problems.
•3D processing of PET sinograms performs better when compared to 2D processing.•Synthetic PET data is useful for training convolutional networks that will be tested with real data.•A short network ...processing 3D PET sinograms can achieve better results than a deep network processing 2D sinograms.•For 3D PET sinograms, a residual architecture speeds up performance time and increases reconstruction quality.•Collection of the synthetic PET sinogram database to train deep learning methods for preclinical PET studies.
Positron emission tomography (PET) has been widely used in nuclear medicine to diagnose cancer. PET images suffer from degradation because of the scanner's physical limitations, the radiotracer's reduced dose, and the acquisition time. In this work, we propose a residual three-dimensional (3D) and convolutional neural network (CNN) to enhance sinograms acquired from a small-animal PET scanner. The network comprises three convolutional layers created with 3D filters of sizes 9, 5, and 5, respectively. For training, we extracted 15250 3D patches from low- and high-count sinograms to build the low- and high-resolution pairs. After training and prediction, the image was reconstructed from the enhanced sinogram using the ordered subset expectation maximization (OSEM) algorithm. The results revealed that the proposed network improves the spillover ratio by up to 4.5% and the uniformity by 55% compared to the U-Net. The NEMA phantom data were obtained in a simulation environment. The network was tested on acquired real data from a mouse. The reconstructed images and the profiles of maximum intensity projection show that the proposed method visually yields sharper images.
2023 Elsevier Ltd. All rights reserved.
In rough set theory, a construct is an attribute subset with the same ability to discern objects belonging to different classes as the whole set of attributes, while maintaining the similarity ...between objects belonging to the same class. Although algorithms for reducts computation can be adapted to compute constructs, practical problems exist where these algorithms cannot compute all constructs within a reasonable time frame. Therefore, this paper introduces an algorithm for computing all constructs of a decision system. The results of experiments with various decision systems (both artificial and real-world) suggest that our algorithm is, in most cases, faster than the state-of-the-art algorithms when the simplified binary discernibility–similarity matrix has a density of less than 0.29.
•An experimental study of the effect of class imbalance on quality measures for contrast patterns is presented.•Our study was performed both regarding and disregarding the class imbalance level.•The ...best quality measures for ranking patterns in class imbalance problems is provided.•A guide for determining, according to the class imbalance ratio, the quality measure to apply, is provided.
Contrast pattern-based classifiers rely on the discriminative power of contrast patterns. For this reason, many quality measures have been proposed to evaluate the quality of a contrast pattern. These measures allow to distinguish among contrast patterns with low and high discriminative ability for classification. In the literature, many comparative studies among quality measures for contrast patterns have been proposed but all of them were performed without taking into account the class imbalance level. However, in many class imbalance problems, those patterns extracted from the minority class have low support, which could negatively affect their discriminative ability. Therefore, in this paper, we present an experimental study of the effect of class imbalance on quality measures for contrast patterns. This study determines which quality measures for contrast patterns are the best for class imbalance problems; both regarding and disregarding the class imbalance level. Also, for the best quality measures we performed a pairwise comparison to determine which other quality measures have statistically similar behavior to them. This will help to simplify future research since it can be used only one quality measure among those with similar performance.
•The concept of closed frequent similar pattern mining is introduced.•Several lemmas to prune the search space are introduced and proved.•A novel closed frequent similar pattern mining algorithm ...(CFSP-Miner), is proposed.•CFSP-Miner is more efficient than the frequent pattern mining algorithms.•CFSP-Miner has excellent scalability properties.
Frequent pattern mining is considered a key task to discover useful information. Despite the quality of solutions given by frequent pattern mining algorithms, most of them face the challenge of how to reduce the number of frequent patterns without information loss. Frequent itemset mining addresses this problem by discovering a reduced set of frequent itemsets, named closed frequent itemsets, from which the entire frequent pattern set can be recovered. However, for frequent similar pattern mining, where the number of patterns is even larger than for Frequent itemset mining, this problem has not been addressed yet. In this paper, we introduce the concept of closed frequent similar pattern mining to discover a reduced set of frequent similar patterns without information loss. Additionally, a novel closed frequent similar pattern mining algorithm, named CFSP-Miner, is proposed. The algorithm discovers frequent patterns by traversing a tree that contains all the closed frequent similar patterns. To do this efficiently, several lemmas to prune the search space are introduced and proven. The results show that CFSP-Miner is more efficient than the state-of-the-art frequent similar pattern mining algorithms, except in cases where the number of frequent similar patterns and closed frequent similar patterns are almost equal. However, CFSP-Miner is able to find the closed similar patterns, yielding a reduced size of the discovered frequent similar pattern set without information loss. Also, CFSP-Miner shows good scalability while maintaining an acceptable runtime performance.
This work sought to estimate population measles seroprevalence and heterogeneity in the antibody concentration distribution that could be explained by the birth-year cohort according to the ...opportunity viral and vaccine exposure, applied to data from Medellín, Colombia.
Prevalence of IgG antibodies was analyzed for measles based on a population study with a random sample of 2098 individuals from 6 to 64 years of age. Finite mixture models were used to estimate global seroprevalence and that of three birth-year cohorts (I: born up to 1982; II: 1983–1994; III: born since 1995). Multiple linear regression permitted adjusting the concentration of antibodies by cohort, zone, and sex.
Globally, seronegativity was 6.5 % (95%CI 4.9–8.6), seropositivity of 78.4 (95%CI 75.1–81.4), and equivocal of 15.1 % (95%CI 12.5–18.1). Two components were found with skewed normal distribution, which reclassified those equivocal as seropositive. Differences were observed by cohort in the geometric mean of antibodies (Cohort I: 1704.6; II: 562.2; III: 802.1 mIU/mL) and seronegativity (Cohort I: 4 %; II:13.3 %; III: 8.9 %). Antibody concentration increased by 1.26 mIU/mL in residents in the rural area, while diminishing in individuals from cohort II (by 3.02 mIU/mL) and cohort III (by 2.14 mIU/mL).
The younger cohorts (II and III) had lower antibody concentration (higher seronegativity), indicating the need to monitor periodically seroprevalence and an eventual reestablishment of the transmission in these groups with higher risk of infection.
A new oversampling method in the string space Briones-Segovia, Víctor A.; Jiménez-Villar, Víctor; Carrasco-Ochoa, Jesús Ariel ...
Expert systems with applications,
11/2021, Letnik:
183
Journal Article
Recenzirano
•Fast oversampling method in the string space based on the edit distance.•Good quality oversampling not based on searching nearest neighbors.•Hierarchical synthetic object generation for class ...imbalance problems.
In syntactic and structural pattern recognition, data represented as strings appear in several supervised classification applications. In some situations, data collections show imbalanced class distributions, which typically results in the classifier biasing its performance to the class representing the majority of objects. To solve this problem, some oversampling methods have been proposed for data represented as strings. However, this type of method has been little studied in the literature. Therefore, in this paper, we present an oversampling method for working in string space that balances the minority class and gets better classification results than state-of-the-art oversampling methods, especially for highly imbalanced problems. Furthermore, according to our experiments, the proposed method is much faster than those reported in the literature.
•We propose estimating the quality of a contrast pattern using unseen objects.•We evaluate quality measures by correlating their values and the estimated quality.•We describe the influence of ...database characteristics in correlations.•We perform a study to find how to synergistically combine quality measures.
Contrast patterns, which lie in the core of most understandable classifiers, are frequently evaluated by quality measures. Since many different quality measures are available, they should be compared to select the most appropriate for each applications. This paper introduces a method to compare quality measures, using a set of mined patterns and a collection of objects not used for mining. The comparison is performed by correlating quality values with a quality estimation of the patterns. Additionally, a meta-learning study is performed to show that combining quality measures could be better than using the best single measures in isolation. The results of this paper can help researchers to create new quality measures or to find new combinations of quality measures to create better understandable classification systems.
