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Copyright of Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics. [ABSTRACT FROM AUTHOR]

In 64 B.C., the Romans turned Syria into a Roman province and this occupation continued, with many vicissitudes, until 638 A.D. when it fell into Arab hands. This occupation originated an important material and social development for almost 400 years. The oil lamps located in Syria were objects located in hypogea (funeral contexts) and in cut pits (domestic contexts) mainly along the Syrian coast, showing aspects of the social, cultural and domestic life.From the data published in the scientific literature, four quantitative variables (length, width, discus diameter and shape index) and two qualitative variables of great archaeological relevance (context and chronology) from Syria oil lamps have been recorded, which has allowed us to analyze the oil lamps by applying bivariate and multivariate statistical analysis. The results obtained indicate different factory patterns regarding the context and chronology as well.The application of Cluster Analysis provides a classification of the oil lamps into a large group consisting of small and very rounded lamps, perhaps dedicated to domestic and everyday use, versus other group with larger and highly stylized lamps, possibly with functionality more associated with burials or ceremonial rituals. Also, the graphics sequences of each variable constitute a pattern for the lamps in each cluster with respect to the metric variables, allowing to graphically comparison between the oil lamps and the variable values also.

I extend multiple indicators multiple causes (MIMIC) models to unveil unbiased, asymmetric, bidirectional influences using indicators of the same items within variable-defined subgroups. The strategy discerns (1) item-variation in interaction (and derivative) terms that capture synergies and cluster together (formative or causal indicators) from (2) item-variation in duplicate terms when items lack synergy and cluster together only (reflective or effect indicators). An item may reveal either or both influences. These symmetric indicators yield estimates of (1) the unique variation and synergy of each formative indicator within the structural model portion of the MIMIC model (based on moderated regression) and (2) the remaining shared variation in the reflective indicator within the measurement model portion (based on confirmatory factor analysis). I reveal two patterns of comorbidity in disease subgroups of specific co-occurring conditions across a community sample of older adults and in age and gender subsamples. First, as structural model indicators, depressive symptoms may display different synergies as they cluster within a disease subgroup of diabetes and specific co-occurring conditions. As measurement model indicators, depressive symptoms capture non-synergistic clustering within the disease subgroup. Second, diabetes may mediate the co-occurring condition when depressive symptoms lack synergies but cluster within the disease subgroup. Researchers should distinguish both comorbidity patterns, which have different implications. I offer insights for adaptive modeling, conceptualizing and screening symptom clusters, metabolomics, and economic or social monitoring. [ABSTRACT FROM AUTHOR]

The proposed method is based on classifying 15 types of plant leaf disease. Hue saturation value was used to delete the background and the healthy areas to show only the affected area in the image. Texture analyses adopted in image features extractions from the R component &G component &B component and creating 3 components which are RG and RB and GB color of the RGB color space images of diseased leaves. Building image classifier using statistical method for classification.

The current lack of a common basis for collecting data on population-level prevention and health promotion interventions causes public health to be relatively invisible within broader health systems, making it vulnerable to funding cuts when there is pressure to reduce spending. Further, the inconsistent use of terms for describing interventions hinders knowledge translation and building an evidence base for public health practice and policy. The International Classification of Health Interventions (ICHI), being developed by the World Health Organization, is a standard statistical classification for interventions across the full scope of health systems. ICHI has potential to meet the need for a common language and structure for describing and capturing information about prevention and health promotion interventions. We report on a developmental appraisal conducted to examine the strengths and limitations of ICHI for coding interventions delivered for public health purposes. Our findings highlight classification challenges in relation to: consistently identifying separate components within multi-component interventions; operationalizing the ICHI concept of intervention target when there are intermediary targets as well as an ultimate target; coding an intervention component that involves more than one ICHI target or action; and standardising what is being counted. We propose that, alongside its purpose as a statistical classification, ICHI can play a valuable role as an 'epistemic hub', to be used flexibly by public health actors to meet a range of information needs, and as a basis for improved communication and exchange. [ABSTRACT FROM AUTHOR]

The proposed method is based on classifying 15 types of plant leaf disease. Hue saturation value was used to delete the background and the healthy areas to show only the affected area in the image. Texture analyses adopted in image features extractions from the R component &G component &B component and creating 3 components which are RG and RB and GB color of the RGB color space images of diseased leaves. Building image classifier using statistical method for classification. [ABSTRACT FROM AUTHOR]

We consider Bayesian multiple statistical classification problem in the case where the unknown source distributions are estimated from the labeled training sequences, then the estimates are used as nominal distributions in a robust hypothesis test. Specifically, we employ the DGL test due to Devroye et al. and provide non-asymptotic, exponential upper bounds on the error probability of classification. The proposed upper bounds are simple to evaluate and reveal the effects of the length of the training sequences, the alphabet size and the numbers of hypothesis on the error exponent. The proposed method can also be used for large alphabet sources when the alphabet grows sub-quadratically in the length of the test sequence. The simulations indicate that the performance of the proposed method gets close to that of optimal hypothesis testing as the length of the training sequences increases. [ABSTRACT FROM AUTHOR]

This paper presents a structured, generically applicable, method for using building performance simulation to aid the development of comfort-driven solar shading controls by mapping predicted occupant comfort conditions to sensor measurements. The method uses confusion matrices as a statistical classification approach to facilitate (i) selection of sensor deployment strategies that offer beneficial trade-offs considering multiple performance aspects and (ii) identification of control algorithms that optimise comfort conditions using non-ideal sensors. The support method requires relatively little effort from a developer, only a small number of simulations and fits well within the current practice of shading control development. The method is tested using a sun-tracking control strategy for indoor roller blinds as a case study, which demonstrates that the method can identify high-performance solutions. Finally, generally applicable features of the method are extrapolated from the case study, and alternative applications and the method's limitations are discussed. [ABSTRACT FROM AUTHOR]

The Tapajós river is among the largest rivers in the world and has been credited as the main affluent of the lower Amazon River. Geomorphological studies in the Tapajós commonly deal with evolutionary, hydrological, and sedimentological issues. Recently, important advances have been made in understanding the morphology and dynamics in the Tapajós, especially in the confluence zone near Santarém, eastern Amazonian Brazil. However, the lack of an independent channel classification system makes it difficult to integrate data obtained from different sources. This work presents a classification system for the lower Tapajós based on morphometric variables extracted from transversal profiles coupled with radar and optical remote sensing data. We used discriminant analysis of principal components for the first time in fluvial geomorphology to provide a clustering-based geomorphological classification, which is statistically supported. We propose a segmentation of the channel into three distinct sections referred to as narrower channel reach, higher ria reach, and lower ria reach. The results showed that the channel has a distinct morphological pattern in each of these reaches, which can also be observed by the variation along the longitudinal profile. Our findings showed that the most variable hydrological and sedimentological patterns are found in the lower ria reach, while the higher ria reach comprises a canyon-shaped, more stable part of the channel. We discuss the implications of this pattern on the ria's evolution. The method developed here could be applied to other areas of the Amazon basin, where the lack of data and logistical difficulties carrying out fieldwork are common obstacles to large-scale investigations. The identified reaches are indended to be considered in the sample design of future works and in the formulation of water resources management strategies. [ABSTRACT FROM AUTHOR]

• We explored two largest smartphone sensor-based datasets for classifying daily activities and transportation mode. • The impact and eficiency of different smartphone sensors have been analyzed for locomotion activities. • We proposed a post-processing classification approach (Mode technique) to refine classifier predictions. • We proposed window length optimization and smartphone orientation independent features. • We evaluated our proposed post-processing technique for both statistical machine learning and deep learning-based models. Recognition of daily human activities in various locomotion and transportation modes has numerous applications like coaching users for behavior modification and maintaining a healthy lifestyle. Besides, applications and user interfaces aware of user mobility through their smartphones can also aid in urban transportation planning, smart parking, and vehicular traffic monitoring. In this paper, we explored smartphone sensor-based two benchmark datasets (Sussex Huawei Locomotion (SHL) and Transportation Mode Detection (TMD)). Firstly, we demonstrated preprocesssing of sensor data, window length optimization based on Akaike Information Criteria (AIC), and introduced smartphone orientation independent features. We also provided an in-depth analysis of different smartphone sensors' importance for classifying daily activities and transportation modes. We justified the sensor relevance by showing the variation of performances with the number of sensors explored. For refining classifier predictions, we also proposed a post-processing approach named "Mode technique". This method primarily concentrates on the statistical analysis of transportation modes and improves the activity recognition rate in statistical classifiers: Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, Logistic Regression, Support Vectors Machine with RBF kernel, Random Forest, and deep learning-based methods: Artificial Neural Network and Recurrent Neural Network by smoothing the outputs of these classifiers. Besides, we showed the use of magnitude and jerk-based features to overcome the overfitting problem due to smartphone orientation. We obtained 97.2% accuracy in the SHL dataset and 99.13% accuracy in the TMD dataset. These results demonstrate that our approach can profoundly recognize various activities in advanced locomotion and transportation modes compared to existing methods in two large-scale datasets. [ABSTRACT FROM AUTHOR]

We present a quality control algorithm to detect spurious precipitation events, which occur at weighing rain gauges in the automated precipitation monitoring network of MeteoSwiss. Although small in intensity, the spurious precipitation events must be removed during routine quality control, because they have a negative impact on climatological and meteorological applications of precipitation data. Automated monitoring, expert inspection and systematic analysis lead to the hypothesis that spurious precipitation is induced by rapid temperature changes at the load cell of the weighing rain gauge. Constrained by the black box nature of the signal processing performed by the firmware, we trained a statistical classifier on features extracted from measurements provided by the weighing gauge, and expert labels obtained from the routine quality control. The high sensitivity and specificity of the trained Support Vector Machine provide strong evidence in favor of the hypothesis. Furthermore, the classifier is suitable for operational deployment as an automated real-time quality control test, flagging individual 10‑minute precipitation intensities as either spurious or non-spurious. Our results also suggest that a modification of the instrument should be possible, such that it will not generate spurious measurements in the first place. We are therefore collaborating with the manufacturer on possible software and hardware improvements to the instrument.

Time series of hourly temperature from 146 weather stations located in Santa Catarina State – South Brazil were used to show that a compact data representation, using probability density functions (pdf) parameters, could be useful to classify homogeneous air temperature areas. The normal distribution fitted well the 146 weather stations temperature time series, presenting a median value of 0.9721 for the Pearson correlation coefficient. The means and standard deviations obtained by adjusting the Gaussian functions for the 146 stations were used as input parameters for two different classifiers: hierarchical and k-means. Both classifiers separated Santa Catarina's weather stations into four distinct groups. These groups had direct relationship with altitude ranges and with the influence of sea. The classification of weather stations in different homogeneous groups was useful to identify climatic behaviors of hourly temperatures. In addition to the characterization of the climate itself, this classification can be useful as a support for the validation of numerical weather forecast models, and for the identification of abnormal temperature time series in a regional spatial context. [ABSTRACT FROM AUTHOR]

Statistical classifications are essential for collecting consistent data that can be compared over space and time. However, a publicly-documented body of practice concerning how to undertake the development and testing of a statistical classification is currently lacking. What aspects of the classification should be tested during the development process? How do we judge whether the classification is fit-for-purpose? How should problems and shortcomings be identified so that they can be remedied? To fill this gap, we drew on existing, authoritative sources to develop an analytic structure for use in the development and testing of statistical classifications. It consists of two components: (1) a statistical classification development and testing framework reflecting the required features of a statistical classification; and (2) a 4-tier model representing the main elements that make up a statistical classification, to use as a heuristic structure within which to locate issues identified and consider how they can be addressed. In this paper, we outline the development of the framework and model, and reflect on their application in testing a draft classification of health interventions. We propose this analytic structure as a new tool to support those engaged in the development of statistical classifications. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When the potential for making accurate classifications with a statistical classifier is limited, a neutral zone classifier can be constructed by adding a no-decision option as a classification outcome. We show how a neutral zone classifier can be constructed from a receiving operating characteristic (ROC) curve. We extend the ROC curve graphic to highlight important performance characteristics of a neutral zone classifier. Additional utility of neutral zone classifiers is illustrated by showing how they can be incorporated into the first stage of a two-stage classification process. At the first stage, a classification is attempted from easily collected or inexpensive features. If the classification falls into the neutral zone, additional relatively more expensive features can be obtained and used to make a definitive classification at the second stage. The methods discussed in the paper are illustrated with an application pertaining to prostate cancer. [ABSTRACT FROM AUTHOR]

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

Data-mining techniques are designed for classification problems in which each observation is a member of one and only one category. The authors formulate ten data representations that could be used to extend those methods to problems in which observations may be full members of multiple categories. They propose an audit matrix methodology for evaluating the performance of three popular data-mining techniques--linear discriminant analysis, neural networks, and decision tree induction--using the representations that each technique can accommodate. They then empirically test their approach on an actual surgical data set. Tree induction gives the lowest rate of false positive predictions, and a version of discriminant analysis yields the lowest rate of false negatives for multiple category problems, but neural networks give the best overall results for the largest multiple classification cases. There is substantial room for improvement in overall performance for all techniques. [ABSTRACT FROM AUTHOR]

Purpose: We report on a statistical method for grouping anti-cancer drugs (GRAD) in single mouse trials (SMT). The method assigns candidate drugs into groups that inhibit or do not inhibit tumor growth in patient-derived xenografts (PDX). It determines the statistical significance of the group assignments without replicate trials of each drug.Methods: The GRAD method applies a longitudinal finite mixture model, implemented in the statistical package PROC TRAJ, to analyze a mixture of tumor growth curves for portions of the same tumor in different mice, each single mouse exposed to a different drug. Each drug is classified into an inhibitory or non-inhibitory group. There are several advantages to the GRAD method for SMT. It determines that probability that the grouping is correct, uses the entire longitudinal tumor growth curve data for each drug treatment, can fit different shape growth curves, accounts for missing growth curve data, and accommodates growth curves of different time periods.Results: We analyzed data for 22 drugs for 18 human colorectal tumors provided by researchers in a previous publication. The GRAD method identified 18 drugs that were inhibitory against at least one tumor, and 10 tumors for which there was at least one inhibitory drug. Analysis of simulated data indicated that the GRAD method has a sensitivity of 84% and a specificity of 98%.Conclusion: A statistical method, GRAD, can group anti-cancer drugs into those that are inhibitory and those that are non-inhibitory in single mouse trials and provide probabilities that the grouping is correct. [ABSTRACT FROM AUTHOR]

• Mesoporous silica: carriers for glibenclamide tablets with improved dissolution rate • The examined silicas parameters were ranked by importance order: pairwise comparison • Data statistical treatment enabled a systematic classification of the silicas and selection of the best. • Data statistical treatment enabled classification and selection of the best silicas. • Syloid®XDP3150 was the most effective silica in enhancing drug dissolution properties. This work evaluated the suitability of various mesoporous silicas as carriers for developing an oral formulation endowed with improved dissolution properties of glibenclamide, hypoglycemic agent poorly water-soluble. The different silicas were examined for solid-state, morphology, and technological and physical-chemical properties (granulometry, specific surface area, wettability, water content, water activity, apparent density, flowability, compactability). A pairwise comparison allowed a ranking, by importance order, of the parameters examined and, for each parameter, a score was assigned to each silica type. Data statistical treatment (JMP software) indicated Neusilin®US2 and Syloid®XDP3150 as the best materials. Different loading methods were tested: physical mixing; addition of drug dissolved in a volatile solvent, subsequently evaporated; addition of drug dissolved in a solvent. Methods involving drug dissolution enabled drug amorphization and intimate dispersion within the silica porous structure. Dissolution tests indicated Syloid®XDP3150 as the most effective silica in enhancing drug dissolution properties, providing a release rate clearly faster than from commercial tablets. Drug amorphization, improved wettability, increased surface area of the drug, finely dispersed into the highly porous silica, were the main factors responsible for this finding. Moreover, the obtained results suggested that drug dissolution rate can be properly tuned, based on the suited choice of the silica type. [ABSTRACT FROM AUTHOR]

Saponins in Caryophyllaceae were subjected to many structural elucidations for several decades leading to big datasets of molecules essentially based on four aglycones: quillaic acid (QA), gypsogenin (Gyp), gypsogenic acid (GA) and 16α-OH-gypsogenic acid (16α-OH-GA). Statistical analyses of structural variability of 231 saponins (published between 1971 and 2016) highlighted several diversification trends associating the four aglycones to different glycosylation (Gly) patterns defined by specific, dominant or relatively well-regulated sugars at the carbons C3, C23 and/or C28. The C28 was frankly more glycosylated than C3 in GA and 16α-OH-GA versus comparable Gly levels in Gyp and QA. Moreover, GA and 16α-OH-GA showed higher regulations of glucose than Gyp and QA. Further structural diversification of GA- and 16α-OH-GA-based saponins was provided by specific occurrence, absence or high regulations of other hexoses (Gal, Man), GlcA and/or pentoses (Xyl, Ara) in presence of Glc (dominant sugar). Under Gyp and QA, Gly patterns showed systematic occurrence of 3-Gal with diversification due (i) to presence-absence of Fuc, Rha, Qui (6-deoxyhexoses), Xyl at C3 or C28 or (ii) to high regulations of 3-Glc, 3-GlcA, 3-Ara, 28-Gal and/or 28-Glc. The highlighted structural organization of saponins showed significant associations with several plant genera. At lower biological scale, roots, seeds and aerial parts were revealed to be more or less favorite compartments for the different saponin classes. Finally, a synthesis of published pharmacological activities was made taking into account affiliations of saponins to different aglycones and plant genera. Preliminary average aglycone-glycosylation patterns were established for the most cited activities (cytotoxic, antiproliferative, immunomodulatory). [ABSTRACT FROM AUTHOR]

This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005) and Ferraty et al. (2006).