Hierarchical Classification Technique Research Articles

Repertórios interculturais de mulheres sobre concepções de HIV/AIDS em cenário interétnico

Abstract Objective to analyze, from the perspective of interculturality, the meanings attributed to HIV/AIDS that make up the repertoires of women in interethnic situations. Method a descriptive, retrospective study, developed based on the primary research database. The sample consisted of 642 records, from the application of the Word Association Test, with 386 non-indigenous women and 256 indigenous women from the municipality of Rio Tinto-PB. The responses learned from the AIDS-inducing stimulus were categorized according to group belonging, age and marital status. Data were processed by the IRaMuTeQ software, and analyzed using the Descending Hierarchical Classification, complementary specificities and factorial correspondence analysis techniques. The discussions were based on the three perspectives that encompass interculturality: relational, functional and critical. Results Three classes were formed: Biomedical Repertoire, Socio-emotional Repertoire and Behavioral Repertoire. Biomedical Repertoire was the most significant for both groups, being led by non-indigenous women; the socio-emotional and behavioral constituents were mostly represented by indigenous women. Final considerations and implications for practice in this interethnic setting, intercultural dialogue materializes in the exchange of a heterogeneous way of thinking-knowing-doing, which unfolds in the light of relational and functional interculturality, demonstrating embryonic movements towards critical interculturality.

Elementos formadores das dimensões do empreendedorismo social

Purpose: this study has a dual objective. First, it aims to identify the main academic literature elements of entrepreneurship which are measurement aspects. Second, to extract the dimensions associated with social entrepreneurship and pointing out its potential categories of analysis. Methodology: it is bibliographic research that reviewed the entrepreneurship models published in the Web of Science and Scopus databases. These models and their elements were analyzed using a qualitative methodology of content analysis with extraction supported by the Iramuteq software. Also, a lexical analysis was applied to allow the use of Descending Hierarchical Classification (CHD) and Similarity analysis techniques. Main findings: the analysis of studies that measure entrepreneurship pointed to the existence of two dimensions. First dimension towards traditional entrepreneurship. The second dimension aims at social entrepreneurship, and despite it is independent, this dimension uses aspects of traditional entrepreneurship. However, this new dimension is gaining its own space and, in the future, it may occupy the status of a paradigm. Theoretical/methodological contributions: qualitative data were treated quantitatively, allowing the statistical analysis of the dimensions used by entrepreneurship researchers and the subfield of social entrepreneurship. Therefore, the research identified relevant variables, which can be empirically validated to develop new instruments for measuring entrepreneurship and social entrepreneurship. Originality/value: the research identified the elements of measurement of entrepreneurship. Thus, the results contribute to guiding researchers in the construction of models or measurement instruments and the findings and present an overview of the field. Practical & Social implications: social entrepreneurship mitigates several social problems. Thus, studies of reviews that support future management tools in this subfield contribute to society and managers.

Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection.

(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.

The Ludic and Human Rights: The Anti-Racist Fight in Science Education for a Political-Scientific Formation through Graffiti Art

Context: With the growing denunciations of violence and injustices in the social relationship, inside and outside schools, education based on human rights is insurgent in the current system of teaching and learning. Using the concept of school as a process of scientific, social and political construction, we planned the teaching and learning process of chemical interactions using the art of graffiti as a playful activity. Objectives: Reflection on chemistry teaching beyond the concepts of natural sciences, but also towards social issues to promote an education that transfigures the traditional model established by the hegemonic power during Brazilian history. Design: We use an ethnographic case study as a method. Scenario and Participants: In this way, we chose to bring graffiti art to chemistry workshops, since the paints are fixed on urban walls through chemical interactions between substances, building images and/or protest phrases that make us rethink the injustices and inequalities existing in Brazilian society and to dialogue the emergence of this art in the black movement with the political aspects of Human Rights. Thirteen students enrolled in a state basic education high school in the city of Goiânia-GO, Brazil, joined the workshops on Human Rights, Graffiti and Chemistry. Eight graffiti artists also participated in the workshop for free. Data collection and analysis: We used transcripts of semi-structured interviews and video-recorded workshops to categorise the data, analysing them with the Descending Hierarchical Classification technique and the use of dendrograms performed by the Iramuteq Software. Results: We obtained categories that evidence the chemical understanding of the content of chemical interactions and the socio-political understanding of human rights, and seven drawings on graffiti murals that show this correlation. Conclusions: The transgression of morals and the empowerment of the subordinate promote playfulness in the individual or collective social visibility of individuals, enabling better assimilation of scientific and social content.

Interface between women's health and violence in the training of nurses in Brazil.

Objective.To analyze the theoretical interfaces of violence against women in the nursing undergraduate curricula of public Higher Education Institutions in Brazil.Methods.Documentary and descriptive study with a qualitative approach. The documentary search happened through the access to the E-mec website for the identification of public Higher Education Institutions for undergraduate nursing degree in Brazil. The menus available online from educational institutions that contained the terms “woman” and “violence” were analyzed. Data processing took place using the IraMuTeQ software, and they were analyzed using the Descending Hierarchical Classification technique.Results.The analysis by the software resulted in an important degree of utilization (72.95%), since, of the 244 segments of texts from the menus, 178 were retained. The analysis by the Descending Hierarchical Classification resulted in four thematic categories: Violence against women as a pathological process linked to sexual and reproductive health; Women’s Health: Care, epidemiological, social and cultural aspects; Gender as an analytical category; and Children’s and Adolescents’ Care.Conclusion.It was found a connection between the terms “woman” and “violence” to the sexual and reproductive aspects of women (physiological and pathological natures) susceptible to intervention; however, the gender approach is recognized as an analytical category for understanding the vulnerabilities of the female audience to illness and violence.

Bioclimatic pattern in a Mediterranean mountain area: assessment from a classification approach on a regional scale.

The present work analyses the main weather patterns over the period 1981-2010 in the Central Apennines (Italy), drawing upon data from 23 monitoring stations spanning a wide elevation range (260-1750 m asl). Cluster analysis was used to identify homogeneous units and to verify the effectiveness of the bioclimatic classification by crossing the results derived from the application of hierarchical and non-hierarchical classification techniques. The results reveal a diversified picture of five clusters that depends on several factors as elevation, the geographic position within or outside the mountainous range, and the regional morphological traits. Although Mediterranean and Temperate climatic features coexist, the Mediterranean pattern in the southern areas and internal valleys better expresses the overall mixed characteristics of Central Italy. The use of a mixed methodology of hierarchic and partitioning methods of cluster analysis improves the bioclimatic classification, especially to quantify the level of humidity and the mediterraneity degree.

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients’ active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.

Fault Detection and Classification for Photovoltaic Systems Based on Hierarchical Classification and Machine Learning Technique

Line-line (LL) and line-ground (LG) faults may not be detected by common protection devices in Photovoltaic (PV) arrays as these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. In this article, we propose a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the dc side of PV systems. For this purpose, the main features of current-voltage curves under different fault events and normal conditions are extracted. The faults are categorized using the hierarchical classification platform. Later, the LL and LG faults are detected and classified by machine learning methods. The proposed method aims to reduce the amount of dataset, which is required for the learning process, and also obtains a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared with other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under different conditions and severity with the accuracy of 96.66% and 91.66%, respectively.

Assumptions of Social Management in the Brazilian Perspective: A Parallel with International Approaches

Social management is a broad multidisciplinary field; thus, it requires consensus among scholars. This paper responds to two questions: What elements and categories are prioritized in social management studies by Brazilian scholars? Is there novelty in the elements of SM at the Brazilian approach or it is a superposition with other international perspectives? The methods were lexical analysis, descending hierarchical classification techniques, correspondence analysis, and similarity analysis. The hierarchical clustering of elements extracted from the literature from 1990 to 2019 reveals that the field of social management has six dimensions and originated from previous studies about social participation, governance, and others. Despite the criticism on the concept, there is consensus among Brazilian authors; however, few new ideas emerge, showing a slow grown in the field, besides a low internationalization level. The advance of the praxis is minimizing the level of abstraction; Brazilian scholars defend the triad society, state, and market, but their studies focused more on state and society. On the contrary, international studies recognized the role of firms in social management. The differentiation of the Brazilian perspective of social management is a cognitive citizenship, educational and pedagogical process.

Parents´ perception of allergic or food intolerant children in relation to disease

Introduction: Cow´s milk protein allergy (CMPA) and lactose intolerance (LI) negatively affect the quality of life of patients and their families. Objective: To analyse the perception parents of children with CMPA or LI regarding aspects of health, emphasising their oral health and the factors involved in the treatment of allergy/food intolerance. Methods: Qualitative research employing focus groups, with a sample of 12 parents of children with CMPA or LI (two groups), including the participation of a researcher/moderator, an observer, and six participants in each group and a plan with guiding questions. Discourses were recorded and transcribed, and textual contents were processed in software IRAMUTEQ and analysed by the descendant hierarchical classification (DHC) technique, content analysis, and word cloud. Results: Six categories were derived from DHC-multivariate analysis: (1) infant formula, (2) oral health, (3) nutritional aspects, (4) treatment, (5) disease´s stigma, and (6) health services. The thematic axes were derived from nutritional, assistance, and care categories. Reports of different oral diseases were given by parents regarding food allergy or intolerance in their children, such as frequent caries, teeth spots, pain, and sensitive teeth. Conclusion: Parents´ perception of CMPA/LI in their children is affected by nutritional factors and the stigma caused by the disease, with special reference to the importance given to children´s oral health and reports about difficulties in the access to health services with multi-professional teams.

Hierarchical Classification Algorithm Based on FastText

At present, methods for automatic assigning labels for literature using the Chinese Library Classification system are mostly based on machine learning methods, and many use the build knowledge base to improve classification effect. These methods are applicable to small-scale dataset, and as the categorical numbers increase, the classification effect will decrease and the time cost will increase dramatically. The paper proposes a hierarchical classification algorithm based on fastText text classification tool to solve classification indexing problem on large-scale literature dataset. The algorithm trains the two-layer classifier from the top-down method and designs an optimization algorithm to reduce the error transmission between the two layers. Experimental results showed that the optimized hierarchical classification technique improved the classification effect compared to the traditional classification technique.

JUTE CROP PRODUCTION ESTIMATION IN MAJOR STATES OF INDIA: A COMPARATIVE STUDY OF LAST 6 YEARS’ FASAL AND DES ESTIMATES

Abstract. In the last few years, remote sensing technique has emerged as a viable technology for crop acreage estimation. Under the FASAL project, the jute acreage estimation was carried out in the last 6 years by using both microwave SAR data (2012–13 to 2016–17) and high resolution optical multi-spectral data (2017–18). In the assessment using SAR data, hierarchical decision rule classification technique and for optical data hybrid classification approach was used. Yield was estimated using, agro-meteorological parameter based statistical models. In the present study, different statistical parameters such as correlation coefficient (r) and RMSE were used for evaluating and comparing the results of the last 6 years (2012–13 to 2017–18) with DES (government) estimates. The RMSE values over the years were found to be 7–20% and 5–13% for area and production, respectively. The correlation coefficient (r) over the years between DES and FASAL estimates ranging between 0.995 to 1.00 and 0.996 to 1.00 in acreage and production estimates respectively. At district level, the correlation coefficient (r) values for the area and production were 0.967 and 0.962 respectively. On the basis of statistical criteria used in this study, FASAL estimates were close to DES estimates and improved over the years. The FASAL jute production estimates could be called better than DES ones in terms of good accuracy, timely reporting and low labour intensive. Thus, the FASAL estimates can be continued for policy purposes as far as jute production forecasts are concerned in India.

On-Body Sensor Positions Hierarchical Classification.

Many motion sensor-based applications have been developed in recent years because they provide useful information about daily activities and current health status of users. However, most of these applications require knowledge of sensor positions. Therefore, this research focused on the problem of detecting sensor positions. We collected standing-still and walking sensor data at various body positions from ten subjects. The offset values were removed by subtracting the sensor data of standing-still phase from the walking data for each axis of each sensor unit. Our hierarchical classification technique is based on optimizing local classifiers. Many common features are computed, and informative features are selected for specific classifications. In this approach, local classifiers such as arm-side and hand-side discriminations yielded F1-scores of 0.99 and 1.00, correspondingly. Overall, the proposed method achieved an F1-score of 0.81 and 0.84 using accelerometers and gyroscopes, respectively. Furthermore, we also discuss contributive features and parameter tuning in this analysis.

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods.

A Factor Graph Approach to Automated GO Annotation.

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.

Particle-Size Analysis for the Pike 1 Project, McMurray Formation

The unconsolidated sands of the Lower Cretaceous McMurray formation are the primary host of the Athabasca oil-sands deposit in Alberta, Canada. Alberta has one of the world’s largest nonconventional hydrocarbon resources with an estimated 1.8 trillion bbl of heavy oil (ERCB ST98-2013). The Pike 1 Project is a joint venture between Devon Canada Corporation (operator) and BP Canada Energy Group ULC. The Pike 1 Project is currently under a multiyear appraisal program to evaluate the McMurray bitumen resources that are amenable to steam-assisted gravity drainage(SAGD). In the Pike 1 Project area, the particle-size distribution (PSD) of the middle McMurray reservoir sands is highly variable because of the complex nature of the depositional environment. In order to understand the McMurray reservoir sands, Devon has exercised rigorous laboratory sampling and quality-control procedures to confirm the comprehensiveness of the PSD data set. By use of an unsupervised hierarchical classification technique, a dynamically growing self-organizing tree algorithm was used to cluster all of the PSD histograms from within the bitumen net-pay zone into one of four sand classes on the basis of similarity. Each sand class has a distinct PSD and permeability range. Using the sand classes, Devon extracted select intervals of core that closely matched the PSD histogram of each class to provide physical samples, termed “sandprints,” to fulfill sand-control-testing objectives. Further work included integrating the sand classes within the Pike 1 geological model, 3D permeability mapping, and upscaled sand-class volumes for SAGD well-pad optimization. This paper describes the process by which Devon has evaluated and classified the Pike 1 PSD data set into distinct sand classes within the unconsolidated middle McMurray reservoir. Devon’s methodology of acquiring these sands, necessary for sand-control testing, is also discussed in detail, emphasizing the overall effectiveness of the process. By use of this innovative PSD classification process as a supporting tool to reservoir characterization, Devon intends to realize the following benefits: • SAGD horizontal-well-pair placement optimization • A more methodical approach to laboratory testing of horizontal-liner technology for SAGD producers and injectors • Improved reservoir management

A Mobile Storage System for Massive Spatial Data

This paper aims at the solution to problems of large amounts of data storage, low efficiency calculate and poor user experience which actually exists in GIS application on mobile devices. In order to solve these issues we use GIS technologies including spatial data organization, map browser, and spatial index. We focused on the research of how to effectively utilize system resources and rational and efficient out organize the spatial data. What’s more, we improved the R-tree indexing algorithm to establish a rapid spatial index structure and used hierarchical classification techniques to optimize the efficiency of the real-time visualization of spatial data for mobile devices.

Analysing spatio-temporal footprints of urbanization on environment of Surat city using satellite-derived bio-physical parameters

Our study examines the relationships among various environmental variables in Surat city using remote sensing. Landsat Thematic Mapper satellite data were used in conjugation with geospatial techniques to study urbanization and correlation among satellite-derived biophysical parameters namely, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), normalized difference bareness index (NDBaI) and land surface temperature (LST). A modified NDWI (MNDWI) was used for extracting areas under water. Land use/land cover classification was performed using hierarchical decision tree classification technique using ERDAS IMAGINE Expert classifier with an accuracy of 90.4% for 1990 and 85% for 2009. It was found that city has expanded over 42.75 sq.km within two decades. Built-up, fallow and sediment land use classes exhibited high dynamics with increase of nearly 200% and 50% and decrease of 55% respectively from 1990 to 2009. Vegetation and water classes were less dynamic with 20% decrease and 15% increase. The transformation of land parcels from vegetation to built-up, vegetation to fallow and fallow to built-up has resulted in increase of LST by 5.5 ± 2.6°C, 6.7 ± 3°C and 3.5 ± 2.9°C, respectively.

Alien Phytogeographic Regions of Southern Africa: Numerical Classification, Possible Drivers, and Regional Threats

The distributions of naturalised alien plant species that have invaded natural or semi-natural habitat are often geographically restricted by the environmental conditions in their new range, implying that alien species with similar environmental requirements and tolerances may form assemblages and characterise particular areas. The aim of this study was to use objective numerical techniques to reveal any possible alien phytogeographic regions (i.e. geographic areas with characteristic alien plant assemblages) in southern Africa. Quarter degree resolution presence records of naturalised alien plant species of South Africa, Lesotho, Swaziland, Namibia and Botswana were analysed through a divisive hierarchical classification technique, and the output was plotted on maps for further interpretation. The analyses revealed two main alien phytogeographic regions that could be subdivided into eight lower level phytogeographic regions. Along with knowledge of the environmental requirements of the characteristic species and supported by further statistical analyses, we hypothesised on the main drivers of alien phytogeographic regions, and suggest that environmental features such as climate and associated biomes were most important, followed by human activities that modify climatic and vegetation features, such as irrigation and agriculture. Most of the characteristic species are not currently well-known as invasive plant species, but many may have potential to become troublesome in the future. Considering the possibility of biotic homogenization, these findings have implications for predicting the characteristics of the plant assemblages of the future. However, the relatively low quality of the dataset necessitates further more in-depth studies with improved data before the findings could be directly beneficial for management.

Classification of emotional speech using 3DEC hierarchical classifier

The recognition of emotion from speech acoustics is an important problem in human–machine interaction, with many potential applications. In this paper, we first compare four ways to extend binary support vector machines (SVMs) to multiclass classification for recognising emotions from speech—namely two standard SVM schemes (one-versus-one and one-versus-rest) and two other methods (DAG and UDT) that form a hierarchy of classifiers, each making a distinct binary decision about class membership. These are trained and tested using 6552 features per speech sample extracted from three databases of acted emotional speech (DES, Berlin and Serbian) and a database of spontaneous speech (FAU Aibo Emotion Corpus) using the OpenEAR toolkit. Analysis of the errors made by these classifiers leads us to apply non-metric multi-dimensional scaling (NMDS) to produce a compact (two-dimensional) representation of the data suitable for guiding the choice of decision hierarchy. This representation can be interpreted in terms of the well-known valence-arousal model of emotion. We find that this model does not give a particularly good fit to the data: although the arousal dimension can be identified easily, valence is not well represented in the transformed data. We describe a new hierarchical classification technique whose structure is based on NMDS, which we call Data-Driven Dimensional Emotion Classification (3DEC). This new method is compared with the best of the four classifiers studied earlier and a state-of-the-art classification method on all four databases. We find no significant difference between these three approaches with respect to speaker-dependent performance. However, for the much more interesting and important case of speaker-independent emotion classification, 3DEC significantly outperforms the competitors.