The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Ontology-based Classification of News in an Electronic Newspaper

This paper deals with the classification of news items in ePaper, a prototype system of a future personalized newspaper service on a mobile reading device. The ePaper system aggregates news items from various news providers and delivers to each subscribed user (reader) a personalized electronic newspaper, utilizing content-based and collaborative filtering methods. The ePaper can also provide users "standard" (i.e., not personalized) editions of selected newspapers, as well as browsing capabilities in the repository of news items. This paper concentrates on the automatic classification of incoming news using hierarchical news ontology. Based on this classification on one hand, and on the users' profiles on the other hand, the personalization engine of the system is able to provide a personalized paper to each user onto her mobile reading device.

Determining the informational, navigational and transactional intent of web queries

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

Analysis and detection of cognitive load and frustration in drivers' speech

Non-driving related cognitive load and variations of emotional state may impact a driver’s capability to control a vehicle and introduces driving errors. Availability of reliable cognitive load and emotion detection in drivers would benefit the design of active safety systems and other intelligent in-vehicle interfaces. In this study, speech produced by 68 subjects while driving in urban areas is analyzed. A particular focus is on speech production differences in two secondary cognitive tasks, interactions with a co-driver and calls to automated spoken dialog systems (SDS), and two emotional states during the SDS interactions - neutral/negative. A number of speech parameters are found to vary across the cognitive/emotion classes. Suitability of selected cepstral- and production-based features for automatic cognitive task/emotion classification is investigated. A fusion of GMM/SVM classifiers yields an accuracy of 94.3% in cognitive task and 81.3% in emotion classification.

Classifying the user intent of web queries using k-means clustering

Purpose: Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries. Design/methodology/approach: For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k-means clustering approach based on a variety of query traits. Findings: The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational. Research limitations/implications: This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs. Practical implications: The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research. Originality/value: This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay-off for web search engines can be quite beneficial. © Emerald Group Publishing Limited.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

OntoFeed um leitor de Feeds com extensão ontológica.

O problema que justifica o presente estudo refere-se à falta de semântica nos mecanismos de busca na Web. Para este problema, o consórcio W3 vem desenvolvendo tecnologias que visam construir uma Web Semântica. Entre estas tecnologias, estão as ontologias de domínio. Neste sentido, o objetivo geral desta dissertação é discutir as possibilidades de se imprimir semântica às buscas nos agregadores de notícia da Web. O objetivo específico é apresentar uma aplicação que usa uma classificação semi-automática de notícias, reunindo, para tanto, as tecnologias de busca da área de recuperação de informação com as ontologias de domínio. O sistema proposto é uma aplicação para a Web capaz de buscar notícias sobre um domínio específico em portais de informação. Ela utiliza a API do Google Maps V1 para a localização georreferenciada da notícia, sempre que esta informação estiver disponível. Para mostrar a viabilidade da proposta, foi desenvolvido um exemplo apoiado em uma ontologia para o domínio de chuvas e suas consequências. Os resultados obtidos por este novo Feed de base ontológica são alocados em um banco de dados e disponibilizados para consulta via Web. A expectativa é que o Feed proposto seja mais relevante em seus resultados do que um Feed comum. Os resultados obtidos com a união de tecnologias patrocinadas pelo consórcio W3 (XML, RSS e ontologia) e ferramentas de busca em página Web foram satisfatórios para o propósito pretendido. As ontologias mostram-se como ferramentas de usos múltiplos, e seu valor de análise em buscas na Web pode ser ampliado com aplicações computacionais adequadas para cada caso. Como no exemplo apresentado nesta dissertação, à palavra chuva agregaram-se outros conceitos, que estavam presentes nos desdobramentos ocasionados por ela. Isto realçou a ligação do evento chuva com as consequências que ela provoca - ação que só foi possível executar através de um recorte do conhecimento formal envolvido.

A Cancer Recognition Method Based on DNA Microarray

The accurate cancer classification is of great importance in clinical treatment. Recently, the DNA microarray technology provides a promising approach to the diagnosis and prognosis of cancer types. However, it has no perfect method for the multiclass classification problem. The difficulty lies in the fact that the data are of high dimensionality with small sample size. This paper proposed an automatic classification method of multiclass cancers based on Biomimetic pattern recognition (BPR). To the public GCM data set, the average correct classification rate reaches 80% under the condition that the correct rejection rate is 81%.

近30年来陕北黄土高原土地利用动态变化分析

基于1975年的LandsatMSS影像,1986年、1997年和2006年的Landsat TM影像,利用遥感影像的计算机自动分类和目视解译,得到这4a的土地利用信息。利用ERDAS中的GIS分析模块和统计方法对这4a的土地利用图进行统计分析,得到1975-1986年、1986-1997年和1997-2006年这3个时间段的土地利用变化的数量、速度、幅度、空间格局特征。结果表明:耕地经历了一个先增加后减少的过程,草地、林地先减少后增加,居民地一直处于增加状态,未利用地从整体上来说处于减少状态,只不过在中期有一定数量的波动;在前期和中期土地利用类型间的相互转化基本一致,后期土地利用类型间的相互转化有所增强;耕地、草地、林地和未利用地是该地区土地利用变化的主导类型,主要是耕地、草地与其它土地利用类型间的相互转化。

Mining Anomalies Using Traffic Feature Distributions

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

La visión artificial, un nuevo aliado para el análisis de imágenes artísticas

En este estudio se evalúa el rendimiento de los métodos de Bag-of-Visualterms (BOV) para la clasificación automática de imágenes digitales de la base de datos del artista Miquel Planas. Estas imágenes intervienen en la ideación y diseño de su producción escultórica. Constituye un interesante desafío dada la dificultad de la categorización de escenas cuando éstas difieren más por los contenidos semánticos que por los objetos que contienen. Hemos empleado un método de reconocimiento basado en Kernels introducido por Lazebnik, Schmid y Ponce en 2006. Los resultados son prometedores, en promedio, la puntuación del rendimiento es aproximadamente del 70%. Los experimentos sugieren que la categorización automática de imágenes basada en métodos de visión artificial puede proporcionar principios objetivos en la catalogación de imágenes y que los resultados obtenidos pueden ser aplicados en diferentes campos de la creación artística.

Algorithmic computation of knot polynomials of secondary structure elements of proteins

The classification of protein structures is an important and still outstanding problem. The purpose of this paper is threefold. First, we utilize a relation between the Tutte and homfly polynomial to show that the Alexander-Conway polynomial can be algorithmically computed for a given planar graph. Second, as special cases of planar graphs, we use polymer graphs of protein structures. More precisely, we use three building blocks of the three-dimensional protein structure-alpha-helix, antiparallel beta-sheet, and parallel beta-sheet-and calculate, for their corresponding polymer graphs, the Tutte polynomials analytically by providing recurrence equations for all three secondary structure elements. Third, we present numerical results comparing the results from our analytical calculations with the numerical results of our algorithm-not only to test consistency, but also to demonstrate that all assigned polynomials are unique labels of the secondary structure elements. This paves the way for an automatic classification of protein structures.

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.

Selection of burst-like transients and stochastic variables using multi-brand image differencing in the Pan-STARRS1 Medium Deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands gP1, rP1, iP1, and zP1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.

Bioassays against pinewood nematode: assessment of a suitable dilution agent and screening for bioactive essential oils

Acetone was investigated and found to be an appropriate alternative to Triton X-100 as a solvent of essential oils in bioassays aimed to investigate their effects on pinewood nematode (Bursaphelenchus xylophilus) mortality. Therefore it was used as dilution agent to screen the effectiveness of fifty two essential oils against this pest. Thirteen essential oils were highly effective, resulting in more than 90% pinewood nematode mortality at 2 mg/mL, with six of them resulting in 100% mortality. LC100 values ranged between 0.50 mg/mL and 0.83 mg/mL for the essential oils of Origanum vulgare and Satureja montana, respectively. Essential oils were submitted to gas chromatography and gas chromatography-mass spectrometry analysis and their chemical composition established. Data from essential oils with 100% mortality at 2 mg/mL and other essential oils previously found to have LC100 ≤ 2 mg/mL was combined, their chemical profiles investigated by correspondences analysis plus automatic classification.