What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.

Geoprocessamento aplicado na identificação de padrões espaciais do perfil de nascimento na região do Vale do Paraíba

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Etnofaulismos e os dicionários monolíngues brasileiros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Content extraction: Identifying the main content in HTML documents

Except the article forming the main content most HTML documents on the WWW contain additional contents such as navigation menus, design elements or commercial banners. In the context of several applications it is necessary to draw the distinction between main and additional content automatically. Content extraction and template detection are the two approaches to solve this task. This thesis gives an extensive overview of existing algorithms from both areas. It contributes an objective way to measure and evaluate the performance of content extraction algorithms under different aspects. These evaluation measures allow to draw the first objective comparison of existing extraction solutions. The newly introduced content code blurring algorithm overcomes several drawbacks of previous approaches and proves to be the best content extraction algorithm at the moment. An analysis of methods to cluster web documents according to their underlying templates is the third major contribution of this thesis. In combination with a localised crawling process this clustering analysis can be used to automatically create sets of training documents for template detection algorithms. As the whole process can be automated it allows to perform template detection on a single document, thereby combining the advantages of single and multi document algorithms.

Special Collections in the Classroom

Bucknell University's Special Collections/University Archives recently acquired various rare books that have been integrated into curricular initiatives.

Bucknell's Google Student Ambassador

Oudam Meas '12 has served as Bucknell's Google Student Ambassador.

Review of Truman Capote Encyclopedia

For the Capote connoisseur, this encyclopedia offers a treasure trove of details about Truman Capote's life and works. There are numerous biographical and critical sources about Capote and his works, including many online reference tools (e.g., Literature Criticism Online, Literature Online LION, Biography Resource Center, as well as other encyclopedias).

Cardiac image fusion from stand-alone SPECT and CT: clinical experience

Myocardial perfusion imaging with SPECT (SPECT-MPI) and 64-slice CT angiography (CTA) are both established techniques for the noninvasive evaluation of coronary artery disease (CAD). Three-dimensional (3D) SPECT/CT image fusion may offer an incremental diagnostic value by integrating both sets of information. We report our first clinical experiences with fused 3D SPECT/CT in CAD patients. METHODS: Thirty-eight consecutive patients with at least 1 perfusion defect on SPECT-MPI (1-d adenosine stress/rest SPECT with (99m)Tc-tetrofosmin) and 64-slice CTA were included. 3D volume-rendered fused SPECT/CT images were generated and compared with the findings from the side-by-side analysis with regard to coronary lesion interpretation by assigning the perfusion defects to their corresponding coronary lesion. RESULTS: The fused SPECT/CT images added information on pathophysiologic lesion severity in 27 coronary stenoses (22%) of 12 patients (29%) (P<0.001). Among 40 equivocal lesions on side-by-side analysis, the fused interpretation confirmed hemodynamic significance in 14 lesions and excluded functional relevance in 10 lesions. In 3 lesions, assignment of perfusion defect and coronary lesion appeared to be reliable on side-by-side analysis but proved to be inaccurate on fused interpretation. Added diagnostic information by SPECT/CT was more commonly found in patients with stenoses of small vessels (P=0.004) and involvement of diagonal branches (P=0.01). CONCLUSION: In addition to being intuitively convincing, 3D SPECT/CT fusion images in CAD may provide added diagnostic information on the functional relevance of coronary artery lesions.