Aydano Pamponet Machado

A framework for building web mining applications in the world of blogs: A case study in product sentiment analysis

Recently there has been much interest in electronic commerce applications that use data mining techniques to explore datasets in the social media context. However, most of the applications have already been developed in an ad hoc manner, mainly, due to the lack of adequate tools, yielding difficulties in customizing applications and requesting high time consuming for constructing and maintaining these applications. This work addresses these problems and proposes a software framework for building Web mining applications in the blog world. The architecture of the proposed framework combines the use of blog crawling and data mining algorithms, in order to provide a complete and flexible solution for building general-purpose Web mining applications. The framework flexibility allows some important customizations, such as the construction of adapters for reading text from different blogs, and the use of different pre-processing techniques and data mining algorithms. In order to improve the efficacy of information extraction from blogs, ontology is used in the blogs description. For this, there are software agents responsible for tracking and indexing blogs related to a specific tag and for mining blog datasets. Moreover, web services are used for encapsulating existing tools and maximize reuse. This framework has been instantiated in order to be applied for helping the blog users to effectively find out relevant information in the blog world. The focus of this paper is on describing the novel software architecture of the general framework (blog crawling and data mining) providing detailed information about the data mining sub-framework, which uses the semantic web services technology for automating service composition and consists on the main research contribution. A case study of an e-commerce application for analyzing the users sentiment regarding specific products is reported and its results considers the effort reduction when creating a web mining application by using the proposed integrated frameworks and existing data mining tools, as well as a qualitative analysis related to quality aspects of the developed application, such as the evolution impact.

Intrastromal corneal ring segment implantation to correct astigmatism after penetrating keratoplasty

PURPOSE: To evaluate the clinical outcomes of implantation of Ferrara intrastromal corneal ring segments (ICRS) in patients with astigmatism after penetrating keratoplasty (PKP). SETTING: Private clinic, Belo Horizonte, Brazil. DESIGN: Retrospective consecutive case series. METHODS: Chart records of post‐PKP patients who had ICRS implantation from May 2005 to September 2009 were retrospectively reviewed. The following parameters were studied: corrected distance visual acuity (CDVA), keratometry (K) values, spherical equivalent (SE), spherical refractive error, corneal topographic astigmatism, minimum K, and maximum K. RESULTS: The study evaluated 59 eyes (54 patients). The mean CDVA (logMAR) improved from 0.45 ± 0.17 (SD) (range 0.18 to 1.00) to 0.30 ± 0.17 (range 0.00 to 1.00). The mean SE was −6.34 ± 3.40 diopters (D) (range 0.37 to −16.50 D) preoperatively and −2.66 ± 2.52 D (range 0.87 to −10.50 D) postoperatively. The mean spherical refractive error decreased from −7.10 ± 3.07 D (range 2.15 to 16.68 D) preoperatively to −3.46 ± 2.05 D (range 0.88 to 10.79 D) postoperatively. No patient lost visual acuity. The mean corneal topographic astigmatism decreased from 3.37 ± 1.51 D preoperatively to 1.69 ± 1.04 D postoperatively. The mean maximum K value decreased from 48.09 ± 2.56 D to 44.17 ± 2.67 D and the mean minimum K value, from 44.90 ± 2.54 D to 42.46 ± 2.63 D. All changes were statistically significant (P<.0001). CONCLUSION: Intrastromal corneal ring segments effectively reduced corneal cylinder in patients with astigmatism after PKP. Financial Disclosure: Drs. Ferrara and Merayo‐Lloves have proprietary interest in the Ferrara ring. Drs. Coscarelli, Torquetti, and Alfonso have no financial or proprietary interest in any material or method mentioned.

Using 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules

The lung cancer is the reason of a lot of deaths on population around the world. An early diagnosis brings a most curable and simpler treatment options. Due to complexity diagnosis of small pulmonary nodules, Computer-Aided Diagnosis (CAD) tools provides an assistance to radiologist aiming the improvement in the diagnosis. Extracting relevant image features is of great importance for these tools. In this work we extracted 3D Texture Features (TF) and 3D Margin Sharpness Features (MSF) from the Lung Image Database Consortium (LIDC) in order to create a classification model to classify small pulmonary nodules with diameters between 3-10mm. We used three machine learning algorithm: k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP) and Random Forest (RF). These algorithms were trained by different set of features from the TF and MSF. The classification model with MLP algorithm using the selected features from the integration of TF and MSF achieved the best AUC of 0.820.

Automatic weighing attribute to retrieve similar lung cancer nodules

BackgroundCancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules.MethodsIn this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes.ResultsThe results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved.ConclusionsBased on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.

Evaluation of Cardiac Function with Radionuclide Angiography in Hypertension

Alterations of left ventricular systolic and diastolic function at rest and during exercise have been described in the first stages of coronary artery disease, hypertrophic cardiomyopathy and essential hypertension. Ab~ormalities in the systolic response to exercise are common in patients with hypertension, and can be caused by diastolic changes that have been reported as very early indications of this disease. Widespread prescribing of ,,-blockers for the treatment of hypertension must take into account that these drugs usually depress cardiac function and therefore aggravate the systolic performance, which is already impaired. For this reason, a new class of ,,-blockers (hybrid drugs with vasodilator properties) that may be devoid of these effects is now under intensive investigation. One of these novel drugs, nebivolol, was used to assess modifications of the cardiac performance in patients with hypertension, using radionuclide angiography (RNA).