AI-based Classification Research Articles

A model-free technique based on computer vision and sEMG for classification in Parkinson’s disease by using computer-assisted handwriting analysis

Patients suffering from Parkinson’s disease are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose a model-free technique for differentiating Parkinson’s disease patients from healthy subjects by using a handwriting analysis tool based on computer vision and surface ElectroMyoGraphy (sEMG) signal-processing techniques and an Artificial Intelligence-based classifier. Experimental tests have been conducted with both healthy and Parkinson’s Disease patients using the proposed technique to address some specific research scientific questions regarding most representative features, best writing patterns, best AI-based classification approach between ANN optimal topology and SVM approaches in terms of both accuracy and repeatability of the results. Finally, the obtained results are reported and discussed to infer some important properties on writing patterns, classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research.

Validation of production data by using an AI-based classification methodology; a case in the Gulf of Mexico

Production data quality is a topic of general interest in the oil industry. It is often the only information available in sufficient amount in mature fields. With the development of information systems the oil companies have large volumes of data, but not all are reliable. The errors may have various origins, starting with problems in data acquisition systems. The existence of contaminated data may cause operational failures and lead to an inappropriate decision making process. A methodology for identification of contaminated data was applied in order to determine the quality of a production dataset of an oil field. To achieve this objective, a methodology based on data mining techniques, combining a fuzzy classification algorithm, neural network modeling and an iterative process, was applied to a real case, a database of an offshore field en México and the result was the classification of data: good, slightly contaminated or bad. The decline behavior of a well was evaluated, with good and slightly contaminated data and the results were appropriate. We concluded that this classification methodology based on intelligent algorithms generated a simple solution to the problem of quality determination of production data. We found that this methodology can be applied to any dataset. Of course, there is a degree of subjectivity in the methodology, and changing the restriction criteria for classification, the data quality determination may change. Furthermore, during the application of the proposed methodology, it was shown the effectiveness of data mining tools in the estimation of missing data.

Gastro-intestinal tract inspired computational model for myocardial infarction diagnosis

Myocardial infarction (MI) has been the major cause of mortality in humans. The diagnostic process of MI in humans is critical and requires careful examination of the general symptoms, heart activity rate and blood chemistry apart from considering the patient history and contextual information of the diagnostic process. The manual diagnostic process often results in high misclassification rate due to the higher level of complexity involved therein. Thus, computer-based diagnostic systems have been developed to assist the medical practitioners for accurately diagnosing MI. However, most of the computer-based techniques are unable to handle and incorporate contextual information associated with the diagnostic process effectively. This paper aims at enhancing the computer aided diagnostic performance and solving the problem of handling contextual information through the development of a novel nature-inspired computational model. The model presented in this paper is based on the defense mechanism associated with the Human Digestive System. It incorporates multi-level decision making approach and the reasoning behind diagnostic indications at different levels of diagnostic process. The proposed diagnostic system, implemented using MATLAB, has been tested on real medical data set. The experimental results of the proposed model have been compared with five standard AI-based classification techniques. The results obtained suggest that the new classification approach presented in this paper is better than the standard AI-based classification solutions in most cases.