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Biomedical time-series analysis is a diverse field that includes biomedical engineering, computer science, and medical achievements, with the goal to save human lives and improve the quality of healthcare. In this paper, we present a preliminary report on construction of an innovative web platform for heterogeneous multivariate biomedical time-series analysis. The platform will feature data upload,...
Biomedical time-series analysis is a diverse field that includes biomedical engineering, computer science, and medical achievements, with the goal to save human lives and improve the quality of healthcare. In this paper, we present a preliminary report on construction of an innovative web platform for heterogeneous multivariate biomedical time-series analysis. The platform will feature data upload,...
Breast cancer is leading cause of death and also one of the most invasive types of cancers among women in worldwide. It happens when cells in the breast start to develop uncontrollably or spread throughout the body. Early detection and effective diagnosis is the only rescue to lessen breast cancer fatality. Accurate classification of breast tumor is an important task in medical diagnosis. Soft computing...
In this paper we present an EEG detection system, which is a low-cost, portable, popularization. It is realized the low cost, mass brain health examination through the mobile phone software and networking in the "cloud" of the EEG pattern recognition expert database. The main functions of the system include: (1) through the mobile phone to control the EEG data acquisition; (2) established...
Automatic classification of tropical wood species is becoming more important especially for timber exporting countries due to the considerable economic challenge as a result of fraudulent labelling of timber species at the custom checkpoints. Hence, a reliable automated wood species recognition system is needed to inspect the wood species labelling at the checkpoints. A tropical wood species classification...
To achieve a state of optimal health, sufficient exercise is imperative in everyday routine. However, it is merely impossible to determine how much exercise is sufficient for an individual without knowing his health and fitness status. This paper presents the detailed design of a personalized exercise prescription system. The system analyzes subject's health and fitness features and provides an optimal...
Esophageal cancer is one of the most common malignant tumors in Henan Province and videoendoscope is an invaluable tool for the diagnosis of early esophageal cancer. Therefore, we propose a diagnosis expert system for the diagnosis of early esophageal cancer on the basis of videoendoscope. We apply the Bag-of-Words model to get the feature representations and group the features using the Information...
Study on method of condition assessment of substation equipment is the key subject in condition maintenance. This article introduced the application of artificial intelligence methods in the substation equipment condition assessment and different artificial intelligence methods, such as artificial neural network (ANN), fuzzy theory and expert system, are introduced respectively, and their advantages...
By analyzing the character of oil equipment detection, the intelligent fusion model of detection information of oil equipment has been established. The feature-level fusion algorithm based on fuzzy neural network and expert system has been proposed, in which the expert system has been embedded into fuzzy neural network so that it could choose the membership function and adjust the network structure...
In this paper an S-transform-based expert system is presented for classification of voltage dips. The S-transform (ST) technique is integrated with expert system (ES) model to construct the classifier. Firstly, ST technique is used to extract and quantify the significant features using amplitude factor, harmonic increment, number of amplitude mutation, and maximum value of phase angle shifting. Then...
Feature extraction methods in pattern recognition tasks seek to transform data variables to abstract mathematical variables such that their scores (called features) reveal hidden data structure of high cognitive value. Various feature extraction methods process raw data from different perspectives. Some depend on statistical correlation or independence such as principal component analysis (PCA), independent...
Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration...
Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non- stationary signals like EEG. In this work, SVM (support vector machine) based classifier was employed to detect epileptic seizure activity from background...
Kernel principal component analysis (KPCA) has been effectively applied as an unsupervised non-linear feature extractor in many machine learning applications and suggested for various data stream classification tasks requiring a nonlinear transformation scheme to reduce dimensions. However, the dimensionality reduction ability is restricted because of KPCA's high time complexity. So the practicality...
Decreasing traffic accidents and events are one of the most significant responsibilities for most of the government in the world. Nevertheless, it is hard to precisely predict the traffic condition. In order to understand the real cause of traffic accidents and restore the occurrence of traffic events, a traffic monitor and event analysis mechanism based on 3D video processing techniques is proposed...
Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays, because of their flexibility, symbolic reasoning, and explanation capabilities. Meanwhile, accurate forecasts on tourism demand and study on the pattern of the tourism demand from various origins is essential for the tourism-related...
Most of the pattern classification systems employ AI techniques. The most popular one is multi-layer perceptron network (MLP) because of its high computational efficiency. However, there may be some drawbacks: long training time, adjustment of hyperparameters, only a single most probable classification can be returned, etc. In this paper, case-based reasoning (CBR) approach is presented to help solve...
Genetic programming is the usage of the paradigm of survival of the fittest in scientific computing. It is applied to evolve solutions to problems where dependencies between multiple input factors are unknown. In this paper we propose and evaluate the application of a specifically adapted genetic programming framework to optimize the rule base of an expert system. The expert system controls a computer-aided-design...
Remote diagnosis system is an effective way to forecast the trends of tobacco diseases and insect pests. The system is a B/S application. It includes User side, Server side and Data collection module. Tobacco farmer can find the prevention and cure methods of tobacco diseases and insect pests with the system and the application can diagnose tobacco diseases and insect pests by images shot by the wireless...
Biometric authentication techniques such as lips, face, and eyes are more reliable and efficient than conventional authentication techniques such as password authentication, token, cards, personal identification number, etc. In this research paper, the emphasis has been laid on the speaker identification based on lip features. In this study, we have presented a detailed comparative analysis for speaker...
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