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This paper presents a series of experiments on the classification of emergency phone conversation records using artificial neural networks (ANNs). Input data which were processed by ANNs were the features of callers and events taken from emergency phone calls. The authors analyzed four variants of classification: the groups of callers which have specified features, the groups of events which have...
Biometrics play a crucial role in establishing an individuals identity. A signature is one of the most widely recognized way to authorize transactions and authenticate the human identity as compared to other electronic identification methods such as fingerprint and retina scans. Due to a huge demand for authentication, fast algorithms need to be assimilated for signature recognition and verification...
This paper presents the results of a study developing artificial neural network system (ANN) for classification of Alzheimer's disease (AD) and healthy patients. The classification is done using biomarkers, from cerebrospinal fluid: albumin ratio (CSF/Serum and/or Plasma), Aβ40 (CSF), Aβ42 (CSF), tau-total (CSF) and tau-phospho (CSF). Neural network input parameters are datasets from Alzbiomarkers...
The dissolved gas-in-oil analysis (DGA) is a prevailing methodology being widely used to detect incipient faults in power transformers. However various methods have been developed to interpret DGA results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI) based methodology is varied with variation of input variable. Thus,...
This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification...
This paper proposes ANN based method for fingerprint ROI (Region of Interest) segmentation. Proposed ANNs where trained with 10000 samples extracted from 20 fingerprint images (in grey-scale and binary modes). The experimental results, including three statistical performance indicators, shows very good performance of the proposed method on a test database of 200 fingerprint images.
This paper describes an implementation of speech recognition that recognizes and suppresses ten (10) defined profane and vulgar Filipino words. The adapted speech recognition architecture was that of the Oregon Graduate Institute's (OGI) Center for Spoken Language and Learning (CSLU). It utilizes a hybrid Hidden Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature...
This paper presents a novel approach to human chromosome classification. Human cell contains 22 pairs of autosomes and a pair of sex chromosomes. In this research, 22 types of autosomes represent 22 classes to be distinguished. New method of classification is based on the special organized committee of 462 simple perceptrons, called Competitive Neural Network Teams (CNNTs). Each perceptron is trained...
This research paper proposes an intelligent classification technique to recognize normal and abnormal MRI brain image. Medical image like ECG, MRI and CT-scan images are important way to diagnose disease of human being efficiently. The manual analysis of tumor based on visual inspection by radiologist/physician is the conventional method, which may lead to wrong classification when a large number...
In this paper, we propose voice conversion based on articulatory-movement (AM) to vocal tract parameter (VTP) mapping. An artificial neural network (ANN) is applied to map AM to VTP and to convert the source speaker's voice to the target speaker's voice. The proposed system is not only text independent voice conversion, but can also be used for an arbitrary source speaker. This means that our approach...
This study elaborates on a design of a face recognition algorithm realized with feature extraction from 2D-LDA and the use of polynomial-based radial basis function neural networks (P-RBF NNS). The overall face recognition system consists of two modules such as the preprocessing part and recognition part. The proposed polynomial-based radial basis function neural networks is used as an the recognition...
This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment‥ In order to train and test the developed automatic person identification system, an in-house hand signatures database is created,...
The research presented in this paper proposes a novel gender classification approach using face image. The approach extracts features from grayscale face images through Infomax ICA and subsequently selects features using k-means clustering and classifies the clustered features employing PNN. All the experimental evaluations are done on cropped face images from FERET database using 280 faces for training...
There are many real-life classification problems where class overlapping severely limits the classification accuracy. In these situations is difficult to build automatic classifiers that obtain good generalization performance. An interesting case is found in the separation of stars and galaxies, which arises in galactic or extragalactic studies. There are many astronomical analysis packages which...
Digital mammography is a preferred method for early detection of breast cancer. However, in most cases, it is very difficult to distinguish benign and malignant masses without a biopsy, hence, misdiagnosis is always possible. In this paper, the Extreme Learning Machine (ELM) algorithm is used to classify the suspicious masses in digitized mammograms available in the Mini-MIAS database. As selection...
In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using standard 12 lead ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. In arrhythmia analysis, it is unavoidable that some attribute values of a person...
Internet Automatic Sales System is be used to assist people in net business. Since part of the talking continent in purchasing is always asked frequently, so the statistic method like TFIDF is applied to deal this issue. Based on the experiment situation, baseline system based on TFIDF achieved very good effect. But the classic algorithm is not enough to reach real application requirement, the Dice...
A human face does not only identify an individual but also communicates useful information about a person's emotional state. No wonder automatic face expression recognition has become an area of immense interest within the computer science, psychology, medicine and human-computer interaction research communities. Various feature extraction techniques based on statistical to geometrical data have been...
It is necessary to study a kind of network intrusion detection method which realizes faster attack detection and response. In order to improve the network intrusion detection precision further, Network intrusion detection method based on Agent and SVM is proposed to recognize the intrusion types in the paper. The network intrusion detection system based Agent and SVM are created. Then, network Intrusion...
Since ECG is huge in size sending large volume data over resource constrained wireless networks is power consuming and will reduce the energy of nodes in Body Sensor Networks (BSN). Therefore, compression of ECGs and diagnosis of diseases from compressed ECGs will play key roles in enhancing the life-time of body sensor networks. Moreover, discrimination between ventricular Tachycardia and Ventricular...
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