An expert system for general symbol recognition

An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.     

An automated vision system for container-code recognition

Automatic container-code recognition is of great importance to the modern container management system. Similar techniques have been proposed for vehicle license plate recognition in past decades. Compared with license plate recognition, automatic container-code recognition faces more challenges due to the severity of nonuniform illumination and invalidation of color information. In this paper, a computer vision based container-code recognition technique is proposed. The system consists of three function modules, namely location, isolation, and character recognition. In location module, we propose a text-line region location algorithm, which takes into account the characteristics of single character as well as the spatial relationship between successive characters. This module locates the text-line regions by using a horizontal high-pass filter and scanline analysis. To resolve nonuniform illumination, a two-step procedure is applied to segment container-code characters, and a projection process is adopted to isolate characters in the isolation module. In character recognition module, the character recognition is achieved by classifying the extracted features, which represent the character image, with trained support vector machines (SVMs). The experimental results demonstrate the efficiency and effectiveness of the proposed technique for practical usage.     

ARCHI: An expert system for biological objects recognition

From the biological sample to the final decision, a computer vision system splits its process in many levels: image acquisition, preprocessing, segmentation, postprocessing, measurement, decision. Most often, the human operator acts upon one or more of these levels, either to drive or to confirm the processing, either to do it himself.ARCHI system acts in two ways: first to drive the acquisition, preprocessing and segmentation steps (driving part), second, to filter contours issued by segmentation according to morphological or color parameters, learned during previous steps (recognition part).At every level, decisions are taken according to (i) the biological knowledge of the field, (ii) the objects which are looked for, (iii) and the associated artefacts. That can possibly imply a modification of previous steps (control and feedback). Moreover, the knowledge of the objects can adapt itself to the sample.     

A fast expert system for electrocardiogram arrhythmia detection

Abstract: A fast expert system for electrocardiogram (ECG) arrhythmia detection has been designed in this study. Selecting proper wavelet details, the ECG signals are denoised and beat locations are detected. Beat locations are later used to locate the peaks of the individual waves present in each cardiac cycle. Onsets and offsets of the P and T waves are also detected. These are considered as ECG features which are later used for arrhythmia detection utilizing a novel fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the proposed procedure. We have evaluated the algorithm on the MIT–BIH arrhythmia database. Application of the wavelet filter with the scaling function which closely resembles the shape of the ECG signal has been shown to provide precise results in this study.     

Intelligent tutoring system using expert knowledge and Kohonen maps with automated training

Neural Computing and Applications - This paper presents an intelligent tutoring system (ITS) model that is capable of driving the didactic transposition of contents. Initially, the tutoring system...     

A new approach for estimation of obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS) is a situation where repeatedly upper airway stops off while the respiratory effort continues during sleep at least for 10 s. Apart from polysomnography, many researchers have concentrated on exploring alternative methods for OSAS detection. However, not much work has been done on using non-Gaussian and nonlinear behavior of the electroencephalogram (EEG) signals. Bispectral analysis is an advanced signal processing technique particularly used for exhibiting quadratic phase-coupling that may arise between signal components with different frequencies. From this perspective, in this study, a new technique for recognizing patients with OSAS was introduced using bispectral characteristics of EEG signal and an artificial neural network (ANN). The amount of Quadratic phase coupling (QPC) in each subband of EEG (namely; delta, theta, alpha, beta and gamma) was calculated over bispectral density of EEG. Then, these QPCs were fed to the input of the designed ANN. The neural network was configured with two outputs: one for OSAS and one for estimation of normal situation. With this technique a global accuracy of 96.15% was achieved. The proposed technique could be used in designing automatic OSAS identification systems which will improve medical service.     

SC3: self-configuring classifier combination for obstructive sleep apnea

Neural Computing and Applications - Obstructive sleep apnea is considered to be one of the most prevalent sleep-related disorders that can affect the general population. However, the gold standard...     

Strategic justification of advanced automated technology systems using an expert system

This paper presents an application of expert systems for the strategic justification of advanced automated technology systems. This expert system is developed using the EXSYS expert system shell in the VAX-VMS mainframe environment. It considers important attributes such as suitability (measure of the AMT's compliance with corporate strategy); system capability; performance of the AMT system; and productivity including the financial benefits of the AMT.     

An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks

Abstract: In this study a wavelet‐based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta‐bar‐delta, extended delta‐bar‐delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT‐BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.     

儿童阻塞性睡眠呼吸暂停计算机人脸辅助诊断综述

利用人脸图片辅助诊断儿童阻塞性睡眠呼吸暂停（OSA）可以减轻医生的负担,提高诊断的准确率。首先,简要阐述了目前儿童OSA临床诊断中的方法及其局限性;然后,在研究了目前已有方法的基础上,结合计算机人脸辅助诊断其他疾病的方法,将计算机人脸辅助诊断儿童OSA的方法分为三种类型：传统的计算机人脸辅助诊断方法、基于迁移学习的诊断方法、基于3D人脸数据的诊断方法,综述了三种类型的方法中的关键步骤,并对这些关键步骤中使用的方法进行了对比研究,为将来儿童OSA计算机人脸辅助诊断的研究提供了不同的切入点;最后,分析了儿童OSA诊断未来研究中的机遇和挑战。     

Detection of severe obstructive sleep apnea through voice analysis

This paper deals with the potential and limitations of using voice and speech processing to detect Obstructive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients who present various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set of features for detecting OSA. We apply various feature selection and reduction schemes (statistical ranking, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, Support Vector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects shows that in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able to discriminate quite well between the presence and absence of OSA. However, this is not the case with mild OSA and healthy snoring patients where voice seems to play a secondary role. We found that the best classification schemes are achieved using a Genetic Algorithm for feature selection/reduction.     

A fuzzy expert system for noise-induced sleep disturbance

In this paper, an attempt has been made to develop a fuzzy expert system for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modelling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. It has been established on the basis of findings of various researchers that the effect of noise on sleep disturbance depends to a large extent on age. The middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, we have compared our model results with some of the findings of researchers reported in International Journals.     

An expert system for laminated plate design using composite materials

Fibre-reinforced composite materials are introduced as being suitable for aircraft design, and the need for computerizing the design process is explained. Following a brief exposition of the linear theory of composite laminated plates, a prototype expert system for the design of simple load-carrying plates or panels is described. Finally the automatic assessment of the relative merits of designs of different materials using uncertainty reasoning techniques is discussed.     

An expert system for sorting

Abstract: Sorting an internal list is an indispensable requirement in numerous data processing applications, and many algorithms have been devised for accomplishing those tasks. Furthermore, it is fairly simple to derive order of magnitude of effort measures for particular sorting strategies, and within classes of equivalent theoretical power, practice has shown which algorithms prevail in which circumstances. Thus there is a useful background for testing a system's ability to learn which problem solving technique should be applied in a given instance, for the expert knowledge is rather concise and structured and facilitates comparison to machine decisions. The paper describes the construction of such a system and analyzes the results.     

An expert system for speaker identification using adaptive wavelet sure entropy

In this study, an expert speaker identification system is presented for speaker identification using Turkish speech signals. Here, a discrete wavelet adaptive network based fuzzy inference system (DWANFIS) model is used for this aim. This model consists of two layers: discrete wavelet and adaptive network based fuzzy inference system. The discrete wavelet layer is used for adaptive feature extraction in the time–frequency domain and is composed of discrete wavelet decomposition and discrete wavelet entropy. The performance of the used system is evaluated by using repeated speech signals. These test results show the effectiveness of the developed intelligent system presented in this paper. The rate of correct classification is about 90.55% for the sample speakers.     

RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

A new expert system (ES) to aid the nonspecialist physician in diagnosing arthritis and collagen diseases has been developed. Here we present the structure of RENOIR and the results of its implementation. This rule-based ES has been programmed using the MILORD environment. This is a shell to develop ES using a closed set of linguistic labels to express uncertainty. A feature of RENOIR is its five levels of knowledge representation, which permits to build a very flexible knowledge base (KB) and express knowledge with high accuracy. Those rules directed to similar goals are grouped in modules to improve computational performance and for higher clarity of the KB. Control of the reasoning process is assured by several mechanisms, one of the main being metarules specifically designed for almost all the knowledge levels of the KB. We have used public domain knowledge (books, criteria tables) and personal heuristics from one of the authors (Belmonte-Serrano) to implement the KB of RENOIR. In its present form, our KB comprises 1 058 rules, 978 facts, 220 metarules, and 34 modules. A first validation process has shown good performance of the ES compared to 12 physicians with diverse levels of experience in rheumatic diseases. New ongoing versions of the system with improved interfaces and reasoning capabilities are expected before verifying RENOIR's clinical acceptability. © 1994 John Wiley & Sons, Inc.     

An expert system for multi-criteria decision making using Dempster Shafer theory

This paper outlines a new software system we have developed that utilises the newly developed method (DS/AHP) which combines aspects of the Analytic Hierarchy Process (AHP) with Dempster–Shafer Theory for the purpose of multi-criteria decision making (MCDM). The method allows a decision maker considerably greater level of control (compared with conventional AHP methods) on the judgements made in identifying levels of favouritism towards groups of decision alternatives. More specifically, the DS/AHP analysis allows for additional analysis, including levels of uncertainty and conflict in the decisions made, for example. In this paper an expert system is introduced which enables the application of DS/AHP to MCDM. The expert system illustrates further the usability of DS/AHP, also including new aspects of analysis and representation offered through using this method. The principal application used to illustrate this expert system is that of identifying those residential properties to visit (view), from those advertised for ales through a real estate brokerage firm.     

腕表式睡眠呼吸暂停监测系统设计

设计了一种便携的腕表式睡眠呼吸暂停监测系统,避免了住院监测带来的高费用和低舒适度.以STM32为处理核心,通过控制传感器,实时监测用户的呼吸气流、血氧饱和度、心电图和胸腹运动情况,判断是否发生了呼吸暂停,记录并显示整晚呼吸暂停的总次数,同时将所测生理参数通过低功耗蓝牙发送到智能手机或平板应用程序,供医生进一步分析.将系统与标准多导睡眠仪测试结果对比,两者测得的呼吸暂停低通气指数(AHI)相关性和一致性较好.系统简便实用、测量结果可靠,扩大了睡眠呼吸暂停综合征的筛查人群.