Aproximação inteligente baseada no design de sistemas integrados para aplicativos de telemedicina

Introduction: The present research was conducted at Sikha ‘O’ Anusandan (deemed to be University) in 2017. Telemedicine application in the field of medicine creates a new age. Accordingly, it requires technology to be compatible. Easy access and fast processing are the major focuses in different applications. In this paper, an approach has been considered to diagnose heart diseases. Methods: The model is designed using fuzzy logic in which the rule-based principle is applied to satisfy the objective. The model is developed keeping a view over the multi-agent system. The diagnosis of the patient is performed using Fuzzy Inference System (fis). Results: The pathological test results will help to form the rules of the model and can work for the diagnosis in a convenient way. Furthermore, the results of detection are communicated through Internet and sms for monitoring and post care purpose of supporting IoT application. Conclusion: The simulated result shows its performance can be helpful to physicians as well as patients from remote places. Originality: The model is proposed for disease detection and monitoring patients on remote locations. Also, distributed agents are proposed to act on a common platform using Internet for the benefit of society. This will save time for physicians and travelling costs for the patient. Limitations: The research results can be practically implemented in new medical equipment for hospitals with earlier equipment.Introducción: la presente investigación se realizó en Sikha ‘O’ Anusandan (la cual se considera una universidad) en 2017. La aplicación de la telemedicina en el campo de la medicina genera una nueva era. En consecuencia, requiere que la tecnología sea compatible. Las características principales que se demandan de dichas aplicaciones son el fácil acceso y el procesamiento rápido. Este estudio se aproxima a la telemedicina para el caso de diagnosis de enfermedades cardíacas. Métodos: el modelo se diseña mediante una lógica difusa en la que se aplica el principio basado en reglas para satisfacer el objetivo. El modelo se desarrolla teniendo en cuenta el sistema de agentes múltiples. El diagnóstico del paciente se realiza con el sistema de inferencia difusa (fis). Resultados: los resultados de la prueba patológica ayudarán a formar las reglas del modelo y pueden aportar para el diagnóstico de manera conveniente. Además, los resultados de la detección se comunican a través de Internet y sms para fines de seguimiento y cuidado posterior de la aplicación IoT. Conclusión: el resultado simulado muestra que su desempeño puede ser útil tanto para médicos como para pacientes en lugares remotos. Originalidad: se propone el modelo para detectar enfermedades y monitorear pacientes situados en locaciones remotas. Además, se propone que agentes distribuidos en una zona actúen sobre una plataforma común utilizando internet para el beneficio de la sociedad, esto ahorrará tiempo a los médicos y costos de traslado o transporte del paciente. Limitaciones: los resultados de la investigación se pueden implementar de forma práctica en nuevos equipos médicos para hospitales con equipos ya existentes.Introdução: a presente pesquisa foi realizada na Universidade de Sikha ‘O’ Anusandan, em 2017. O aplicativo de telemedicina no campo da medicina gera uma nova era. Em consequência, requer que a tecnologia seja compatível. O acesso fácil e o processamento rápido são as principais características esperadas dos diferentes aplicativos. Neste estudo foi considerada uma aproximação para diagnosticar as doenças cardíacas.Métodos: o design do modelo é feito através de uma lógica difusa, na qual o princípio baseado em regras para satisfazer o objetivo é utilizado. O modelo é desenvolvido tendo em conta o sistema de agentes múltiplos. O diagnóstico do paciente é realizado utilizando o sistema de inferência difusa (fis).Resultados: os resultados do exame patológico ajudarão a formar as regras do modelo e podem contribuir para o diagnóstico de forma conveniente. Além disso, os resultados do exame são comunicados, por internet e sms, para fins de seguimento e cuidado posterior do aplicativo IoT.Conclusão: o resultado simulado mostra que seu desempenho pode ser útil tanto para médicos quanto para pacientes em lugares remotos.Originalidade: é proposto o modelo para detectar doenças e monitorar pacientes situados em lugares remotos. Além disso, é proposto que agentes distribuídos em determinadas zonas utilizem uma plataforma comum, fazendo uso da internet para beneficiar a sociedade, o que economizará tempo para os médicos e custos de traslado e/ou transporte do paciente.Limitações: os resultados da pesquisa podem ser inseridos de forma prática em novas equipes médicas para hospitais com equipes já existentes

Variable Sign-Sign Wilcoxon Algorithm: A Novel Approach for System Identification

Behavioral study of a system is an important task. It is mostly used in real world environments and became an emergent research area. Various approaches have been proposed since last two decades. In this paper, we have proposed a Variable Step-Size Sign-Sign Wilcoxon Approach, that is robust against outliers in the desired data and also convergence speed is faster than Wilcoxon norm based approach. In initial stage, Sign-Sign Wilcoxon norm based approach has been verified. Next to it, the proposed approach is verified and compared for the application in Linear and Non-linear system identification problems in presence of outliers.DOI:http://dx.doi.org/10.11591/ijece.v2i4.83

Classification of Emotional Speech of Children Using Probabilistic Neural Network

Child emotions are highly flexible and overlapping. The recognition is a difficult task when single emotion conveys multiple informations. We analyze the relevance and importance of these features and use that information to design classifier architecture. Designing of a system for recognition of children emotions with reasonable accuracy is still a challenge specifically with reduced feature set. In this paper, Probabilistic neural network (PNN) has been designed for such task of classification. PNN has faster training ability with continuous class probability density functions. It provides better classification even with reduced feature set. LP_VQC and pH vectors are used as the features for the classifier. It has been attempted to design the PNN classifier with these features. Various emotions like angry, bore, sad and happy have been considered for this piece of work. All these emotions have been collected from children in three different languages as English, Hindi, and Odia. Result shows remarkable classification accuracy for these classes of emotions. It has been verified in standard databse EMO-DB to validate the result

Performance Analysis of HE Methods for Low Contrast Images

AbstractThe image enhancement is one of the important issues in image processing. The main purpose is to highlight certain characteristic of image such as: contrast, sharpening. Histogram equalization is the well-known method for image enhancement. Histogram equalization became a popular technique because it is simple and effective. However Histogram equalization cause excessive contrast enhancement which cause visual artifacts of processed image. In this paper new forms of histogram equalization are overviewed to overcome this drawback. The major difference among the methods is the way to divide the input histogram. Recursive exposure based sub-image histogram equalization (R_ESIHE) use average intensity value as the separating point. Median-mean based sub-image clipped histogram equalization (MMSICHE) and Quadrants dynamic histogram equalization for contrast enhancement (QDHE) use median intensity value as separating point. Here objective parameters are Peak signal to noise ratio (PSNR) and Absolute Mean Brightness Error (AMBE)used to compare the quality of enhancement

A Statistical Approach for Voiced Speech Detection

Detection of Voice in speech signal is a challenging problem in developing high-performance systems used in noisy environments. In this paper, we present an efficient algorithm for robust voiced speech detection and for the application to variable-rate speech coding. The key idea of the algorithm is considering speech energy and zero crossings rate (ZCR) information simultaneously when processing speech signals and finding the end point of the signal. Next to it a decision rule and a background noise statistics estimator, by applying a statistical model. A robust decision rule is derived from the generalized likelihood ratio test (LRT) by assuming that the noise statistics are known a priori. The algorithm is most efficient for the time-varying noise. According to our simulation results, the proposed algorithm shows significantly better performance in low signal-to-noise ratio and in noisy environments

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having  HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications

Impulsive Noise Cancellation from ECG Signal using Adaptive Filters and their Comparison

Impulsive Noise is the sudden burst noise of short duration. Mostly it causes by electronic devices and electrosurgical noise in biomedical signals at the time of acquisition. In this work, Electrocardiograph (ECG) signal is considered and tried to remove impulsive noise from it. Impulsive noise in ECG signal is random type of noise. The objective of this work is to remove the noise using different adaptive algorithms and comparison is made among those algorithms. Initially the impulsive noise in sinusoidal signal is synthesized and tested for different algorithms like LMS, NLMS, RLS and SSRLS. Further those algorithms are modified in a new way to weight variation. The proposed novel approach is applied in the corrupted ECG signal to remove the noise. The effectiveness of the proposed approach is verified for ECG signal with impulsive noise as compared to the traditional approaches as well as previously proposed approaches. Also the performance of our approach is validated by SNR computation. Significant improvement in SNR is achieved after removal of noise