From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy

This chapter addresses the following question: What are the advantages of extending a fuzzy expert system (FES) to an artificial neural network (ANN), within a computer‐based speech therapy system (CBST)? We briefly describe the key concepts underlying the principles behind the FES and ANN and their applications in assisted speech therapy. We explain the importance of an intelligent system in order to design an appropriate model for real‐life situations. We present data from 1‐year application of these concepts in the field of assisted speech therapy. Using an artificial intelligent system for improving speech would allow designing a training program for pronunciation, which can be individualized based on specialty needs, previous experiences, and the child\u27s prior therapeutical progress. Neural networks add a great plus value when dealing with data that do not normally match our previous designed pattern. Using an integrated approach that combines FES and ANN allows our system to accomplish three main objectives: (1) develop a personalized therapy program; (2) gradually replace some human expert duties; (3) use “self‐learning” capabilities, a component traditionally reserved for humans. The results demonstrate the viability of the hybrid approach in the context of speech therapy that can be extended when designing similar applications

Parkinson’s Disease Prediction Based on Multistate Markov Models

In the real medical world, there are many symptoms or chronic diseases that cannot be characterized in a deterministic way, and which must be examined in a random way. In the study of these stochastic processes, Markov chains are used. There is a wide variety of phenomena that suggest a behavior in a Markov process manner such as: the probability that a patient's health to improve, to get worse, to remain stable or to progress to death within a certain time slot, depending on what happened in the previous time window. Our goal is to show that the Markov chains can be applied to the patients with Parkinson’s disease in order to predict the evolution of the disease over time. So the doctor may decide a therapeutic solution that is adapted to the patient's needs, and that can improve the quality of the patient's life with Parkinson's disease in terminal stage

Use of Deep Learning and Blockchain Technologies in Healthcare Industry

This editorial summarises the special issue entitled "Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems", which deals with the intersection and use of deep learning and blockchain technologies in the healthcare industry. This special issue consists of eleven scientific articles

Motion analysis using global navigation satellite system and physiological data

Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and k-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems

Investigations of Novel High-Temperature Resistant Polymers for Electro-Optical Applications in Signal Processing Systems

In this paper the novel high-temperature resistant polymers with nonlinear optical properties have been synthesized, characterized and tested for use in electro-optical components with high bit rate optical signal processing systems and for dynamic holography. The characterization that has been reported include the measurement of second-order nonlinear susceptibility by second harmonic generation, UV-VIS spectroscopy, XRD measurement dielectric relaxation, glass transition temperature and molecular weight distribution before and after artificial ageing. Also, we have done AFM investigations and profilometry measurements for stamp patterning layers. The application of the new polyimides for electro-optic devices has been evaluated by creation of thin oriented polymer films on various substrates and preparation of planar and strip waveguides

Human Signature Identification Using IoT Technology and Gait Recognition

This study aimed to develop an autonomous design system for recognizing the subject by gait posture. Gait posture is a type of non-verbal communication characteristic of each person, and can be considered a signature used in identification. This system can be used for diagnosis. The system helps aging or disabled subjects to identify incorrect posture to recover the gait. Gait posture gives information for subject identification using leg movements and step distance as characteristic parameters. In the current study, the inertial measurement units (IMUs) located in a mobile phone were used to provide information about the movement of the upper and lower leg parts. A resistive flex sensor (RFS) was used to obtain information about the foot contact with the ground. The data were collected from a target group comprising subjects of different age, height, and mass. A comparative study was undertaken to identify the subject after the gait posture. Statistical analysis and a machine learning algorithm were used for data processing. The errors obtained after training data are presented at the end of the paper and the obtained results are encouraging. This article proposes a method of acquiring data available to anyone by using indispensable devices purchased by all users such as mobile phones

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness’s are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime

The Challenges and Compatibility of Mobility Management Solutions for Future Networks

Wireless network devices can attain the required level of quality of service (QoS) and maintain connectivity even after detaching from a current point of access. This detachment (mobility) requires various mobility management (MM) mechanisms, which present numerous challenges due to the exponential growth of wireless devices and the demands of users. The network must be heterogeneous and dense to manage such a heightened escalation of network traffic, increased number of devices, and different types of user demands. Such factors will seriously challenge MM solutions, eventually making the networks non-feasible from the dependability, adaptability, extensibility, and power consumption points of view. Therefore, novel perspectives on MM mechanisms are desired for 5G networks and beyond. This paper introduces an innovative discussion of the functional requirements of MM mechanisms for advanced wireless networks. We present comprehensive arguments on whether the prevailing mechanisms perceived by standard bodies attempt to fulfill the stated requirements. We complete this discussion through innovative qualitative evaluation. We assess each of the discussed mechanisms in terms of their capability to fulfill the dependability, adaptability, extensibility, and power consumption benchmarks for upcoming MM schemes. Hereafter, we demonstrate the outcome and the identified gaps/challenges for the planning and deployment of 5G MM frameworks and beyond. Next, we present the capabilities and possible MM solutions to tackle the gaps/difficulties. We complete our discussion by proposing a 6G MM architecture based on defined parameters

The Challenges and Compatibility of Mobility Management Solutions for Future Networks

Wireless network devices can attain the required level of quality of service (QoS) and maintain connectivity even after detaching from a current point of access. This detachment (mobility) requires various mobility management (MM) mechanisms, which present numerous challenges due to the exponential growth of wireless devices and the demands of users. The network must be heterogeneous and dense to manage such a heightened escalation of network traffic, increased number of devices, and different types of user demands. Such factors will seriously challenge MM solutions, eventually making the networks non-feasible from the dependability, adaptability, extensibility, and power consumption points of view. Therefore, novel perspectives on MM mechanisms are desired for 5G networks and beyond. This paper introduces an innovative discussion of the functional requirements of MM mechanisms for advanced wireless networks. We present comprehensive arguments on whether the prevailing mechanisms perceived by standard bodies attempt to fulfill the stated requirements. We complete this discussion through innovative qualitative evaluation. We assess each of the discussed mechanisms in terms of their capability to fulfill the dependability, adaptability, extensibility, and power consumption benchmarks for upcoming MM schemes. Hereafter, we demonstrate the outcome and the identified gaps/challenges for the planning and deployment of 5G MM frameworks and beyond. Next, we present the capabilities and possible MM solutions to tackle the gaps/difficulties. We complete our discussion by proposing a 6G MM architecture based on defined parameters

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness’s are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime