17 research outputs found
Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification
A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy
Fuzzy qualitative trigonometry
AbstractThis paper presents a fuzzy qualitative representation of conventional trigonometry with the goal of bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in the domain of physical systems, especially in intelligent robotics. Fuzzy qualitative coordinates are defined by replacing a unit circle with a fuzzy qualitative circle; a Cartesian translation and orientation are defined by their normalized fuzzy partitions. Conventional trigonometric functions, rules and the extensions to triangles in Euclidean space are converted into their counterparts in fuzzy qualitative coordinates using fuzzy logic and qualitative reasoning techniques. This approach provides a promising representation transformation interface to analyze general trigonometry-related physical systems from an artificial intelligence perspective.Fuzzy qualitative trigonometry has been implemented as a MATLAB toolbox named XTRIG in terms of 4-tuple fuzzy numbers. Examples are given throughout the paper to demonstrate the characteristics of fuzzy qualitative trigonometry. One of the examples focuses on robot kinematics and also explains how contributions could be made by fuzzy qualitative trigonometry to the intelligent connection of low-level sensing & control tasks to high-level cognitive tasks
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System
A key challenge in eXplainable Artificial Intelligence is the well-known
tradeoff between the transparency of an algorithm (i.e., how easily a human can
directly understand the algorithm, as opposed to receiving a post-hoc
explanation), and its accuracy. We report on the design of a new deep network
that achieves improved transparency without sacrificing accuracy. We design a
deep convolutional neuro-fuzzy inference system (DCNFIS) by hybridizing fuzzy
logic and deep learning models and show that DCNFIS performs as accurately as
three existing convolutional neural networks on four well-known datasets. We
furthermore that DCNFIS outperforms state-of-the-art deep fuzzy systems. We
then exploit the transparency of fuzzy logic by deriving explanations, in the
form of saliency maps, from the fuzzy rules encoded in DCNFIS. We investigate
the properties of these explanations in greater depth using the Fashion-MNIST
dataset
Dépistage de pathologies par analyse de cris néonataux à l'aide de réseaux de neurones
Cette étude porte sur le dépistage automatisé de pathologies chez les nourrissons par analyse de leurs cris.
Dans le cadre de ce projet, nous avons tentĂ© de rĂ©aliser cette tĂąche Ă lâaide de techniques de classification par rĂ©seaux de neurones artificiels, entraĂźnĂ©s de façon supervisĂ©e.
Les enregistrements provenaient de la base de donnĂ©es de lâĂcole de technologie supĂ©rieure. Seuls les cris de nourrissons nĂ©s Ă terme et ĂągĂ©s de 30 jours ou moins ont Ă©tĂ© considĂ©rĂ©s. Les segments expiratoires des cris furent sĂ©parĂ©s en Ă©chantillons de 400 millisecondes, puis ces Ă©chantillons furent subdivisĂ©s en 8 trames de 50 millisecondes. Chaque Ă©chantillon Ă©tait reprĂ©sentĂ© par les coefficients du cepstre de frĂ©quences mel (MFCC) de ses trames.
Nous avons Ă©valuĂ© trois architectures de rĂ©seaux de neurones diffĂ©rentes : Les perceptrons multicouches (MLP), les rĂ©seaux convolutionnels (CNN) et les rĂ©seaux rĂ©currents de type long short-term memory (LSTM). Nous avons entraĂźnĂ© ces rĂ©seaux Ă reconnaĂźtre diverses pathologies, notamment lâhyperbilirubinĂ©mie et la dĂ©tresse respiratoire. La performance des classifieurs Ă©tait mesurĂ©e Ă lâaide de la validation croisĂ©e Ă k folds.
Nous avons aussi reproduit, autant que possible, le dataset employĂ© dans une autre Ă©tude, afin de permettre un comparaison Ă©quitable. Nous avons entraĂźnĂ© et Ă©valuĂ© notre systĂšme sur ce dataset. La comparaison des performances obtenues avec celles rapportĂ©es par lâĂ©tude de rĂ©fĂ©rence nous mĂšne Ă conclure que notre approche a du potentiel, mais demeure pour lâinstant infĂ©rieure Ă la leur.
Pour finir, nous avons Ă©galement dĂ©montrĂ© quâune partition inadĂ©quate des donnĂ©es entre les ensembles dâentraĂźnement et de validation pouvait produire une sous-estimation trĂšs importante de lâerreur de gĂ©nĂ©ralisation rĂ©elle. Nous avons soulevĂ© des soupçons par rapport Ă la façon don't les donnĂ©es furent partitionnĂ©es dans plusieurs autres Ă©tudes sur la reconnaissance automatisĂ©e de pathologies chez les nourrissons par analyse de leurs cris
Methods in Industrial Biotechnology for Chemical Engineers
In keeping with the definition that biotechnology is really no more than a
name given to a set of techniques and processes, the authors apply some set of
fuzzy techniques to chemical industry problems such as finding the proper
proportion of raw mix to control pollution, to study flow rates, to find out
the better quality of products. We use fuzzy control theory, fuzzy neural
networks, fuzzy relational equations, genetic algorithms to these problems for
solutions. When the solution to the problem can have certain concepts or
attributes as indeterminate, the only model that can tackle such a situation is
the neutrosophic model. The authors have also used these models in this book to
study the use of biotechnology in chemical industries.
This book has six chapters. First chapter gives a brief description of
biotechnology. Second chapter deals will proper proportion of mix of raw
materials in cement industries to minimize pollution using fuzzy control
theory. Chapter three gives the method of determination of temperature set
point for crude oil in oil refineries. Chapter four studies the flow rates in
chemical industries using fuzzy neutral networks. Chapter five gives the method
of minimization of waste gas flow in chemical industries using fuzzy linear
programming. The final chapter suggests when in these studies indeterminancy is
an attribute or concept involved, the notion of neutrosophic methods can be
adopted.Comment: 125 pages, 20 figure
System for audio capture and classification of baby cry samples
We explore multiclass classification of infants' cries and the relation between the age of the infant and the accuracy of classification. Additionally we explore secure cloud storage and cloud data processing. We compare several state-of-the-art multiclass classification models with recurrent neural networks. Classification accuracy was obtained on data from infants of various ages. For data storage and processing we used the Django Rest API and the opensource cloud platform OpenStack. Multiclass classification models successfully differentiated between different classes of crying, but no age effect has been found. We have demonstrated the aptness of the Django Rest API and OpenStack platform for data storing and processing in the cloud
Infant Cry Signal Processing, Analysis, and Classification with Artificial Neural Networks
As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals.
We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures the diversity of variations within infant cries and the result outperforms all other related studies on asphyxiated baby crying classification. We propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of infant vocal tract development as early as 4-month age. Experiments discover the pattern and tendency of the vocal tract changes and predict the abnormality of infant vocal tract by classifying the cry signals into younger age category. We propose an approach of generating hybrid feature set and using prior knowledge in a multi-stage CNNs model for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound and the experimental results give encouraging improvements on two relative databases. We propose an approach of graph convolutional network (GCN) with transfer learning for robust infant cry reason classification. Non-fully connected graphs based on the similarities among the relevant nodes are built to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. With as limited as 20% of labeled training data, our model outperforms that of the CNN model with 80% labeled training data in both supervised and semi-supervised settings. Lastly, we apply mel-spectrogram decomposition to infant cry classification and propose a fusion method to further improve the infant cry classification performance
Models and Analysis of Vocal Emissions for Biomedical Applications
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies
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Modelling and design of the eco-system of causality for real-time systems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection
Fuzzy Relational Neural Network
In this paper a fuzzy neural network based on a fuzzy relational ââIF-THENââ reasoning scheme is designed. To define the structure of the model different t-norms and t-conorms are proposed. The
fuzzification and the defuzzification phases are then added to the model so that we can consider the model like a controller. A learning algorithm to tune the parameters that is based on a backpropagation algorithm and a recursive pseudoinverse matrix technique is introduced. Different experiments on synthetic and benchmark data are made. Several results using the UCI repository of Machine learning database are showed for classification and approximation tasks. The model is also compared with some other methods known in literature