3,550 research outputs found
Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network
Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase
CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey
Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.
Development of soft computing and applications in agricultural and biological engineering
Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed
A survey on artificial intelligence based techniques for diagnosis of hepatitis variants
Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System
Collision avoidance is a critical task in many applications, such as ADAS
(advanced driver-assistance systems), industrial automation and robotics. In an
industrial automation setting, certain areas should be off limits to an
automated vehicle for protection of people and high-valued assets. These areas
can be quarantined by mapping (e.g., GPS) or via beacons that delineate a
no-entry area. We propose a delineation method where the industrial vehicle
utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to
detect passive beacons and model-predictive control to stop the vehicle from
entering a restricted space. The beacons are standard orange traffic cones with
a highly reflective vertical pole attached. The LiDAR can readily detect these
beacons, but suffers from false positives due to other reflective surfaces such
as worker safety vests. Herein, we put forth a method for reducing false
positive detection from the LiDAR by projecting the beacons in the camera
imagery via a deep learning method and validating the detection using a neural
network-learned projection from the camera to the LiDAR space. Experimental
data collected at Mississippi State University's Center for Advanced Vehicular
Systems (CAVS) shows the effectiveness of the proposed system in keeping the
true detection while mitigating false positives.Comment: 34 page
A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions
Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measurements are selected as fault indicators to extract meaningful features, using a discrete wavelet transform. These features are further reduced via the application of orthogonal fuzzy neighbourhood discriminative analysis. A recurrent neural network is then used to detect and classify the presence of bearing faults. The proposed system is implemented and tested in simulation on data collected from an experimental setup, to verify its effectiveness and reliability in accurately detecting and classifying the various faults
Connectionist techniques to approach sustainability modelling
When defining a context of sustainability, capturing the complexity of data and extracting as much information as possible are fundamental challenges. Normally, quantitative and qualitative indicators are defined. While the definition and calculation of the former is direct, the latter are
difficult to manage. This document provides tools based on connectionist techniques for
managing complex information combining the use of imprecise and qualitative variables. The application of these tools to evaluate non-numerical sustainability indicators is presented. The results obtained in some first approaches are briefly presented to illustrate the connectionist paradigm
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Predicting business failure using artificial intelligence system
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information
for decision making for financial institutions, investors, creditors, and other participants in the
business market. Financial reporting systems provide relevant information that can be used to
assess the financial position of firms. It is crucial to have classification and prediction models
that can analyse this financial information and provide accurate assurance for users about
business health. Recent studies have explored the use of machine learning tools as substitute
for traditional statistical methods to develop classification models to classify firm insolvency
according to financial statement information. However, these models have no ideal classifier,
since each provides a certain percentage of wrong outputs, which is a crucial consideration;
every percentage of wrong response can mean massive financial losses for stakeholders.
Therefore, this study proposes new insolvency classification and perdition models based on
machine learning modelling techniques to develop an improved classifier.
Individual modelling techniques using statistical methods and machine learning were used to
develop the classification model of business insolvency. The results showed that machine
learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest
performance based on all performance measurements used in the study, and it was the best
individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble-
Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it
has been proven that DPL modelling approach is useful for business insolvency classification.
A key contribution in enhancing individual classifier outputs is the use of traditional combining
methods with two new aggregation methods in business insolvency (Fuzzy Logic and
Consensus Approach). The Consensus Approach showed the best improvement in the results
of all individual classifiers with average accuracy of 97.7%, and it is considered the best
classification method not only in comparison with individual classifiers, but also with
traditional combiners.
This study pioneers the development of a time series business insolvency prediction model
with Big Data for UK businesses. The aim of the model is to provide early prediction about a
business health. Three prediction models were developed based on Nonlinear Autoregressive
with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR),
and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of
83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in
comparison with the best individual classifier (deep learning)
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