14 research outputs found
Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification
Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian
data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan
pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan
Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan
corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q
dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model
hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik
prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi
mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma
Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM.
Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan
huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi
menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai
telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen
dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai
jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan
individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM
serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua
daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal
model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM
dalam menerajui tugas-tugas pengelasan corak.
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Pattern classification is one of the primary issues in various data mining
tasks. In this study, the main research focus is on the design and
development of hybrid models, combining the supervised Adaptive
Resonance Theory (ART) neural network and Reinforcement Learning (RL)
models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM)
network and Q-learning are adopted as the backbone for designing and
developing the hybrid models. A new QFAM model is first introduced to
improve the classification performance of FAM network. A pruning strategy
is incorporated to reduce the complexity of QFAM. To overcome the
opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then
rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide
predictions with explanations using only a few antecedents. To further
improve the robustness of QFAM-based models, the notion of multi agent
systems is employed. As a result, an agent-based QFAM ensemble model
with a new trust measurement and negotiation method is proposed. A variety
of benchmark problems are used for evaluation of individual and ensemble
QFAM-based models. The results are analyzed and compared with those
from FAM as well as other models reported in the literature. In addition, two
real-world problems are used to demonstrate the practicality of the hybrid
models. The outcomes indicate the effectiveness of QFAM-based models in
tackling pattern classification tasks
Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
Ubiquitous in-home health monitoring systems have become popular in recent
years due to the rise of digital health technologies and the growing demand for
remote health monitoring. These systems enable individuals to increase their
independence by allowing them to monitor their health from the home and by
allowing more control over their well-being. In this study, we perform a
comprehensive survey on this topic by reviewing a large number of literature in
the area. We investigate these systems from various aspects, namely sensing
technologies, communication technologies, intelligent and computing systems,
and application areas. Specifically, we provide an overview of in-home health
monitoring systems and identify their main components. We then present each
component and discuss its role within in-home health monitoring systems. In
addition, we provide an overview of the practical use of ubiquitous
technologies in the home for health monitoring. Finally, we identify the main
challenges and limitations based on the existing literature and provide eight
recommendations for potential future research directions toward the development
of in-home health monitoring systems. We conclude that despite extensive
research on various components needed for the development of effective in-home
health monitoring systems, the development of effective in-home health
monitoring systems still requires further investigation.Comment: 35 pages, 5 figure
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
Magnetic resonance (MR) and computer tomography (CT) images are two typical
types of medical images that provide mutually-complementary information for
accurate clinical diagnosis and treatment. However, obtaining both images may
be limited due to some considerations such as cost, radiation dose and modality
missing. Recently, medical image synthesis has aroused gaining research
interest to cope with this limitation. In this paper, we propose a
bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN),
to synthesize medical images from unpaired data. Specifically, a dual contrast
loss is introduced into the discriminators to indirectly build constraints
between real source and synthetic images by taking advantage of samples from
the source domain as negative samples and enforce the synthetic images to fall
far away from the source domain. In addition, cross-entropy and structural
similarity index (SSIM) are integrated into the DC-cycleGAN in order to
consider both the luminance and structure of samples when synthesizing images.
The experimental results indicate that DC-cycleGAN is able to produce promising
results as compared with other cycleGAN-based medical image synthesis methods
such as cycleGAN, RegGAN, DualGAN, and NiceGAN. The code will be available at
https://github.com/JiayuanWang-JW/DC-cycleGAN
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
Most semi-supervised learning (SSL) models entail complex structures and
iterative training processes as well as face difficulties in interpreting their
predictions to users. To address these issues, this paper proposes a new
interpretable SSL model using the supervised and unsupervised Adaptive
Resonance Theory (ART) family of networks, which is denoted as SSL-ART.
Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of
prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy
ARTMAP structure to map the established prototype nodes to the target classes
using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is
devised to associate a prototype node with more than one class label. The main
advantages of SSL-ART include the capability of: (i) performing online
learning, (ii) reducing the number of redundant prototype nodes through the OtM
mapping scheme and minimizing the effects of noisy samples, and (iii) providing
an explanation facility for users to interpret the predicted outcomes. In
addition, a weighted voting strategy is introduced to form an ensemble SSL-ART
model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART,
assigns {\color{black}a different weight} to each class based on its
performance pertaining to the corresponding class. The aim is to mitigate the
effects of training data sequences on all SSL-ART members and improve the
overall performance of WESSL-ART. The experimental results on eighteen
benchmark data sets, three artificially generated data sets, and a real-world
case study indicate the benefits of the proposed SSL-ART and WESSL-ART models
for tackling pattern classification problems.Comment: 13 pages, 8 figure
A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction
A two-stage hybrid model for data classification and rule extraction is proposed. The first stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for incremental learning of data samples, while the second stage uses a Genetic Algorithm (GA) for rule extraction from QFAM. Given a new data sample, the resulting hybrid model, known as QFAM-GA, is able to provide prediction pertaining to the target class of the data sample as well as to give a fuzzy if-then rule to explain the prediction. To reduce the network complexity, a pruning scheme using Q-values is applied to reduce the number of prototypes generated by QFAM. A \u27don\u27t care\u27 technique is employed to minimize the number of input features using the GA. A number of benchmark problems are used to evaluate the effectiveness of QFAM-GA in terms of test accuracy, noise tolerance, model complexity (number of rules and total rule length). The results are comparable, if not better, than many other models reported in the literature. The main significance of this research is a usable and useful intelligent model (i.e., QFAM-GA) for data classification in noisy conditions with the capability of yielding a set of explanatory rules with minimum antecedents. In addition, QFAM-GA is able to maximize accuracy and minimize model complexity simultaneously. The empirical outcome positively demonstrate the potential impact of QFAM-GA in the practical environment, i.e., providing an accurate prediction with a concise justification pertaining to the prediction to the domain users, therefore allowing domain users to adopt QFAM-GA as a useful decision support tool in assisting their decision-making processes
Employing machine learning techniques in monitoring autocorrelated profiles
In profile monitoring, it is usually assumed that the observations between or within each profile are independent of each other. However, this assumption is often violated in manufacturing practice, and it is of utmost importance to carefully consider autocorrelation effects in the underlying models for profile monitoring. For this reason, various statistical control charts have been proposed to monitor profiles when between- or within-data is correlated in Phase II, in which the main aim is to develop control charts with quicker detection ability. As a novel approach, this study aims to employ machine learning techniques as control charts instead of statistical approaches in monitoring profiles with between-profile autocorrelations. Specifically, new input features based on conventional statistical control chart statistics and normalized estimated parameters are defined that are capable of adequately accounting for the between-autocorrelation effect of profiles. In addition, six machine learning techniques are extended and compared by means of Monte Carlo simulations. The simulation results indicate that machine learning techniques can obtain more accurate results compared with statistical control charts. Moreover, adaptive neuro-fuzzy inference systems outperform other machine learning techniques and the conventional statistical control charts. 2023, The Author(s).Scopu
A Q-learning-based multi-agent system for data classification
In this paper, a multi-agent classifier system with Q-learning is proposed for tackling data classification problems. A trust measurement using a combination of Q-learning and Bayesian formalism is formulated. Specifically, a number of learning agents comprising hybrid neural networks with Q-learning, which we have formulated in our previous work, are devised to form the proposed Q-learning Multi-Agent Classifier System (QMACS). The time complexity of QMACS is analyzed using the big O-notation method. In addition, a number of benchmark problems are employed to evaluate the effectiveness of QMACS, which include small and large data sets with and without noise. To analyze the QMACS performance statistically, the bootstrap method with 95% confidence interval is used. The results from QMACS are compared with those from its constituents and other models reported in the literature. The outcome indicates the effectiveness of QMACS in combining the predictions from its learning agents to improve the overall classification performance