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The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks
This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi
Automated classification of African embroidery patterns using cellular learning automata and support vector machines
Embroidery is the art that is majorly practised in Nigeria, which requires creativity and skills. However, differentiating between two standard embroidery patterns pose challenges to wearers of the patterns. This study developed a classification system to improve the embroiderer to user relationship. The specific characteristics are used as feature sets to classify two common African embroidery patterns (handmade and tinko) are shape, brightness, thickness and colour. The system developed and simulated in MATLAB 2016a environment employed Cellular Learning Automata (CLA) and Support Vector Machine (SVM) as its classifier. The classification performance of the proposed system was evaluated using precision, recall, and accuracy. The system obtained an average precision of 0.93, average recall of 0.81, and average accuracy of 0.97 in classifying the handmade and tinko embroidery patterns considered in this study. This study also presented an experimental result of three validation models for training and testing the dataset used in this study. The model developed an improved and refined embroiderer for eliminating stress related to the manual pattern identification process
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Automatic detection and classification of leukaemia cells
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)
Local Causal States and Discrete Coherent Structures
Coherent structures form spontaneously in nonlinear spatiotemporal systems
and are found at all spatial scales in natural phenomena from laboratory
hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary
climate dynamics. Phenomenologically, they appear as key components that
organize the macroscopic behaviors in such systems. Despite a century of
effort, they have eluded rigorous analysis and empirical prediction, with
progress being made only recently. As a step in this, we present a formal
theory of coherent structures in fully-discrete dynamical field theories. It
builds on the notion of structure introduced by computational mechanics,
generalizing it to a local spatiotemporal setting. The analysis' main tool
employs the \localstates, which are used to uncover a system's hidden
spatiotemporal symmetries and which identify coherent structures as
spatially-localized deviations from those symmetries. The approach is
behavior-driven in the sense that it does not rely on directly analyzing
spatiotemporal equations of motion, rather it considers only the spatiotemporal
fields a system generates. As such, it offers an unsupervised approach to
discover and describe coherent structures. We illustrate the approach by
analyzing coherent structures generated by elementary cellular automata,
comparing the results with an earlier, dynamic-invariant-set approach that
decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht
The identification of cellular automata
Although cellular automata have been widely studied as a class of the spatio temporal systems, very few investigators have studied how to identify the CA rules given observations of the patterns. A solution using a polynomial realization to describe the CA rule is reviewed in the present study based on the application of an orthogonal least squares algorithm. Three new neighbourhood detection methods are then reviewed as important preliminary analysis procedures to reduce the complexity of the estimation. The identification of excitable media is discussed using simulation examples and real data sets and a new method for the identification of
hybrid CA is introduced
Новый генератор псевдослучайных последовательностей чисел на основе клеточного автомата
Розглядається новий генератор псевдовипадкових послідовностей біт, який реалізований на клітинному автоматі. Представлена апаратна реалізація генератора і виконано його програмне моделювання. За допомогою програмної моделі проведене тестування розробленого генератора псевдовипадкових чисел. Використані тести показали позитивний результат, який підтверджує високі статистичні властивості сформованої випадкової послідовності.This paper considers a novel pseudo-random bit sequence generator, which is implemented on a cellular automaton. It presents the hardware implementation of the generator and it the software simulation. With the help of the software model is testing of the random number generator was conducted. Tests showed a positive result, which confirms the high statistical properties of the generated random sequence.Рассматривается новый генератор псевдослучайных последовательностей бит, который реализован на клеточном автомате. Представлена аппаратная реализация генератора и выполнено его программное моделирование. С помощью программной модели проведено тестирование разработанного генератора псевдослучайных чисел. Использованные тесты показали положительный результат, который подтверждает высокие статистические свойства сформированной случайной последовательности
Layered Cellular Automata
Layered Cellular Automata (LCA) extends the concept of traditional cellular
automata (CA) to model complex systems and phenomena. In LCA, each cell's next
state is determined by the interaction of two layers of computation, allowing
for more dynamic and realistic simulations. This thesis explores the design,
dynamics, and applications of LCA, with a focus on its potential in pattern
recognition and classification. The research begins by introducing the
limitations of traditional CA in capturing the complexity of real-world
systems. It then presents the concept of LCA, where layer 0 corresponds to a
predefined model, and layer 1 represents the proposed model with additional
influence. The interlayer rules, denoted as f and g, enable interactions not
only from adjacent neighboring cells but also from some far-away neighboring
cells, capturing long-range dependencies. The thesis explores various LCA
models, including those based on averaging, maximization, minimization, and
modified ECA neighborhoods. Additionally, the implementation of LCA on the 2-D
cellular automaton Game of Life is discussed, showcasing intriguing patterns
and behaviors. Through extensive experiments, the dynamics of different LCA
models are analyzed, revealing their sensitivity to rule changes and block size
variations. Convergent LCAs, which converge to fixed points from any initial
configuration, are identified and used to design a two-class pattern
classifier. Comparative evaluations demonstrate the competitive performance of
the LCA-based classifier against existing algorithms. Theoretical analysis of
LCA properties contributes to a deeper understanding of its computational
capabilities and behaviors. The research also suggests potential future
directions, such as exploring advanced LCA models, higher-dimensional
simulations, and hybrid approaches integrating LCA with other computational
models.Comment: This thesis represents the culmination of my M.Tech research,
conducted under the guidance of Dr. Sukanta Das, Associate Professor at the
Department of Information Technology, Indian Institute of Engineering Science
and Technology, Shibpur, West Bengal, India. arXiv admin note: substantial
text overlap with arXiv:2210.13971 by other author
Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks
The use of complex networks as a modern approach to understanding the world
and its dynamics is well-established in literature. The adjacency matrix, which
provides a one-to-one representation of a complex network, can also yield
several metrics of the graph. However, it is not always clear that this
representation is unique, as the permutation of lines and rows in the matrix
can represent the same graph. To address this issue, the proposed methodology
employs a sorting algorithm to rearrange the elements of the adjacency matrix
of a complex graph in a specific order. The resulting sorted adjacency matrix
is then used as input for feature extraction and machine learning algorithms to
classify the networks. The results indicate that the proposed methodology
outperforms previous literature results on synthetic and real-world data.Comment: 12 pages, 10 figure
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