Andrzej Bargiela

Infohub Ltd

Granular Computing: An Introduction covers a full spectrum of granular computing from the basic methodology through algorithms and granular worlds, to a representative spectrum of applications. This book will appeal to all who are developing intelligent systems, either working at the methodological level or interested in detailed system realisation...

Human-centered information processing has been pioneered by Zadeh through his introduction of the concept of fuzzy sets in the mid 1960s. The insights that were afforded through this formalism have led to the development of the granular computing (GrC) paradigm in the late 1990s. Subsequent research has highlighted the fact that many founding princ...

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an alg...

International Journal of Simulation: Systems, Science & Technology DOI: 10.5013/IJSSST.a.22.04.06 Web address: https://ijssst.info/Vol-22/No-4/paper6.pdf

Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despite many approaches proposed in the timetabling literature, it has been observed that there is no sing...

Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despite many approaches proposed in the timetabling literature, it has been observed that there is no sing...

Automatic extraction of distinctive features from a visual information stream is challenging due to the large amount of information contained in most image data. In recent years deep neural networks (DNNs) have gained outstanding popularity for solving visual information processing tasks. This study reports novel contributions, including a new DNN...

Marking the 30th anniversary of the European Conference on Modelling and Simulation (ECMS), this inspirational text/reference reviews significant advances in the field of modelling and simulation, as well as key applications of simulation in other disciplines. The broad-ranging volume presents contributions from a varied selection of distinguished...

In this study, we are incorporating the Late Acceptance Hill Climbing (LAHC) strategy in the proposed Domain Transformation Approach (DTA) to solve the university examination scheduling problem. This is with the aim to test whether LAHC can substitute the original traditional greedy Hill Climbing (HC) in the proposed framework, in order to clarify...

In this paper, we report an interesting observation pertaining to new image processing pipeline for membrane detection suggested by optimization experiments. Denoising is usually performed in order to minimize the detrimental effects that noise has on the subsequent stages of an algorithm. Thus Denoising is typically carried out as an early pre-pro...

The nurse scheduling problem (NSP) is a complex optimisation problem of allocating nurses to duty rosters in hospitals. The objective is usually to ensure that there are always sufficient nurses on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that...

Although there have been a few approaches to achieve the goal of fault tolerance by diversifying redundancy of the individual networks that make up a neural network ensemble, some of which include ensembles of neural networks of different sizes, and ensembles of different models of neural networks such as Radial Basis Function Networks and Multilay...

The nurse scheduling problem (NSP) is a complex optimisation problem regarding the allocation of nurses to duty rosters in hospitals. The objective is to ensure that there are sufficient nurses on duty while considering individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employ...

Background: Nurse scheduling is a complex combinatorial optimization problem. Objective: This paper presents a novel approach to solving the nurse scheduling problem by simplifying it through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Results: In genera...

Through the introduction of local receptive fields, we improve the fidelity of restricted Boltzmann machine (RBM) based representations to encodings extracted by visual processing neurons. Our biologically inspired Gaussian receptive field constraints encourage learning of localized features and can seamlessly integrate into RBMs. Moreover, we prop...

In this paper, we formulate nurse scheduling problem in modern hospital environment as an integer programming (IP) problem. We simplify the nurse scheduling problem by transforming it through Information Granulation which enables the original problem to be expressed and solved more easily. The solution of the simplified problem is followed by a ref...

In this paper we report an interesting observation pertaining to denoising based on the optimization of image processing chains. Although often a goal in itself, denoising is usually performed in order to minimize the detrimental effects of noise in the subsequent stages of an algorithm. Typically, denoising is carried out as an early pre-processin...

In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreo...

This paper is concerned with the problem of optimizing deep neural networks with diverse transfer functions using evolutionary methods. Standard evolutionary (SEDeeNN) and cooperative coevolutionary methods (CoDeeNN) were applied to three different architectures characterized by different constraints on neural diversity. It was found that (1) SEDee...

Recent research has demonstrated the great capability of deep belief networks for solving a variety of visual recognition tasks. However, primary focus has been on modelling higher level visual features and later stages of visual processing found in the brain. Lower level processes such as those found in the retina have gone ignored. In this paper,...

Nurse scheduling is a complex combinatorial optimization problem. With increasing healthcare costs, and a shortage of trained staff it is becoming increasingly important for hospital management to make good operational decisions. A major element of hospital expenditure is staff cost. In order to help Kajang Hospital to make decisions about staffing...

Local Contrast Hole Filling Algorithm for Neural Slices Membrane Detection (LCHF) algorithm is non-learning, simple, easily adopted, and undependable on ground-truth; and it can recognize membrane and eliminates organelles, using a very simple algorithm that consist of short sequences of basic processing steps yet can be relatively competitive. Her...

Problem signatures are patterns that reveal a glimpse of the computational strategy most likely to be suitable for a given problem. Such a pattern could be the preferred choice of the activation and output functions for a given problem in neural networks that implement transfer functions optimization. We refer to these patterns as first-order signa...

In this paper, we investigate adaptive linear combinations of graph coloring heuristics with a heuristic modifier to address the examination timetabling problem. We invoke a normalisation strategy for each parameter in order to generalise the specific problem data. Two graph coloring heuristics were used in this study (largest degree and saturation...

Artificial Neural Networks are robust in their appli-cations; however, choosing the appropriate neural network model best suited for a particular problem is usually just a case of trial and error. In this paper, we present self-adaptive learning for Artificial Neural Networks as a direction towards efficient learning. Our hypothesis is that the neu...

In this paper we report an interesting observation pertaining to denoising based on the optimization of image processing chains. Although often a goal in itself, denoising is usually performed in order to minimize the detrimental effects of noise in the subsequent stages of an algorithm. Typically, denoising is carried out as an early pre-processin...

Incremental algorithms for fuzzy classifiers are studied in this paper. It is assumed that not all training patterns are given a priori for training classifiers, but are gradually made available over time. It is also assumed that the previously available training patterns can not be used afterwards. Thus, fuzzy classifiers should be modified by upd...

Transfer functions play an important role in artificial neural networks. They enable neural networks to form decision boundaries of different shapes and forms. However, transfer function optimisation has received relatively little research attention. In this paper, we present an approach for training neural networks that use transfer functions pool...

Many successful approaches to examination timetabling consist of multiple stages, in which a constructive approach is used for finding a good initial solution, and then one or more improvement approaches are employed successively to further enhance the quality of the solution obtained during the previous stage. Moreover, there is a growing number o...

This paper discusses and analyses the tradeoff between the flexibility afforded with greater number of staff and the implied cost of employing extra staff in the context of the nurse-scheduling problem. If the number of staff is constant, our study allows quantification of the degree of pressure put on the staff resulting from the schedules that do...

Simulation modelling of the initial assignments of exams to time-slots provides an alternative approach to the establishment of a set of feasible solutions that are subsequently optimized. In this research, we analyze two backtracking strategies for reassigning exams after the initial allocation of exams to time-slots. We propose two approaches for...

The electrode resolution of current retinal prostheses is still far from matching the densities of retinal neurons. Decreasing electrode diameter increases impedance levels thus deterring effective stimulation of neurons. One solution is to increase the surface roughness of electrodes, which can be done via nanoparticle coatings. This paper explore...

In this paper we introduce a new optimization method for the examinations scheduling problem. Rather than attempting direct optimization of assignments of exams to specific time-slots, we perform permutations of slots and reassignments of exams upon the feasible (but not optimal) schedules obtained by the standard graph colouring method with Larges...

The Min/Max classification and clustering has a distinct advantage of generating easily interpretable information granules - represented as hyperboxes in the multi-dimensional feature space of the data. However, while such an information abstraction lends itself to easy interpretation it leaves open the question whether the granules represent well...

In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) optimization in our proposed methodology in solving the examination scheduling problem. It is shown that our greedy HC optimization outperforms the GA in all cases when tested on the benchmark datasets. In our implementation, HC consumes more time to execut...

The Hermann grid was first described and discussed by the physiologist Ludimar Hermann in 1870. It is composed of white horizontal and vertical bars on a black background [1]. Subjects perceive black or gray smudges at the intersections of white bars when looking at the grid. This effect was discussed by Baumgartner who proposed a theory related to...

Formed of amino acids combinations, proteins are essential components of living beings which participate in the regulation of bodily functions. Each protein is assigned specific function(s) and reacts to external agents of the best fits. Most binding activities take place on surfaces specifically in regions of high complementarity. The structure an...

Abstract—Incremental construction of fuzzy rule-based classifiers is studied in this paper. It is assumed that not all training patterns are given a priori for training classifiers, but are gradually made available over time. It is also assumed the previously available training patterns can not be used in the following time steps. Thus fuzzy rule-b...

The interaction between proteins and their binding agents take place on surfaces and involve factors such as chemical and shape complementarity. It was shown in past studies that protein-protein interactions involve flatter regions whereas protein-ligand bindings are associated with crevices. Many approaches have been implemented which focus on the...

Most protein-ligand interactions take place on surfaces and include but not limited to factors such as chemical composition, hydrophobicity, electronegavitiy and shape complementarity. Past studies showed that protein-protein interactions occur on comparatively flat regions whereas protein-ligand bindings involve crevices. In the search for such si...

The interaction between proteins and their binding agents take place on surfaces and involve factors such as chemical and shape complementarity. It was shown in past studies that protein-protein interactions involve flatter regions whereas protein-ligand bindings are associated with crevices. Many approaches have been implemented which focus on the...

Incremental construction of fuzzy rule-based classifiers is studied in this paper. It is assumed that not all training patterns are given a priori for training clas- sifiers, but are gradually made available over time. It is also assumed the previ- ously available training patterns can not be used in the following time steps. Thus fuzzy rule-based...

Clustering forms one of the most visible conceptual and algorithmic framework of developing information granules. In spite of the algorithm being used, the representation of information granules-clusters is predominantly numeric (coming in the form of prototypes, partition matrices, dendrograms, etc.). In this paper, we consider a concept of granul...

Ranking plays important role in contemporary information search and retrieval systems. Among existing ranking algorithms, link analysis based algorithms have been proved to be effective for ranking documents retrieved from large-scale text repositories such as the current Web. Recent developments in semantic Web raise considerable interest in desig...

This paper describes a simulation model of nanoparticle assemblies formed by spray deposition. The simulation deposits nanoparticles via a semi-mechanistic process, which in spite of its simplicity generates morphologies of considerable complexity. The experiments reveal several relationships between nanoparticle parameters such as their relative p...

Cybernetics studies information processing in the context of interaction with physical systems. Since, such information is sometimes vague; it can only be discerned using approximate representations. Machine learning provides solutions that create approximate models of information and decision trees are one of its main components. However, decision...

The retina still poses many structural and computational questions. Structurally, for example, it is not yet clear how many distinct horizontal cell (HC) types the primate retina contains and what the exact patterns of connections between photoreceptors (PRs) and HCs consist of. Computationally, it is not yet clear, for instance, what functions are...

Age-related macular degeneration and retinitis pigmentosa are two of the most common diseases that cause degeneration in the outer retina, which can lead to several visual impairments up to blindness. Vision restoration is an important goal for which several different research approaches are currently being pursued. We are concerned with restoratio...

Incremental construction of fuzzy rule-based classifiers is studied in this paper. It is assumed that not all training patterns are given a priori for training classifiers, but are gradually made available over time. It is also assumed the previously available training patterns can not be used in the following time steps. Thus fuzzy rule-based clas...

The problem of learning concept hierarchies and terminological ontologies can be decomposed into two sub-tasks: concept extraction and relation learning. We describe an new approach to learn relations automatically from unstructured text corpus based on one of the probabilistic topic models, Latent Dirichlet Allocation. We rst provide denition (Inf...

Fuzzy clustering being focused on the discovery of structure in multivariable data is of relational nature in the sense of not distinguishing between the natures of the individual variables (features) encountered in the problem. In this study, we revisit the generic approach to clustering by studying situations in which there are families of featur...

Probabilistic topic models were originally developed and utilized for document modeling and topic extraction in Information Retrieval. In this paper, we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques...

In this paper a new model of the Outer Plexiform Layer (OPL) of the human retina is presented. The model, which is a multi-resolution Linear Recurrent Neural Net- work (LRNN) defined by 31 parameters, was subjected to several optimization experiments targeting different low-level visual functions involving the control of noise, brightness, contrast...

Proteins play a vital role in maintaining the balance of bodily functions in all living beings. However their functional properties are difficult to predict since they depend not only on the sequence of constituent amino acids but also on the 3D folding of the protein. This paper presents a new statistical method for extraction of surface atoms of...

In this paper we introduce a new clustering method and apply it to brain magnetic resonance imaging (MRI) lateral ventricular compartments segmentation. The method uses Gaussian smoothing to enable fuzzy c-mean (FCM) to create both a more homogeneous clustering result and reduce effect caused by noise. With the objective of finding the optimal clus...

In this study, we investigate an adaptive decomposition strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set. Moreover, the examinations within each set are...

In this study, we are concerned with information granulation realized both in supervised and unsupervised mode. Our focus is on the exploitation of the technology of hyperboxes and fuzzy sets as a fundamental conceptual vehicle of information granulation. In case of supervised learning (classification), each class is described by one or more fuzzy...

Visual tracking is an important scientific problem; the human visual system is capable of tracking moving objects in a wide variety of situations. It is also of considerable practical importance; many actual and potential applications of visual tracking algorithms exist in domains such as surveillance, medicine, robotics and the media. Although man...

In this paper, we combine graph coloring heuristics, namely largest degree and saturation degree with the concept of a heuristic modifier under the framework of squeaky wheel optimization for solving a set of examination timetabling problems. Both components interact adaptively to determine the best ordering of examinations to be processed at each...

Many of the analyses of time series that arise in real-life situations require the adoption of various simplifying assumptions so as to cope with the complexity of the phenomena under consideration. Whilst accepting that these simplifications lead to heuristics providing less accurate processing of information compared to the solution of analytical...

Detecting occlusion and camouflage in visual tracking via process-behaviour charts

Nurse Rostering problems represent a subclass of scheduling problems that are hard to solve. Their complexity is due to the large solution spaces and the many objectives and constraints that need to be fulfilled. In this study, we propose a hierarchical method of granulation of problem domain through pre-processing of constraints. A set of zero-cos...

In this paper, we are introducing a new method of granular exam-to-slot allocation based on the pre- processing of the basic student-exam information into a more abstract (granulated) entity of conflict chains. Since the conflict chains are designed to capture the mutual dependencies between exams, they enable us to reason about the exam-to-slot al...