The navigation of mobile robots using heuristic algorithms is one of the important issues in computer and control sciences. Path planning and obstacle avoidance are current topics of navigational challenges for mobile robots. The major drawbacks of conventional methods are the inability to plan motion in a dynamic and unknown environment, failure in crowded and complex environments, and inability to

Over the last decades, the number of Swarm Intelligence algorithms proposed in the literature has increased considerably. However, most algorithms do not follow an adequate scientific rigour neither the principles of Swarm Intelligence, reproducing similar computational procedures of many other approaches, but by means of a different metaphor. In this scenario, the aim of this paper is to propose a

Age estimation can obtain biological age which is helpful for diagnosis of healthy status and disease. The current age estimation methods do not consider the deep relationships of instances, which limits the potential improvement of the age estimation performance. A hierarchical age estimation mechanism with adaboost-based deep instance weighted fusion is proposed to solve this problem. First, a circulation

Supervised classification and target recognition of Hyperspectral images (HSI) is a challenging task due to high dimensionality and spectral mixing. Straightforward cognitive computation and target classification lead to high computation cost and low recognition accuracy. Limited availability of training samples makes the recognition process very slow and inaccurate. The main purpose of this work is

This paper presents an evolutionary method to find the optimally designed gripper configuration for the automated material handling process. The optimal design of the robot gripper is a non-linear, multicriteria and multi-constraint problem. Evolutionary computational methods are introduced to overcome the difficulty associated while finding the optimal dimensions of the gripper. In this study, an

In this paper, a class of generalised inertial competitive neural networks with time-varying delays is proposed. The existence and global exponential stability of anti-periodic solutions for this class of neural networks are investigated. By using a continuation theorem of coincidence degree theory, the Wirtinger inequality and constructing an appropriate Lyapunov function, some sufficient conditions

Drought is one of the most recurrent natural disasters with cataclysmic effects on water budget, crop production, economic progression and public health. These consequences are magnified by the climate change leading to more intense drought conditions. A number of drought indices have been presented to calibrate the drought severity with its own strengths and limitations. Many of them are region-specific

Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a validation dataset is a common alternative to prevent overfitting in many Machine Learning (ML) techniques, including GP. But, there is one key point which differentiates

This work, primarily, addresses the problem of automated diagnosis of lung cancer by classifying malignant nodules present in the lung, if any. To achieve the goal, we have posed a weighted dual objective optimisation problem so as to reduce the feature subset required for automated classification of malignant lung nodules and at the same time, we endeavour to increase the classification accuracy to

Recently, deep learning has developed rapidly and contributed in many fields like the classification in radar and sonar applications. In some special fields like the underwater acoustic signals, the dataset for training may be scarce due to the reason of security or other restrictions, which affects the performance of the deep learning methods as those need a big dataset to ensure high accuracy. Furthermore

In this paper, we consider a new neural network model to simply solve the convex second-order cone constrained variational inequality problem. Based on a smoothing method, the variational inequality (VI) problem is first converted to a convex second-order cone programming (CSOCP). Using a high-performance model, the obtained convex programming problem is solved. According to Karush-Kuhn-Tucker conditions

Cloud computing services provide a wide range of resource pool for maintaining a large amount of data. Cloud services are commonly used as the private or public data forum based on the demand, and the increase in usage has lead to security concerns. The information in the cloud comes under threat due to hackers, and the most common attack on the cloud data is considered as the Distributed Denial of

The drastic increase in atmospheric pollutants has resulted in the prevalence of hazardous diseases like Asthma, Ischaemic heart disease, and Pulmonary disease around the world. IoT technology has the capability to acquire and monitor air quality parameters in the ambient environment of an individual. Inspired from these aspects, this paper proposes an IoT-based automated framework for monitoring and

ABSTRACT In order to actively respond to the severe situation of the current African swine fever (ASF) epidemic, this paper proposed a double-quantitative decision rough set method over two universes. First, a double-quantitative decision rough set model over two universes based on compatibility relation is defined. Furthermore, the characteristics of ASF decision-making problems are fully considered

Case-based reasoning (CBR) is a nature-inspired machine learning technique. It solves a new problem using the existing similar problems with their solutions stored in central repository known as case-base. It results in continuous growth of the case-base enhancing the problem solving capability of the system but at the same time compromising the performance. First performance challenge is continuous

The traditional method of extracting entities and relations not only disregards the dependency between the two subtasks of entities and relations but also facilitates the cumulative propagation of errors. To solve these problems, a method – called MSBD – of joint extraction of entities and relations based on the Character Graph Convolutional Network (CGCN) and Multi-Head Self-Attention Mechanism (MS)

Multiverse optimisation (MVO) algorithm is an excellent meta-heuristic algorithm based on laws of physics. However, it is easy to fall into local optimum when solving complex multimodal optimisation problems with high dimensions. The global optimisation performance of the algorithm is still unsatisfactory. In this paper, we propose a hybrid multiverse optimisation (DE-SMVO) algorithm. First, in order

Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different

This paper presents a class of Cohen–Grossberg neural networks (CGNNs) with discontinuous activations and time-varying delays. Firstly, under the framework of Filippov solution, we derive some general sufficient conditions to guarantee the global existence of the solutions to the proposed CGNNs with discontinuous activations and time-varying delays. Then, by constructing the new Lyapunov–Krasovskii

There is great interest in the pursuit of process-driven, algorithmic allocation of health-care resources, including computer-aided medical diagnosis and treatment (MDT). Little is understood regarding the computational complexity of MDT; however, and if determined, how relevant the complexity would be to automating MDT. We approach analysing the computational complexity of MDT in several ways: (1)

In this scientific research, a new technique to solve a linear quadratic optimal control problem with fuzzy variables is proposed. The problem by an efficient transformation reduces to a crisp problem, and the solution is obtained from the direct method. In fact, we use the approximation method with artificial neural network, and to obtain the solution, the perceptron neural network (PNN) is given

In this paper, we present a non-reduced order approach to study the exponential stability of delayed inertial genetic regulatory networks. Sufficient conditions guaranteeing the networks to be exponentially stable are established by introducing a novel Lyapunov–Krasovskii functional. Compared with the existing ones in literature, the proposed methods significantly reduce the computational complexity

Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration

The current landmine clearance methods mostly rely on the manual use of metal detectors (MDs) and on the deminer’s experience in differentiating between the sounds emitted due to the presence of a landmine or of harmless clutter. This process suffers from high false-alarm rates, which renders the demining effort slow and costly. In this paper, we report our attempts in using machine learning for decision

Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy

Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-cloud, on the other hand, can be trustable paradigm to obviate obstacles such as aforesaid unpleasant

In this paper, we study the robust H∞ performance for discrete-time T-S fuzzy switched memristive stochastic neural networks with mixed time-varying delays and switching signal design. The neural network under consideration is subject to time-varying and norm bounded parameter uncertainties. Decomposing of the delay interval approach is employed in both the discrete delays and distributed delays. By

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant

In tacit coordination games people manage to converge on prominent solutions, which are known as focal points. There is still no accepted explanation of how players manage to converge on the same solution. It could be that the limited explanatory power arises from the fact that existing theories rely on pure strategies to describe behaviour. The aim of the current study is to construct a cognitive

Prediction of metrological, botanical characteristics is extremely important for different directions in agriculture. The availability of these data allows us to adjust the process of growing crops, which has a huge impact on yield, speed of ripening and the presence of vitamins in the grown culture. Increasing yields due to changes in culture growing conditions without the use of gene mutations and

The fitness evaluation (FE) management has been successfully applied to improve the performance of multi-population methods for dynamic optimisation problems (DOPs). In this work, we extend one of its variants to address DOPs which was recently proposed by the authors. The aim of our proposal is to increase the efficiency of the FE management. To this end, we propose a technique based on hierarchical

There is a rising trend in exploring the capability of inverse reinforcement learning (IRL) in high dimensional demonstrations. Our aim is to recognise human intents from video data within an IRL framework. For this, we present a two-layered maximum likelihood IRL model. The usefulness of knowledge representation (KR) schemes and availability of advisors at different layers is exploited through this

In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuses odometry and vision data based on dimensionality reduction technique, hierarchically. These two types of data are first fused and then considered as inputs to the place cell model. Place cells do the clustering

The genetic algorithm (GA) is one of the most common solutions to solve many optimisation problems. Its distributed version, Island Model GA (IMGA), was introduced to overcome more complex and scalable cases. However, there is a recurrent problem in IMGA called premature convergence as a consequence of selection in the migration. This process is a mechanism of migrating individuals from one into another

This article mainly explores a class of proportional delayed high-order inertial Hopfield neural networks (HIHNNs) with time-varying coefficients. Without using the reduced-order method, by combining Lyapunov function method with differential inequality approach, some sufficient criteria on the existence of anti-periodic solutions including its uniqueness and exponential stability are built up. The

This paper presents new analytic and numerical analysis of signalling games that give rise to informational bottlenecks – that is to say, signalling games with more state/act pairs than available signals to communicate information about the world. I show via simulation that agents learning to coordinate tend to favour partitions of nature which provide maximal information transfer. This is true despite

Recently, graphics processing unit (GPU) gained lots of attention from academia and industry for its applicability in high-performance computing. It has been successfully applied to many fields, such as image processing, machine learning, object detection, etc. In our previous work, GPU was adopted to accelerate the computation of rough set approximation (RSA), which is the core step in most of the

Electroencephalogram (EEG) signals are commonly used in analysing the brain activity. The EEG signals have small amplitude and hence, they are often affected by the artefacts. For the efficient processing, it is necessary to remove the artefacts from the EEG signals. This paper develops a technique through deep learning scheme for removing the artefacts present in the EEG signal. Initially, the EEG

Lower back Pain (LBP) is pathological and occurs in about 80% of the population at least once in their life. Physiotherapists personalise manual treatments to heal or relieve pain according to the patient characteristics. The contribution of this methodological paper is the description and evaluation of the configuration software associated to a therapy machine that executes back segment mobilisations

A number of software programms are developed in several releases. Before developing any new release, a set of requirements is suggested for inclusion in the release. Having multiple constraints, it is impossible to develop all the requirements proposed in the next release. The presence of competing companies, replication of product ideas, shortening of the development time and lack of project funding

In the multi-criteria decision-making (MCDM), multi-objective optimisation by ratio analysis (MOORA) plus the full multiplicative form (MULTIMOORA) are a useful method, which has the flexibility and robustness properties. The aim of this paper is to present a more robust method of MULTIMOORA to solve the MCDM evaluations with interval-valued Pythagorean fuzzy sets (IVPFSs). More specifically, considering

Optimisation algorithm is currently one of the most applicable techniques to solve real-world problems by finding the best solution from all feasible solutions in the search space. This paper proposes enhanced multiverse optimiser algorithm that is inspired from the physics multiverse theory. The proposed algorithm suggests an enhancement of multiverse optimiser algorithm . It enhances the performance

As the generalisation of intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy set (PFS), the q-rung orthopair fuzzy set (q-ROFS) is more useful to express fuzzy and ambiguous information. Meanwhile, to consider human’s hesitance, the concept of q-rung orthopair hesitant fuzzy set (q-ROHFS) is presented, which can be more valid for handling real multiple attribute decision-making (MADM) problems.

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of the combination of prototypical concepts. The proposed logic relies on the logic of typicality ALC+TR, whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept

In this article, the ability of estimating the weight and the failure load of a structure through image processing has been investigated. To this end, the well-known civil engineering practice, spaghetti bridge, has been used as a test structure. To set up uniform experiments and to simplify the construction process, only 2 dimensional bridges were considered. By defining the failure load as the load

Cognitive Science research is hard to conduct, because researchers must take phenomena from the world and turn them into laboratory tasks for which a reasonable level of experimental control can be achieved. Consequently, research necessarily makes tradeoffs between internal validity (experimental control) and external validity (the degree to which a task represents behavior outside of the lab). Researchers

Max-margin-based discriminative feature learning method (MMLDF) learns a low-dimensional feature representation such that the global margin of the data is maximised and samples of the same class become as close as possible. Non-linear version of MMLDF uses kernel method. In this paper, we first prove that the kernel MMLDF cannot be used for discriminative feature learning. Strictly speaking, the kernel

In this study, an improved invasive weed optimisation (CMIWO) algorithm is investigated to solve the dynamic economic dispatch (DED) problem with valve-point effects. In the proposed algorithm, a hybrid operator including selective crossover, random mutation and row crossover is proposed to improve the exploration and exploitation abilities. Moreover, a self-adaption repair method is developed and

This article introduces two tools aimed at improving our understanding of the relationship between human and artificial rationality and helping us identify agents that are false positives or negatives. The first is a framework that systematically exposes where and how discrepancies between human and artificial rationalities can arise. The second is a test that utilises the insight gained from applying

In satisfiability (SAT) the task is to determine whether a propositional formula can evaluate to true under some assignment to its variables. The formula is either represented in CNF or in circuit form. Solvers are available for both representations with comparable performance. CNF-based approach, being simpler in encoding and solver design, is more popular, in which a circuit is translated into CNF

Speech recognition performance of the machine has been greatly improved using artificial intelligence. However, compared with the superior recognition ability of human auditory system, the machine still has some problems to deal with. Based on the existing physiological principle of human auditory system, this paper proposes a novel emergent auditory model. This model simulates each key part of the

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance

Over recent decades, it has been asserted that the essence of developmental learning processes is change through learning. However, capturing the essence of change in learning processes remains an open question. To study learning processes, we take up a maze problem and conduct an experiment in which a participant draws a route for the maze problem over 10 sessions. To understand how a participant

Scores of machine learning algorithms depend on a good distance measure to achieve high performance. The inverted specific-class distance measure, simply ISCDM, is proposed to find reasonable distance measure between each pair of instances with nominal attributes only. ISCDM does not depend on the attribute value of the training instance, which makes it less sensitive to missing values in the training

An electric-resistance-welded (ERW) tube is one of the leading contributor products for automotive manufacturers. Such tubes tend to fail due to weak weld quality that may be the result of several parameters, such as inappropriate selection of weld power, positions of forming rolls and fin pass rolls, mill operating speed and many more. This paper presents an application of classifier algorithm to

Migration transition occurring in artificial agent societies is one of the vital transitions which is responsible for their development and growth in addition to fertility and mortality transitions occurring in them. The migration transition introduces while its realisation, into a society or societies, a set of macro, micro, and meso-level dynamics depending upon whether migration process is realised

In this paper, without using reduced-order method, a class of non-autonomous inertial neural networks with time-varying connection weights and unbounded continuously distributed delays are considered. Based upon Lyapunov function method and differential inequality technique, some sufficient conditions are established to reveal that all solutions of the addressed model and their derivatives converge

ABSTRACT We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its

In this paper, we study a class of genetic regulatory networks with time-varying delays, new criteria for the existence and global exponential stability of almost positive periodic solutions are established by using the theory of dichotomy and contraction mapping principle, together with some novel analysis techniques. Finally, an example is given to numerically demonstrate the theoretical results

Arrhythmia classification is an interesting research field that serves as the solution for most of the cardiac-related diseases. The patients with cardiac diseases are experiencing the greatest risk rate of death, and hence, there is a need to identify the presence of arrhythmia in patients to reduce the fatality rate. This paper proposes an arrhythmia classification method, which offers better classification