ABSTRACT Due to exponential growth in demand for radio spectrum for wireless communication networking, the radio spectrum has become over-crowded. The fixed spectrum allocation policy of the radio spectrum leads to inefficient utilisation of the available spectrum, which diverted the attention of researchers towards different intelligent techniques to access the spectrum dynamically and efficiently

ABSTRACT Improvement of prediction accuracy and early detection of the Alzheimer’s disease is becoming increasingly important for managing its impact on lives of affected patients. Many machine learning approaches have been applied to support the diagnosis and prediction of this illness. In this paper we propose an approach for improving the Alzheimer’s disease classification accuracy by using data

Current methods for human-object interaction detection often use the spatial relation between a human and an object as an interaction pattern. However, this strategy is relatively simple and has low discrimination in similar interactions. To solve this drawback, the spatial relation between skeletons and objects is proposed to model the interaction pattern and improve the detection accuracy. First

Multiple watermarking techniques for images is receiving more attention in recent years for its wide variety of applications in different fields such as piracy of digital data, and copyrights protection. Current approaches rely on adding many watermarks in different bands or channels utilising scaling factor, and embedding locations that are mainly defined by experts. This brought many challenges in

The integration of Lattice Boltzmann (LB) and adaptive-network-based fuzzy inference system was utilised to simulate mesoscale fluid interface in a multiphase fluid system. This method uses data generated by LB and based on the local population and density data. The interface between dispersed and matrix phases on the neural points was simulated. The neural mesh of interface was created by the ANFIS

Crowdsourcing services provide an economic and efficient means of acquiring multiple noisy labels for each training instance in supervised learning. Ground truth inference methods, also known as consensus methods, are then used to obtain the integrated labels of training instances. Although consensus methods are effective, there still exists a level of noise in the set of integrated labels. Therefore

In this paper, global asymptotic stability for a class of discrete-time Cohen-Grossberg Neural Networks with finite and infinite delays is investigated. By combining graph theoretic approach with Homeomorphism concept as well as Lyapunov functional method, two new sufficient conditions ensuring the global asymptotic stability of equilibrium point for above neural networks are established. Combining

Image captured in bad weather conditions confines scene prominence, appears grey and diminishes image contrast. This usually happens due to atmospheric dispersing phenomenon that affects the quality of outdoor computer vision frameworks. This deprivation relies on the gap between the object point and the camera and mostly differs for every pixel present in an image. Transmission coefficients, which

In this paper, a ranking procedure is proposed for the order-of-magnitude qualitative reasoning problem. To this end, a preference degree was suggested for comparing a set of k-dimensional vectors of qualitative labels to provide a criterion to interpret the concept of ‘preference’ between two k-dimensional vectors of qualitative labels. Some useful properties of the proposed preference degree were

Goal-driven autonomy is an agent model for managing a dynamic environment by reasoning about current and potential goals while planning and acting. Since unexpected events and conditions may cause an agent’s goals and plans to become invalid or infeasible, an agent with goal-driven autonomy should monitor the environment against its expectations. Designed for dynamic, open, and partially observable

In the object recognition task, due to changes in size, colour, illumination, position, viewing angle, and environmental background, great uncertainty is caused. The invariant recognition capability required to adapt to such diverse uncertainties has also become one of the most challenging goals of artificial intelligence, as only biometric systems can do this. The approach to dealing with uncertainty

We present ConArgLib, a C++ library implemented to help programmers solve some of the most important problems related to extension-based abstract Argumentation. The library is based on ConArg, which exploits Constraint Programming and, in particular, Gecode, a toolkit for developing constraint-based systems and applications. Given a semantics, such problems consist, for example, in enumerating all

Any evolutionary algorithm tends to end up in a local optimum. A new approach based on an evolutionary algorithm named as Diversity Driven Multi-Parent Evolutionary Algorithm with Adaptive non-uniform mutation is presented. In the proposed algorithm, Non-uniform mutation is used to maintain diversity in the explored solutions. Fitness variance, which signifies solution space aggregation, is used to

Autonomous transfer vehicles (ATVs) can be considered as one of the critical components of context-aware structured smart factories in Industry 4.0 era. Conventional mapping methods such as grid maps can provide information for navigation, but they are not enough for complex environments that require interactions. On the other hand, high-definition (HD) mapping, which is mainly used in traffic networks

Existing ant colony algorithms only have one kind of pheromone. They use non-dominated solutions to update it while not making use of dominated solutions, which can provide valuable information for guiding the subsequent foraging process. To make full use of dominated solutions, we create a new kind of pheromone temporarily called a negative pheromone and propose a new ant colony optimisation algorithm

The Satin Bowerbird Optimisation (SBO) was inspired by the Satin Bowerbirds living in Australia’s rainforests and other mesic habitats. Like other meta-heuristic algorithms, the main problem faced by the SBO is that it has been empirically demonstrated to become easily trapped into local optimal solutions, creating low precision and slow convergence speeds. To overcome these deficiencies, we propose

Online social media such as Twitter are growing so rapidly. Recently, Twitter has become one of the popular microblogging services on the Internet. It lets millions of users to communicate and interact by sending short messages of up to 140 characters. The massive amount of information over the web from Twitter requires an automatic tool that can determine the topics that people are talking about.

Intelligence has been defined in many ways like logic, awareness, reasoning, critical thinking, etc. Many researchers insist on the possibility of a Technological Singularity shortly, which may see machines gaining intelligence similar to, or greater than humans. While many researchers believe that Technological Singularity is at an arm’s length, many counter-question the possibility of the same due

One of the most important assumptions in machine learning tasks is the fact that training data points and test data points are extracted from the same distribution. However, this paper assumes the situation in which this fact does no longer hold. Therefore, a task named space adjustment, through which the distribution of the data points in the training-data space and the distribution of the data points

Aspect-based sentiment analysis is one of the challenging problems among the various type of tasks in sentiment analysis. Sequential models specifically deep neural networks (like Recurrent Neural Networks) have been found to handle this problem in an efficient way. This paper presents a deep neural network model named ATE-SPD for aspect-based sentiment analysis that simultaneously extracts aspect-terms

Recognition of emotion in speech is a difficult task due to many speaker factors like gender, age, and the cultural background (nationality, ethnicity, and region) as well as the acoustical environment. Among these factors, the cultural background of the speaker has a strong influence on the expression of emotion. The reason for the unsatisfactory performance of an emotion recognition engine built

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