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Fast copula variational inference J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2021-01-15 Jinjin Chi; Jihong Ouyang; Ang Zhang; Xinhua Wang; Ximing Li
ABSTRACT Mean-field variational inference, built on fully factorisations, can be efficiently solved; however, it ignores the dependencies between latent variables, resulting in lower performance. To address this, the copula variational inference (CVI) method is proposed by using the well-established copulas to effectively capture posterior dependencies, leading to better approximations. However, it
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Correlation-based Oversampling aided Cost Sensitive Ensemble learning technique for Treatment of Class Imbalance J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2021-01-13 Debashree Devi; Saroj K. Biswas; Biswajit Purkayastha
ABSTRACT The issue of class imbalance and its consequences over the conventional learning models is a well-investigated topic, as it highly influences performances of real-life classification tasks. Amongst the available solutions, Synthetic Minority Oversampling Technique (SMOTE) imprints efficacy in balancing the data through synthetic minority instance generation. However, SMOTE suffers from the
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The synchronization and stability analysis of delayed fuzzy Cohen-Grossberg neural networks via nonlinear measure method J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2021-01-11 Meryem Abdelaziz; Farouk Chérif
ABSTRACT This paper examines the problem of master-slave synchronization for a class of fuzzy Cohen-Grossberg neural networks (FCGNNs) subject to fuzzy effects and time-delays (time-varying and distributed). Some sufficient and new conditions are given in order to establish the exponential lag synchronization for the considered model. Also, the existence, the uniqueness, and exponential stability of
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Topological machine learning for multivariate time series J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2021-01-11 Chengyuan Wu; Carol Anne Hargreaves
ABSTRACT We develop a method for analyzing multivariate time series using topological data analysis (TDA) methods. The proposed methodology involves converting the multivariate time series to point cloud data, calculating Wasserstein distances between the persistence diagrams and using the k -nearest neighbours algorithm ( k -NN) for supervised machine learning. Two methods (symmetry-breaking and anchor
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Boosting symbiotic organism search algorithm with ecosystem service for dynamic blood allocation in blood banking system J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2021-01-11 Prinolan Govender; Absalom E Ezugwu
ABSTRACT Blood is a valuable commodity in society due to its ability to save lives during crises. Furthermore, because of the scarcity of blood donors, blood assignment by blood banks requires meticulous planning and solid issuing policy. The multiple components of a blood banking system contribute to the complexity of maintaining an efficient structure for such a system. One particular aspect relates
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A systematic mapping study on agent mining J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2021-01-11 Emmanuelle Grislin-Le Strugeon; Kathia Marcal de Oliveira; Marie Thilliez; Dorian Petit
ABSTRACT Over the past two decades, many studies have been published in diverse fields of application combining agent abilities (knowledge processing, communication, learning, mobility, etc.) and data mining approaches (clustering, decision trees, ontologies, etc.). We performed a systematic mapping study to quantitatively analyse these contributions about agent mining. We determined that most of the
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Hybrid optimisation dependent deep belief network for lane detection J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-12-15 Suvarna Shirke; R. Udayakumar
ABSTRACT Nowadays, in research introducing an advanced driver assistance system for improving driving is considered as the trending one. In this research, concentrate more on proposing lane detection model and assist in driving. This work develops a lane detection model through the deep learning scheme. The proposed scheme has two major phases, such as image transformation and lane detection. Initially
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Machine translation model for effective translation of Hindi poetries into English J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-11-26 Rajesh Kumar Chakrawarti; Jayshri Bansal; Pratosh Bansal
ABSTRACT The Word Sense Disambiguation (WSD) is a process of disambiguating the sense of the text according to its context. Machine translation is one of the challenging task since it requires effective representation of the text to capture semantic relation between Hindi lyrics in English normal language behaviour. This paper focuses on WSD methods to deal with dialects that convert Hindi lyrics to
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Stability for patch structure Nicholson’s blowflies systems involving distinctive maturation and feedback delays J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-11-05 Gang Yang; Qian Cao
ABSTRACT This article explores a class of patch structure Nicholson’s blowflies systems involving nonlinear density-dependent mortality terms and multiple pairs of distinctive maturation and feedback delays. By utilising the fluctuation lemma and differential inequality techniques, a delay-dependent criterion on the global asymptotic stability of the addressed systems is established, which refines
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A genetic algorithm for supplier selection problem under collaboration opportunities J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-10-30 Sihem Ben Jouida; Saoussen Krichen
ABSTRACT In this paper, we propose a collaborative model for the supplier selection for the purchasing activity in the supply chain. The problem addresses a set of firms that try to look for a cost saving configuration to optimise their ordering plans, given a set of suppliers with quantity discounts options. Possible collaborations between firms, modelled as a coalition formation, can be beneficial
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Information flow in context-dependent hierarchical Bayesian inference J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-10-30 Chris Fields; James F. Glazebrook
ABSTRACT Recent theories developing broad notions of context and its effects on inference are becoming increasingly important in fields as diverse as cognitive psychology, information science and quantum information theory and computing. Here we introduce a novel and general approach to the characterisation of contextuality using the techniques of Chu spaces and Channel Theory viewed as general theories
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A learning-based resource provisioning approach in the fog computing environment J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-10-20 Masoumeh Etemadi; Mostafa Ghobaei-Arani; Ali Shahidinejad
ABSTRACT With the recent advancements in distributed computing technologies, the fog computing model has emerged to provide resource capabilities at the edge of the network for executing IoT applications. However, due to the rapid growth of IoT applications and variability their workload over time, achieving an efficient resource provisioning solution to deal with time-varying workloads as one of the
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A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-10-14 Amandeep Kaur; Krishan Kumar
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
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Improving Alzheimer’s disease classification by performing data fusion with vascular dementia and stroke data J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-09-29 Zoran Bosnić; Brankica Bratić; Mirjana Ivanović; Marija Semnic; Iztok Oder; Vladimir Kurbalija; Tijana Vujanić Stankov; Vojislava Bugarski Ignjatović
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
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Improved human-object interaction detection through skeleton-object relations J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-09-05 Hong-Bo Zhang; Yi-Zhong Zhou; Ji-Xiang Du; Jin-Long Huang; Qing Lei; Lijie Yang
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
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A new colour image copyright protection approach using evolution-based dual watermarking J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-08-31 Saad M. Darwish; Osama F. Hassan
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
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Prediction of fluid interface between dispersed and matrix phases by Lattice Boltzmann-adaptive network-based fuzzy inference system J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-08-27 Hao Liang; Meisam Babanezhad; Narjes Nabipour; Maryam Heidarifard; Mashallah Rezakazemi; Saeed Shirazian
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
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Resampling-based noise correction for crowdsourcing J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-08-17 Wenqiang Xu; Liangxiao Jiang; Chaoqun Li
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
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Global asymptotic stability for discrete-time Cohen-Grossberg neural networks with delays by combining graph theoretic approach with Homeomorphism concept J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-08-12 Zheng Zhou; Huaying Liao; Zhengqiu Zhang
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
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Frequency component vectorisation for image dehazing J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-20 Nazeer Muhammad; Hira Khan; Nargis Bibi; Muhammad Usman; Naseer Ahmed; Shahid Nawaz Khan; Zahid Mahmood
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
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A preference degree for ranking k-dimensional vectors of qualitative labels and its application in multi-attribute group decision-making J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-20 Gholamreza Hesamian; Ali Dehghani
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
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Expectations for agents with goal-driven autonomy J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-14 Dustin Dannenhauer; Héctor Muñoz-Avila; Michael T. Cox
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
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The uncertainty and explainability in object recognition J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-09 Wei Hui; Liping Yu
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
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ConArgLib: an argumentation library with support to search strategies and parallel search J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-07 Stefano Bistarelli; Fabio Rossi; Francesco Santini
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
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Diversity driven multi-parent evolutionary algorithm with adaptive non-uniform mutation J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-07 Sumika Chauhan; Manmohan Singh; Ashwani Kumar Aggarwal
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
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High-definition map update framework for intelligent autonomous transfer vehicles J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-04 Muhammed Oguz Tas; Hasan Serhan Yavuz; Ahmet Yazici
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
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A multi-objective decomposition-based ant colony optimisation algorithm with negative pheromone J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-07-04 Jiaxu Ning; Qidong Zhao; Peng Sun; Yunfei Feng
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
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Adaptive chaotic satin bowerbird optimisation algorithm for numerical function optimisation J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-06-28 Tanachapong Wangkhamhan
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
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A review of approaches for topic detection in Twitter J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-06-28 Zeynab Mottaghinia; Mohammad-Reza Feizi-Derakhshi; Leili Farzinvash; Pedram Salehpour
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.
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Intelligence in cyberspace: the road to cyber singularity J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-06-28 Ishaani Priyadarshini; Chase Cotton
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
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A linear space adjustment by mapping data into an intermediate space and keeping low level data structures J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-06-16 Weiqing Fu; Hamid Parvin; Mohammad Reza Mahmoudi; Bui Anh Tuan; Kim-Hung Pho
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
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ATE-SPD: simultaneous extraction of aspect-term and aspect sentiment polarity using Bi-LSTM-CRF neural network J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-06-04 Ashish Kumar; Sharad Verma; Aditi Sharan
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
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Hierarchical classifier design for speech emotion recognition in the mixed-cultural environment J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-06-04 P. Vasuki; Chandrabose Aravindan
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
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Online path planning of mobile robot using grasshopper algorithm in a dynamic and unknown environment J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-05-18 Zahra Elmi; Mehmet Önder Efe
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
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A framework for the analysis and synthesis of Swarm Intelligence algorithms J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-05-18 Dávila Patrícia Ferreira Cruz; Renato Dourado Maia; Leandro Nunes de Castro
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
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Hierarchical age estimation mechanism with adaBoost-based deep instance weighted fusion J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-05-18 Yongming Li; Fan Li; Yuanlin Zheng; Pin Wang; Mingfeng Jiang; Xinke Li
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
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Deep CNN feature fusion with manifold learning and regression for pixel classification in HSI images J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-08-05 Vishal Srivastava; Bhaskar Biswas
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
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An improved multi-objective antlion optimization algorithm for the optimal design of the robotic gripper J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-08-02 Golak Bihari Mahanta; Amruta Rout; Deepak B. B. V. L; Bibhuti Bhusan Biswal
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
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Existence and global exponential stability of anti-periodic solutions for generalised inertial competitive neural networks with time-varying delays J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-08-02 Yongkun Li; Jiali Qin
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
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Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-08-09 Amandeep Kaur; Sandeep K. Sood
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
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Population subset selection for the use of a validation dataset for overfitting control in genetic programming J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-07-31 Daniel Rivero; Enrique Fernandez-Blanco; Carlos Fernandez-Lozano; Alejandro Pazos
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
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Adaptive weighted aggregation in Group Improvised Harmony Search for lung nodule classification J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-07-31 Subhajit Kar; Kaushik Das Sharma; Madhubanti Maitra
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
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Deep learning-based framework for expansion, recognition and classification of underwater acoustic signal J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-08-05 Guanghao Jin; Fan Liu; Hao Wu; Qingzeng Song
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
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A new neural network framework for solving convex second-order cone constrained variational inequality problems with an application in multi-finger robot hands J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2019-08-12 Alireza Nazemi; Atiye Sabeghi
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
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Detection of distributed denial of service attack in cloud computing using the optimization-based deep networks J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-04-15 S. Velliangiri; P. Karthikeyan; V. Vinoth Kumar
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
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Personalised healthcare model for monitoring and prediction of airpollution: machine learning approach J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-04-13 Veerawali Behal; Ramandeep Singh
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
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Double-quantitative decision rough set over two universes and application to African swine fever decision-making J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-04-13 Xiaoyuan Hu; Bingzhen Sun; Ting Wang; Chao Jiang
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
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Performance evaluation of fuzzy clustered case-based reasoning J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-04-03 Malik Jahan Khan; Cynthia Khan
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
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Joint extraction of entities and relations based on character graph convolutional network and Multi-Head Self-Attention Mechanism J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-25 Zhao Meng; Shengwei Tian; Long Yu; Yalong Lv
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)
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A hybrid multiverse optimisation algorithm based on differential evolution and adaptive mutation J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-16 Lei Chen; Lvjie Li; Wenyue Kuang
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
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A comprehensive review of moth-flame optimisation: variants, hybrids, and applications J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-16 Abdelazim G. Hussien; Mohamed Amin; Mohamed Abd El Aziz
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
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Global exponential stability of anti-periodic solutions for discontinuous Cohen–Grossberg neural networks with time-varying delays J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-16 Chengfeng Xu; Fanchao Kong
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
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Medical diagnosis and treatment is NP-complete J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-11 Jeffrey. E. Arle; Kristen W. Carlson
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)
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Linear quadratic optimal control problem with fuzzy variables via neural network J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-10 Sohrab Effati; Amin Mansoori; Mohammad Eshaghnezhad
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
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A non-reduced order approach to stability analysis of delayed inertial genetic regulatory networks J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-10 Qian Wang; Hui Wei; Zhiwen Long
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
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A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-06 Yılmaz Kaya; Melih Kuncan; Kaplan Kaplan; Mehmet Recep Minaz; H.Metin Ertunç
ABSTRACT 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
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Detection and classification of landmines using machine learning applied to metal detector data J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-03-02 L. Safatly; M. Baydoun; M. Alipour; A. Al-Takach; K. Atab; M. Al-Husseini; A. El-Hajj; H. Ghaziri
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
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Improved butterfly optimisation algorithm based on guiding weight and population restart J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-02-26 Yanju Guo; Xianjie Liu; Lei Chen
ABSTRACT 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
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Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-02-18 Mirsaeid Hosseini Shirvani
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
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Robust H∞ performance for discrete time T-S fuzzy switched memristive stochasticneural networks with mixed time-varying delays J. Exp. Theor. Artif. Intell. (IF 2.039) Pub Date : 2020-02-16 R. Vadivel; M. Syed Ali; Young Hoon Joo
ABSTRACT 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