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1.	Dahlstrand, F (författare) Consequence analysis theory for alarm analysis 2002 Ingår i: Knowledge-Based Systems. - 0950-7051. ; 15:1-2, s. 27-36 Tidskriftsartikel (refereegranskat)abstract Alarm analysis is the task of finding the root cause of a failure situation in an industrial process. In this article, a new approach for performing alarm analysis using multilevel flow models (MFM) is introduced. This approach is compared with existing methods for alarm analysis using MFM. The result of the alarm analysis presented in this article is a set of explanations that fits the observed state of the process. Furthermore, it is shown that the old alarm analysis algorithms are a special case of the method presented here. (C) 2002 Elsevier Science B.V. All rights reserved.
2.	Larsson, Jan Eric (författare) Diagnostic reasoning based on means-end models: experiences and future prospects 2002 Ingår i: Knowledge-Based Systems. - 0950-7051. ; 15:1-2, s. 103-110 Tidskriftsartikel (refereegranskat)abstract Multilevel flow models (MFM) are graphical models of goals and functions of technical systems. MFM was invented by Morten Lind at the Technical University of Denmark and several new algorithms and implementations have been contributed by the group headed by Jan Eric Larsson at Lund Institute of Technology. MFM has several properties which makes for a relatively easy knowledge engineering task, compared to mathematical models as used in classical control theory and compared to the rule bases used in standard expert systems. In addition, MFM allows for diagnostic algorithms with excellent real-time properties. This paper gives an overview of existing MFM algorithms, and different MFM projects which have been performed, or are currently in progress.
3.	Jones, Richard W., et al. (författare) A framework for intelligent medical diagnosis using the theory of evidence 2002 Ingår i: Knowledge-Based Systems. - 0950-7051 .- 1872-7409. ; 15:1-2, s. 77-84 Tidskriftsartikel (refereegranskat)abstract In designing fuzzy logic systems for fault diagnosis, problems can be encountered in the choice of symptoms to use fuzzy operators and an inability to convey the reliability of the diagnosis using just one degree of membership for the conclusion. By turning to an evidential framework, these problems can be resolved whilst still preserving a fuzzy relational model structure. The theory of evidence allows for utilisation of all available information. Relationships between sources of evidence determine appropriate combination rules. By generating belief and plausibility measures it also communicates the reliability of the diagnosis, and completeness of information. In this contribution medical diagnosis is considered using the theory of evidence, in particular the diagnosis of inadequate analgesia is considered
4.	Kruusmaa, Maarja, et al. (författare) Covering the path space : a casebase analysis for mobile robot path planning 2003 Ingår i: Knowledge-Based Systems. - Guildford, Surrey : Elsevier. - 0950-7051 .- 1872-7409. ; 16:5-6, s. 235-242 Tidskriftsartikel (refereegranskat)abstract This paper presents a theoretical analysis of a casebase used for mobile robot path planning in dynamic environments. Unlike other case-based path planning approaches, we use a grid map to represent the environment that permits the robot to operate in unstructured environments. The objective of the mobile robot is to learn to choose paths that are less risky to follow. Our experiments with real robots have shown the efficiency of our concept. In this paper, we replace a heuristic path planning algorithm of the mobile robot with a seed casebase and prove the upper and lower bounds for the cardinality of the casebase. The proofs indicate that it is realistic to seed the casebase with some solutions to a path-finding problem so that no possible solution differs too much from some path in the casebase. This guarantees that the robot would theoretically find all paths from start to goal. The proof of the upper bound of the casebase cardinality shows that the casebase would in a long run grow too large and all possible solutions cannot be stored. In order to keep only the most efficient solutions the casebase has to be revised at run-time or some other measure of path difference has to be considered.
5.	Verikas, Antanas, et al. (författare) An intelligent system for tuning magnetic field of a cathode ray tube deflection yoke 2003 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier Science. - 0950-7051 .- 1872-7409. ; 16:3, s. 161-164 Tidskriftsartikel (refereegranskat)abstract This short communication concerns identification of the number of magnetic correction shunts and their positions for deflection yoke tuning to correct the misconvergence of colours of a cathode ray tube. The misconvergence of colours is characterised by the distances measured between the traces of red and blue beams. The method proposed consists of two phases, namely, learning and optimisation. In the learning phase, the radial basis function neural network is trained to learn a mapping: correction shunt position→changes in misconvergence. In the optimisation phase, the trained neural network is used to predict changes in misconvergence depending on a correction shunt position. An optimisation procedure based on the predictions returned by the neural net is then executed in order to find the minimal number of correction shunts needed and their positions. During the experimental investigations, 98% of the deflection yokes analysed have been tuned successfully using the technique proposed.
6.	Amon, B., et al. (författare) From first-order logic to automated word generation for Lyee 2003 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 16:07-8, s. 413-429 Tidskriftsartikel (refereegranskat)abstract A conceptual schema can be viewed as a language to describe the phenomena in a system to be modelled, i.e. a set of derivation rules and integrity constraints as well as a set of event-rules describing the behaviour of an object system. In this paper, we investigate the relationship between the Lyee software requirements concepts with various constructs in conceptual modelling. Within our work we choose the Unified Modelling Language (UML) as a modelling notation for explaining conceptual models. The result obtained models a fully expressive set of UML and First Order Logic constructs mapped into Lyee concepts.
7.	Bayram, Firas, et al. (författare) From concept drift to model degradation : An overview on performance-aware drift detectors 2022 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 245 Forskningsöversikt (refereegranskat)abstract The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system’s life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model’s performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model’s performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions.
8.	Cacciarelli, Davide, et al. (författare) Stream-based active learning with linear models 2022 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 254 Tidskriftsartikel (refereegranskat)abstract The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.
9.	Chen, Jifu, et al. (författare) QoS prediction for web services in cloud environments based on swarm intelligence search 2023 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 259 Tidskriftsartikel (refereegranskat)
10.	Danielson, Mats, et al. (författare) A second-order-based decision tool for evaluating decisions under conditions of severe uncertainty 2020 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 191 Tidskriftsartikel (refereegranskat)abstract The requirement to assign precise numerical values to model entities such as criteria weights, probabilities, and utilities is too strong in most real-life decision situations, and hence alternative representations and evaluation mechanisms are important to consider. In this paper, we discuss the DecideIT 3.0 state-of-the-art software decision tool and demonstrate its functionality using a real-life case. The tool is based on a belief mass interpretation of the decision information, where the components are imprecise by means of intervals and qualitative estimates, and we discuss how multiplicative and additive aggregations influence the resulting distribution over the expected values.
11.	Danielson, Mats, et al. (författare) An improvement to swing techniques for elicitation in MCDM methods 2019 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 168, s. 70-79 Tidskriftsartikel (refereegranskat)abstract Several approaches that utilise various questioning procedures to elicit criteria weights exist, ranging from direct rating and point allocation to more elaborate methods. However, decision makers often find it difficult to understand how these methods work and how they should be comprehended. This article discusses the SWING family of elicitation techniques and suggests a refined method: the P-SWING method. Based on this, we provide an integrated framework for elicitation, modelling and evaluation of multi-criteria decision problems.
12.	Davies, Guy, et al. (författare) Model correspondence as a basis for schema domination 2010 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 23:7, s. 693-703 Tidskriftsartikel (refereegranskat)abstract Conceptual schemata each representing some component of a system in the making, can be integrated in a variety of ways. Herein, we explore some fundamental notions of this. In particular, we examine some ways in which integration using correspondence assertions affects the interrelationship of two component schemata. Our analysis of the logic leads us to reject the commonly asserted requirement of constraining correspondence assertions to single predicates from a source schema. Much previous work has focussed on dominance with regard to preservation of information capacity as a primary integration criterion. However, even though it is desirable that the information capacity of a combined schema dominate one or both of its constituent schemata, we here discuss some aspects of why domination based on information capacity alone is insufficient for the integration to be semantically satisfactory, and we provide a framework for detecting mappings that prevent schema domination.
13.	Deng, Jifei, et al. (författare) Imbalanced multiclass classification with active learning in strip rolling process 2022 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 255 Tidskriftsartikel (refereegranskat)abstract In the strip rolling process, conventional supervised methods cannot effectively address data with an imbalanced number of normal and faulty instances. In this paper, based on a deep belief network, a resampling method is combined with active learning (AL) to address imbalanced multiclass problems. The support vector machine-based synthetic minority oversampling technique was adapted to enrich the training data, whereas the true data distribution and model generalization were changed. A new selection strategy of AL is proposed that forms a function using uncertainty and diversity. AL uses an optimizing set that has a similar distribution with the whole dataset to calculate the informativeness of instances to optimize the model. Based on this step, the model study instances approach decision boundaries to promote performance. The proposed method is validated by five UCI benchmark datasets and strip rolling data, and experiments show that it outperforms conventional methods in tackling imbalanced multiclass problems.
14.	Deshmukh, Shradha, et al. (författare) Explainable quantum clustering method to model medical data 2023 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 267, s. 1-13 Tidskriftsartikel (refereegranskat)abstract Medical experts are often skeptical of data-driven models due to the lack of their explainability. Several experimental studies commence with wide-ranging unsupervised learning and precisely with clustering to obtain existing patterns without prior knowledge of newly acquired data. Explainable Artificial Intelligence (XAI) increases the trust between virtual assistance by Machine Learning models and medical experts. Awareness about how data is analyzed and what factors are considered during the decision-making process can be confidently answered with the help of XAI. In this paper, we introduce an improved hybrid classical-quantum clustering (improved qk-means algorithm) approach with the additional explainable method. The proposed model uses learning strategies such as the Local Interpretable Model-agnostic Explanations (LIME) method and improved quantum k-means (qk-means) algorithm to diagnose abnormal activities based on breast cancer images and Knee Magnetic Resonance Imaging (MRI) datasets to generate an explanation of the predictions. Compared with existing algorithms, the clustering accuracy of the generated clusters increases trust in the model-generated explanations. In practice, the experiment uses 600 breast cancer (BC) patient records with seven features and 510 knee MRI records with five features. The result shows that the improved hybrid approach outperforms the classical one in the BC and Knee MRI datasets. © 2023 Elsevier B.V.
15.	Ekenberg, Love, et al. (författare) A framework for determining design correctness 2004 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 17:07-8, s. 249-262 Tidskriftsartikel (refereegranskat)abstract Quality is one of the main concerns in today's systems and software development and use. One important instrument in verification is the use of formal methods, which means that requirements and designs are analyzed formally to determine their relationships. Furthermore, since professional software design is to an increasing extent a distributed process, the issue of integrating different systems to an entity is of great importance in modem system development and design. Various candidates for formalizing system development and integration have prevailed, but very often, particularly for dynamic conflict detection, these introduce non-standard objects and formalisms, leading to severe confusion, both regarding the semantics and the computability. In contrast to such, we introduce a framework for defining requirement fulfillment by designs, detecting conflicts of various kinds as well as integration of heterogeneous schemata. The framework introduced transcends ordinary logical consequence, as it takes into account static and dynamic aspects of design consistency and, in particular, the specific features of the state space of a specification. Another feature of the approach is that it provides a unifying framework for design conflict analysis and schema integration.
16.	Galozy, Alexander, 1991-, et al. (författare) Information-gathering in latent bandits 2023 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 260 Tidskriftsartikel (refereegranskat)abstract In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. © 2022 The Author(s).
17.	Gao, Kun, 1993, et al. (författare) Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory 2021 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 218 Tidskriftsartikel (refereegranskat)abstract Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods.
18.	Guo, Xiaoyi, et al. (författare) Subspace projection-based weighted echo state networks for predicting therapeutic peptides 2023 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 263 Tidskriftsartikel (refereegranskat)abstract Detection of therapeutic peptide is a major research direction in the current biopharmaceutical field. However, traditional biochemical experimental detection methods take a lot of time. As supplementary methods for biochemical experiments, the computational methods can improve the efficiency of therapeutic peptide detection. Currently, most machine learning-based therapeutic peptide identification algorithms do not consider the processing of noisy samples. We propose a therapeutic peptide classifier, called weighted echo state networks based on subspace projection (WESN-SP), which reduces the bias caused by high-dimensional noisy features and noisy samples. WESN-SP is trained by sparse Bayesian learning algorithm (SBL) and introduces a weight coefficient for each sample by kernel dependence maximization-based subspace projection. The experimental results show that WESN-SP has better performance than other existing methods. © 2023 The Author(s). Published by Elsevier B.V.
19.	Haidong, Shao, et al. (författare) Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing 2020 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 188 Tidskriftsartikel (refereegranskat)abstract Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods.
20.	Hashim, Fatma A., et al. (författare) Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization 2023 Ingår i: Knowledge-Based Systems. - : ELSEVIER. - 0950-7051 .- 1872-7409. ; 260 Tidskriftsartikel (refereegranskat)abstract Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Ficks law optimization (FLA) is presented, in which Ficks first rule of diffusion is utilized. According to Ficks law of diffusion, molecules tend to diffuse from higher to lower concentration areas. Many experimental series are done to test FLAs performance and ability in solving different optimization problems. Firstly, FLA is tested using twenty well-known benchmark functions and thirty CEC2017 test functions. Secondly, five real-world engineering problems are utilized to demonstrate the feasibility of the proposed FLA. The findings are compared with 12 well-known and powerful optimizers. A Wilcoxon rank-sum test is carried out to evaluate the comparable statistical performance of competing algorithms. Results prove that FLA achieves competitive and promising findings, a good convergence curve rate, and a good balance between exploration and exploitation. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/121033-fick-s-law-algorithm-fla.(c) 2022 Elsevier B.V. All rights reserved.
21.	Hashim, Fatma A., et al. (författare) Snake Optimizer: A novel meta-heuristic optimization algorithm 2022 Ingår i: Knowledge-Based Systems. - : ELSEVIER. - 0950-7051 .- 1872-7409. ; 242 Tidskriftsartikel (refereegranskat)abstract In recent years, several metaheuristic algorithms have been introduced in engineering and scientific fields to address real-life optimization problems. In this study, a novel nature-inspired metaheuristics algorithm named as Snake Optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes. Each snake (male/female) fights to have the best partner if the existed quantity of food is enough and the temperature is low. This study mathematically mimics and models such foraging and reproduction behaviors and patterns to present a simple and efficient optimization algorithm. To verify the validity and superiority of the proposed method, SO is tested on 29 unconstrained Congress on Evolutionary Computation (CEC) 2017 benchmark functions and four constrained real-world engineering problems. SO is compared with other 9 well-known and newly developed algorithms such as Linear population size reduction-Success-History Adaptation for Differential Evolution (L-SHADE), Ensemble Sinusoidal incorporated with L-SHADE (LSHADE-EpSin), Covariance matrix adaptation evolution strategy (CMAES), Coyote Optimization Algorithm (COA), Moth-flame Optimization, Harris Hawks Optimizer, Thermal Exchange optimization, Grasshopper Optimization Algorithm, and Whale Optimization Algorithm. Experimental results and statistical comparisons prove the effectiveness and efficiency of SO on different landscapes with respect to exploration-exploitation balance and convergence curve speed.
22.	Hatamzad, Mahshid, et al. (författare) Intelligent cost-effective winter road maintenance by predicting road surface temperature using machine learning techniques 2022 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 247 Tidskriftsartikel (refereegranskat)abstract Since Winter Road Maintenance (WRM) is an important activity in Nordic countries, accurate intelligent cost-effective WRM can create precise advance plans for developing decision support systems to improve traffic safety on the roads, while reducing cost and negative environmental impacts. Lack of comprehensive knowledge and inaccurate WRM information would lead to a certain loss of WRM budget, safety reduction, and irreparable environmental damage. This study proposes an intelligent methodology that uses data envelopment analysis and machine learning techniques. In the proposed methodology, WRM efficiency is calculated by data envelopment analysis for different decision-making units (roads), and inefficient units need to be considered for further assessments. Therefore, road surface temperature is predicted by means of machine learning methods, in order to achieve efficient and effective WRM on the roads during winter in cold regions. In total, four different methods have been used to predict road surface temperature on an inefficient road. One of these is linear regression, which is a classical statistical regression technique (ordinary least square regression); the other three methods are machine-learning techniques, including support vector regression, multilayer perceptron artificial neural network, and random forest regression. Graphical and numerical results indicate that support vector regression is the most accurate method.
23.	He, Zhiyi, et al. (författare) Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples 2020 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 191 Tidskriftsartikel (refereegranskat)abstract Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.
24.	Hossain, Liaquat, et al. (författare) Exponential random graph modeling of emergency collaboration networks 2015 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 77, s. 68-79 Tidskriftsartikel (refereegranskat)abstract Effective response to bushfires requires collaboration involving a set of interdependent complex tasks that need to be carried out in a synergistic manner. Improved response to bushfires has been attributed to how effective different emergency management agencies carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among emergency management personnel on the effective outcome in delivering improved bushfire response. There are, however, very few systematic empirical studies with a focus on the effect of collaboration networks among emergency management personnel and bushfire response. Given that collaboration evolves among emergency management personnel when they communicate, in this study, we first propose an approach to map the collaboration network among emergency management personnel. Then, we use Exponential Random Graph (ERG) models to explore the micro-level network structures of emergency management networks and their impact on performance. ERG Models are probabilistic models presented by locally determined explanatory variables and that can effectively identify structural properties of networks. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the 2009 Royal Commission Report dataset, we construct and model emergency management response networks. We notice that alternative-k-star, and alternative-k-two-path parameters of ERG have impact on bushfire response. The findings of this study may be utilized by emergency managers or administrators for developing an emergency practice culture to optimize response within an emergency management context. (C) 2015 Elsevier B.V. All rights reserved.
25.	Jin, Qiangguo, et al. (författare) DUNet : A deformable network for retinal vessel segmentation 2019 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 178, s. 149-162 Tidskriftsartikel (refereegranskat)abstract Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.
26.	Johannesson, Paul, et al. (författare) Special issue on 'legacy systems and software change' 2004 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 17:7-8, s. 237-238 Tidskriftsartikel (refereegranskat)
27.	Li, Ping, et al. (författare) Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases 2022 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 258 Tidskriftsartikel (refereegranskat)abstract Current human biomedical research shows that human diseases are closely related to non-coding RNAs, so it is of great significance for human medicine to study the relationship between diseases and non-coding RNAs. Current research has found associations between non-coding RNAs and human diseases through a variety of effective methods, but most of the methods are complex and targeted at a single RNA or disease. Therefore, we urgently need an effective and simple method to discover the associations between non-coding RNAs and human diseases. In this paper, we propose a sparse regularized joint projection model (SRJP) to identify the associations between non-coding RNAs and diseases. First, we extract information through a series of ncRNA similarity matrices and disease similarity matrices and assign average weights to the similarity matrices of the two sides. Then we decompose the similarity matrices of the two spaces into low-rank matrices and put them into SRJP. In SRJP, we innovatively use the projection matrix to combine the ncRNA side and the disease side to identify the associations between ncRNAs and diseases. Finally, the regularization term in SRJP effectively improves the robustness and generalization ability of the model. We test our model on different datasets involving three types of ncRNAs: circRNA, microRNA and long non-coding RNA. The experimental results show that SRJP has superior ability to identify and predict the associations between ncRNAs and diseases. © 2022 The Author(s)
28.	Li, Xian, et al. (författare) GCDB-UNet : A novel robust cloud detection approach for remote sensing images 2022 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 238 Tidskriftsartikel (refereegranskat)abstract Cloud detection is a prerequisite in many remote sensing applications, and it has been tackled through different approaches from simple thresholding to complicated deep network training. On the other hand, existing approaches are susceptible to failures while handling thin clouds, largely because of their small sizes, sparse distributions, as well as high transparency and similarity to the non-cloud background regions. This paper presents global context dense block U-Net (GCDB-UNet), a robust cloud detection network that embeds global context dense block (GCDB) into the U-Net framework and is capable of detecting thin clouds effectively. GCDB consists of two feature extraction units for addressing the challenges in thin cloud detection, namely, a non-local self-attention unit that extracts sample correlation features by aggregating the sparsely distributed thin clouds and a squeeze excitation unit that extracts channel correlated features by differentiating their importance. In addition, a dense connection scheme is designed to exploit the multi-level fine-grained representations from the two types of extracted features and a recurrent refinement module is introduced for gradual enhancement of the predicted classification map. We also created a fully annotated cloud detection MODIS dataset that consists of 1192 training images, 80 validation images and 150 test images. Extensive experiments on Landsat8, SPARCS and MODIS datasets show that the proposed GCDB-UNet achieves superior cloud detection performance as compared with state-of-the-art methods. Our created MODIS cloud detection dataset is available at https://github.com/xiachangxue/MODIS-Dataset-for-Cloud-Detection.
29.	Liljeroth, Erland, et al. (författare) Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning 2021 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 214 Tidskriftsartikel (refereegranskat)abstract The plant pathogen Phytophthora infestans causes the severe disease late blight in potato, which can result in huge yield loss for potato production. Automatic and accurate disease lesion segmentation enables fast evaluation of disease severity and assessment of disease progress. In tasks requiring computer vision, deep learning has recently gained tremendous success for image classification, object detection and semantic segmentation. To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected similar to 500 field RGB images in a set of diverse potato genotypes with different disease severity (0%-70%), resulting in 2100 cropped images. 1600 of these cropped images were used as the dataset for training deep neural networks and 250 cropped images were randomly selected as the validation dataset. Finally, the developed model was tested on the remaining 250 cropped images. The results show that the values for intersection over union (IoU) of the classes background (leaf and soil) and disease lesion in the test dataset were 0.996 and 0.386, respectively. Furthermore, we established a linear relationship (R-2 = 0.655) between manual visual scores of late blight and the number of lesions detected by deep learning at the canopy level. We also showed that imbalance weights of lesion and background classes improved segmentation performance, and that fused masks based on the majority voting of the multiple masks enhanced the correlation with the visual disease scores. This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery, which could aid breeding for crop resistance in field environments, and also benefit precision farming. (C) 2021 Elsevier B.V. All rights reserved.
30.	Liu, Junkai, et al. (författare) AMDGT : Attention aware multi-modal fusion using a dual graph transformer for drug–disease associations prediction 2024 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 284 Tidskriftsartikel (refereegranskat)abstract Identification of new indications for existing drugs is crucial through the various stages of drug discovery. Computational methods are valuable in establishing meaningful associations between drugs and diseases. However, most methods predict the drug–disease associations based solely on similarity data, neglecting valuable biological and chemical information. These methods often use basic concatenation to integrate information from different modalities, limiting their ability to capture features from a comprehensive and in-depth perspective. Therefore, a novel multimodal framework called AMDGT was proposed to predict new drug associations based on dual-graph transformer modules. By combining similarity data and complex biochemical information, AMDGT understands the multimodal feature fusion of drugs and diseases effectively and comprehensively with an attention-aware modality interaction architecture. Extensive experimental results indicate that AMDGT surpasses state-of-the-art methods in real-world datasets. Moreover, case and molecular docking studies demonstrated that AMDGT is an effective tool for drug repositioning. Our code is available at GitHub: https://github.com/JK-Liu7/AMDGT. © 2023 The Author(s)
31.	Lundström, Jens, 1981-, et al. (författare) Assessing print quality by machine in offset colour printing 2013 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 37, s. 70-79 Tidskriftsartikel (refereegranskat)abstract Information processing steps in printing industry are highly automated, except the last one print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several printquality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer. (C) 2012 Elsevier B.V. All rights reserved.
32.	Mahdavi, Ehsan, et al. (författare) ITL-IDS : Incremental Transfer Learning for Intrusion Detection Systems 2022 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 253 Tidskriftsartikel (refereegranskat)abstract Utilizing machine learning methods to detect intrusion into computer networks is a trending topic in information security research. The limitation of labeled samples is one of the challenges in this area. This challenge makes it difficult to build accurate learning models for intrusion detection. Transfer learning is one of the methods to counter such a challenge in machine learning topics. On the other hand, the emergence of new technologies and applications might bring new vulnerabilities to computer networks. Therefore, the learning process cannot occur all at once. Incremental learning is a practical standpoint to confront this challenge. This research presents a new framework for intrusion detection systems called ITL-IDS that can potentially start learning in a network without prior knowledge. It begins with an incremental clustering algorithm to detect clusters’ numbers and shape without prior assumptions about the attacks. The outcomes are candidates to transfer knowledge between other instances of ITL-IDS. In each iteration, transfer learning provides target environments with incremental knowledge. Our evaluation shows that this method can combine incremental and transfer learning to identify new attacks. © 2022 Published by Elsevier B.V.
33.	Ming, Hong, et al. (författare) Few-shot nested named entity recognition 2024 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 293 Tidskriftsartikel (refereegranskat)abstract While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures commonly existing in NER datasets. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories. This paper posits that the few-shot nested NER task warrants its own dedicated attention and proposes a Global-Biaffine Positive-Enhanced (GBPE) framework for this new task. Within the GBPE framework, we first develop the new Global-Biaffine span representation to capture the span global dependency information for each entity span to distinguish nested entities. We then formulate a unique positive-enhanced contrastive loss function to enhance the utility of specific positive samples in contrastive learning for larger margins. Lastly, by using these enlarged margins, we obtain better margin constraints and incorporate them into the nearest neighbor inference to predict the unlabeled entities. Extensive experiments on three nested NER datasets in English, German, and Russian show that GBPE outperforms baseline models on the 1-shot and 5-shot tasks in terms of F1 score.
34.	Nakhaei, Niknaz, et al. (författare) A solution technique to cascading link failure prediction 2022 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 258 Tidskriftsartikel (refereegranskat)abstract The study of complex networks is a new powerful tool that can provide a profitable skeleton to better elucidate technology-related phenomena and interactions between components of real-world networks However, it is not easy to predict the communal behavior of such systems from their elements and on the other hand, the failure of one or few elements can trigger the failure of other elements throughout the network, resulting in network breakdown and catastrophic events at large scales. Therefore, developing predictive mathematical techniques to examine complex networks is one of the biggest challenges of the present time. Knowing that link failure prediction is less studied in the OR literature, the present study articulates a method to predict link failures in complex networks, which is primarily based on Bayesian Belief Networks (BBN) and TOPSIS. The method aims to predict failures based on the affective factors of failures in networks. To this end, effective factors of failures are first detected, and then the graph of the relationship of factors along with their weight is determined. After all, the method provides the prediction for future damaged components. The functionality of the method is validated by an extensive computational analysis employing simulation in scale-free, random, and actual international aviation networks and its performance is compared with other benchmark algorithms. The results and sensitivity analysis experiments arrive at prominent managerial insights and efficacious implications and show that our method can generate high-quality solutions in many networks.
35.	Pastrana, S., et al. (författare) Evaluation of classification algorithms for intrusion detection in MANETs 2012 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 36, s. 217-225 Tidskriftsartikel (refereegranskat)abstract Mobile Ad hoc Networks (MANETs) are wireless networks without fixed infrastructure based on the cooperation of independent mobile nodes. The proliferation of these networks and their use in critical scenarios (like battlefield communications or vehicular networks) require new security mechanisms and policies to guarantee the integrity, confidentiality and availability of the data transmitted. Intrusion Detection Systems used in wired networks are inappropriate in this kind of networks since different vulnerabilities may appear due to resource constraints of the participating nodes and the nature of the communication. This article presents a comparison of the effectiveness of six different classifiers to detect malicious activities in MANETs. Results show that Genetic Programming and Support Vector Machines may help considerably in detecting malicious activities in MANETs. © 2012 Elsevier B.V. All rights reserved.
36.	Perera, Charith, et al. (författare) A Knowledge-Based Resource Discovery for Internet of Things 2016 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 109, s. 122-136 Tidskriftsartikel (refereegranskat)abstract In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform.
37.	Rani, Sita, et al. (författare) Federated learning for secure IoMT-applications in smart healthcare systems : A comprehensive review 2023 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 274 Forskningsöversikt (refereegranskat)abstract Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks. © 2023 The Author(s)
38.	Saberi-Movahed, Farid, et al. (författare) Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with application in gene selection 2022 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 256 Tidskriftsartikel (refereegranskat)abstract Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task. © 2022 The Author(s)
39.	Sari, Anny Kartika, et al. (författare) Archetype sub-ontology : Improving constraint-based clinical knowledge model in electronic health records 2012 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 26, s. 75-85 Tidskriftsartikel (refereegranskat)abstract The global effort in the standardization of electronic health records has driven the need for a model to allow medical practitioners to interact with the newly standardized medical information system by focusing on the actual medical concepts/processes rather than the underlying data representations. An archetype has been introduced as a model that represents functional health concepts or processes such as admission record, which enables capturing all information relevant to the processes transparently to the users. However, it is necessary to ensure that the archetypes capture accurately all information relevant to the archetype concepts. Therefore, a semantic backbone is required for each of the archetype.In this paper, we propose the development of an archetype sub-ontology for each archetype to represent the semantic content of the corresponding archetype. The sub-ontology is semi-automatically extracted from a standard health ontology, in this case SNOMED CT. Two steps performed to build an archetype sub-ontology are the annotation process and the extraction process, in which some rules have to be applied to maintain the validity of sub-ontology. The approach is evaluated by utilizing the archetype sub-ontologies produced in the development of a new archetype to ensure that only relevant archetypes can be linked to the archetype being developed, so that the only relevant data are captured using the particular archetype. It is shown that the method produces better results than the current approach in which an archetype sub-ontology is not used. We conclude that the archetype sub-ontology can represent well the semantic content of archetype.
40.	Seyed Jalaleddin, Mousavirad, et al. (författare) How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding? 2023 Ingår i: Knowledge-Based Systems. - : Elsevier B.V.. - 0950-7051 .- 1872-7409. ; 272 Tidskriftsartikel (refereegranskat)abstract Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.
41.	Seyed Jalaleddin, Mousavirad, et al. (författare) Population-based self-adaptive Generalised Masi Entropy for image segmentation : A novel representation 2022 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 245 Tidskriftsartikel (refereegranskat)abstract Image segmentation is an indispensable part of computer vision applications, and image thresholding is a popular one due to its simplicity and robustness. Generalised Masi entropy (GME) is an image thresholding method that exploits the additive/non-extensive information using entropic measure (). shows the measure of degree of extensibility and non-extensibility available in an image. From the literature, all research considered it as a fixed coefficient, while finding a proper value for can enhance the efficacy of thresholding. This paper proposes a simple yet effective approach for adaptively finding a proper value for without any background knowledge regarding the distribution of histogram. To this end, a new representation is proposed so that it can be used with any type of population-based metaheuristic (PBMH) algorithms. For the optimisation process, we use differential evolution (DE), as a representative. In addition, to further improve efficacy, we improve DE algorithm based on one-step -means clustering, random-based sampling, Gaussian-based sampling, and opposition-based learning. Our extensive experiments compared to the most recent approaches on a set of benchmark images and in terms of several criteria clearly show that the proposed approach not only can find the proper value for automatically but also it can improve the efficacy of GME-based image thresholding methods.
42.	Yu, Zaiyang, et al. (författare) MV-ReID : 3D Multi-view Transformation Network for Occluded Person Re-Identification 2024 Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 283 Tidskriftsartikel (refereegranskat)abstract Re-identification (ReID) of occluded persons is a challenging task due to the loss of information in scenes with occlusions. Most existing methods for occluded ReID use 2D-based network structures to directly extract representations from 2D RGB (red, green, and blue) images, which can result in reduced performance in occluded scenes. However, since a person is a 3D non-grid object, learning semantic representations in a 2D space can limit the ability to accurately profile an occluded person. Therefore, it is crucial to explore alternative approaches that can effectively handle occlusions and leverage the full 3D nature of a person. To tackle these challenges, in this study, we employ a 3D view-based approach that fully utilizes the geometric information of 3D objects while leveraging advancements in 2D-based networks for feature extraction. Our study is the first to introduce a 3D view-based method in the areas of holistic and occluded ReID. To implement this approach, we propose a random rendering strategy that converts 2D RGB images into 3D multi-view images. We then use a 3D Multi-View Transformation Network for ReID (MV-ReID) to group and aggregate these images into a unified feature space. Compared to 2D RGB images, multi-view images can reconstruct occluded portions of a person in 3D space, enabling a more comprehensive understanding of occluded individuals. The experiments on benchmark datasets demonstrate that the proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks. These results also suggest that our approach has the potential to solve occlusion problems and contribute to the field of ReID. The source code and dataset are available at https://github.com/yuzaiyang123/MV-Reid. © 2023 Elsevier B.V.
43.	Zhang, Chongsheng, et al. (författare) Multi-Imbalance : An open-source software for multi-class imbalance learning 2019 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 174, s. 137-143 Tidskriftsartikel (refereegranskat)abstract Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class imbalanced data classification. It provides users with seven different categories of multi-class imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance.
44.	Zhang, Liangwei, et al. (författare) Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems 2018 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 139:1, s. 50-63 Tidskriftsartikel (refereegranskat)abstract This paper presents an unsupervised, density-based approach to anomaly detection. The purpose is to define a smooth yet effective measure of outlierness that can be used to detect anomalies in nonlinear systems. The approach assigns each sample a local outlier score indicating how much one sample deviates from others in its locality. Specifically, the local outlier score is defined as a relative measure of local density between a sample and a set of its neighboring samples. To achieve smoothness in the measure, we adopt the Gaussian kernel function. Further, to enhance its discriminating power, we use adaptive kernel width: in high-density regions, we apply wide kernel widths to smooth out the discrepancy between normal samples; in low-density regions, we use narrow kernel widths to intensify the abnormality of potentially anomalous samples. The approach is extended to an online mode with the purpose of detecting anomalies in stationary data streams. To validate the proposed approach, we compare it with several alternatives using synthetic datasets; the approach is found superior in terms of smoothness, effectiveness and robustness. A further experiment on a real-world dataset demonstrated the applicability of the proposed approach in fault detection tasks.
45.	Zhao, Haifeng, et al. (författare) SmartWiki : A reliable and conflict-refrained Wiki model based on reader differentiation and social context analysis 2013 Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 47, s. 53-64 Tidskriftsartikel (refereegranskat)abstract Wiki systems, such as Wikipedia, provide a multitude of opportunities for large-scale online knowledge collaboration. Despite Wikipedia's successes with the open editing model, dissenting voices give rise to unreliable content due to conflicts amongst contributors. Frequently modified controversial articles by dissent editors hardly present reliable knowledge. Some overheated controversial articles may be locked by Wikipedia administrators who might leave their own bias in the topic. It could undermine both the neutrality and freedom policies of Wikipedia. As Richard Rorty suggested "Take Care of Freedom and Truth Will Take Care of Itself"[1], we present a new open Wiki model in this paper, called TrustWiki, which bridge readers closer to the reliable information while allowing editors to freely contribute. From our perspective, the conflict issue results from presenting the same knowledge to all readers, without regard for the difference of readers and the revealing of the underlying social context, which both causes the bias of contributors and affects the knowledge perception of readers. TrustWiki differentiates two types of readers, "value adherents" who prefer compatible viewpoints and "truth diggers" who crave for the truth. It provides two different knowledge representation models to cater for both types of readers. Social context, including social background and relationship information, is embedded in both knowledge representations to present readers with personalized and credible knowledge. To our knowledge, this is the first paper on knowledge representation combining both psychological acceptance and truth reveal to meet the needs of different readers. Although this new Wiki model focuses on reducing conflicts and reinforcing the neutrality policy of Wikipedia, it also casts light on the other content reliability problems in Wiki systems, such as vandalism and minority opinion suppression.
46.	Öztayşi, Başar, et al. (författare) Fuzzy Analytic Hierarchy Process with Interval Type-2 Fuzzy Sets 2014 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 59, s. 48-57 Tidskriftsartikel (refereegranskat)abstract The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1’s, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

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