Search

Abstract: This paper is concerned with a technique for detecting and monitoring abnormal paper formation variations in machine direction online in various frequency regions. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the time series block. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the kernel canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The experimental investigations performed have shown good repetitiveness and stability of the paper formation abnormalities detection results. The tools developed are used for online paper formation monitoring in a paper mill. [Copyright &y& Elsevier]

Type 2 diabetic patients benefit significantly if the disease is well controlled through behavioral changes, namely adopting a healthy lifestyle. Currently, there is some evidence that technological strategies can help patient self-management. However, few studies have specifically targeted individuals who solely engage in automatic and personalized self-management practices. This study aims to synthesize the literature regarding personalized feedback recommendation systems to promote behavior change without health professionals' direct intervention for the management of type 2 diabetes and to verify if the use of these systems improves health-related outcomes. A systematic review was performed from inception to April 13, 2021, based on a search conducted in six databases. According to the defined search expression, studies addressing type 2 diabetic patients and recommendation systems were included. In total, 2186 papers were initially identified, but only 22 met the specific inclusion criteria after screening. Discrepancies in the selection of studies were discussed in consensus meetings. To assess the quality of the articles, two tools were employed according to the types of articles retrieved. Selected papers were summarized regarding specific characteristics such as clinical and technological outcomes. Studies incorporating a recommendation system into their technological solution showed a positive effect on the evaluated outcomes, except for those with longer duration, where the effect was not statistically significant. Although most studies did not report the type of system used, expert systems (rule-based) were found to be the most prevalent among those that did. As behaviors are often difficult to change quickly, it is recommended that future studies extend their follow-up timeframe. Nevertheless, the data obtained suggest that technological solutions incorporating a recommendation system show potential to improve health-related outcomes and demonstrated good usability. [ABSTRACT FROM AUTHOR]

Fake news refuting is an essential part of online social networks (OSNs) management. Identifying the potential voluntary fake news refuters who can spontaneously repost the fake news refutation messages on the OSNs, and intentionally exposing such messages to them can contribute a lot to fake news regulation. Therefore, to fully utilize available user features and enhance fake news refuter identification, the paper proposes a novel paradigm for refuter classification problem, which leverages the pre-trained techniques and mixture of expert system to unify the structured and unstructured data. Specifically, the unstructured text records are first understood by the pre-trained ERNIE2 model, and then feed into Chi-Merge grounded Convolutional Neural Networks, which are expert classifiers in our framework, along with the structured profile data for further transformation. Besides, features extracted from text data are used for determining weights for multiple expert classifiers. Experiments on near 60 k real-world Weibo user data prove that our framework outperforms the large-scale pre-trained deep learning methods and traditional machine learning classification models. This paper provides a novel perspective of fake news refutation from the refuter's side and sheds light on artificial intelligence-driven OSNs management. [ABSTRACT FROM AUTHOR]

In recent years, feature selection research has quickly advanced to keep up with the age of developing expert systems. This is because the applications of these systems sometimes need massive datasets. Researchers who have an interest in creating novel feature selection methods or enhancing existing technologies have grown their interest in this topic. The current version of binary PSO (BPSO) is not developed as well as continuous PSO and does not follow the main principles of the standard PSO algorithm. Unlike the continuous version of the PSO, in BPSO, particle position is restricted to the Hamming space. Thus, there is no risk of swarm divergence, but the problem of premature convergence of the swarm arises. This paper presents an enhanced particle swarm optimization employed for feature selection to tackle the limitations of the current version of binary BPSO. Its primary quality is the application of a position update mechanism to appropriately select optimal features. The position update mechanism is designed to avoid the premature convergence issue suffered by BPSO, by determining the probability of the particles switching positions using position values rather than velocity. Two variant methods for determining the probability of changing the position of a particle element were introduced. These result in the two variants of enhanced binary particle swarm optimization (EBPSO) for feature selection, called EBPSO1 and EBPSO2. Several experiments were performed to analyze the effectiveness of the proposed method and compare it with state-of-the-art feature selection methods based on eighteen frequently used benchmark datasets. The experimental findings demonstrated that the proposed approach generated the most accurate classification in contrast to the alternative feature selection approach on most of the datasets. As a result, it can be inferred based on the outcomes obtained that this proposed approach performs significantly superior to the other state-of-the-art feature selection approaches. [ABSTRACT FROM AUTHOR]

• The paper reviews the features used for latent palmprint identification. • The paper evaluates the minutiae descriptors used for latent palmprint identification. • The paper shows that there is room for improvements of the feature representations. Latent palmprint identification is a crucial element for both law enforcement and integrated automated fingerprint identification systems because approximately 30% of the imprints found in a crime scene originate from a human's palms. To find the person whom the palmprint belongs to, forensic experts use systems that automatically compare the imprints found, called latent, against thousands of potential palmprints. Identification systems rely on features obtained from the palmprint, and different feature representations to include discriminative information. However, there is no consensus as to which representation allows for a better matching between latent palmprints, and those with a known identity. Furthermore, evaluating the identification performance when matching palmprints obtained when using different representations has not been done fairly. The current manner of evaluating palmprint identification methods uses different datasets, performance measures, and does not allow to discern the contributions of the feature representation and the methods for matching the palmprints. In this study, we have reviewed those features used for latent palmprint identification, and also we propose an evaluation methodology that allows for a fair comparison of minutiae-based features. Using our methodology, we evaluated each representation performing more than 5 billion comparisons. Our experiments are done using a dataset that includes information about the matching minutiae according to an expert. We aim with our results to provide a baseline for new research in latent palmprint identification feature representations, allowing for a fair comparison of newly developed representations in the future, which would enhance the whole latent palmprint identification methods. For this purpose, we also publicly provide our dataset, methodology implementation, and the feature representations implementation tested in our experiments. [ABSTRACT FROM AUTHOR]

• A survey of kernel clustering methods is provided. • Inspiring from the mechanism of flight guidance in particle swarm a fast kernel clustering method. • Presenting a framework for an automatic parameter tuning for the proposed method. • Application of the proposed method to image and video segmentation tasks. • Developing enhancement strategies to improve image and video segmentation results. Machine learning has become a pillar of today's expert and intelligent systems where a special attention has been drawn to unsupervised methods. Support vector data description is one of the most interesting methods proposed in this context that addresses two main challenges: the absence of ground truth labels and the presence of outliers in data. It is well known that the ultimate problem has a great impact on a system performance since it typically prevents and limits its generalization and hence its failure. Even though the strengths of the Support vector data description method, its application into multi class and large scale context, as computer vision systems, raises bottleneck issues due to its runtime complexity and mono-class context application. We propose, in this paper, an approach inspired from the movement behavior of particle swarm and relies on the use of core set paradigm to overcome the aforementioned drawbacks of Support vector data description method. Indeed, since the proposed method uses some parameters corresponding to kernel function and swarm's movement formulation, we present and study in this paper some strategies to ensure a dynamic adaptation of these parameters. The main qualities of the proposed method is that it provides a fast scheme for clustering a set of points without neither a prior knowledge on the number of naturally occurring cluster in the data nor an assumption on clusters shapes. A quantitative and comparative performance assessment is carried out over the Berkeley and BuffaloXiph datasets. The results witness the efficiency of the method in terms of both classification accuracy and computational performance. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas. [ABSTRACT FROM AUTHOR]

Abstract: In this paper it is introduced a recognition method for Mexican banknotes by using artificial vision. It is shown that the Mexican banknotes can be classified by extracting their color and texture features, with the RGB space and the Local Binary Patterns, respectively. We show the classification results performed with the current Mexican banknotes. We state that the proposed method can be applied to recognize banknotes of other countries which employ colors to distinguish the denominations. [Copyright &y& Elsevier]

Crowd counting in adverse hazy weather is inevitable and significant to the scene understanding in real-world application. For the crucial and challenging hazy-weather crowd counting issue, this paper proposes a single-stage hazy-weather crowd counting method based on direction-aware attention aggregation. The method develops a Directional Context Attention (DCA) block to realize embedding the positional information to the channel attention aggregation and to capture the long-range dependencies about the crowd structure, both of which guide the counting method readily locate the crowd of interest. Due to the lack of the hazy-weather crowd counting benchmark datasets, we also generate the hazy-weather crowd counting datasets to evaluate the proposed method. Experimental results and the discussions demonstrate our method is effective greatly on the hazy-weather crowd counting task, especially the design of the core DCA block. In this research work, both the proposed method and the generated benchmark datasets in hazy-weather scene facilitate the development of the crowd counting in the adverse weather conditions and the universal intelligent surveillance technologies. The generated hazy-weather crowd counting benchmarks datasets (Hazy-ShanghaiTechRGBD and Hazy-JHU) in this paper would be released in https://github.com/312524/Hazy-CC after peer-review process of this work to facilitate further research. • Introduce the hazy-weather crowd counting task for adverse-weather expert systems. • Design a direction-aware attention aggregation hazy-weather crowd counting network. • Develop a directional context attention block for mighty direction-aware learning. • Generate and release two crowd counting benchmarks specially for hazy- weather scene. • Realize the competitive performance on two generated adverse counting benchmarks. [ABSTRACT FROM AUTHOR]

Highlights • A new multi-agent dynamic system MADS architecture for the complex computer vision tasks. • An adaption of the proposed architecture to online multi-face tracking in video sequences. • A logical representation model of relations and mental attitudes of agents involved in the MADS. • The integration of two cognitively different type of agents for specific problem solving. • The integration of state-of-the-art methods for face detection and tracking with BDI agents. Abstract The paper presents a new architecture framework in the field of expert and intelligent systems which is based on four paradigms: a novel multi-agent dynamic system architecture (MADS), an extended Belief Desire Intention (EBDI) agent community, autonomy-oriented entities (AOEs), and deep learning concepts. The main impact of the proposed framework is a new approach, or even a new way of thinking, which enables integration of the concepts of deep learning, a conventional approach to solving the domain problem, cognitive agents with mental attitudes, and concepts of nature-inspired computing. All these allow the effective use of the framework in the field of intelligent and expert systems. The significance of the framework lies in its flexibility and adaptability based on the formal logical description of EBDI agents, the definition of the behaviour of AOEs, the use of classical modules for domain problem solving, and modules based on deep-learning concepts. We believe that the example of the adaptation of the proposed architecture framework to robust multi-face tracking illustrates the significance of the proposed framework. In this paper, MADS is adapted to the first two stages of a face de-identification pipeline: robust face detection and multi-face tracking. The proposed architecture of MADS has a two-level hierarchical organization. At the first level there is a manager designed as an Extended Belief Desire Intention (mEBDI) agent. The extension of a manager BDI agent consists of a convolutional neural network-based face detector, a set of autonomous-oriented entities for the elimination of false positive face detections, and a trajectory memory. At the second level, there are many tracking agents (trEBDIs) which consist of a basic BDI agent extended with a face tracker based on position and scale correlation filters, a visual appearance memory, and a trajectory memory. The mEBDI and trEBDI agents are defined by the modal logic and are described at the implementation level. The proposed architecture for a robust multi-face tracking system was tested on a subset of YouTube music videos. The qualitative results, as well as the preliminary quantitative results expressed by the standard testing metrics, demonstrate the effective adaptation of the proposed multi-agent dynamic architecture to a robust multi-face tracking system. [ABSTRACT FROM AUTHOR]

Highlights • The paper combines domain knowledge (DK) with machine learning (ML). • Patterns of logical analysis of data (LAD) are used by experts to enrich the DK. • The LAD patterns identify fault causes that are not represented in the DK. • The identified causes are used to enrich the fault trees in industrial plants. Abstract Experts in industrial processes rely on domain knowledge (DK) repositories to identify the causes of abnormal situations in order to make appropriate decisions that mitigate the negative effects of such events. These DK repositories need to be enriched and updated continuously as different unexpected events occur. A common causality analysis method in DK repositories is the fault tree analysis (FTA). The major limitation of updating a fault tree is that it requires in-depth system knowledge, which involves a high level of human experience. Data exploitation based on machine learning (ML) can address this limitation by deeply analyzing process historical data to discover hidden phenomena that are difficult for human experts to identify and to analyze. This paper proposes an innovative methodology that combines domain knowledge, in the form of FTA, with additional knowledge extracted by a descriptive ML method called logical analysis of data (LAD). More specifically, LAD is a classification method, which provides as a by-product a set of interpretable rules (patterns) explaining the classification results. The patterns extracted from historical data represent an important and complementary source of knowledge that provides experts with insights and allows them to better understand the process operations. The objective of using these patterns in the proposed methodology is to provide automatic enrichment and updating of existing fault trees in order to achieve accurate fault detection and diagnosis (FDD) in industrial processes. The proposed methodology is demonstrated using fault trees constructed for two different systems in the process industry. The fault tree for each system was updated successfully with minimal effort from process experts. [ABSTRACT FROM AUTHOR]

Highlights • Improved underwater glider path planning mission scenarios optimization with L-SHADE. • New proposed algorithm yielded stable and competitive output trajectories. • Outranked all other previous results from literature and ranked first in comparison. • Several configured algorithms are also compared to, analysed, and further improved. Abstract This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from literature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is targeting higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms existing literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision-makers, when they use the improved UGPP expert system yielding better trajectories. [ABSTRACT FROM AUTHOR]

Ambiguous or inconsistent information is frequently present in the input of any real-world system that relies on multiple data sources. In the presence of inconsistencies, an expert system based on classical logic is trivialised due to the principle of explosion, that forces any proposition (and its negation) to be inferred. Instead, a family of non-classical logics, called Paraconsistent Logics, formalises the idea that even from inconsistent premises, useful conclusions can be drawn. Within this context, the Paraconsistent Annotated Logics with Annotation of Two Values (PAL2v), which use a pair of favourable and unfavourable evidences as annotations, have been particularly successful in engineering applications. Structures called Para-Analysers can be built with these logics that provide the appropriate means to analyse the truth value of a proposition under conflicting/contradictory evidences. The work reported in this paper extends the traditional Para-Analysers with an evidence filter. This new model can be represented as a cubic structure representing various Para-Analyser lattices, each of which is selected according to the quality of the evidence, that is updated with respect to measurements executed at run time. The resulting three-dimensional paraconsistent analysis structure is encapsulated as an expert-system cell called Cubic Paraconsistent Analyser with Evidence Filter and Temporal Analysis (CPAet). Sets of CPAet nodes are combined constituting distinct network topologies that are used to infer the operating conditions of equipment in data networks of electrical systems control and supervision, resulting in a fault detection and classification system. Tests on simulated systems show that the CPAet networks were effective to deal with inconsistencies without trivialising the inferences, while also providing a more informed (finer grained) decision on the data network equipment state, when compared to the traditional paraconsistent analysers. • The extension of a paraconsistent logic model considering the quality of evidences. • A novel paraconsistent algorithm for the analysis of equipment of data networks. • The encapsulation of this algorithm as nodes in a network for system analysis. • The identification of failure in engineering installations using sets system analysis networks. • Complete investigation paraconsistent analysis applied to complex electrical systems. [ABSTRACT FROM AUTHOR]

Homomorphic Encryption (HE) is a set of powerful properties of certain cryptosystems that allow privacy-preserving operation over the encrypted text. Still, HE is not widespread due to limitations in terms of efficiency and usability. Among the challenges of HE, scheme parametrization (i.e., the selection of appropriate parameters within the algorithms) is a relevant multi-faced problem. First, the parametrization needs to comply with a set of properties to guarantee the security of the underlying scheme. Second, parametrization requires a deep understanding of the low-level primitives since the parameters have a confronting impact on the scheme's precision, performance, and security. Finally, the circuit to be executed influences, and it is influenced by, the parametrization. Thus, there is no general optimal selection of parameters, and this selection depends on the circuit and the scenario of the application. Currently, most existing HE frameworks require cryptographers to address these considerations manually. It requires a minimum of expertise acquired through a steep learning curve. In this paper, we propose a unified solution for the aforementioned challenges. Concretely, we present an expert system combining Fuzzy Logic and Linear Programming. The Fuzzy Logic Modules receive a user selection of high-level priorities for the security, efficiency, and performance of the cryptosystem. Based on these preferences, the expert system generates a Linear Programming Model that obtains optimal combinations of parameters by considering those priorities while preserving a minimum level of security for the cryptosystem. We conduct an extended evaluation showing that an expert system generates optimal parameter selections that maintain user preferences without undergoing the inherent complexity of analyzing the circuit. • Homomorphic Encryption scheme parametrization is a complex procedure. • Scheme parametrizations can be represented as Linear Programming Tasks. • Fuzzy Logic loosens the constraints of the Linear Programming Task. • Together Fuzzy Logic and Linear Programming provide near-optimal parametrizations. • The parametrization complexity is reduced and couples with user preferences. [ABSTRACT FROM AUTHOR]

This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, a robust catalogue is obtained when incomplete records and those with uncertainty are eliminated from a catalogue. Therefore, a robust catalogue is a collection of association rules with 100% confidence and unitary support. In this paper we demonstrate that robust catalogues contain the same association rules as the datasets from which they were obtained, but can be managed in memory without eliminating any data from the analysis. In fact, the experiments performed show that all robust catalogues contained in a classification dataset are easily obtained, providing millions of associative classification rules with 100% confidence to the expert researcher in data mining. [ABSTRACT FROM AUTHOR]

Sparse representation methods based on l 1 and/or l 2 regularization have shown promising performance in different applications. Previous studies show that the l 1 regularization based representation has more sparse property, while the l 2 regularization based representation is much simpler and faster. However, when dealing with noisy data, both naive l 1 and l 2 regularization suffer from the issue of unsatisfactory robustness. In this paper, we explore the method to implement an antinoise sparse representation method for robust face recognition based on a joint version of l 1 and l 2 regularization. The contributions of this paper are mainly shown in the following aspects. First, a novel objective function combining both l 1 and l 2 regularization is proposed to implement an antinoise sparse representation. An iterative fitting operation via l 1 regularization is integrated with l 2 norm minimization, to obtain an antinoise classification. Second, the rationale how the proposed method produces promising discriminative and antinoise performance for face recognition is analyzed. The l 2 regularization enhances robustness and runs fast, and l 1 regularization helps cope with the noisy data. Third, the classification robustness of the proposed method is demonstrated by extensive experiments on several benchmark facial datasets. The method can be considered as an option for the expert systems for biometrics and other recognition problems facing unstable and noisy data. [ABSTRACT FROM AUTHOR]

Recently, fingerprint crowdsourcing from pedestrian movement trajectories has been promoted to alleviate the site survey burden for radio map construction in fingerprinting-based indoor localization. Indoor corners, as one of the most common indoor landmarks, play an important role in movement trajectory analysis. This paper studies the problem of indoor corner recognition in crowdsourced movement trajectories. In a movement trajectory, smartphone internal sensor measurements experience some signal changes when passing by a corner. However, the state-of-the-art solutions based on signal change detection cannot well deal with the fake corner problem and pose diversity problem in most practical movement trajectories. In this paper, we study the corner recognition problem from an expert system viewpoint by applying machine learning techniques. In particular, we extract recognition features from both the time and frequency domain and propose a hierarchical corner recognition scheme consisting of three classifiers. The first pose classifier is to classify various poses into only two groups according to whether or not a smartphone is kept in a fixed position relative to a user upper body when collecting sensor measurements. Feature selection is then applied to train two corner classifiers each for one pose group. Field experiments are conducted to compare our proposed scheme with three state-of-the-art algorithms. In all cases, our scheme outperforms the best of these algorithms in terms of much higher F1-measure and precision for corner recognition. The results also provide insights on the potentials of using more advanced techniques from expert systems in indoor localization. [ABSTRACT FROM AUTHOR]

Many information systems record executed process instances in the event log , a very rich source of information for several process management tasks, like process mining and trace comparison. In this paper, we present a framework, able to convert activities in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Our abstraction mechanism manages non trivial situations, such as interleaved activities or delays between two activities that abstract to the same concept. Abstracted traces can then be provided as an input to an intelligent system, meant to implement a variety of process management tasks, significantly enhancing the quality and the usefulness of its output. In particular, in the paper we demonstrate how trace abstraction can impact on the quality of process discovery, showing that it is possible to obtain more readable and understandable process models. We also prove, through our experimental results, the impact of our approach on the capability of trace comparison and clustering (realized by means of a metric able to take into account abstraction phase penalties) to highlight (in)correct behaviors, abstracting from details. [ABSTRACT FROM AUTHOR]

Mental models play a crucial role in explaining and driving human innovation activities. To help researchers clarify the changes of mental models in various innovation situations, an exploration of its topic dynamic evolution changes is urgently needed. However, most existing works have discussed the topic-semantic distributions of collected documents along the overall timeline, which ignores the semantic details of fusion and evolution between topics in continuous time. This paper discovers and reveals the multi-level information evolving of topics, by integrating latent Dirichlet allocation (LDA) and Word2vec harmoniously to generate the topic evolution maps of the corpus from global to local perspectives. Which include topic distribution trends and their dynamic evolution under the overall time series, as well as the merging and splitting of semantic information between topics in the adjacent time span. These reveal the correlation between topics and the full life cycle of a topic emerging, developing, maturing, and fading. Then, the integrated method was used to perform an analysis of topic evolution with 3984 abstracts of mental model-related papers published between 1980 and 2020. Finally, the performance of the proposed method was compared to that of three traditional topic evolution generated methods based on the standard evaluation metrics. The experimental results demonstrated that our method outperforms other methods both in terms of the content and strength of topic evolution. The proposed method could mine the latent evolution information more clearly and comprehensively from a vast number of papers and is also suited to the various applications of expert systems related to information mining works. [ABSTRACT FROM AUTHOR]

• We propose a proposal for attribute reduction based on stripped quotient sets. • The proposed proposal has solid theoretical background and efficient algorithms. • The proposed proposal reports the superior results when compared to the state-of-the-art methods. Data scenarios on nowadays comprise an enormous number of attributes and instances while not all attributes are necessary and useful for data analytics and knowledge extraction in framing expert and intelligent systems. In such scenarios, removing irrelevant and redundant attributes is a very important task and a challenging research problem. In rough set approach, the attribute reduction method based on the positive region has reported the promising performance in consistent decision tables, while conditional information entropies are employed instead for inconsistent decision tables. However, the complexity of existing methods is still quite high. This paper proposes efficient attribute reduction approaches in inconsistent decision tables by using the concept of stripped quotient sets. Firstly, we present a method for fast determining reducts based on the conditional information entropies including Shannon's and Liang's entropies, called ERED-SQS. Secondly, we define an original notion of attribute reduction based on the stripped quotient set and propose an efficient SRED algorithm to cope with this reduct type. Finally, a simulation performed on 16 datasets varying in a number of objects, up to 5 millions, and a number of attributes, up to 20 thousands, to validate the proposed methods in comparison with the state-of-the-art methods. Experimental results show that the proposed algorithms, ERED-SQS and SRED, have significantly improved the computational time, especially for large data sets. Moreover, the classification accuracy of the reducts obtained from SRED algorithm outperforms that of the reducts based on Shannons and Liangs conditional entropies. This verifies that our proposal is applicable and effective to applications regarding attribute reduction in inconsistent decision tables. [ABSTRACT FROM AUTHOR]

• Clustering the features into some sets with highly related features and low innerredundancy. • Defining a neural tree exploiting an ELM and an inference engine in any node. • Transferring the rules extracted from any ELM to other nodes of neural tree. • Proposing Peer-to-Peer (P2P) and Server-Client (SC) models for knowledge transferring. • Classifying high dimensional data with high accuracy and without overfitting. Classification of the high-dimensional data by a new expert system is followed in the current paper. The proposed system defines some non-disjoint clusters of highly relevant features with the least inner-redundancy. For each cluster, a neural tree is implemented exploiting an Extreme Learning Machine (ELM) together an inference engine in any node. The derived classification rules from ELM are stored in the rule-base of the inference engine to recognize the classes. A majority voting is used to unify the results of the different neural trees. This structure is refereed as the Forest of Extreme Learning Machines with Rule-base Transferring (FELM-RT). The contribution of FELM-RT is to decrease the duplicated computations by using two novel interaction models between the neural trees. In the first interaction model, namely Peer-to-Peer (P2P) model, each node can share its rule-base with the other nodes of the various neural trees. In the second that is referred as Server-to-Client (S2C) model, a neural tree that works on a cluster with the best relevancy and redundancy, shares the rules with the other neural trees. In both of the models, a fuzzy aggregation technique is used to adjust the certainty of the rules. The processing time of FELM-RT decreases essentially and it improves the classification accuracy. The high results of F-measure and G-mean, show that FELM-RT classifies the high-dimensional datasets without over-fitting. The comparison between FELM-RT and some state-of-the-art classifiers reveals that FELM-RT overcomes them specially on the datasets with more than 3 million features. [ABSTRACT FROM AUTHOR]

• A model-free reinforcement learning algorithm for adaptive low-level PID control. • Double Q-learning provides excelent performance with low computational cost. • Incremental state and action spaces discretization for fast reinforcement learning. • The Double Q-PID algorithm is effectively applied to multiple robotic platforms. • The proposed algorithm effectively improves the adaptability of the PID controllers. Many expert systems have been developed for self-adaptive PID controllers of mobile robots. However, the high computational requirements of the expert systems layers, developed for the tuning of the PID controllers, still require previous expert knowledge and high efficiency in algorithmic and software execution for real-time applications. To address these problems, in this paper we propose an expert agent-based system, based on a reinforcement learning agent, for self-adapting multiple low-level PID controllers in mobile robots. For the formulation of the artificial expert agent, we develop an incremental model-free algorithm version of the double Q -Learning algorithm for fast on-line adaptation of multiple low-level PID controllers. Fast learning and high on-line adaptability of the artificial expert agent is achieved by means of a proposed incremental active-learning exploration-exploitation procedure, for a non-uniform state space exploration, along with an experience replay mechanism for multiple value functions updates in the double Q -learning algorithm. A comprehensive comparative simulation study and experiments in a real mobile robot demonstrate the high performance of the proposed algorithm for a real-time simultaneous tuning of multiple adaptive low-level PID controllers of mobile robots in real world conditions. [ABSTRACT FROM AUTHOR]

• Face recognition methods are proposed on the dependence structure of Gabor wavelets. • We develop lightweight copula model of Gabor wavelets to extract the face features. • PSO is use to improve the performance of lightweight copula model of Gabor wavelets. Low resolution, difficult illumination and noise are the important factors that affect the performance of face recognition system. In order to counteract these adverse factors, in this paper we propose copula probability models based on Gabor wavelets for face recognition. Gabor wavelets have robust performance under lighting and noise conditions. The strong dependencies exist in the domain of Gabor wavelets due to their non-orthogonal property. In the light of the structure characteristic of Gabor wavelet subbands, the proposed methods use copula to capture the dependencies to represent the face image. Three probability-model-based methods CF-GW (Copula Function of Gabor Wavelets), LCM-GW (Lightweight Copula Model of Gabor Wavelets) and LCM-GW-PSO (Lightweight Copula Model of Gabor Wavelets with Particle Swarm Optimization) are proposed for face recognition. Experiments of face recognition show our proposed methods are more robust under the conditions of low resolution, lighting and noise than the popular methods such as the LBP-based methods and other Gabor-based methods. The face features extracted by our methods belong to the Riemannian manifold which is different to Euclidean space. In order to deal the issue of face recognition in complex environment, we can combine the face features in Riemannian manifold with the face features in Euclidean space to obtain the more robust face recognition system by using expert system technologies such as reasoning model and multi-classifier fusion. [ABSTRACT FROM AUTHOR]

• This paper deals with a novel touchstroke authentication by frequency vs time features. • Touch times of tap-password are converted into signal and transformed by STFT. • STFT is manipulated to reach for more precise spectrograms by two window functions. • Classification is made by one-class SVM. • We achieved two of the lowest EERs as 1.40%. and 2.01% by manipulated STFT. Emerging security requirements lead to new validation protocols to be implemented to recent authentication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time-domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (STFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%–2.12% and 2.01%–3.21% equal error rates (EER) with mSTFT; while with regular STFT the classifiers produced quite higher EER, 7.49%–11.95% and 6.93%–10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches. [ABSTRACT FROM AUTHOR]

• A deep neuroevolution system is presented. • Our system is based on Probabilistic Model Building Genetic Programming approach. • Deep neural networks discovered by the system can diagnose heart rhythm problems. • Performance of the proposed network is evaluated on a physiological dataset. • Our network can outperform several machine learning algorithms in diagnosis. Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachycardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle different tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2% of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved. [ABSTRACT FROM AUTHOR]

• Customised sentiment analysis can monitor opinion/emotions changes in employees. • Modified skipgram model can comprehend implicit social network information. • Proposed employee profiles can be ranked by anomaly to identify potential insiders. Historically, the harm caused by insiders has proven to be one of the greatest concerns for any organization. As such, it has received considerable attention from both the industrial and research communities. Existing works mainly focused on modeling the employees' normal biometric behavior (e.g., human to device interaction pattern) to detect anomalous behavior which corresponds to the insider activity. However, it is unattainable to stop the insider at the final moment when the malicious act is being carried out. In this paper, we propose a novel framework which performs employee profiling based on aspect-based sentiments and social network information and examine its applicability for early detection of potential insider threats. On the contrary to the traditional sentiment analysis, aspect-based sentiment analysis provides more fine-grained information on the employee. Our framework employs a combination of deep learning techniques such as Gated Recurrent Unit (GRU) and skipgram to build temporal sentiment profiles for the employees. It then performs anomaly detection on the profiles and ranks the employees based on their respective anomaly score. Due to the absence of relevant benchmark dataset, we augmented the publicly available real-world Enron email corpus with an insider threat scenario to evaluate our framework. The evaluation results demonstrate that the augmentation is indeed reflected in the augmented employee's anomaly ranking (i.e., from normal to abnormal) and her close associates are indeed placed closely to her when the profiles are visualized in the 2D space. The profiles obtained from our framework can also be used to complement any existing expert and intelligent systems with additional capabilities in handling textual information such as, integration with profiles obtained from biometric behavior to form a more comprehensive threat detection system. [ABSTRACT FROM AUTHOR]

• Current proximity based network embedding models ignore the type information. • Meta path is an effective concept to depict vertex proximity in HIN. • Using meta path to guide embedding learning can preserve heterogeneous proximity. • Exact proximity measurement is time consuming. • Sampling based proximity measurement is more efficient for network embedding. We study the problem of representation learning in heterogeneous information networks. Its unique challenges come from the existence of multiple types of vertices and edges. Existing proximity-based network embedding techniques ignore the type information when evaluating the proximity and limits their usage in heterogeneous scenario. In this paper, we propose a heterogeneous proximity preserving network embedding model via meta path guided random walk, which is capable of capturing the high-order proximity between vertices specified by the given path. To improve the learning efficiency, we introduce a sampling based learning strategy which can incrementally learn representations. We conduct experiments on two real world heterogeneous information networks. Experimental results on several mining tasks prove the effectiveness of our approach over many competitive baselines. The model is very efficient and is able to learn embeddings for large networks both in offline and online scenarios. Besides, for expert system, our approach can be applied to improve the representation of knowledge entities by depicting the knowledge base as a heterogeneous information network. [ABSTRACT FROM AUTHOR]

• A simultaneous prototypes and local metric learning algorithm is proposed. • The approach is used in biometrics and object recognition based on image set. • Learned prototypes dramatically reduces the storage and time costs. • Learned local metric significantly improves the discrimination ability of a set. • Experimental results of our method superior/comparable to the literatures. Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifically, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classification error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label "closer" to each other, while pushing dissimilar ones "far away". Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on image set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications. [ABSTRACT FROM AUTHOR]

• A novel expert system for checking the correctness of memory systems. • The expert system properly combines simulation and metamorphic testing. • The expert system automatically generates test cases to check memory models. • Mutation testing has been applied to check the effectiveness of the ES. • The ES provides promising results, detecting 99. During the last few years, computer performance has reached a turning point where computing power is no longer the only important concern. This way, the emphasis is shifting from an exclusive focus on the optimisation of the computing system to optimising other systems, like the memory system. Broadly speaking, testing memory systems entails two main challenges: the oracle problem and the reliable test set problem. The former consists in deciding if the outputs of a test suite are correct. The latter refers to providing an appropriate test suite for determining the correctness of the system under test. In this paper we propose an expert system for checking the correctness of memory systems. In order to face these challenges, our proposed system combines two orthogonal techniques – simulation and metamorphic testing – enabling the automatic generation of appropriate test cases and deciding if their outputs are correct. In contrast to conventional expert systems, our system includes a factual database containing the results of previous simulations, and a simulation platform for computing the behaviour of memory systems. The knowledge of the expert is represented in the form of metamorphic relations, which are properties of the analysed system involving multiple inputs and their outputs. Thus, the main contribution of this work is two-fold: a method to automatise the testing process of memory systems, and a novel expert system design focusing on increasing the overall performance of the testing process. To show the applicability of our system, we have performed a thorough evaluation using 500 memory configurations and 4 different memory management algorithms, which entailed the execution of more than one million of simulations. The evaluation used mutation testing, injecting faults in the memory management algorithms. The developed expert system was able to detect over 99% of the critical injected faults, hence obtaining very promising results, and outperforming other standard techniques like random testing. [ABSTRACT FROM AUTHOR]

• Fast learning and data imputation using neighbour artificial immune system with a genetic algorithm. • Sustained and reliable predictions as the number of missing data increases in the datasets. • Sustained or improve recommendations of items to users from common methods used in collaborative filtering as missing data increases. In collaborative filtering, one of the main challenges that researchers face is sparseness in the data, which is caused by users rating fewer items as the number of items increase in the dataset. The effect is poor predictions and recommendations of items to users by expert and intelligent systems like the recommender system. This paper proposes a Nearest Neighbour Artificial Immune System with a Genetic Algorithm (NNAISGA) to perform fast data imputations to reduce sparseness. The main impact is sustained and reliable predictions as the number of missing data increases in the dataset. The second benefit is to help sustain or improve recommendations of items to users from common methods used in collaborative filtering such as the User-based, Item-based, Slope-one, Tendencies-based and Non-Negative Matrix Factorisation (NNMF) methods. We show that NNAISGA is the desired method for learning and imputation over the traditional genetic algorithm. The findings show that using the NNAISGA as a fast imputation method yields promising results. All of the methods, except for the User-based method, show significant improvements or sustained accuracies in terms of predictions and recommendations. [ABSTRACT FROM AUTHOR]

• An approach to prioritizing the customers is proposed. • The approach includes a two–stage fuzzy inference system. • Four criteria and arrival time is used for prioritization. • The results show fair prioritization with lower waiting times. This paper presents a fuzzy expert system for prioritization of heterogeneous customers with different needs. In this way, it is possible to improve the average waiting cost of a queuing system and enhance customer satisfaction as the basic component of organizations. Each customer has his own specific characteristics and demands and expects the system to understand his value and provide the best service. In this study, a two-stage Mamdani fuzzy inference system (FIS) was applied as a decision-making approach to prioritize customers based on Service Duration, Service Value, Customer Loyalty, Maximum Toleration, and Waiting Time. For this purpose, an expert system was developed that can be applied in various fields perfectly. To evaluate and analyze the performance of Fuzzy Prioritization System (FPS), the proposed model is compared with the First-In-First-Out (FIFO) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods as common approaches for prioritizing customers. The results of numerical experiments revealed the efficiency of the proposed system compared to these two methods. [ABSTRACT FROM AUTHOR]

• A guideline for the annotation process. • A study on the quality of video summaries collected from different assessment scales. • A novel metric based on compression limitation of user annotations. Hours of video are uploaded to streaming platforms every minute, with recommender systems suggesting popular and relevant videos that can help users save time in the searching process. Recommender systems regularly require video summarization as an expert system to automatically identify suitable video entities and events. Since there is no well-established methodology to evaluate the relevance of summarized videos, some studies have made use of user annotations to gather evidence about the effectiveness of summarization methods. Aimed at modelling the user's perceptions, which ultimately form the basis for testing video summarization systems, this paper seeks to propose: (i) A guideline to collect unrestricted user annotations, (ii) a novel metric called compression level of user annotation (CLUSA) to gauge the performance of video summarization methods, and (iii) a study on the quality of annotated video summaries collected from different assessment scales. These contributions lead to benchmarking video summarization methods with no constraints, even if user annotations are collected from different assessment scales for each method. Our experiments showed that CLUSA is less susceptible to unbalanced compression data sets in comparison to other metrics, hence achieving higher reliability estimates. CLUSA also allows to compare results from different video summarizing approaches. [ABSTRACT FROM AUTHOR]

• We proposed a new ensemble learning framework in the setting of granular computing. • We conducted data transformation by information granulation for in-depth training. • We developed multi-level fusion strategies for in-depth classifiers fusion. • The proposed framework outperforms popular ensemble learning methods. • The proposed framework outperforms popular standard learning methods. Classification is a special type of machine learning tasks, which is essentially achieved by training a classifier that can be used to classify new instances. In order to train a high performance classifier, it is crucial to extract representative features from raw data, such as text and images. In reality, instances could be highly diverse even if they belong to the same class, which indicates different instances of the same class could represent very different characteristics. For example, in a facial expression recognition task, some instances may be better described by Histogram of Oriented Gradients features, while others may be better presented by Local Binary Patterns features. From this point of view, it is necessary to adopt ensemble learning to train different classifiers on different feature sets and to fuse these classifiers towards more accurate classification of each instance. On the other hand, different algorithms are likely to show different suitability for training classifiers on different feature sets. It shows again the necessity to adopt ensemble learning towards advances in the classification performance. Furthermore, a multi-class classification task would become increasingly more complex when the number of classes is increased, i.e. it would lead to the increased difficulty in terms of discriminating different classes. In this paper, we propose an ensemble learning framework that involves transforming a multi-class classification task into a number of binary classification tasks and fusion of classifiers trained on different feature sets by using different learning algorithms. We report experimental studies on a UCI data set on Sonar and the CK+ data set on facial expression recognition. The results show that our proposed ensemble learning approach leads to considerable advances in classification performance, in comparison with popular learning approaches including decision tree ensembles and deep neural networks. In practice, the proposed approach can be used effectively to build an ensemble of ensembles acting as a group of expert systems, which show the capability to achieve more stable performance of pattern recognition, in comparison with building a single classifier that acts as a single expert system. [ABSTRACT FROM AUTHOR]

• A multi-objective blind source separation approach is proposed. • Solutions include the ones found by mono-objective optimization of single criterion. • One may find non-dominate solutions closer to mean squared error ones. • The set of estimates can be used by the expert in order to take the decision. Several problems in signal processing are addressed by expert systems which take into account a set of priors on the sought signals and systems. For instance, blind source separation is often tackled by means of a mono-objective formulation which relies on a separation criterion associated with a given property of the sought signals (sources). However, in many practical situations, there are more than one property to be exploited and, as a consequence, a set of separation criteria may be used to recover the original signals. In this context, this paper addresses the separation problem by means of an approach based on multi-objective optimization. Differently from the existing methods, which provide only one estimate for the original signals, our proposal leads to a set of solutions that can be utilized by the system user to take his/her decision. Results obtained through numerical experiments over a set of biomedical signals highlight the viability of the proposed approach, which provides estimations closer to the mean squared error solutions compared to the ones achieved via a mono-objective formulation. Moreover, since our proposal is quite general, this work also contributes to encourage future researches to develop expert systems that exploit the multi-objective formulation in different source separation problems. [ABSTRACT FROM AUTHOR]

• Test dataset can never be modified. • Optimal Training dataset composition for detection of minority classes. • Feature selection without under-sampling performs better. • Under-sampling Normal class instances is more fruitful compared to oversampling U2R and R2L category of attacks. • Naïve Bayes Classifier has good detection rate for U2R and Probe category of attacks and can be utilized for the same in a multistage classifier. This paper analyses the recently proposed article "A feature reduced intrusion detection system using ANN classifier" by Akashdeep, Ishfaq Manzoor & Neeraj Kumar, (Expert systems with Applications, 2017) which has a limitation in its experimental setup. The work of Akashdeep et.al has crafted the test dataset to attain improved accuracy. They have utilized 5 fractional test datasets for performance evaluation. The reduced list of features obtained in their work does not give the asserted performance for the original test dataset. Table 18 of the above article by Akashdeep et.al gives the performance comparison of their work with existing works which isn't appropriate as these works have different test dataset composition. Another issue with the work of Akashdeep et.al is the utilization of partial training dataset for determining the reduced list of features. Their work reduces the training dataset by random undersampling of the majority class instances and random replication of the minority class instances. The reduced list of features obtained by Akashdeep et.al comprises 25 features. This work applies the feature selection algorithm proposed by Akashdeep et.al on the original training dataset leading to a feature subset having 29 features. It has been observed experimentally that the reduced feature subset (29 features) obtained in later outperforms the former reduced feature set (25 features). This work uses the classification algorithms c4.5, Naive Bayes and Random Forest for performance comparison of these reduced feature sets. Oversampling one class may deteriorate the performance of another class. This work also evaluates random undersampling/oversampling of a specific class to design an optimal training dataset. The results show that the classification models developed using this training dataset have a better detection rate for the minority classes. [ABSTRACT FROM AUTHOR]

• HPSO-SSM is proposed based on original particle swarm optimization. • A new wrapper-based feature selection approach based on HPSO-SSM is proposed. • The logistic map sequence is used to enhance the diversity in the search process. • An innovative position update model is presented to improve the position quality. • Our method outperforms seventeen extremely competitive methods in terms of accuracy. The "curse of dimensionality" is one of the largest problems that influences the quality of the optimization process in most data mining, pattern recognition, and machine learning tasks. Using high-dimensional datasets to train a classification model may reduce the generalization performance of the learned model. In addition, high dimensionality of the dataset results in high computational and memory costs. Feature selection is an important data preprocessing approach in many practical application domains that are relevant to expert and intelligent systems. Feature selection aims at selecting a subset of informative and relevant features from an original feature dataset. Therefore, using a feature selection approach to process the original data prior to the learning process is essential for enhancing the performance on the classification task. In this paper, hybrid particle swarm optimization with a spiral-shaped mechanism (HPSO-SSM) is proposed for selecting the optimal feature subset for classification via a wrapper-based approach. In HPSO-SSM, we make three improvements: First, a logistic map sequence is used to enhance the diversity in the search process. Second, two new parameters are introduced into the original position update formula, which can effectively improve the position quality of the next generation. Finally, a spiral-shaped mechanism is adopted as a local search operator around the known optimal solution region. For a complete evaluation, the proposed HPSO-SSM method is compared with six state-of-the-art meta-heuristic optimization algorithms, ten well-known wrapper-based feature selection techniques, and six classic filter-based feature selection methods. Various assessment indicators are used to properly evaluate and compare the performances of these approaches on twenty classic benchmark classification datasets from the UCI machine learning repository. According to the experimental results and statistical tests, the developed methods effectively and efficiently improve the classification accuracy compared with other wrapper-based approaches and filter-based approaches. The results demonstrate the high performance of the HPSO-SSM method in searching the feasible feature space and selecting the most informative attributes for solving classification problems. Therefore, the HPSO-SSM method has broad application prospects as a new feature selection approach. [ABSTRACT FROM AUTHOR]

Highlights • We present a new extension of the Tourist Trip Design Problem (TTDP). • The TTDP with Clustered POIs (TTDP-Clu) have POIs are grouped in clusters. • A MIP model is developed for TTDP-Clu. • A Fuzzy GRASP metaheuristic is proposed to generate solutions of the problem. • The metaheuristic solutions are compared with MIP model. Abstract In tourist sector, expert and intelligent systems should perform at least two main tasks or services: point of interest recommendation and route generation. In this regard, the personalized electronic tourist guide, generally implemented on hand-held device, such as mobile applications or in web. These tools must work like an expert and intelligent system that perform the services mentioned above then they should need low computation effort. In this paper we focus on the route generation based on scores of the points of interest and the distance or time between them. We consider a new extension of the Tourist Trip Design Problem, named Tourist Trip Design Problem with Clustered Points of Interest, where points of interest are grouped in clusters representing different types of attraction sites. Moreover, minimum/maximum limits are imposed on the number of points of interest belonging to the each clusters that are visited in the same route. Since it is a novel problem, we generate two sets of instances in order to evaluate the accuracy of our solution approach. A Fuzzy GRASP (Greedy Randomized Adaptive Search Procedure), in which both distance based and score based evaluation criteria are used to guide the candidates selection in the construction phase is proposed. The results provided by our heuristic are compared with those obtained by solving the MIP formulation. Computational results carried out on real and real-like instances show the effectiveness and efficiency of the proposed approach and its suitability to be part of a Personalized Electronic Tourist Guide in hand-held devices. [ABSTRACT FROM AUTHOR]

Highlights • Fuzzy inference system for dynamic delivery of language learning material. • Knowledge inference relationships among learning objects for personalized learning. • Hybrid model for misconception diagnosis and identification using machine learning. • Framework of machine learning and fuzzy logic for individualized language learning. • Implementation of expert system and evaluation using an established framework. Abstract Adaptive e-learning employs algorithmic mechanisms in order to orchestrate the pace of instruction and provide tailored learning objects to support the unique educational experience of each learner. Taking this into consideration, this research work presents a fully operating and evaluated adaptive and intelligent e-learning system for second language acquisition. This system uses a hybrid model for misconception detection and identification (MDI) and an inference system for the dynamic delivery of the learning objects tailored to learners' needs. More specifically, the MDI mechanism incorporates the Fuzzy String Searching and The String Interpreting Resemblance algorithms in order to reason between possible learners' misconceptions. Furthermore, the inference system utilizes the knowledge inference relationship between the learning objects and creates a personalized learning environment for each student. The paper presents examples of operation and the system is evaluated using an evaluation model. The results are very encouraging and promising since they reveal that the hybrid model for misconception detection and identification and the inference system operate collaboratively and enhance the adaptivity of the students' needs and preferences. [ABSTRACT FROM AUTHOR]

Highlights • We propose transfer learning approaches for predicting drug candidates. • We compare the proposed approaches against existing baseline approaches. • We conduct experimental results for several prediction tasks of drug candidates. • Our approaches achieve high and significant performance results over baselines. Abstract The prediction of drug candidates for given tissues of organisms based on expression data is a critical biological problem. By correctly predicting drug candidates for given tissues, biologists can (1) avoid an experimental process of high-throughput screening that requires excessive time and costly equipment and (2) accelerate the drug discovery process by automatically assigning drug candidates. Although high throughput screening for therapeutic compounds lead to the generation of expression data, the process of correctly assigning candidate drugs based on such data remains a rigorous task. Hence, the design of high-performance machine learning (ML) algorithms is crucial for data analysts who work with clinicians. Clinicians incorporate advanced ML tools into expert and intelligent systems to improve the drug discovery process by accurately identifying drug candidates. The transfer learning approaches that are necessary to improve the prediction performance of several tasks that are involved in identifying drug candidates are presented in this paper. The performances of machine learning algorithms are compared in the transfer learning setting by employing several evaluation measures on real data that are obtained from experiments conducted on rats to identify drug candidates. The experimental results show that the proposed transfer learning approaches outperform baseline approaches in terms of prediction performance and statistical significance. [ABSTRACT FROM AUTHOR]

In order to effectively integrate medical data and alleviate the problem of uneven distribution of medical resources. In this paper, we combine the techniques of expert systems, graph neural networks, and knowledge graphs to propose a disease guidance model combining semi-supervised graph neural networks and knowledge graphs. We use the MASR speech recognition module combined with gated convolutional units for effective text processing of different types of speech; then we use the LTP module in natural language processing for semantic analysis and segmentation matching of interrogative sentences; we combine keywords with the number of diseases and divide and construct the set of nodes with knowledge graphs. And we use semi-supervised graph neural network type analysis to give treatment results and rehabilitation suggestions effectively. We optimize the Chinese and English corpora respectively, adding consideration for local dialect audiences. We performed a comprehensive comparison of the accuracy and training time of several mainstream GCN algorithms and our GCN semi-supervised (SGS) under various graphical text datasets to validate the efficiency and accuracy of our own algorithm choices. We preprocess the number of different symptoms for classification and simplify the redundant nodes to optimize the running time while taking into account the overall convergence. The operational mechanism of the model as well as the convergence and hits under different symptom parameters are explained through hit rate and convergence rate metrics to demonstrate the effectiveness and stability of the model under proprietary medical conditions. [ABSTRACT FROM AUTHOR]

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset. [ABSTRACT FROM AUTHOR]

Fault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty. [ABSTRACT FROM AUTHOR]

While the rapid development of information technology has made easy to store and access the huge amount of data, it also brings another problem, that of how to extract potentially useful knowledge not only in an efficient way but also in a way that could be easily understandable by humans. One of the solutions to this problem is linguistic summarization, aim of which is to generate explicit and concise summaries from data that is more compatible with human cognitive mechanism. The most crucial step in linguistic summarization is certainly the evaluation of linguistic summaries since they are the most important element of fuzzy rule based systems commonly used in expert systems and intelligent systems. Therefore, the selection of appropriate method for evaluating linguistic summaries in sense of different views such as quality, quantity, relevance and simplicity becomes vital. The aim of this paper is to review the state of art on linguistic summarization in the framework of fuzzy sets, focusing on the methods for evaluating linguistic summaries and the current applications. A taxonomy is proposed to identify the existing methods depending on the type of fuzzy sets (i.e., type-1 fuzzy set and type-2 fuzzy set) and the type of cardinalities (i.e., scalar cardinality and fuzzy cardinality). The recent studies on linguistic summarization are also presented to give a comprehensive framework for the future directions. The paper ends with conclusions, addressing some important issues and open questions which can be subject for future research. [ABSTRACT FROM AUTHOR]

This paper introduces an Expert System acting as an intelligent agent to interrogate past aircraft occurrence data to support the aircraft occurrence investigators. This Expert System can potentially identify similar occurrences that provide indicators of contributing factors that may otherwise have been missed. This intelligence comes from the Expert System's ability to learn from past occurrence data using the combined Fuzzy Logic System (FLS) inference technique and AcciMaps to emulate the aircraft occurrence investigators' evidence analytical reason model. The learned expert knowledge is presented graphically and interactively to the user (investigator) using the AcciMaps. The paper includes an overview of the Australian Transport Safety Bureau (ATSB) investigation evidence analysis workflow, popular mainstream inference techniques used in Expert Systems, evaluation of the proposed Fuzzy Logic System (FLS) inference technique and AcciMap, including an example study. The FLS inference technique has met the Expert System requirements to support aviation occurrence investigations. [ABSTRACT FROM AUTHOR]

Given the large numbers of publications in software engineering, frequent literature reviews are required to keep current on work in specific areas. One tedious work in literature reviews is to find relevant studies amongst thousands of non-relevant search results. In theory, expert systems can assist in finding relevant work but those systems have primarily been tested in simulations rather than in application to actual literature reviews. Hence, few researchers have faith in such expert systems. Accordingly, using a realistic case study, this paper assesses how well our state-of-the-art expert system can help with literature reviews. The assessed literature review aimed at identifying test case prioritization techniques for automated UI testing, specifically from 8,349 papers on IEEE Xplore. This corpus was studied with an expert system that incorporates an incrementally updated human-in-the-loop active learning tool. Using that expert system, in three hours, we found 242 relevant papers from which we identified 12 techniques representing the state-of-the-art in test case prioritization when source code information is not available. These results were then validated by six other graduate students manually exploring the same corpus. Without the expert system, this task would have required 53 h and would have found 27 additional papers. That is, our expert system achieved 90% recall with 6% of the human effort cost when compared to a conventional manual method. Significantly, the same 12 state-of-the-art test case prioritization techniques were identified by both the expert system and the manual method. That is, the 27 papers missed by the expert system would not have changed the conclusion of the literature review. Hence, if this result generalizes, it endorses the use of our expert system to assist in literature reviews. • First paper reporting a literature review assisted by FAST2. • FAST2 saved 50 man-hours of human effort while missing 10% relevant papers. • The missing papers do not change the conclusions of the literature review. • FAST2 imposed a bias onto the distribution of the included papers. • The literature review process was scripted online for reproduction and experiments. [ABSTRACT FROM AUTHOR]

• Bibliometric study on fuzzy systems research. • A combined methodology designed to maximize accurate results. • Novel visualizations of results, based on big data processing software. • Expert systems with Applications as a top fuzzy systems research jorunal. Fuzzy systems research (FSR) is a continuously growing academic field which developments and applications extend to a wide-ranging variety of scientific areas. Academic journals play a decisive role in the scientific communication process and the dissemination of novel ideas. The current developments in information technologies bring both challenges and opportunities for scientific journals to analyze, debate, and share scientific findings. Applying bibliometric and network visualization techniques, this paper seeks to analyze the evolution of the FSR field based on journals from 1965 to 2019. The aim is to visualize the current landscape of key highly oriented FSR journals, focusing on productivity, influence, and synergy. The paper uses data retrieved from the Web of Science scientific database and uses a combined methodology specifically designed to maximize the retrieval of significant documents. Results show that the journals with the highest productivity analyzed by period are Mathematical Analysis and Applications (1965–1979), Fuzzy Sets and Systems [FSS](1980–2009), and the Journal of Intelligent Fuzzy Systems (2010–2019). The most cited journals of the last 20 years are FSS, IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Systems Man Cybernetics Systems, while the most relevant for impact factor is Information Fusion. Moreover, a bibliographic coupling analysis by decades is proposed, showing not only the natural impact and relation that the interaction between journals generate but the impact that the rapid development in communications has in the diversification and dissemination of influence, citations, and productivity of scientific journals. [ABSTRACT FROM AUTHOR]

Highlights • DMU ranking approach that synergistically combines different EIS techniques. • TOPSIS used to integrate second-order features extracted from raw input-output data. • In-strength, PageRank, inefficiency radius come from dominance network analysis. • Crowding distance, hypervolume, spacing indicators from multiobjective optimization. • The proposed approach has been applied to ranking major US airlines. Abstract This paper presents a new approach for ranking organizational units within a benchmarking context. Instead of the conventional optimization-based techniques, the proposed approach uses Social network analysis and Multiobjective optimization concepts to extract second-order features from the input-output data, integrating the resulting multidimensional information using TOPSIS. It shows how several expert and intelligent systems techniques can be harmoniously integrated and applied to performance assessment. The proposed approach has been used for ranking the performance of 27 major US airlines, comparing the results with some existing Data Envelopment Analysis methods. It is shown that the use of a richer information set instead of the raw input-output data leads to an innovative and more effective way of discriminating between efficient units. [ABSTRACT FROM AUTHOR]

Highlights • A novel intelligent, collaborative health knowledge system supporting diagnosis. • Temporal rule and knowledge management. • An inference engine handling the embedded time and uncertainty. • A conflict detection policy for supporting rule generation. • A well-designed and user-friendly web interface with strong collaboration features. Abstract The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms. [ABSTRACT FROM AUTHOR]

Highlights • Basic probability assignment function is used to extract expert judgment information. • Two types of super fuzzy relationship matrices on grade's power set are constructed. • Multi-objective programming is established to derive belief distribution on grades. • Algorithm is proposed to solve group fuzzy comprehensive evaluation under ignorance. • The proposed method is compared with three kinds of relevant methods. Abstract This paper aims at solving such a group fuzzy comprehensive evaluation (FCE) problem that the global or local ignorance may exist in judgments made by experts and the importance degrees of experts are different. The basic probability assignment (BPA) function is used to extract the expert's judgment information and the super fuzzy relationship matrices consisting of the individual type and the general type are constructed by Shafer's discounting and Dempster's rule. Then each type of super fuzzy relationship matrix is combined with factor weight set via a specified fuzzy operator and the comprehensive evaluation result that is a belief distribution on the power set of grade levels is obtained. A multi-objective programming model is established to compute the optimal belief distribution on each grade level and an algorithm is summarized to derive the final grade level that the evaluated alternative belongs to. Moreover, the numerical comparisons between the proposed method and relevant existing methods are given to clarify the advantages of the proposed method. Finally, an illustrative example is provided to demonstrate the applicability of the proposed method and algorithm. It is worth noting that the proposed method can be easily converted into a core algorithm, which is benefit for developing fuzzy expert system from the perspective of ignorance, and thus it has an important impact and significance on expert and intelligent systems. [ABSTRACT FROM AUTHOR]

Highlights • Enhanced nonlinearities may improve expert systems performance. • Proposal of the activation function DReLU. • DReLU presents the best training speed in all cases. • DReLU enhances the ReLU performance in all scenarios. • DReLU provides the best test accuracy in almost all experiments. Abstract A substantial number of expert and intelligent systems rely on deep learning methods to solve problems in areas such as economics, physics, and medicine. Improving the accuracy of the activation functions used by such methods can directly and positively impact the overall performance and quality of the mentioned systems at no cost whatsoever. In this sense, enhancing the design of such theoretical fundamental blocks is of great significance as it immediately impacts a broad range of current and future real-world deep learning based applications. Therefore, in this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is practically a mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These Convolutional Neural Networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p < 0.05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios. Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance. Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function. [ABSTRACT FROM AUTHOR]

Highlights • An architecture for numerical design of experiment configuration is proposed. • A bayesian network with a "multi-net" strategy is proposed. • Models are trained from historical data and expert knowledge. • The performances of the proposed method are validated through a case study. Abstract This paper describes a specific knowledge-based system (KBS) to assist designers in configuring numerical design of experiments (NDoE) processes efficiently. NDoE processes are applied in product design to improve the quality of product, by taking into account variabilities and uncertainties. NDoE processes are defined by various and complex methodologies to achieve several objectives, as optimization, surrogate modeling or sensitivity analysis. On the other hand, NDoE processes may demand huge computing resources to execute hundreds simulations, and also advanced expert knowledge to set the best configuration amongst numerous possibilities. Designers aim to obtain most useful results with a minimal computational cost as soon as possible. Thus, the configuration step must be as fast as possible, and it must lead to an efficient combination of complex methods, algorithms and hyper-parameters, to obtain valuable information on the product. The proposed KBS and its inference engine, a bayesian network, is detailed and applied to a product developed by automotive industry. The KBS propose new efficient configurations to achieve designers' goal. This application shorten the configuration step of the NDoE process, and enables designers to use more complex methods. It also allows designers to capitalize knowledge and learn from each past NDoE process. [ABSTRACT FROM AUTHOR]