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Abstract: The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior. [Copyright &y& Elsevier]

Abstract: Evolutionary computing (EC) techniques have been used traditionally used for solving challenging optimization problems. But the increase in data and information has reduced the performance capacity of the GA, but highlighted the cost of finding a solution by GA. In addition, the genetic algorithm employed in previous literature is modeled to solve one problem exactly. The GA needs to be redesigned, at a cost, for it to be applied to another problem. For these two reasons, this paper proposes a method for incorporating the GA and rough set theory. The superiority of the proposed GA in this paper lies in its ability to model problems and explore solutions generically. The advantages of the proposed solution approach include: (i) solving problems that can be decomposed into functional requirements, and (ii) improving the performance of the GA by reducing the domain range of the initial population and constrained crossover using rough set theory. The solution approach is exemplified by solving the problem of web services composition, where currently the general analysis and selection of services can be excessively complex and un-systemic. Based on our experimental results, this approach has shown great promise and operates effectively. [Copyright &y& Elsevier]

Abstract: This paper proposes a self-generating fuzzy system with a learning ability from a combination of the on-line self-aligning clustering (OSAC) algorithm and ant and particle swarm cooperative optimization (APSCO). The proposed OSAC algorithm not only helps generate rules from on-line training data, but also helps avoid generating highly overlapping fuzzy sets. Once a new rule is generated, APSCO optimizes the corresponding antecedent and consequent parameters. In APSCO, ant colony and particle swarm coexist in a population, and both search for an optimal parameter solution simultaneously in each iteration. Ant paths not only help determine the consequent parameters of generated rules, they also help generate auxiliary particles. Well-performing particles are selected from the auxiliary particles and original particles. And these selected particles cooperate to find a better solution through particle swarm optimization. This paper applies the proposed self-generating fuzzy system to different fuzzy controller design problems, and compares it with other genetic and swarm intelligence algorithms and their hybrids to verify system performance. [Copyright &y& Elsevier]

Abstract: This paper discusses an experimental study of the Japanese candlestick method as used in hybrid stock market forecasting models. Two models are presented in this paper. Model 1 is a committee machine with simple generalized regression neural networks (GRNN) experts. This model also has a simple gating network. Model 2 has a similar committee machine along with a hybrid type gating network that contains fuzzy logic. Model 1 was developed to introduce the candlestick method and examine whether using the candlestick method improves performance. Model 2 is developed to determine whether the application of fuzzy logic could improve the former model. This model uses standard IF-THEN rules based fuzzy logic. In the experiment, a few simple Japanese candlestick patterns are integrated into the models. Both models use the same simple candlestick patterns to provide a basis for comparison. The Japanese candlestick method is implemented in the gating network. Model 1 uses features of candlestick patterns in the gating network. Model 2 uses candlestick patterns for recognizing the strength of market conditions. To investigate the performance of these models, the daily stock quotes of Hewlett-Packard, Bank of America, Ford, DuPont, and Yahoo are used as input data sets. The performance of the models was satisfactory based on the mean squared error. [Copyright &y& Elsevier]

Abstract: In this paper, we have made an extensive study of artificial intelligence (AI) techniques like ensemble of classifiers and feature selection for the identification of students with learning disabilities. The experimental results show that our best method, which combines both ensemble of classifiers and feature selection, can correctly identify up to 50% of the learning disabilities (LD) students with 100% confidence. Also when predicting samples in “junior high school” using model built on the “elementary school” students and when the “junior high school” samples are used to build the model we predict the samples in the “elementary school” dataset. In particular, we propose variants of two recent Feature Transform-based ensemble methods (Rotation Forest and Input Decimated Ensemble). In the Rotation Forest, the feature set is randomly split into subsets and Principal Component Analysis (PCA) is used to transform the features that belong to a subset. The Input Decimated Ensemble first singles out a given class i and runs PCA on this data only. This transformation is applied to the whole dataset and a classifier Di is trained using these transformed patterns. This choice limits the size of the ensemble to the number of classes. In this paper, we perform an empirical comparison varying the Feature Transform method used in the Rotation Forest technique and we propose a clustering method to overcome the drawback of the Input Decimated Ensemble. [Copyright &y& Elsevier]

Abstract: Fish disease and health management is becoming a key barrier and major problem that currently limits expanded production and sustainable development of aquaculture and bring out food-safety issues. There has been a marked growth of interest in developing expert system to aid fish disease/health management. The literature shows that a few have been used to any extent due to the lack of deep user requirement analysis and long-term support and maintenance. This paper presents the effort to apply evolutionary prototyping model in developing an intelligent decision support system for fish disease/health management. The paper describes in detail about the four evolution stages as the system development experienced, every stage including the system design and implementation, feedback and requirement from system evaluation, and system redevelopment until a blown system. At last the evolution process is concluded from three directions. The evolutionary prototyping benefits and challenges experienced during the development and promotion are also discussed from the project management perceptive. [Copyright &y& Elsevier]

Abstract: In this paper, the usage of an automatic decision system based on the combination of support vector machines (SVM), which recognizes the some analog modulation types, is introduced. Here, automatic analog modulation recognition performance of this system is compared with a multi-layer perceptrons (MLP) classifier. The discrete wavelet transform (DWT) and wavelet entropy (WE) methods are used for the extraction effective features from analog modulation signals in the feature extraction stages of both these SVM and MLP systems. In this study, some experiments were performed for finding the optimal C (cost) and σ (sigma) kernel parameters of SVM. The analog modulated signals used in this study are amplitude modulation (AM), double side band (DSB), upper single band (USB), lower single band (LSB), frequency modulation (FM), and phase modulation (PM). The performance of both SVM and MLP classifiers is evaluated by using total 3240 analog modulated signals. These test results show the effectiveness of the system proposed in this paper. The rate of correct classification is about 96.419753% for the sample analog modulated signals. [Copyright &y& Elsevier]

Abstract: This paper proposed a new improved method for back propagation neural network, and used an efficient method to reduce the dimension and improve the performance. The traditional back propagation neural network (BPNN) has the drawbacks of slow learning and is easy to trap into a local minimum, and it will lead to a poor performance and efficiency. In this paper, we propose the learning phase evaluation back propagation neural network (LPEBP) to improve the traditional BPNN. We adopt a singular value decomposition (SVD) technique to reduce the dimension and construct the latent semantics between terms. Experimental results show that the LPEBP is much faster than the traditional BPNN. It also enhances the performance of the traditional BPNN. The SVD technique cannot only greatly reduce the high dimensionality but also enhance the performance. So SVD is to further improve the document classification systems precisely and efficiently. [Copyright &y& Elsevier]

Abstract: In this paper, we study the recommendation problem as formed by two tasks: (i) to filter useful/interesting items, (ii) to guide the user to good recommendations. The first task has been widely studied in the field of recommender systems. In fact, the most common characterization of these systems is based on the algorithms that select (filter) the items to be recommended (e.g. collaborative filtering, content-based, etc.). Through this paper, we will focus on the second task: the task of guiding the user. We claim that this task needs a closer attention. In this paper, we report an experiment to provide evidence for this fact. Actually, the experiment shows that machine learning algorithms commonly applied to the first task become useless when applied to the task of guiding. [Copyright &y& Elsevier]

Abstract: Over the last few decades pattern classification has been one of the most challenging area of research. In the present-age pattern classification problems, the support vector machines (SVMs) have been extensively adopted as machine learning tools. SVM achieves higher generalization performance, as it utilizes an induction principle called structural risk minimization (SRM) principle. The SRM principle seeks to minimize the upper bound of the generalization error consisting of the sum of the training error and a confidence interval. SVMs are basically designed for binary classification problems and employs supervised learning to find the optimal separating hyperplane between the two classes of data. The main objective of this paper is to introduce a most promising pattern recognition technique called cross-correlation aided SVM based classifier. The idea of using cross-correlation for feature extraction is relatively new in the domain of pattern recognition. In this paper, the proposed technique has been utilized for binary classification of EEG signals. The binary classifiers employ suitable features extracted from crosscorrelograms of EEG signals. These cross-correlation aided SVM classifiers have been employed for some benchmark EEG signals and the proposed method could achieve classification accuracy as high as 95.96% compared to a recently proposed method where the reported accuracy was 94.5%. [Copyright &y& Elsevier]

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method. [Copyright &y& Elsevier]

• Automatic Artificial Intelligence Algorithm Selection. • Automatic Hyperparameter Optimization. • Particle Swarm Optimization which can handle continuous and discrete values. • Particle Swarm Optimization with Simulated Annealing acceptance criteria. • Global Optimal Solution. Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way. [ABSTRACT FROM AUTHOR]

• Narrative review of machine learning methods used on end-stage kidney disease. • Techniques to predict survival in kidney transplantation and dialysis therapy. • Models used to analyze the incidence of infection or tacrolimus toxicity. • Algorithms used to analyze other dialysis variables to predict mortality. In the field of medicine, decision-making has traditionally been carried out based on the best available scientific information and the experience of specialists using data found in analog formats such as radiographies, medical reports, and handwritten notes, among others. In this sense, the Big Data phenomenon is changing the world of medicine since the technologies that have been developed have made available to researchers and clinicians enormous amounts of data in digital formats that can be used to complement or help in complex tasks such as mentioned decision making. A key element in this process is data analysis techniques, since without them it is not possible to exploit the information. Currently the most used techniques are based on algorithms in the area of artificial intelligence and more specifically machine learning. This paper focuses on a specific domain of medicine, renal replacement therapies for end-stage renal disease, where machine learning is beginning to be used as a complementary tool to predict or make decisions. This paper provides a narrative review of the main machine learning methods that are being used to conduct end-stage renal disease treatment analyses. [ABSTRACT FROM AUTHOR]

Over the years, many Artificial Intelligence (AI) approaches have dealt with the diagnosis problem and its application in complex environments such as medical domains. Model-Based Reasoning (MBR) is one of the approaches that traditionally have tried to solve this problem thanks to its capacity for modelling and reasoning. The consideration of the temporal dimension in these domains is a challenging topic in MBR, especially if temporal imprecision is taken into account. Unfortunately, despite there being many successful MBR systems, there are still two fundamental problems in their development at the aforementioned domains: (1) the degree of dependency between the model used and the domain; and (2) the reutilization of the systems when the domain changes. First this paper proposes a set of basic requirements for the design of Knowledge-Based Systems that will help to solve the problem of temporal diagnosis for environments of high conceptual complexity. From these principles and through a deep analysis of the various approaches present in AI we establish a generic framework that addresses both goals by integrating MBR and ontologies for domain knowledge representation in order to describe a intermediate model representation to facilitate the low dependency between the model and the application domain. Finally, this paper demonstrates the use of the framework by developing a diagnosis system within a real medical environment (Intensive Care Unit) with a step-by-step description of the process, from the architecture through to implementation. [Copyright &y& Elsevier]

Abstract: As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. Therefore, B2B ecommerce decision making will be a challenge for organizations in the foreseeable future. Some literature shows that important issues to reduce uncertainty in the development of long-term relationships among B2B commerce partners. In this sense, this paper proposes a new negotiation support system to incorporate causal relationships of negotiation terms in the process of B2B negotiation, on the basis of a cognitive map. The proposed a CAKES-NEGO (CAusal Knowledge-driven Expert System) suggests that causal relationships of negotiation terms could be explicitly represented by using the cognitive map as knowledge representation vehicle as well as inference engine. Cognitive maps can illustrate causal relationships among the factors describing a given object and/or problem, and it can also describe experts’ tacit knowledge about a certain object. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCM, a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain, is used for the causal knowledge acquisition. The objectives of this paper are to (1) suggest a fuzzy cognitive mapping based expert system that support decision process of decision makers and (2) apply it to the illustrative examples, which are B2B negotiation problems, to show the validity of our proposed system. In addition, statistical tests proved that the proposed negotiation mechanism could improve decision performance significantly in B2B negotiations. [Copyright &y& Elsevier]

Abstract: This paper presents methodologies to select equities based on soft-computing models which focus on applying fundamental analysis for equities screening. This paper compares the performance of three soft-computing models, namely multi-layer perceptrons (MLP), adaptive neuro-fuzzy inference systems (ANFIS) and general growing and pruning radial basis function (GGAP-RBF). It studies their computational time complexity; applies several benchmark matrices to compare their performance, such as generalize rate, recall rate, confusion matrices, and correlation to appreciation. This paper also suggests how equities can be picked systematically by using relative operating characteristics (ROC) curve. [Copyright &y& Elsevier]

Abstract: Financial engineering such as trading decision is an emerging research area and also has great commercial potentials. A successful stock buying/selling generally occurs near price trend turning point. Traditional technical analysis relies on some statistics (i.e. technical indicators) to predict turning point of the trend. However, these indicators can not guarantee the accuracy of prediction in chaotic domain. In this paper, we propose an intelligent financial trading system through a new approach: learn trading strategy by probabilistic model from high-level representation of time series–turning points and technical indicators. The main contributions of this paper are two-fold. First, we utilize high-level representation (turning point and technical indicators). High-level representation has several advantages such as insensitive to noise and intuitive to human being. However, it is rarely used in past research. Technical indicator is the knowledge from professional investors, which can generally characterize the market. Second, by combining high-level representation with probabilistic model, the randomness and uncertainty of chaotic system is further reduced. In this way, we achieve great results (comprehensive experiments on S&P500 components) in a chaotic domain in which the prediction is thought impossible in the past. [Copyright &y& Elsevier]

Abstract: Based on a scope of 10,120 articles on ANNs, this paper uses data mining including association rules and cluster analysis, to survey these ANNs papers through keyword classification and clustering of articles from 1995 to 2005, exploring the ANNs methodologies and application developments during that period. The four decision variables of keywords, author’s nationality, research category, and year of publication, are implemented for data mining with total of 110,080 data items. The research findings show that some specific ANNs methodologies and applications pattern can be extracted from the mining results, and these describe the ANNs development over this period. In addition, using more data mining approaches for analysis could provide different explanations for ANNs development. Finally, discussion and brief conclusion are presented. [Copyright &y& Elsevier]

Abstract: The paper describes JavaDON, an open-source expert systems shell based on the OBOA framework for developing intelligent systems. The central idea of the JavaDON project was to make an easy-to-use and easy-to-extend tool for building practical expert systems. Since JavaDON is rooted in a sound theoretical framework, it is well-suited for building even complex expert system applications, both stand-alone and Web-based ones. JavaDON knowledge representation scheme supports using multimedia elements along with traditional techniques, such as rules and frames. Another important feature of JavaDON is its capability of saving knowledge bases in XML format (in addition to the shell''s native format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. So far, JavaDON has been used to build several practical expert systems, as well as a practical knowledge engineering tool to support both introductory and advanced university courses on expert systems. The paper presents design details of JavaDON, explains its links with the underlying OBOA framework, and shows examples of using JavaDON in expert system development. It also discusses some of the current efforts in extending JavaDON. [Copyright &y& Elsevier]

Abstract: This paper introduces a method of classifying partial discharges of unknown origin. The innovative trend of using Artificial Neural Network (ANN) towards classification of Partial Discharge (PD) patterns is cogent and discernible. The Adaptive Resonance Theory (ART), a type of neural network which is suitable for PD pattern recognition is explained here. To ensure the suitability and reliability of chosen network for PD pattern recognition, the network is tested with the well known Iris plant database and alphabet character for recognition & classification. Further more the network is trained with various combinations of ϕ–q–n distributions of PD patterns and tested. It is shown that the ART 2 network is able to classify the PD patterns. The paper ends with analyzing the efficacy of multifarious features selected in the measurement space. Also the validation of input features is done using ‘Hold-One-Out’ method and partial set training technique [Copyright &y& Elsevier]

Abstract: Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary SVM output. For the first difficulty, we propose a way to alleviate or solve it through a novel informative sampling. For the latter two difficulties, we provide guidelines within SVM framework so that one can readily use the paper as a quick reference for SVM response modeling: use of different costs for different classes and use of distance to decision boundary, respectively. This paper also provides various evaluation measures for response models in terms of accuracies, lift chart analysis, and computational efficiency. [Copyright &y& Elsevier]

Abstract: Pattern recognition has a long history within electrical engineering but has recently become much more widespread as the automated capture of signal and images has been cheaper. Very many of the application of neural networks are to classification, and so are within the field of pattern recognition and classification. In this paper, we explore how probabilistic neural networks fit into the earlier framework of pattern recognition of partial discharge patterns since the PD patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. Also this paper describes a method for the automated recognition of PRPD patterns using a novel complex probabilistic neural network system for the actual classification task. The efficacy of composite neural network developed using probabilistic neural network is examined. [Copyright &y& Elsevier]

Abstract: This paper proposes an artificial neural network based element extraction method for automatic finite element mesh generation. A finite element mesh is a discretized representation of a geometric domain. A domain is discretized into elements, which may be triangles, quadrilaterals, tetrahedron, or hexahedron. The element extraction method repeatedly generates element (s) within the domain using some predefined ‘if-then’ rules until the whole domain is filled with required elements. This method has an advantage of creating meshes for domains with complex boundaries. However, the ‘if-then’ rules for element extraction are usually difficult to acquire, because these rules not only generate elements but also change the problem they are designed to solve. In this paper, a Back-Propagation (BP) neural network is used to represent the ‘if-then’ element extraction rules and to train the relationship behind these rules. The input for this network includes the coordinates of some boundary points while the output defines the parameters for extracting an element. In order to generate good quality element while keeping the updated problem still solvable, the design and definition of the neural network is more complex than those in the traditional classification problems. This paper discusses issues related to the design of the neural network for element extraction. Numerical experiments on quadrilateral mesh generation have shown that this method is effective. [Copyright &y& Elsevier]

This paper surveys expert systems (ES) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how ES methodologies and applications have developed during this period. Based on the scope of 166 articles from 78 academic journals (retrieved from five online database) of ES applications, this paper surveys and classifies ES methodologies using the following eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, object-oriented methodology, case-based reasoning, system architecture, intelligent agent systems, database methodology, modeling, and ontology together with their applications for different research and problem domains. Discussion is presented, indicating the followings future development directions for ES methodologies and applications: (1) ES methodologies are tending to develop towards expertise orientation and ES applications development is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science, and human behavior could implement ES as another kind of methodology. (3) The ability to continually change and obtain new understanding is the driving power of ES methodologies, and should be the ES application of future works. [Copyright &y& Elsevier]

This paper describes a prototype of a decision support system developed to assess patients with osteoporosis. The system was embedded into the clinical workflow environment. This system is being used in two clinics and one hospital center in Canada. Osteoporosis is a disorder with major physical and economical implications. Assessing and properly managing patients with osteoporosis is complex and requires high level of expertise. Expertise alone does not totally address the problem as it involves the attitudes and behavioral states of the general practitioner, the patient and the specialist. The system''s architecture, subcomponents and integrated workflow are described. The system was developed based on the experiences of a specialist with 50 patients. As of the end of 2003, the system was used for 45 patients. The reports generated were shown to general practitioners who were not able to distinguish them from an actual specialist report. In this paper we present one case and demonstrate the main differences between the report generated by the system and a report produced by the specialist. Experiences in using the system as it is integrated into the clinical workflow are discussed. Valuable insights into the integration and use of web-based decision support systems are highlighted. [Copyright &y& Elsevier]

Conjoint analysis is used to understand how consumers develop preferences for products or services, which encompass, as usual, multi-attributes and multi-attribute levels. Conjoint analysis has been one of the popular tools for multi-attribute decision-making problems on products and services for consumers over the last 30 years. It has also been used to market segmentation and optimal product positioning. In spite of its popularity and commercial success, a major weakness of conjoint analysis has been pointed such that respondents participating in conjoint experiment have to evaluate a number of hypothetical product profiles. To reduce the number of hypothetical products, this paper proposes a systematic method, called data fusion, and explores the usability of various data fusion techniques. The paper evaluates traditional data fusion (correlation-based), hierarchical Bayesian-based data fusion, and neural network-based data fusion. [Copyright &y& Elsevier]

AI virtual assistants are steadily expanding their way into multiple domains such as healthcare, hospitality, and telecommunication. However, such virtual assistants still suffer from a limited level of distinctive characteristics of domain-specific knowledge. In this paper, we improve the Named Entity Recognition (NER) ability by facilitating (i) the assistants' understanding of both general and domain-specific terms by building new datasets in the automotive domain and using adversarial training and (ii) NER's robustness against spacing errors using multi-task learning. We verify the proposed method in both Korean and English NER datasets and the experimental results demonstrate that the proposed NER model outperforms the baseline model in both general and automotive domains. The proposed method improves automotive NER performance by 1.85 points and 1.13 points in English and Korean, respectively. Furthermore, we validate proposed NER model on the in-house question answering system where queries are taken from real users. • We construct the automotive NER dataset in both Korean and English. • We propose ADMit that utilizes domain adversarial and multi-task learning for NER. • We show that ADMit achieves state-of-the-art in the automotive NER datasets. • ADMit improves the performance of automotive FAQ system for real user's queries. [ABSTRACT FROM AUTHOR]

Sentiment analysis using artificial intelligence is a great tool for analyzing human linguistic capabilities. The deep learning techniques combined with Natural Language Processing (NLP) has said to offer higher performance in analyzing the sentiments in implicit aspects present in high-quality training datasets. However, the different complexities associated with deep learning techniques such as high computational and space complexity, sensitivity to learning rate, high training time, etc make them hard to implement in real-time applications. However, very few researchers have used the randomization-based neural network for sentimental analysis which offers different benefits such as increased generalization capacity, improved accuracy, low training time, and universal approximator for non-linear functions. Motivated by this problem, this paper presents an Artificial Rabbits optimized Robust Random Vector Functional Link Network (RRVFLN) for improving sentiment analysis accuracy. Initially, the data is preprocessed and the word vectors are efficiently analyzed by the Fast text method by considering each token as an n-gram character. Artificial Rabbits Optimization (ARO) is utilized to RRVFLN model's input weights and to optimize the hidden layer biases. The ARO-optimized RRVFLN model classifies the outcomes obtained in the three datasets namely the News Headline Sentiment dataset, Sentiment140 dataset, and 4000 Short Story dataset into positive and negative. The simulation results obtained by comparing the proposed model with the various existing techniques display the effectiveness in terms of F1 score, precision, classification accuracy, error rate prediction, and kappa statistics. [ABSTRACT FROM AUTHOR]

Data-driven intelligent systems provide a possible solution to condition-based maintenance of wind turbines without experts' knowledge or mechanism models. However, the accuracy of fault diagnosis results is easily impaired by the data imbalance in real-world applications. In this paper, a novel method is proposed to address the mentioned problem. Specifically, spatial and temporal information of supervisory control and data acquisition (SCADA) data is extracted, and a contrastive learning strategy is developed to obtain the data representations correlated with the health conditions. Additionally, the bollards of the data distributions are determined in the representation space, and they are matched with the data centers of each health state. Under this circumstance, the unexpected effects of data imbalance on data representations are relieved, leading to effective decision boundaries regardless of the sample numbers. Then, a classifier is trained on the learned representations to recognize the faults in wind turbines. Compared with several baselines and state-of-the-art approaches, the impressive performance of the proposed method is demonstrated with simulated data and actual measurements from a utility-scale wind turbine. This research provides useful insights into the efficient development of wind energy with data-driven intelligent systems. [ABSTRACT FROM AUTHOR]

Dempster–Shafer Theory (DST) of belief function is a basic theory of artificial intelligence, which can represent the underlying knowledge more reasonably than Probability Theory (ProbT). Because of the computation complexity exploding exponentially with the increasing number of elements, the practical application scenarios of DST are limited. In this paper, we encode Basic Belief Assignments (BBA) into quantum superposition states and propose the implementation and operation methods of BBA on quantum circuits. We decrease the computation complexity of the Matrix Evolution on BBA (MEoB) on quantum circuits. Based on the MEoB, we realize the quantum belief functions' implementation, the similarity measurements of BBAs, evidence Combination Rules (CR), and probability transformation on quantum circuits. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) have obtained wide attention in recent years, which is relied on their attractive socio-economic benefits and potential to improve safety. One of the main characteristics of MASS is that it can use perception information to replace Officers on Watch (OOW) to make different navigation decisions through expert and intelligent systems, so as to make navigation more intelligent, reliable and safer, which is mainly based on the Maritime Autonomous Navigation Decision-making System (MANDS). However, the state-of-the-art study lacks a systematic description and research on MANDS, and most of them mainly focus on solving a single navigation decision-making task, which is not aligned with the meaning of autonomous navigation. Therefore, this study develops a novel MANDS and installs it on a 300TEU intelligent container ship to successfully carry out real ship trials of autonomous navigation. In this paper, the definition of autonomous navigation is first proposed, which mainly contains four different navigation tasks, namely "Keep route", "Route Optimization", "Collision Avoidance", "Return Back". A well-designed architecture for MANDS is presented which includes corresponding decision-making models and communication interfaces with external systems. Furthermore, in order to realize the switching and deployment between different navigation tasks in the process of autonomous navigation, a novel hierarchical decision-making mechanism is proposed and analyzed, and finally the MANDS is integrated with perception system and ship control system respectively. Before the real ship trial, this system is tested and verified in a simulation-based environment which is composed of the electronic chart platform and ship motion simulator, we also design a scenario library for the testing work. At last, real ship trials of autonomous navigation for MANDS are carried out, which can convincingly prove the performance of MANDS. This study proposes a relative complete solution for autonomous navigation, including system modelling, integration and real ship trial. These findings offer new insight into the research of ship autonomous navigation and promote the improvement of ship intelligence and informatization level. [ABSTRACT FROM AUTHOR]

This paper shows the development of a tool with which to solve the most critical aspect of the porthole die design problem using a predictive model based on machine learning (ML). The model relies on a large amount of geometrical data regarding successful porthole die designs, information on which was obtained thanks to a collaboration with a leading extrusion company. In all cases, the dies were made of H-13 hot work steel and the billet material was 6063 aluminium alloy. The predictive model was chosen from a series of probes with different algorithms belonging to various ML families, which were applied to the analysis of geometrical data corresponding to 596 ports from 88 first trial dies. Algorithms based on the generation of multiple decision trees together with the boosting technique obtained the most promising results, the best by far being the CatBoost algorithm. The explainability of this model is based on a post-hoc approach using the SHAP (SHapley Additive exPlanations) tool. The results obtained with this ML-based model are notably better than those of a previous model based on linear regression as regards both the R2 metric and the results obtained with the application examples. An additional practical advantage is its explainability, which is a great help when deciding the best way in which to adjust an initial design to the predictive model. This ML-based model is, therefore, an optimal means to integrate the experience and know-how accumulated through many designs over time in order to apply it to new designs. It will also provide an aid in generating the starting point for the design of high-difficulty dies, in order to minimise the number of FEM (finite element method) simulation/correction iterations required until an optimal solution is achieved. It is not aimed to eliminate FEM simulation from the design tasks, but rather to help improve and accelerate the whole process of designing porthole dies. The work presented herein addresses a validation model for a very common porthole die typology: four cavity and four port per cavity dies for 6xxx series aluminium alloys. However, a wide range of research regarding the generalisation of this model or its extension to other porthole die typologies must still be carried out. [ABSTRACT FROM AUTHOR]

Reducing emissions and energy consumption of autonomous vehicles is critical in the modern era. This paper presents an intelligent energy management system based on Reinforcement Learning (RL) for conventional autonomous vehicles. Furthermore, in order to improve the efficiency, a new exploration strategy is proposed to replace the traditional decayed ε-greedy strategy in the Q-learning algorithm associated with RL. Unlike traditional Q-learning algorithms, the proposed self-adaptive Q-learning (SAQ-learning) can be applied in real-time. The learning capability of the controllers can help the vehicle deal with unknown situations in real-time. Numerical simulations show that compared to other controllers, Q-learning and SAQ-learning controllers can generate the desired engine torque based on the vehicle road power demand and control the air/fuel ratio by changing the throttle angle efficiently in real-time. Also, the proposed real-time SAQ-learning is shown to improve the operational time by 23% compared to standard Q-learning. Our simulations reveal the effectiveness of the proposed control system compared to other methods, namely dynamic programming and fuzzy logic methods. [ABSTRACT FROM AUTHOR]

In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0. 4 % and a mean absolute error in the order of 1 0 − 4 , respectively. [ABSTRACT FROM AUTHOR]

• A review on Machine and Deep Learning methods applied to industrial problems. • Papers from 2000 are analyzed and hierarchically catalogued. • Application domains, trend, and evolutions are investigated. Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management. [ABSTRACT FROM AUTHOR]

• Systematic literature review based on 136 papers on artificial intelligence applications in supply chain. • The distribution of the different AI techniques used across the SCOR areas is presented. • A classification of the research articles is proposed based on four critical structural dimensions. • Future research perspectives and areas needing further investigations are suggested. Today's supply chains are very different from those of just a few years ago, and they continue to evolve within an extremely competitive economy. Dynamic supply chain processes require a technology that can cope with their increasing complexity. In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged, yet very few studies have addressed the applications of AI in supply chain processes. Machine learning, natural language processing, and robotics are all potential enablers of supply chain transformation. Aware of the potential advantages of AI implementation in supply chains and of the paucity of work done regarding it, we explore what researchers have done so far with respect to AI and what needs further exploration. We reviewed 136 research papers published between 1996 and 2020 from the Scopus database and provided a classification of the research material according to four critical structural dimensions (level of analytics, AI algorithms or techniques, sector or industry of application, and supply chain processes). This study is the first attempt to study the AI applications in SC from a process perspective and provides a decisional framework for adequate use of AI techniques in the different SC processes. [ABSTRACT FROM AUTHOR]

• The Multi-Robot systems have high applicability in a wide variety of works. • The autonomous and coordinate navigation makes the MRS a tool for emergencies. • Path planning methods play a crucial role in autonomous navigation algorithms. • The paper analyses path planning oriented to MRS, comparing the most critical methods. • The paper review the applications that MRS are a crucial tool, e.g. search and rescue. In the multiple fields covered by Artificial Intelligence (AI), path planning is undoubtedly one of the issues that cover a wide range of research lines. To be able to find an optimal solution, which allows one or several vehicles to establish a safe and effective way to reach a final state from an initial state, is a challenge that continues to be studied today. The increasingly widespread use of autonomous vehicles, both aerial and ground-based, make path planning an essential aspect for incorporating these systems into an endless number of applications. Besides, in recent years, the use of Multi-Robot Systems (MRS) has spread, consisting of both Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), gaining versatility and robustness in their operation. The possibility of using heterogeneous robotic teams allows tackling, autonomously, and simultaneously, a wide range of tasks with different characteristics in the same environment. For this purpose, path planning becomes a crucial aspect and, for this reason, this work aims to offer a general vision of trajectory planning, to establish a comparison between the methods and algorithms present in the literature for the resolution of this problem within MRS, and finally, to show the applicability of these methods in different areas, together with the importance of these methods for achieving autonomous and safe navigation of different types of vehicles. [ABSTRACT FROM AUTHOR]

• Reinforcement learning (RL) formalization of the algorithmic trading problem. • Novel trading strategy based on deep reinforcement learning (DRL), denominated TDQN. • Rigorous performance assessment methodology for algorithmic trading. • TDQN algorithm delivers promising results surpassing benchmark strategies. This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in the stock market. It proposes a novel DRL trading policy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new DRL approach is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN algorithm. [ABSTRACT FROM AUTHOR]

• Features of leaf image used in image processing. • Different combination of features which make identification of plant, efficient. • Review and comparison of different feature extraction techniques. • Review and comparison of different classification techniques. This paper provides a comprehensive survey of various techniques used in computer vision for the automatic identification of plants with the help of leaf images. The extracted information is used by the botanists to identify different species of plants and use their medicinal or other properties. With the upsurge of human interference, the number of plants seems to decrease, but their automatic identification can lead to conservation. The Leaf images may be acquired by a phone camera or a digital camera mounted on a tripod stand. The leaves may be covered by dirt, shadows, or hidden under other leaves. Real-life applications based on the automatic identification of plants can successfully identify even similar-looking plant leaves in all environmental conditions. This paper provides a state-of-the-art review of different leaf extraction techniques which are categorized according to the features of leaf used and their pros and cons. We also discuss and compare the various classifiers used in the identification process. The conclusion of the paper also provides different areas of improvement and future work. [ABSTRACT FROM AUTHOR]

• Incremental AI tool for Static Warning Identification. • A case study to help developers Ignoring falsely reported static warnings. • Self-adaptive active learning process. • Reducing human inspection cost in software mining on early life cycle. Knowledge-based systems reason over some knowledge base. Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses active learning methods for acquiring knowledge for "static code warnings". Static code analysis is a widely-used method for detecting bugs and security vulnerabilities in software systems. As software becomes more complex, analysis tools also report lists of increasingly complex warnings that developers need to address on a daily basis. Such static code analysis tools are usually over-cautious; i.e. they often offer many warnings about spurious issues. Previous research work shows that about 35% to 91 % warnings reported as bugs by SA tools are actually unactionable (i.e., warnings that would not be acted on by developers because they are falsely suggested as bugs). Experienced developers know which errors are important and which can be safely ignored. How can we capture that experience? This paper reports on an incremental AI tool that watches humans reading false alarm reports. Using an incremental support vector machine mechanism, this AI tool can quickly learn to distinguish spurious false alarms from more serious matters that deserve further attention. In this work, nine open-source projects are employed to evaluate our proposed model on the features extracted by previous researchers and identify the actionable warnings in a priority order given by our algorithm. We observe that our model can identify over 90% of actionable warnings when our methods tell humans to ignore 70 to 80% of the warnings. [ABSTRACT FROM AUTHOR]

The growing number of e-commerce orders is leading to increased risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoring (CS) is employed to identify customers’ default probability. CS has been widely studied, and many computational methods have been proposed. The primary aim of this work is to develop a CS model to replace the pre-risk check of the e-commerce risk management system Risk Solution Services (RSS), which is currently one of the most used systems to estimate customers’ default probability. The pre-risk check uses data from the order process and includes exclusion rules and a generic CS model. The new model is supposed to replace the whole pre-risk check and has to work both in isolation and in integration with the RSS main risk check. An application of genetic programming (GP) to CS is presented in this paper. The model was developed on a real-world dataset provided by a well-known German financial solutions company. The dataset contains order requests processed by RSS. The results show that GP outperforms the generic CS model of the pre-risk check in both classification accuracy and profit. GP achieved competitive classificatory accuracy with several state-of-the-art machine learning methods, such as logistic regression, support vector machines and boosted trees. Furthermore, the GP model can be used in combination with the RSS main risk check to create a model with even higher discriminatory power. [ABSTRACT FROM AUTHOR]

Traditional topic modeling has been widely studied and popularly employed in expert systems and information systems. However, traditional topic models cannot discover structural relations among topics, thus losing the chance to explore the data more deeply. Hierarchical topic modeling has the capability of learning topics, as well as discovering the hierarchical topic structure from text data. But purely unsupervised models tend to generate weak topic hierarchies. To solve this problem, we propose a novel knowledge-based hierarchical topic model (KHTM), which can incorporate prior knowledge into topic hierarchy building. A key novelty of this model is that it can mine prior knowledge automatically from the topic hierarchies of multiple domains corpora. In this paper, the knowledge is represented as the word pairs which satisfy the requirement of frequent co-occurrence, and knowledge is organized in form of hierarchical structure. We also propose an iterative learning algorithm. For evaluation, we crawled two new multi-domain datasets and conducted comprehensive experiments. The experimental results show that our algorithm and model can generate more coherent topics, and more reasonable hierarchical structure. [ABSTRACT FROM AUTHOR]

In this paper, a new strategy for dealing with time series exhibiting a seasonal pattern is proposed. The strategy is applied in the context of time series forecasting using k NN regression. The key idea is to forecast every different season using a different specialized k NN learner. Each learner is specialized because its training set only contains examples whose targets belong to the season that is able to forecast. This way, the forecast of a specialized k NN learner is an aggregation of target values of the same season, reducing the likelihood of misleading forecasts. Although the strategy is applied to k NN, we think that other computational intelligence approaches could take advantage of it. [ABSTRACT FROM AUTHOR]

Predictive analytics has become an important topic in expert and intelligent systems, with broad applications across various engineering and business domains, such as the prediction of exchange rate in finance, weather and demand for energy using mixture of experts. However, selection of the number of experts and assignment of the input to individual experts remain elusive, especially for highly nonlinear and nonstationary systems. This paper presents a novel mixture of experts, namely, nonparametric multi-scale Gaussian process (MGP) experts to predict the dynamic evolution of such complex systems. Concretely, intrinsic time-scale decomposition is first used to iteratively decompose the time series generated from such complex systems into a series of proper rotation components and a baseline trend component. Those components delineate the true time-frequency-energy patterns of the complex systems at different granularity. A Gaussian process (GP) expert is then applied on each component to predict the system evolution at each scale. MGP circumvent the tedious selection and assignment problems via the nonparametric ITD. Summation of those individual forecasts represents the overall evolution of the original time series. Case studies using synthetic and real-world data elucidated that the proposed MGP model significantly outperforms conventional autoregressive models, composite GP model, and support vector regression in terms of prediction accuracy, and it is particularly effective for multi-step forecasting. [ABSTRACT FROM AUTHOR]

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE ( S mart A daptation through C ontextual RE quirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains. [ABSTRACT FROM AUTHOR]

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics. [ABSTRACT FROM AUTHOR]

Case-based Reasoning (CBR) has been extensively used as its capability to reuse previous solvable problems in recommending new solution through its adaptation strategy. In our work, CBR is chosen as the strategy to identify the leader of specific domain within the online community based on the profile that was developed through one's social participation and contribution as well as feedbacks from the community, called CBRIKL (CBR for Identifying Knowledge Leader). CBRIKL continuously builds leader profiles based on the identified knowledge domains and problems are assigned to them based on their expertise. The novelty introduced in the paper is on building a user profiling based on mixed sources in identifying leader and measuring and comparing strategy on expertise skill set in locating new knowledge leader. The methods and approaches that are developed are made to be usable to other problem domains [ABSTRACT FROM AUTHOR]

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings. [ABSTRACT FROM AUTHOR]

Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants’ average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems. [ABSTRACT FROM AUTHOR]

In recent years, credit scoring has become an efficient tool that allows financial institutions to differentiate their potential default borrowers. Accordingly, researchers have developed a myriad of approaches, including statistical and artificial intelligence techniques, to fulfill the task of credit scoring. Recent studies have shown that ensemble methods, which combine multiple algorithms that process different hypotheses to form a new hypothesis, generally outperform the other credit scoring approaches. In this paper, we propose a novel heterogeneous ensemble credit model that integrates the bagging algorithm with the stacking method. The proposed model differs from the extant ensemble credit models in three aspects, namely, pool generation, selection of base learners, and trainable fuser. Four popular evaluation metrics, including accuracy, area under the curve (AUC), AUC-H measure, and Brier score, are employed to measure the performance of alternative models. To confirm the efficiency of the proposed bstacking approach, a wide range of models, including individual classifiers, homogeneous ensemble model, and heterogeneous ensemble model, are introduced as benchmarks. We also provided a discussion on the accurate yet complex credit scoring model (e.g., bstacking) from a regulatory perspective. [ABSTRACT FROM AUTHOR]