Bankruptcy forecasting using case-based reasoning: The CRePERIE approach

Bankruptcy prediction is a very important research trend: although statistical methods are mainly used in literature, techniques based on Artificial Intelligence are interesting from many points of view. Among them, Case-Based Reasoning (CBR) could be useful to cluster enterprises according to opportune similarity metrics as well as suggest proper actions to take for avoiding bankruptcy in border-line situations. In this paper, we present a new and still under development CBR approach to this problem, that seems to return better results than previous attempts. The approach is based on different kinds of similarity metrics and is focused on the implementation of innovative revise algorithms. In particular, the paper shows how the revise step is crucial to improve the accuracy of the bankruptcy prediction model.     

Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning

In this paper, we present a medical diagnosis decision support model for gastrointestinal cancer. It should be used by general practitioners whenever there is a suspicion that a patient has this type of cancer. To build our model, we used Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR). We used real patient data as inputs to our model. We applied RBR to improve the CBR retrieve process. The model’s output presents the probability of the patient having a specific cancer. In order to adjust the attributes weights, we collected data from a general practitioner. To validate our model, we used K-fold cross validation and the paired t-test. The results showed that, with our approach, the accuracy of the diagnosis increased by 22.92% when compared to a CBR approach not using RBR in case retrieval. Furthermore, we evaluated our approach with an online questionnaire and semi-structured interviews. Even though, given the number of respondents, we cannot generalize our conclusions, the results indicate that our approach would be useful for general practitioners.     

Business failure prediction using hybrid2 case-based reasoning (H2CBR)

We have investigated business failure prediction (BFP) by a combination of decision-aid, statistical, and artificial intelligence techniques. The goal is to construct a hybrid forecasting method for BFP by combining various outranking preference functions with case-based reasoning (CBR), whose heart is the k-nearest neighbor (k-NN) algorithm, and to empirically test the predictive performance of its modules. The hybrid² CBR (H²CBR) forecasting method was constructed by integrating six hybrid CBR modules. These hybrid CBR modules were built up by combining and modifying six outranking preference functions with the algorithm of k-NN inside CBR. A trial-and-error iterative process was employed to identify the optimal hybrid CBR module of the H²CBR forecasting system. The prediction of the optimal module is the final output of the H²CBR forecasting method. We have compared the predictive performance of the six hybrid CBR modules in BFP of Chinese listed companies. In this empirical study, the classical CBR algorithm based on the Euclidean metric, and the two classical statistical methods of logistic regression (Logit) and multivariate discriminant analysis (MDA) were used as baseline models for comparison. Feature subsets were selected with the stepwise method of MDA. The predictive performance of the H²CBR system is promising; the most preferred hybrid CBR for short-term BFP of Chinese listed companies is based on the ranking-order preference function.     

A personalized counseling system using case-based reasoning with neural symbolic feature weighting (CANSY)

In this article, we introduce a personalized counseling system based on context mining. As a technique for context mining, we have developed an algorithm called CANSY. It adopts trained neural networks for feature weighting and a value difference metric in order to measure distances between all possible values of symbolic features. CANSY plays a core role in classifying and presenting most similar cases from a case base. Experimental results show that CANSY along with a rule base can provide personalized information with a relatively high level of accuracy, and it is capable of recommending appropriate products or services. An erratum to this article can be found at     

Supply chain planning (SCP) — Concepts and case studies

Supply Chain Planning (SCP) Systems are becoming the primary focus in today's materials management environment. This paper reviews the fundamentals of SCP and discusses the future directions of these types of systems in the 1990's, with an emphasis on solving engineering logistic problems with computer solutions.     

The role of context in case-based legal reasoning: teleological, temporal, and procedural

Computational models of relevance in case-based legal reasoning have traditionallybeen based on algorithms for comparing the facts and substantive legal issues of aprior case to those of a new case. In this paper we argue that robust models ofcase-based legal reasoning must also consider the broader social and jurisprudentialcontext in which legal precedents are decided. We analyze three aspects of legalcontext: the teleological relations that connect legal precedents to the socialvalues and policies they serve, the temporal relations between prior andsubsequent cases in a legal domain, and the procedural posture of legal cases,which defines the scope of their precedential relevance. Using real examples drawnfrom appellate courts of New York and Massachusetts, we show with the courts' ownarguments that the doctrine of stare decisis (i.e., similar facts should lead to similar results) is subject to contextual constraints and influences. For each of the three aspects of legal context, we outline an expanded computational framework for case-based legal reasoning that encompasses the reasoning of the examples, and provides a foundation for generating a more robust set of legal arguments.     

Integrating case-based reasoning and hypermedia documentation: an application for the diagnosis of a welding robot at Odense steel shipyard

Reliable and effective maintenance support is a vital consideration for the management within today’s manufacturing environment. This paper discusses the development of a maintenance system for the world’s largest robot welding facility. The development system combines a case-based reasoning approach for diagnosis with context information, as electronic on-line manuals, linked using open hypermedia technology. The work discussed in this paper delivers not only a maintenance system for the robot stations under consideration, but also a design framework for developing maintenance systems for other similar applications.     

Job-shop scheduling using automated reasoning: A case study of the car-sequencing problem

The job-shop scheduling problem is known to be NP-complete. The version of interest in this paper concerns an assembly line designed to produce various cars, each of which requires a (possibly different) set of options. The combinatorics of the problem preclude seeking a maximal solution. Nevertheless, because of the underlying economic considerations, an approach that yields a good sequence of cars, given the specific required options for each, would be most valuable. In this paper, we focus on an environment for seeking, studying, and evaluating approaches for yielding good sequences. The environment we discuss relies on the automated reasoning program ITP. Automated reasoning programs of this type offer a wide variety of ways to reason, strategies for controlling the reasoning, and auxiliary procedures that contribute to the effective study of problems of this kind. We view the study presented in this paper as a prototype of what can be accomplished with the assistance of an automated reasoning program.     

NIASHPT: a novel intelligent agent-based strategy using hello packet table (HPT) function for trust Internet of Things

The Journal of Supercomputing - Internet of Things (IoTs) is a new concept in computer science that connects the objects with limited resources to unreliable internet through different...     

Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble

In multivariate statistical process control (MSPC), most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistic. But these charts do not relieve the need for pinpointing source(s) of the out-of-control signals. Neural networks (NNs) have excellent noise tolerance and high pattern identification capability in real time, which have been applied successfully in MSPC. This study proposed a selective NN ensemble approach DPSOEN, where several selected NNs are jointly used to classify source(s) of out-of-control signals in multivariate processes. The immediate location of the abnormal source(s) can greatly narrow down the set of possible assignable causes, facilitating rapid analysis and corrective action by quality operators. The performance of DPSOEN is analyzed in multivariate processes. It shows improved generalization performance that outperforms those of single NNs and Ensemble All approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to identify abnormal source(s) in bivariate statistical process control (SPC) with potential application for MSPC in general.     

Modal Change Logic (MCL): Specifying the reasoning of knowledge-based systems

We investigate the formal specification of the reasoning process of knowledge-based systems in this paper. We analyze the corresponding parts of the KADS specification languages KARL and (ML)² and deduce some general requirements. The essense of these languages is that they integrate a declarative specification of inferences with control information. The languages differ in the way they achieve this integration and each of them has shortcomings. We propose a unifying semantical framework that integrates the core of the different solutions and overcomes their problems. We define a semantics and axiomatization with the Modal Change Logic (MCL). The main contribution of the paper is not to introduce yet another specification language. Instead we aim at four goals: (1) defining a framework for describing the dynamic reasoning behavior of knowledge-based systems which integrates existing approaches; (2) defining a semantics for the specification of the dynamic reasoning behavior of a knowledge-based system within the states as algebras setting that overcomes several shortcomings and ad hoc solutions of existing approaches; and (3) providing an axiomatization that enables the development of mechanized proof support. (4) Through conceptual and semantical clarity, we investigate the relationships to similar work in software engineering and database engineering opening possibilities for further cross-fertilization of these fields.     

Variable structure model-reference adaptive control (VS-MRAC) usingonly input and output measurements: the general case

A globally asymptotically stable VS-MRAC for the general case of plants with arbitrary relative degree is presented. It is shown that the VS approach leads to general adaptive controllers with outstanding performance. The controllers use only I/O measurements and are free of differentiators. The results indicate that several of the usual drawbacks of conventional MRACs (e.g. unsatisfactory transient behavior, lack of robustness, difficulties in designing controllers for time-varying or nonlinear plants) can be circumvented     

3D modeling using geognostic data: The case of the low valley of Foglia river (Italy)

In specific geological contexts, such as alluvial environments, the lithology is highly heterogeneous and laterally variable. As a result, a large number of lithological data are collected (generally random and under sampled) for stratigraphic reconstruction.The integrated management of geo-lithologic data causes major methodological problems, because data obtained from geognostic investigations have to be normalized and correlated. Data are often affected by their degree of precision, a detail depending on the investigation technique and on the geologist's interpretation.This paper presents a methodological approach to organizing, integrating, and interpolating subsoil, mechanical (boreholes, penetration tests), and geophysical (seismic profiles, resistivity sounding) investigations for the construction of a three-dimensional geological model supported by geographical information systems and geostatistics.A practical application concerning the lower Foglia river alluvial deposits (Northern Marche, Italy) was carried out to investigate the geometry of these sedimentary bodies.This integrated approach offers many advantages for the understanding and in-depth visualization of the geometry of the deposits. Moreover, it allows for the acquisition of qualitative and quantitative information about hydrogeological settings, which is useful for water resources exploitation and protection.The achieved goals are the design of a data management workflow for geo-lithological data, the construction of a three-dimensional geological model of the lower Foglia river alluvial plain, a comparison between the geological sections obtained from the model and those made in a traditional manual fashion, and, in addition, new stratigraphic and hydrogeological considerations about the study area.     

Supply Hub in Industrial Park (SHIP): The value of freight consolidation

Industrial parks, characterized as a cluster of enterprises situated in one location to share common resources, have played an indispensible role in boosting regional economic and industrial development. However, further development has been impeded by the shortage of land space, especially for the construction of warehouses. This paper proposes the concept of Supply Hub in Industrial Park (SHIP) as a promising approach addressing this challenge. SHIP is defined as a public provider of warehousing and logistics services for manufacturing enterprises located within an industrial park. The research reported in this paper focuses on evaluating the value of freight consolidation, one of the typical benefits of applying the SHIP approach. Two mathematical models of the supply chain in a typical industrial park are formulated: with and without SHIP. Genetic Algorithm (GA) is applied for solving the two models, and sensitivity experiments are conducted for comparative analysis between the two scenarios. The computational results show that through consolidating shipments, SHIP brings benefits to the whole industrial park. Total cost savings resulted from the application of SHIP would increase with the size of the supply chain, the vehicle capacity, and the rates of fixed transportation costs and holding costs of finished products at manufacturers.     

Validating a neural network application: The case of financial diagnosis

It has been argued that neural network applications should be benchmarked using several data sets of realistic and real problems, and competing algorithms (Prechelt, 1995). However, if applying a neural network model to a particular real problem is in focus, validation should be considered as a suitability evaluation in which several bases of evaluation are combined in a composite judgment. In this paper, five bases of such evaluation are introduced and applied to the validation of a neural network model of financial diagnosis.     

On the structure at infinity of linear block-decouplable systems: The general case

The aim of this note is to show that a linear system (C, A, B) is block-decouplable by means of static state feedback laws (F, G) withGinvertible if and only if the infinite structure of (C, A, B) equals the union of the infinite structures of the subsystems (C_{i}, A, B) extracted from the given output partition. This result generalizes the ones recently obtained for Morgan's problem [6] and for the particular cases of right-invertible [7] or left-invertibie [8] systems.     

A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: A case study

This paper considers simultaneous optimization of strategic design and distribution decisions for three-echelon supply chain architecture consisting of following three players; suppliers, production plants, and distribution centers (DCs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to distribution centers, so as to minimize the combined facility location, production, inventory, and shipment costs and maximize fill rate. To achieve this, three-echelon network model is mathematically represented and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization algorithm (MOHPSO). This heuristic incorporates non-dominated sorting (NDS) procedure to achieve bi-objective optimization of two conflicting objectives. The applicability of proposed optimization algorithm was then tested by applying it to standard test problems found in literature. On achieving comparable results, the approach was applied to actual data of a pump manufacturing industry. The results show that the proposed solution approach performs efficiently.