Abstract: Current research in automatic train operation concentrates on optimizing an energy-efficient speed profile and designing control algorithms to track the speed profile, which may reduce the comfort of passengers and impair the intelligence of train operation. Different from previous studies, this paper presents two intelligent train operation (ITO) algorithms without using precise train model information and offline optimized speed profiles. The first algorithm, i.e., ITOe, is based on an expert system that contains expert rules and a heuristic expert inference method. Then, in order to minimize the energy consumption of train operation online, an ITOr algorithm based on reinforcement learning (RL) is developed via designing an RL policy, reward, and value function. In addition, from the field data in the Yizhuang Line of the Beijing Subway, we choose the manual driving data with the best performance as ITOm. Finally, we present some numerical examples to test the ITO algorithms on the simulation platform established with actual data. The results indicate that, compared with ITOm, both ITOe and ITOr can improve punctuality and reduce energy consumption on the basis of ensuring passenger comfort. Moreover, ITOr can save about 10% energy consumption more than ITOe. In addition, ITOr is capable of adjusting the trip time dynamically, even in the case of accidents.

Abstract: Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness” of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance.

Abstract: The construction project schedule is one of the most important tools for project managers in the Architecture, Engineering, and Construction (AEC) industry that makes them able to track and manage the time, cost, and quality (a.k.a. Project Management Triangle) of projects. Developing project schedules is almost always troublesome, since it is heavily dependent on project planners' knowledge of work packages, on-the-job-experience, planning capability and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a 3-dimensional representation of a project (a.k.a. Building Information Model or BIM) has recently drawn attention to the construction industry. In this paper, the authors demonstrate a novel approach of retrieving enough information from the BIM of a project and then develop construction sequencing for the installation of the project elements. For this reason a computer application is developed that can automatically derive a structurally (statically) stable construction sequence, using the concept of the Genetic Algorithm (GA). The term ''structurally stable sequencing'' in this article refers to the sequencing order of erection in which the structure remains statically stable locally and globally during the entire installation process. To validate the proposed methodology, the authors designed 21 different experiments and used the proposed method for generating stable construction schedules, which all were successfully accomplished. Therefore, this methodology proposes a novel approach of construction project application of the GA, as an Expert System tool.

Abstract: In this paper we propose a classifier capable of recognizing human body static poses and body gestures in real time. The method is called the gesture description language (GDL). The proposed methodology is intuitive, easily thought and reusable for any kind of body gestures. The very heart of our approach is an automated reasoning module. It performs forward chaining reasoning (like a classic expert system) with its inference engine every time new portion of data arrives from the feature extraction library. All rules of the knowledge base are organized in GDL scripts having the form of text files that are parsed with a LALR-1 grammar. The main novelty of this paper is a complete description of our GDL script language, its validation on a large dataset (1,600 recorded movement sequences) and the presentation of its possible application. The recognition rate for examined gestures is within the range of 80.5---98.5 %. We have also implemented an application that uses our method: it is a three-dimensional desktop for visualizing 3D medical datasets that is controlled by gestures recognized by the GDL module.

Abstract: Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer's measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers.

Abstract: Leaf spots can be indicative of crop diseases, where leaf batches (spots) are usually examined and subjected to expert opinion. In our proposed system, we are going to develop an integrated image processing system to help automated inspection of these leaf batches and helps identify the disease type. Conventional Expert systems mainly those which used to diagnose the disease in agriculture domain depends only on textual input. Usually abnormalities for a given crop are manifested as symptoms on various plant parts. To enable an expert system to produce correct results, end user must be capable of mapping what they see in a form of abnormal symptoms to answer to questions asked by that expert system. This mapping may be inconsistent if a full understanding of the abnormalities does not exist. The proposed system consists of four stages; the first is the enhancement, which includes HIS transformation, histogram analysis, and intensity adjustment. The second stage is segmentation, which includes adaptation of fuzzy c-means algorithm. Feature extraction is the third stage, which deals with three features, namely color size and shape of spot. The fourth stage is classification, which comprises back propagation based neural networks.

Abstract: This paper offers a new approach that applies the signatures to expert systems modelling. Signatures and their operators, viewed as a generalization of fuzzy signatures, represent a convenient framework for the symbolic representation of data. The models are derived by a three-step algorithm that maps the signatures onto expert systems. An expert systems modelling algorithm is given. Our algorithm has two inputs, the knowledge base, i.e., the rules, and the data base, i.e., the facts, and it constructs the signatures which represent models of expert systems. The algorithm is advantageous because of its systematic and general formulation allowing for the modelling of uncertain expert systems. The theoretical results are exemplified by a case study which produces models of a Bayesian expert system with mechatronics applications.

Abstract: The subject matter of this paper refers to the improvement of the acoustic emission (AE) method when used for detection, measurement and location of partial discharges (PDs) in oil insulation systems of power appliances. In particular, presents the basic assumptions and describe the individual elements of an expert diagnostic system, which uses an acoustic method to asset the state of the measured power transformer insulation, performed during their normal work in industrial conditions. The system will consist of four basic modules, i.e. measuring system, processing-analyzing system, knowledge base and classifier. These modules are characterized successively in Point 2 of the paper. Special attention is given to the description of the group of multiparametric descriptors characterizing the AE signals in the time, frequency and time-frequency domains, to descriptive statistics indexes and correlative parameters. Accordant descriptors make it possible, at strictly defined metrological conditions, to recognize basic PD forms that may occur in paper-oil insulation. In this way a catalogued knowledge base containing the socalled 'fingerprints' was created for basic types of high-voltage defects of insulation system.

Abstract: The development of an effective mechanism to detect suspicious transactions is a critical problem for financial institutions in their endeavor to prevent anti-money laundering activities. This research addresses this problem by proposing an ontology based expert-system for suspicious transaction detection. The ontology consists of domain knowledge and a set of (SWRL) rules that together constitute an expert system. The native reasoning support in ontology is used to deduce new knowledge from the predefined rules about suspicious transactions. The presented expert-system has been tested on a real data set of more than 8 million transactions of a commercial bank. The novelty of the approach lies in the use of ontology driven technique that not only minimizes the data modeling cost but also makes the expert-system extendable and reusable for different applications.

Abstract: In the past decades, medical data mining has become a popular data mining subject. Researchers have proposed several tools and various methodologies for developing effective medical expert systems. Diagnosing heart diseases is one of the important topics and many researchers have tried to develop intelligent medical expert systems to help the physicians. In this paper, we propose the use of PSO algorithm with a boosting approach to extract rules for recognizing the presence or absence of coronary artery disease in a patient. The weight of training examples that are classified properly by the new rules is reduced by a boosting mechanism. Therefore, in the next rule generation cycle, the focus is on those fuzzy rules that account for the currently misclassified or uncovered instances. We have used coronary artery disease data sets taken from University of California Irvine, (UCI), to evaluate our new classification approach. Results show that the proposed method can detect the coronary artery disease with an acceptable accuracy. Also, the discovered rules have significant interpretability as well.

Abstract: Search for a reliable and efficient insulation diagnostic tool has always been the interest of power utilities. Today a large number of methods are available that can be used for insulation condition monitoring. These methods include both traditional and newer techniques. However due to complex aging process of oil paper insulation under the influence of different types of stresses, insulation condition assessment is generally performed by experts after carefully evaluating different measurement data. Furthermore, measurement data are influenced by various factors (like conductive aging byproducts, furanic compounds, paper and oil-moisture) in addition to measurement error (if any). This makes prediction of insulation condition based on single type of measurement rather difficult. This paper presents an Expert System designed to perform insulation diagnosis. The Expert System considers measurement data obtained using both traditional and newer techniques in order to come to a definitive conclusion. The Expert System extracts insulation condition sensitive information from data obtained using different techniques and then uses these to devise an optimized insulation model. This optimized model is used to predict paper-moisture content and other insulation condition sensitive parameters. Since these values are predicted using optimized model, they are not dependent on a single type of measurement and hence are less likely to be affected by error of any specific measurement. The performance of the developed Expert System is first tested on a laboratory sample and then on several real life power transformers belonging to NTPC Ltd.

Abstract: Purpose Managing processed food products’ safety and recall is a challenge for industry and governments. Contaminated food items can create a significant public health hazard with potential for acute and chronic food borne illnesses. This industry study examines the challenges companies face while managing a processed food recall situation and devise a responsive and reliable knowledge management framework for product safety and recall supply chain for the focal global manufacturing and distribution enterprise. Method Drawing upon the knowledge management and product safety and recall literature and reliability engineering theory, this study uses a holistic single case based approach to develop a knowledge management framework with Failure Mode Effects and Criticality Analysis (FMECA) decision model. This knowledge management decision framework facilitates analysis of the root causes for each potential major recall issue and assesses the reliability of the product safety and recall supply chain system and its critical components. Results The main reasons highlighted for a recall and associated failure modes in a knowledge management framework are to devise appropriate deployment of resources, technology and procedures to recall supply chain. This study underscores specific information described by managers of a global processed food manufacturer and their perspectives about the product safety and recall process, and its complexities. Full scale implementation of product safety and recall supply chain in the proposed knowledge management framework after the current pilot study will be carried out eventually through expert systems. This operational system when fully implemented will capture the essence of decision making environments comprising goals and objectives, courses of action, resources, constraints, technology and procedures. Implications The study recognizes the significance of communication, integration, failsafe knowledge management process design framework, leveraging technology such as Radio Frequency Identification (RFID) within all levels of supply chain for product traceability and the proactive steps to help companies successfully manage a recall process and also reestablish trust among the consumers. The proposed knowledge management framework can also preempt product recall by acting as an early warning system. A formal knowledge management framework will enable a company’s knowledge be cumulative for product safety and recall and serve as an important integrating and coordinating role for the organization.

Abstract: Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then’ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.

Abstract: Academic advising of students is an expert task that requires a lot of time, and intellectual investments from the human agent saddled with such a responsibility. In addition, good quality academic advising is subject to availability of experienced and committed personnel to undertake the task. However, there are instances when there is paucity of capable human adviser, or where qualified persons are not readily available because of other pressing commitments, which will make system-based decision support desirable and useful. In this work, we present the design and implementation of an intelligent Course Advisory Expert System (CAES) that uses a combination of rule based reasoning (RBR) and case based reasoning (CBR) to recommend courses that a student should register in a specific semester, by making recommendation based on the student’s academic history. The evaluation of CAES yielded satisfactory performance in terms of credibility of its recommendations and usability.

Abstract: Diseases should be treated well and on time. If they are not treated on time, they can lead to many health problems and these problems may become the cause of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In an effort to address such problems, studies made attempts to design and develop expert systems which can provide advice for physicians and patients to facilitate the diagnosis and recommend treatment of patients. This review paper presents a comprehensive study of medical expert systems for diagnosis of various diseases. It provides a brief overview of medical diagnostic expert systems and presents an analysis of already existing studies. General Terms Artificial intelligence, Expert system, Medical knowledge.

Abstract: Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.

Abstract: Logistics and supply chain management (LSCM) has become an integral component of global operations strategy in the 21st century global market and enterprise operations. In order to take advantage of the global resources and market, companies around the world are setting up supply chain operations in different countries. This poses a great challenge in integrating the activities of partnering firms around the globe. One of the major enablers of such integration is the application of Information Technology/Systems (IT/IS) in logistics and supply chains. The role of logistics in particular 3PL has become a paramount important for companies to succeed in sustainable global enterprise operations. Since there is a multiple organizational environment in global operations which are characterized by different culture, language, socio-economic and industrial systems. Some of the barriers arise from these differences can be overcome by suitable automation of information and material flows along the supply chain operations. These automations can be achieved in logistics and supply chain management by various information technologies/systems such as Enterprise Resource Planning (ERP), Radio Frequency Identification (RFID), Expert Systems (ES) and Artificial Intelligence (AI). It is hard to imagine a well integrated global logistics and supply chain without the application of these technologies and systems. However, the research and applications related to the application of ES and AI in logistics and supply chain are rather limited though they play a major role in a physically distributed enterprise environment. Realizing the importance of ES and AI in LSCM for the 21st century organizational competitiveness, this special issue for Expert Systems with Applications (ESWA) has been proposed to encourage research in ES and AI applications in LSCM. The aim of this special issue is to help researchers and decision makers to understand the various issues involved in the development of ES and AI for LSCM. With the help of the articles appearing in this special issue, one should be able to understand better the decisions, development and application of ES and AI in LSCM. The special issue consists of twenty papers and an overview of them is presented here under. The paper, ‘‘A Hierarchical Model of the Impact of RFID Practices on Retail Supply Chain Performance’’ by Vlachos evaluates the impact of Radio Frequency Identification (RFID) practices on supply chain performance. It examines eight variables of RFID applications grouped in two categories: location (supplier’s warehouse, retailer’s central warehouse, retailer’s local warehouse, retailer’s owned stores) and utilisation (standards, transportation, pallet level, specialised software). Given the inherent difficulty in assessing supply

Abstract: Expert systems are basically developed to help in solving complex problems by reasoning about knowledge already known like a human expert does. It does not follow the procedure as followed in the conventional programming by a developer. In this paper basic introduction of expert systems consisting of their composition, basic characteristics and advantages of expert systems are covered. Apart from this, considering the development process of expert systems, it's not as easy to develop successful expert systems as it seems. There are certain factors which can lead to failure of expert systems and among them requirement engineering for expert systems is the one. While developing expert systems developers pay least attention to the requirement engineering process. Instead requirement engineering is very crucial to gather all the requirements that are needed for an expert system. If the requirements do not fulfill all of the client's wishes and needs, then in that case expert system is considered fail even though it works perfectly. Therefore, for successful development of expert systems its necessary that emphasize on requirement engineering process of expert systems should be laid down. Here, analysis of expert system attributes, requirement engineering processes in expert system development and the possible techniques that can be applied to expert system development are done. Next, the most appropriate techniques for the expert system development based on the analysis are proposed.

Abstract: Artificial Intelligence (AI) is one of the most advanced technologies in the world. This paper attempts to demonstrate how AI is helping in the development of accounting system as per Perrow’s sociological framework as a basis for comparative organizational analysis of the impact of expert systems on organizational issues. The study analyzes the relative impact of AI on two different types of accounting works auditing and tax. Accounting tasks involve a wide range of structured, semi-structured and unstructured decisions. The heart of auditing and assurance involves the less-structured decisions and analyses that include much uncertainty caused by risks and lack of information. The discussion indicates an impact on factors that ultimately improve productivity. In aggregate, it indicates that expert systems are found to allow the user substantial control of search for solutions and discretion on whether to follow system recommendations, increased access to top management, and a decrease in the need for supervision.

Abstract: Background. The early detection of rheumatic diseases and the treatment to target have become of utmost importance to control the disease and improve its prognosis. However, establishing a diagnosis in early stages is challenging as many diseases initially present with similar symptoms and signs. Expert systems are computer programs designed to support the human decision making and have been developed in almost every field of medicine. Methods. This review focuses on the developments in the field of rheumatology to give a comprehensive insight. Medline, Embase, and Cochrane Library were searched. Results. Reports of 25 expert systems with different design and field of application were found. The performance of 19 of the identified expert systems was evaluated. The proportion of correctly diagnosed cases was between 43.1 and 99.9%. Sensitivity and specificity ranged from 62 to 100 and 88 to 98%, respectively. Conclusions. Promising diagnostic expert systems with moderate to excellent performance were identified. The validation process was in general underappreciated. None of the systems, however, seemed to have succeeded in daily practice. This review identifies optimal characteristics to increase the survival rate of expert systems and may serve as valuable information for future developments in the field.

Abstract: The representation and processing of uncertainty information is one of the key basic issues of the intelligent information processing in the face of growing vast information, especially in the era of network. There have been many theories, such as probability statistics, evidence theory, fuzzy set, rough set, cloud model, etc., to deal with uncertainty information from different perspectives, and they have been applied into obtaining the rules and knowledge from amount of data, for example, data mining, knowledge discovery, machine learning, expert system, etc. Simply, This is a cognitive transformation process from data to knowledge (FDtoK). However, the cognitive transformation process from knowledge to data (FKtoD) is what often happens in human brain, but it is lack of research. As an effective cognition model, cloud model provides a cognitive transformation way to realize both processes of FDtoK and FKtoD via forward cloud transformation (FCT) and backward cloud transformation (BCT). In this paper, the authors introduce the FCT and BCT firstly, and make a depth analysis for the two existing single-step BCT algorithms. We find that these two BCT algorithms lack stability and sometimes are invalid. For this reason we propose a new multi-step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR) which is more precise than the existing methods. Furthermore, the effectiveness and convergence of new method is analyzed in detail, and how to set the parameters m, r appeared in MBCT-SR is also analyzed. Finally, we have error analysis and comparison to demonstrate the efficiency of the proposed backward cloud transformation algorithm for some simulation experiments.

Abstract: In recognition of the importance of historical knowledge in decision making, case based reasoning (CBR) is utilized as a form of an expert system to tackle construction management issues such as quantity takeoff in the proposal development phase of a project. It builds on a proposition that past projects similar to the new one would suggest a reasonable range of craft quantities. This paper finds that when measuring the similarity between the new project and historical projects, traditional similarity measure methods fail to consider the nonlinearity and muticollinearity embedded in the problem, as well as differences across crafts. An innovative similarity measurement algorithm was therefore proposed to tackle the above issues with a carefully designed orthogonalization process and Sobol’s total sensitivity analysis. The application of the proposed algorithm to the craft quantity takeoff of a power plant project was introduced, demonstrating a better result compared with traditional methods. It i...

Abstract: The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.