Abstract: Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

Abstract: This paper presents a particle swarm optimization (PSO)-based fuzzy expert system for the diagnosis of coronary artery disease (CAD). The designed system is based on the Cleveland and Hungarian Heart Disease datasets. Since the datasets consist of many input attributes, decision tree (DT) was used to unravel the attributes that contribute towards the diagnosis. The output of the DT was converted into crisp if-then rules and then transformed into fuzzy rule base. PSO was employed to tune the fuzzy membership functions (MFs). Having applied the optimized MFs, the generated fuzzy expert system has yielded 93.27% classification accuracy. The major advantage of this approach is the ability to interpret the decisions made from the created fuzzy expert system, when compared with other approaches.

Abstract: This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems' areas. We also observe that many new industrial applications are developed using hybrid expert systems recently.

Abstract: A natural language authoring system that organizes technical, financial, legal and market information into Point of View specific analytical, visual and narrative decision-support content. The expert system transforms a user's point of view into a tailored narrative and/or visualization report. Expert rules embed interactive advertising, such as affiliate URL links, into analytical, visual and narrative and statistical content. The rules may be modified by one or more users, thereby capturing knowledge as the rules are utilized by users of the system.

Abstract: The volume presents an international collection of mature expert system projects. Work from countries ranging from Japan to France, Spain to China, and from the United States is reported. Following on the second stage of expert system development, discussion focusses on issues that have become prominent through the application of systems to real-world problems. The incorporation of substantial numeric knowledge into systems is the first issue covered. It arises in many technical domains, which are likely to have already been subject to statistical or algebraic description. The second issue concerns the explicit representation of control knowledge within knowledge-bases. This gives systems the capability of explicitly reasoning about the control strategy applicable to each sub-problem arising in a complex problem domain.

Abstract: In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

Abstract: Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reliable than the system they are observing. This is in part due to the sensors' operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electro-mechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.

Abstract: Highlights? We model a decision-making framework for portfolio management under uncertainty. ? We adopt the strategic bucket, scoring models and portfolio matrixes. ? We develop a fuzzy-based portfolio expert system. ? The system was applied to the portfolio analysis in an electronics firm. The importance of new product development (NPD) for a company's growth and prosperity is emphasized and a number of methods have been suggested to help decision-making for NPD project portfolio management. In spite of their utilities, however, little attention was paid to develop a supporting system for portfolio management that can help quick but careful decision-makings under uncertainties. Therefore, this research proposes a decision-making framework that uses a fuzzy expert system in portfolio management for dealing with the uncertainty of the fuzzy front-end of product development. For the purpose of developing the framework, we adopted the three tools - strategic bucket for strategic resource allocation, scoring models for evaluating projects and portfolio matrixes for balancing projects - to find an optimal set of projects in the portfolio. In particular, this research established fuzzy inference-based models for evaluation criteria which are too ambiguous to be numerically described. Also, based on the evaluation results, the final selection of projects is made by an expert system, which can encompass the operational knowledge and company strategy in the rule-based system. The suggested framework was applied to the portfolio analysis in an electronics firm in Korea and verified its feasibility.

Abstract: Offline clinical guidelines are typically designed to integrate a clinical knowledge base, patient data and an inference engine to generate case specific advice. In this regard, offline clinical guidelines are still popular among the healthcare professionals for updating and support of clinical guidelines. Although their current format and development process have several limitations, these could be improved with artificial intelligence approaches such as expert systems/decision support systems. This paper first, presents up to date critical review of existing clinical expert systems namely AAPHelpm, MYCIN, EMYCIN, PIP, GLIF and PROforma. Additionally, an analysis is performed to evaluate all these fundamental clinical expert systems. Finally, this paper presents the proposed research and development of a clinical expert system to help healthcare professionals for treatment. (Saba T, Al-Zaharani S, Rehman A. Expert System for Offline Clinical Guidelines and Treatment Life Sci J 2012;9(4):2639-2658) (ISSN:1097-8135). http://www.lifesciencesite.com 393.

Abstract: The need for energy efficient and intelligent systemic solutions, has lead many researchers around the world to investigate and evaluate the existing technologies in order to create solutions that would be adopted for the near future intelligent homes and buildings, aiding in smart grid initiatives. In this paper an algorithm based on the adaptable learning system principles is presented. The proposed algorithm utilizes the adaptable learning system concepts. The Observe, Learn, and Adapt (OLA) algorithm proposed is the result of integration of wireless sensors and artificial intelligence concepts towards the same objective: adding more intelligence to a programmable communicating thermostat (PCT), for better energy management and conservation in smart homes. A house simulator was developed and used as an “expert system shell” to assist in implementation and verification of the OLA algorithm. The role of PCT is to provide consumer with a means to manage and reduce energy use, while accommodating their every day schedules, preferences and needs. In this paper, the actual results of learning and adaptability of a PCT equipped with OLA, as a result of the occupant's pattern/schedule changes, and in general, the overall system improvements with respect to energy consumption and savings are demonstrated via simulation for the zone controlled home equipped with OLA and Knowledge Base, versus a home without zone control, Knowledge Base nor OLA.

Abstract: During the recent decades, using expert systems has been developed in a vast level in all sectors of human being life, in particular in the field of medicine. The main objective of this research was to design an expert system for diagnosis all types of diabetes. After data acquisition and designing a rule-based expert system, this system has been coded with VP_Expert Shell and tested in Shahid Hasheminezhad Teaching Hospital affiliated to Tehran University of Medical Sciences and final expert system has been presented. Findings of this research showed that in many parts of medical science and health care the expert systems have been used effectively. The acquisitive knowledge was represented in the diagrams, charts and tables. The related source code using of the expert system was given and after testing the system, finally its validation has been done. It has been concluded here the expert system can be used effectively in all areas of medical sciences. In particular, in terms of vast number diabetics throughout the world, the expert system can be highly helpful for the patients. These patients in many cases are not aware of their disease and how to control it. In addition, some of these patients do not access to the physicians during necessary times. Therefore, such a system can provide necessary information about the indications and diagnosis. Since this expert system gathers its knowledge from several medical specialists, the system has a broader scope and can be more helpful to the patients -- in comparison to just one physician.

Abstract: This paper focuses on the design and development of an expert system for on-line detection of various control chart patterns so as to enable the quality control practitioners to initiate prompt corrective actions for an out-of-control manufacturing process. Using this expert system developed in Visual BASIC 6, all the nine most commonly observed control chart patterns, e.g., normal, stratification, systematic, increasing trend, decreasing trend, upward shift, downward shift, cyclic, and mixture can be recognized well, employing an optimal set of seven shape features. Based on an observation window of 32 data points, it can plot the control chart, compute the control limits, identify the control chart pattern, calculate the process capability index, determine the maximum run length, and identify the starting point of the maximum run length. After pattern recognition, it can also inform the users about various root assignable causes associated with a particular pattern along with the necessary pre-emptive actions. It opens up wide opportunities for quality improvement and real-time applications in diverse manufacturing processes. This developed expert system is built for a vertical drilling process and its recognition performance is tested using simulated process data.

Abstract: Generally, stock trading expert systems (STES) called also ''mechanical trading systems'' are based on the technical analysis, i.e., on methods for evaluating securities by analyzing statistics generated by the market activity, such as past prices and volumes (number of transactions during a unit of a timeframe). In other words, such STES are based on the Level 1 information. Nevertheless, currently the Level 2 information is available for the most of traders and can be successfully used to develop trading strategies especially for the day trading when a significant amount of transactions are made during one trading session. The Level 2 tools show in-depth information on a particular stock. Traders can see not only the ''best'' bid (buying) and ask (selling) orders, but the whole spectrum of buy and sell orders at different volumes and different prices. In this paper, we propose some new technical analysis indices bases on the Level 2 and Level 1 information which are used to develop a stock trading expert system. For this purpose we adapt a new method for the rule-base evidential reasoning which was presented and used in our recent paper for building the stock trading expert system based the Level 1 information. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Warsaw Stock Exchange.

Abstract: Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.

Abstract: Interpretation of satellite images is a complex issue. Remote sensing experts and the maticians interpret and use information contained in satellite images depending on their knowledge and expertise in a given application domain. This knowledge is usually ambiguous and consequently cannot be used in an automatic process. Formalizing expert knowledge thus appears as a prerequisite toward an automatic semantic interpretation of remote sensing images. In computer sciences, ontologies have proven to be efficient for formally expressing remote sensing expert knowledge. This paper aims to demonstrate how expert knowledge explanation via ontologies can improve automation of satellite image exploitation. We argue that ontologies can be used to link this knowledge with the content of remote sensing images by conceptually describing them. For this purpose, we first built an image ontology for describing image segments based on spectral, pseudo-spectral and textural features. Then we used those concepts to build a remote sensing knowledge ontology describing the way experts identify land cover classes in satellite images. Third, image ontology is also used to describe image facts which populate image ontology. We finally tested a concrete application of our approach using an automatic reasoner for classifying remote sensing images.

Abstract: Applications in fault diagnosis are continuously being implemented to serve different sectors. Car failure detection is a sequence of diagnostic processes that necessitates the deployment of expertise. The Expert System (ES) is one of the leading Artificial Intelligence (AI) techniques that have been adopted to handle such task. This paper presents the imperatives for an ES in developing car failure detection model and the requirements of constructing successful Knowledge-Based Systems (KBS) for such model. In addition, it exhibits the adaptation of the ES in the development of Car Failure and Malfunction Diagnosis Assistance System (CFMDAS). However, CFMDAS development faces many challenges such as collecting the required data for building the knowledge base and performing the inferencing. Furthermore, diagnosis of car faults requires high technical skills and experienced mechanics who are typically scarce and expensive to get. Thus, systems such as CFMDAS can be highly useful in assisting mechanics for failure detection and training purposes. Moreover, capturing and retaining valuable knowledge on such domain yield more accurate and less time consuming models.

Abstract: Highlights? RL agent that supervises the WWTP 24h/day. ? RL agent that adapts to each particular WWTP by itself. ? Better performance than standard BSM1 control strategy. ? Control strategy learned autonomously adapted to different locations. One of the main problems in the automation of the control of wastewater treatment plants (WWTPs) appears when the control system does not respond as it should because of changes on influent load or flow. To tackle this difficult task, the application of Artificial Intelligence is not new, and in fact, currently Expert Systems may supervise the plant 24h/day assisting the plant operators in their daily work. However, the knowledge of the Expert System must be elicited previously from interviews to plant operators and/or extracted from data previously stored in databases. Although this approach still has a place in the control of wastewater treatment plants, it should aim to develop autonomous systems that learn from the direct interaction with the WWTP and that can operate taking into account changing environmental circumstances. In this paper we present an approach based on an agent with learning capabilities. In this approach, the agent's knowledge emerges from the interaction with the plant. In order to show the validity of our assertions, we have implemented such an emergent approach for the N-Ammonia removal process in a well established simulated WWTP known as Benchmark Simulation Model No.1 (BSM1).

Abstract: Highlights? Help reduce the difficulties in safety plan development using knowledge-based expert system. ? Help achieve improvement in operation management. ? Help achieve improvement in timeframe for resource assignment. ? Help achieve improvement in customer satisfaction and quality. Food safety plan is being promoted in the food industry by the Hong Kong Government as a preliminary quality control tool. However, it appears to be a challenging task for Distribution Centers (DC) that handles food inventory since most of them are lack of knowledge and know how technology to manage information in a real time base. This paper proposes a Radio Frequency Identification based Food Operations Assignment System (RFID-FOAS) to help DC facilitates the food safety control activities in receiving areas by generating a proper safety plan. The system has adopted the Radio Frequency Identification (RFID) technology and the Case-Based Reasoning (CBR) technique to facilitate the inventory data-capturing process and assist in formulating decisions, respectively. The developed system aims to help reduce the difficulties in safety plan development using a knowledge-based expert system. The significance and contribution of RFID-FOAS in the context of managing the inventory quality in DC for safety plan development is demonstrated through the adoption of the system in a Hong Kong-based logistics company. The generated results show that the decision-making process of the safety plan development is facilitated. Moreover, the real-time data capturing nature of RFID technology has further improved the efficiency and timeframe requested for the actions. With the support of RFID-FOAS, the data capture system and the decision-making time is minimized. As a result, inventory quality and customer satisfaction level are significantly improved.

Abstract: This paper presents a method to creation of a servicing expert system including an artificial neural network. The theoretical basis was presented with the model of an operation process of objects in the form of the following models: mathematical (analytical), graphical and descriptional. For the tests, a model was developed of an organization of a servicing technical system of those technical objects which require short shutdown times (aircrafts, radiolocation systems, etc.). The mathematical basis was presented for the execution of the task of servicing of a technical object. The idea of the servicing of the object was described as a transformation of the properties of the operational function of the object from the space of the current servicing to the form of the space of the features of the nominal (model) operation of the object. The results were presented of the radar system.

Abstract: The operational complexity of modern ships requires the use of advanced applications, called damage control systems (DCSs), able to assist crew members in the effective handling of dangerous events and accidents. In this article we describe the development of a knowledge-based decision support system (KDSS) integrated within a DCS designed for a national navy. The KDSS uses a hybrid design and runtime knowledge model to assist damage control operators through a kill card function which supports damage identification, action scheduling and system reconfiguration. We report a fire fighting scenario as illustrative application and discuss a preliminary evaluation of benefits allowed by the system in terms of critical performance measures. Our work can support further research aimed to apply expert systems to improve shipboard security and suggest similar applications in other contexts where situational awareness and damage management are crucial.

Abstract: Highlights? This study describes a causal knowledge-based expert system. ? This study describes FCM for planning an Internet-based stock trading system. ? The case base consists of the qualities of ISTS use, ISTS use, and user satisfaction. ? This study uses structural equation modeling to estimate the relevant relationships. ? This study develops rules for the identification of the qualities for performance. This study describes a causal knowledge-based expert system for planning an Internet-based stock trading system, abbreviated CAKES-ISTS. The case base of this system consists of the qualities that promote ISTS use, two specific facets of ISTS use (stock amount purchased and frequency of use), and user satisfaction. Planning ISTS requires consideration of the complex causal relationships between system qualities, system use, and performance (i.e., user satisfaction). This study also examines the factors affecting the level of system usage and performance. First, this study uses a fuzzy cognitive map (FCM) to develop the causal knowledge base of the expert system for ISTS planning. Second, this study uses structural equation modeling to estimate the relevant relationships among FCM components as well as their direction and strength. Third, this study develops rules based on system qualities to predict the usage and performance level of ISTS, allowing the identification of the qualities essential to enhance system usage and performance. This clearly shows how effective ISTS planning is possible through the inference process provided by CAKES-ISTS.

Abstract: This paper presents a novel approach for the classification of the religious scriptures, the Hadith (sayings of Prophet Muhammad (plural Ahadith)). Muhadith is a distributed, Cloud based expert system that uses the Hadith science to classify Ahadith among 24 types from seven broad categories. Classification of the Hadith is a complex and sensitive task, and can only be performed by an expert of the Hadith sciences. Muhadith expert system is designed to imitate the Hadith experts for Hadith classification, and to enable a computer to behave like a Hadith expert to discriminate the authentic Ahadith from unauthentic ones. This paper presents the relationship and mapping of the expert system technology onto Hadith sciences, and technicalities involved in designing of the Muhadith expert system. We also propose solutions for the communicational and interoperability problems faced by the legacy web based distributed expert systems. We employ service oriented architecture to overcome the communicational problem and a candidature for the Software as a Service (SaaS) for the Cloud computing. The expert system also provides a reasoning facility that enables the user to look into the classification details. Muhadith expert system has been designed by merging the ideas from the domains of expert systems, Web technologies, and distributed computing systems. This type of an effort on the topic is rare and applying them in the domain of Hadith is our humble contribution.

Abstract: Artificial intelligence (AI) has been used for more than two decades as a development tool for solutions in several areas of the EP (b) approximately 50% of respondents declared they were somehow engaged in applying workflow automation, automatic process control, rule-based case reasoning, data mining, proxy models, and virtual environments; (c) production is the area most impacted by the applications of AI technologies; (d) the perceived level of available literature and public knowledge of AI technologies is generally low; and (e) although availability of information is generally low, it is not perceived equally among different roles. This work aims to be a guide for personnel responsible for production and asset management on how AI-based applications can add more value and improve their decision making. The results of the survey offer a guideline on which tools to consider for each particular oil and gas challenge. It also illustrates how AI techniques will play an important role in future developments of IT solutions in the E&P industry. Introduction While there is hardly a rigorous definition of the term artificial intelligence (AI) that is unequivocally accepted, the tools of AI and its intended uses have been well studied for decades and many applications have appeared. Loosely speaking, AI is the capability of machines (usually in the form of computer hardware and software) to mimic or exceed human intelligence in everyday engineering and scientific tasks associated with perceiving, reasoning, and acting. Since human intelligence is multifaceted, so is AI, comprising goals that range from knowledge representation and reasoning, to learning, to visual perception and language understanding (Winston 1992). AI techniques have been present in the E&P industry for many years. A quick literature search reveals application of AI in SPE scientific and engineering papers as early as in the 1970s. There are numerous references about the applications of neural networks, fuzzy logic, genetic algorithms, expert systems, and other artificial techniques in the resolution of problems in diverse areas, such as reservoir simulation, production optimization, process control, and fault detection and diagnosis, among many others. AI is an area of great interest in the E&P industry, mainly in applications related to production control and optimization, proxy model simulation, and virtual sensing. The most popular techniques are artificial neural networks, fuzzy logic, and genetic algorithms, with interesting developments in hybrid and nontraditional techniques. There has been recent increase in such AI-based commercial applications for production management. While the full impact of such applications is still being realized, there are already solutions in the market with a positive impact in the E&P industry.

Abstract: Expert system - a branch of Artificial Intelligence is a collection of programs which has the ability to reason, justify and answer the queries in a particular domain as a human expert would do. It can be applied to various fields. Expert system in agriculture is gathering momentum and this paper aims at tackling the control and remedial measures for disease management for the staple food crop of Karnataka – Finger Millets popularly known as Ragi. The introduction section consists of contributions of expert systems in agriculture. The second section explains the process in Integrated Disease Management (IDM).The third section is about knowledge engineering process which consists of knowledge acquisition and knowledge representation. The fourth section is about the application of fuzzy logic in IDM. The fifth section briefs about defuzzification of IDM. Keywords withExpert System, Knowledge Base, Fuzzy Logic, Integrated Disease Management, Pathomerty. management, disease and pest management, nutrient and

Abstract: In order to prevent and reduce water pollution, promote a sustainable use, protect the environment and enhance the status of aquatic ecosystems, this article deals with the application of advanced mathematical techniques designed to aid in the management of records of different water quality monitoring networks. These studies include the development of a software tool for decision support, based on the application of fuzzy logic techniques, which can indicate water quality episodes from the behavior of variables measured at continuous automatic water control networks. Using a few physical-chemical variables recorded continuously, the expert system is able to obtain water quality phenomena indicators, which can be associated, with a high probability of cause-effect relationship, with human pressure on the water environment, such as urban discharges or diffuse agricultural pollution. In this sense, at the proposed expert system, automatic water quality control networks complement manual sampling of official administrative networks and laboratory analysis, providing information related to specific events (discharges) or continuous processes (eutrophication, fish risk) which can hardly be detected by discrete sampling.