Visible to the public Expert Systems

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Expert Systems

Expert systems based on fuzzy logic hold promise for solving many problems. The research presented here address black hole attacks in wireless sensor networks, a fuzzy tool for conducting information security risk assessments, and expert code generator, and other topics. These works were presented between January and August of 2014.

  • Taylor, V.F.; Fokum, D.T., "Mitigating Black Hole Attacks In Wireless Sensor Networks Using Node-Resident Expert Systems," Wireless Telecommunications Symposium (WTS), 2014, pp.1, 7, 9-11 April 2014. doi: 10.1109/WTS.2014.6835013 Wireless sensor networks consist of autonomous, self-organizing, low-power nodes which collaboratively measure data in an environment and cooperate to route this data to its intended destination. Black hole attacks are potentially devastating attacks on wireless sensor networks in which a malicious node uses spurious route updates to attract network traffic that it then drops. We propose a robust and flexible attack detection scheme that uses a watchdog mechanism and lightweight expert system on each node to detect anomalies in the behaviour of neighbouring nodes. Using this scheme, even if malicious nodes are inserted into the network, good nodes will be able to identify them based on their behaviour as inferred from their network traffic. We examine the resource-preserving mechanisms of our system using simulations and demonstrate that we can allow groups of nodes to collectively evaluate network traffic and identify attacks while respecting the limited hardware resources (processing, memory and storage) that are typically available on wireless sensor network nodes.
    Keywords: expert systems; telecommunication computing; telecommunication network routing; telecommunication security; telecommunication traffic; wireless sensor networks; autonomous self-organizing low-power nodes; black hole attacks; flexible attack detection scheme; lightweight expert system; malicious node; network traffic; node-resident expert systems; resource-preserving mechanisms; spurious route updates; watchdog mechanism ;wireless sensor networks; Cryptography; Expert systems; Intrusion detection; Monitoring; Routing; Routing protocols; Wireless sensor networks (ID#:14-2825)
  • Bartos, J.; Walek, B.; Klimes, C.; Farana, R., "Fuzzy Tool For Conducting Information Security Risk Analysis," Control Conference (ICCC), 2014 15th International Carpathian, pp.28,33, 28-30 May 2014. doi: 10.1109/CarpathianCC.2014.6843564 The following article proposes fuzzy tool for processing risk analysis in the area of information security. The paper reviews today's approaches (qualitative and quantitative methodologies) and together with already published results proposes a fuzzy tool to support our novel approach. In this paper the fuzzy tool itself is proposed and also every main part of this tool is described. The proposed fuzzy tool is connected with expert system and methodology which is the part of more complex approach to decision making process. The knowledge base of expert system is created based on user input values and the knowledge of the problem domain. The proposed fuzzy tool is demonstrated on examples and problems from the area of information security.
    Keywords: expert systems; fuzzy set theory; risk analysis; security of data; decision making process; expert system; fuzzy tool; information security risk analysis; qualitative methodologies; quantitative methodologies; Expert systems; Information security; Organizations; Risk management; expert system; fuzzy; fuzzy tool; information security; risk analysis; uncertainty (ID#:14-2826)
  • Imam, AT.; Rousan, T.; Aljawarneh, S., "An Expert Code Generator Using Rule-Based And Frames Knowledge Representation Techniques," Information and Communication Systems (ICICS), 2014 5th International Conference on, pp.1 , 6, 1-3 April 2014. doi: 10.1109/IACS.2014.6841951 This paper aims to demonstrate the development of an expert code generator using rule-based and frames knowledge representation techniques (ECG-RF). The ECG-RF system presented in this paper is a passive code generator that carries out the task of automatic code generation in fixed-structure software. To develop an ECG-RF system, the artificial intelligence (AI) of rule-based system and frames knowledge representation techniques was applied to a code generation task. ECG-RF fills a predefined frame of a certain fixed-structure program with code chunks retrieved from ECG-RF's knowledge base. The filling operation is achieved by ECG-RF's inference engine and is guided by the information collected from the user via a graphic user interface (GUI). In this paper, an ECG-RF system for generating a device driver program is presented and implemented with VBasic software. The results show that the ECG-RF design concept is reasonably reliable.
    Keywords: graphical user interfaces; inference mechanisms ;knowledge based systems; program compilers; ECG-RF design concept; ECG-RF inference engine ;ECG-RF knowledge base; ECG-RF system; GUI; VBasic software; artificial intelligence; automatic code generation; code chunks; code generation task; device driver program; expert code generator; fixed-structure program; fixed-structure software; frames knowledge representation techniques; graphic user interface; passive code generator;r ule-based system; Engines; Generators; Graphical user interfaces; Knowledge representation; Programming; Software; Software engineering; Automatic Code Generation; Expert System; Frames Knowledge Representation Techniques; Software Development (ID#:14-2827)
  • Mavropoulos, C.; Ping-Tsai Chung, "A Rule-based Expert System: Speakeasy - Smart Drink Dispenser," Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island, pp.1,6, 2-2 May 2014. doi: 10.1109/LISAT.2014.6845224 In this paper, we develop a knowledge-based expert system case study called Speakeasy Expert System (S.E.S.) for exercising the rule-based expert system programming in both CLIPS and VisiRule. CLIPS stands for "C Language Integrated Production System" and it's an expert system tool created to facilitate the development of software to model human knowledge or expertise. VisiRule is a tool that allows experts to build decision models using a graphical paradigm, but one that can be annotated using code and or Boolean logic and then executed and exported to other programs and processes. Nowadays, there are billions of computing devices are interconnected in computing and communications. These devices include from various desktop personal computers, laptops, servers, embedded computers to small ones such as mobile phones. This growth shows no signs of slowing down and becomes the cause of a new technology in computing and communications. This new technology is called Internet of Things (IOT). In this study, we propose and extend the S.E.S into a Smart Drink Dispenser using IOT Technology. We present Data Flow Diagram of S.E.S in IOT Environment and its IOT architecture, and propose the usage and implementation of S.E.S.
    Keywords: Boolean functions; C language; decision making; expert systems; Boolean logic; C language integrated production system; CLIPS; SES; VisiRule; decision models; graphical paradigm; human knowledge; knowledge-based expert system; rule-based expert system programming; smart drink dispenser; speakeasy expert system; Alcoholic beverages; Business; Decision trees; Expert systems; Internet of Things; Artificial Intelligence (AI);CLIPS; Decision Making Information System ;Internet of Things (IOT);Knowledge-based Expert Systems; Radio-frequency Identification (RFID); VisiRule (ID#:14-2828)
  • Yuzuguzel, H.; Cemgil, AT.; Anarim, E., "Query Ranking Strategies In Probabilistic Expert Systems," Signal Processing and Communications Applications Conference (SIU), 2014 22nd, pp.1199, 1202, 23-25 April 2014. doi: 10.1109/SIU.2014.6830450 The number of features are quite high in many fields. For instance, the number of symptoms are around thousands in probabilistic medical expert systems. Since it is not practical to query all the symptoms to reach the diagnosis, query choice becomes important. In this work, 3 query ranking strategies in probabilistic expert systems are proposed and their performances on synthetic data are evaluated.
    Keywords: medical diagnostic computing; medical expert systems; probability; query processing; medical diagnosis; probabilistic expert systems; probabilistic medical expert systems; query ranking strategies; Conferences; Entropy; Expert systems; Inference algorithms; Probabilistic logic; Sequential diagnosis; Signal processing; medical diagnosis; relative-entropy; sequential diagnosis (ID#:14-2829)
  • GaneshKumar, P.; Rani, C.; Devaraj, D.; Victoire, T.AA, "Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification," Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol.11, no.2, pp.347, 360, March-April 2014. doi: 10.1109/TCBB.2014.2307325 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.
    Keywords: ant colony optimisation; classification; fuzzy systems; genetic algorithms; genetics; genomics; medical expert systems; ABA; ACO; GSA; Genetic Swarm Algorithm approach; accuracy maximization; ant colony optimization; artificial bee colony algorithm; classification accuracy; combinatorial optimization task; complexity minimization; continuous expression values; formless expression values; fuzzy expert system based microarray data classification; fuzzy partition; gene expression data sets; gene expression values; global pheromone updation; hybrid ant bee algorithm; if-then rules; informative gene identification; integer numbers; interpretability-accuracy tradeoff; local pheromone updation; membership function; mutual information; rule generation; rule set; sample classification; simulation study; Accuracy; Computational biology; Data models; Expert systems; Fuzzy systems; Gene expression; IEEE transactions; Microarray data; ant colony optimization; artificial bee colony; fuzzy expert system; mutual information (ID#:14-2830)
  • Carreto, C.; Baltazar, M., "An Expert System for Mobile Devices Based On Cloud Computing," Information Systems and Technologies (CISTI), 2014 9th Iberian Conference on, pp.1, 6, 18-21 June 2014. doi: 10.1109/CISTI.2014.6876953 This paper describes the implementation of an Expert System for Android mobile devices, directed to the common user and the ability to use different knowledge bases, selectable by the user. The system uses a cloud computing-based architecture to facilitate the creation and distribution of different knowledge bases.
    Keywords: cloud computing; expert systems; mobile computing; smart phones; Android mobile devices; cloud computing-based architecture; expert system; knowledge base; mobile devices; Androids; Engines; Expert systems; Google; Humanoid robots; Mobile communication; Android; Cloud computing; Expert System (ID#:14-2831)
  • Pokhrel, J.; Lalanne, F.; Cavalli, A; Mallouli, W., "QoE Estimation for Web Service Selection Using a Fuzzy-Rough Hybrid Expert System," Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on, pp.629,634, 13-16 May 2014. doi: 10.1109/AINA.2014.77 With the proliferation of web services on the Inter-net, it has become important for service providers to select the best services for their clients in accordance to their functional and non-functional requirements. Generally, QoS parameters are used to select the most performing web services, however, these parameters do not necessarily reflect the user's satisfaction. Therefore, it is necessary to estimate the quality of web services on the basis of user satisfaction, i.e., Quality of Experience(QoE). In this paper, we propose a novel method based on a fuzzy-rough hybrid expert system for estimating QoE of web services for web service selection. It also presents how different QoS parameters impact the QoE of web services. For this, we conducted subjective tests in controlled environment with real users to correlate QoS parameters to subjective QoE. Based on this subjective test, we derive membership functions and inference rules for the fuzzy system. Membership functions are derived using a probabilistic approach and inference rules are generated using Rough Set Theory (RST). We evaluated our system in a simulated environment in MATLAB. The simulation results show that the estimated web quality from our system has a high correlation with the subjective QoE obtained from the participants in controlled tests.
    Keywords: Web services; expert systems; fuzzy set theory; probability; quality of experience; rough set theory; Internet; MATLAB; QoE estimation; QoS parameters; RST; fuzzy system; fuzzy-rough hybrid expert system; inference rules; membership functions; probabilistic approach; quality of experience; rough set theory; user satisfaction; web service selection; web services proliferation; Availability; Estimation; Expert systems; Quality of service; Set theory; Web services; QoE; QoS; Web Services; intelligent systems (ID#:14-2832)
  • Kaur, B.; Madan, S., "A Fuzzy Expert System To Evaluate Customer's Trust In B2C E-Commerce Websites," Computing for Sustainable Global Development (INDIACom), 2014 International Conference on, pp.394,399, 5-7 March 2014. doi: 10.1109/IndiaCom.2014.6828166 With the profound Internet perforation being the most significant advancement in the technology of the last few years, the platform for e-Commerce growth is set. E-Commerce industry has experienced astounding growth in recent years. For the successful implementation of a B2C E-business, it is necessary to understand the trust issues associated with the online environment which holds the customer back from shopping online. This paper proposes a model to discern the impact of trust factors pertaining in Indian E-Commerce marketplace on the customers' intention to purchase from an e-store. The model is based on Mamdani Fuzzy Inference System which is used for computation of the trust index of an e-store in order to assess the confidence level of the customers in the online store. The study first identifies the trust factors and thereby investigates the experts on them in order to examine the significance of the factors. Thereafter, the customers' responses regarding B2C E-Commerce websites with respect to the trust parameters are studied which leads to the development of the fuzzy system. The questionnaire survey method was used to gather primary data which was later used for the purpose of rule formation for the fuzzy inference system.
    Keywords: Web sites; consumer behaviour; electronic commerce; expert systems; fuzzy reasoning; purchasing; retail data processing ;trusted computing; B2C e-business; B2C e-commerce Websites; Indian e-commerce marketplace; Internet perforation; Mamdani fuzzy inference system; customer confidence level; customer intention; customer trust; e-commerce growth ;e-commerce industry; e-store; fuzzy expert system; fuzzy system development; online environment; online shopping; online store; purchasing; trust factors; trust index; trust issues; trust parameters; Business; Computational modeling; Expert systems; Fuzzy logic; Fuzzy systems ;Indexes; Internet; Customer's Trust; E-Commerce Trust; Fuzzy System; Online Trust; Trust; Trust Factors; Trust Index (ID#:14-2833)
  • Wen-xue Geng; Fan'e Kong; Dong-qian Ma, "Study on Tactical Decision Of UAV Medium-Range Air Combat," Control and Decision Conference (2014 CCDC), The 26th Chinesepp.135,139, May 31 2014-June 2 2014. doi: 10.1109/CCDC.2014.6852132 To process the uncertainty of decision-making environment and the real-time during the tactical decision of UAV medium-range air combat, a hybrid tactical decision-making method based on rule sets and Fuzzy Bayesian network (FBN) was proposed. By studying the process of UAV air combat, the main factors that affect the tactical decision were analyzed. A corresponding FBN and expert system were built up. The hybrid system retained the advantage of expert system by the first call to it. In the meantime, the system could also process the uncertainty of decision-making environment by means of the FBN. Finally, through the air combat simulation, the correctness, real-time and effectiveness in an uncertain environment of the hybrid tactical decision-making method were verified.
    Keywords: aerospace computing; autonomous aerial vehicles; belief networks; control engineering computing; decision making; expert systems; fuzzy control; fuzzy neural nets; military aircraft; military computing; neurocontrollers; FBN; UAV air combat; UAV medium-range air combat; air combat simulation; decision-making environment; expert system; fuzzy Bayesian network; hybrid system; hybrid tactical decision-making method; rule sets; uncertain environment; Atmospheric modeling; Bayes methods; Decision making; Expert systems; Missiles; Uncertainty; Fuzzy Bayesian network; UAV; expert system; medium-range air combat (ID#:14-2834)
  • Pozna, Claudiu; Foldesi, Peter; Precup, Radu-Emil; Koczy, Laszlo T., "On the Development Of Signatures For Artificial Intelligence Applications," Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference onpp.1304,1310, 6-11 July 2014. doi: 10.1109/FUZZ-IEEE.2014.6891636 This paper illustrates developments of signatures for Artificial Intelligence (AI) applications. Since the signatures are data structures with efficient results in modeling of fuzzy inference systems and of uncertain expert systems, the paper starts with the analysis of the data structures used in AI applications from the knowledge representation and manipulation point of view. An overview on the signatures, on the operators on signatures and on classes of signatures is next given. Using the proto fuzzy inference system, these operators are applied in a new application of fuzzy inference system modeled by means of signatures and of classes of signatures.
    Keywords: Adaptation models; Artificial intelligence; Data structures; Educational institutions; Fuzzy logic; Fuzzy sets; Unified modeling language; Artificial Intelligence; expert systems; knowledge representation; proto fuzzy inference systems; signatures (ID#:14-2835)


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