The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform
James F. Smith - One of the best experts on this subject based on the ideXlab platform.
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ICINCO-SPSMC - Evolutionary Data Mining Approach to Creating Digital Logic
2010Co-Authors: James F. Smith, Thanhvu NguyenAbstract:Abstract : A Data Mining based procedure for automated reverse engineering has been developed. The Data Mining algorithm for reverse engineering uses a genetic program (GP) as a Data Mining Function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness Function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a Database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness Function for the genetic program. Genetic program based Data Mining is then conducted. This procedure incorporates not only the experts? rules into the fitness Function, but also the information in the Database. The information extracted through this process is the internal design specifications of the sensor. Significant mathematical formalism and experimental results related to GP based Data Mining for reverse engineering will be provided.
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Fuzzy Decision Trees for Planning and Autonomous Control of a Coordinated Team of UAVs
Signal Processing Sensor Fusion and Target Recognition XVI, 2007Co-Authors: James F. Smith, Thanhvu NguyenAbstract:ABSTRACT A fuzzy logic resource manager that enables a collection of unmanned aerial vehicles (UAVs) to automatically cooperate to make meteorological measurements will be discussed. Once in flight no human intervention is required. Planning and real-time control algorithms determine the optimal trajectory and points each UAV will sample, while taking into account the UAVs risk, risk tolerance, reliability, mission priority, fuel limitations, mission cost, and related uncertainties. The control algorithm permits newly obtained information about weather and other events to be introduced to allow the UAVs to be more effective. The appr oach is illustrated by a discussion of the fuzzy decision tree for UAV path assignment and related simulation. The different fuzzy membership Functions on the tree are described in mathematical detail. The different methods by which this tree is obtained are summarized including a method based on using a genetic program as a Data Mining Function. A second fuzzy decision tree that allows the UAVs to automatically collaborate without human intervention is discussed. This tree permits three different types of collaborative behavior between the UAVs. Simulations illustrating how the tree allows the different types of collaboration to be automated are provided. Simulations also show the ability of the control al gorithm to allow UAVs to effectively cooperate to increase the UAV teams likelihood of success. Keywords: resource management, fuzzy logic, planning algorithms, decision support algorithms, cooperative behavior
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IEEE Congress on Evolutionary Computation - Evolutionary Data Mining of digital logic and the effects of uncertainty
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: James F. SmithAbstract:A Data Mining based procedure for automated reverse engineering has been developed. The Data Mining algorithm for reverse engineering uses a genetic program (GP) as a Data Mining Function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness Function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a Database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness Function for the genetic program. Genetic program based Data Mining is then conducted. This procedure incorporates not only the experts' rules into the fitness Function, but also the information in the Database. The information extracted through this process is the internal design specifications of the sensor. Significant experimental and theoretical results related to GP based Data Mining for reverse engineering and the related uncertainties will be provided.
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Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security - Genetic program based Data Mining to reverse engineer digital logic
Data Mining Intrusion Detection Information Assurance and Data Networks Security 2006, 2006Co-Authors: James F. Smith, Thanhvu NguyenAbstract:A Data Mining based procedure for automated reverse engineering and defect discovery has been developed. The Data Mining algorithm for reverse engineering uses a genetic program (GP) as a Data Mining Function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness Function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a Database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness Function for the genetic program. Genetic program based Data Mining is then conducted. This procedure incorporates not only the experts' rules into the fitness Function, but also the information in the Database. The information extracted through this process is the internal design specifications of the sensor. Uncertainty related to the input-output Database and the expert based rule set can significantly alter the reverse engineering results. Significant experimental and theoretical results related to GP based Data Mining for reverse engineering will be provided. Methods of quantifying uncertainty and its effects will be presented. Finally methods for reducing the uncertainty will be examined.
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Genetic-program-based Data Mining for hybrid decision-theoretic algorithms and theories
Intelligent Computing: Theory and Applications III, 2005Co-Authors: James F. SmithAbstract:A genetic program (GP) based Data Mining (DM) procedure has been developed that automatically creates decision theoretic algorithms. A GP is an algorithm that uses the theory of evolution to automatically evolve other computer programs or mathematical expressions. The output of the GP is a computer program or mathematical expression that is optimal in the sense that it maximizes a fitness Function. The decision theoretic algorithms created by the DM algorithm are typically designed for making real-time decisions about the behavior of systems. The Database that is mined by the DM typically consists of many scenarios characterized by sensor output and labeled by experts as to the status of the scenario. The DM procedure will call a GP as a Data Mining Function. The GP incorporates the Database and expert’s rules into its fitness Function to evolve an optimal decision theoretic algorithm. A decision theoretic algorithm created through this process will be discussed as well as validation efforts showing the utility of the decision theoretic algorithm created by the DM process. GP based Data Mining to determine equations related to scientific theories and automatic simplification methods based on computer algebra will also be discussed.
A J Krishna Mohan - One of the best experts on this subject based on the ideXlab platform.
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SD Miner - A Spatial Data Mining System
International Journal of Research, 2014Co-Authors: Y J Akhila, Akshaya Naik, Bhagyashree Hegde, Pooja Shetty, A J Krishna MohanAbstract:nfluenced by the GIS technology, a vast volume of bulk Data have been stored known as spatial Data, using the spatial Data Mining technique. In this paper we propose a new spatial Data Mining known as SD-Miner. It consists of three parts. They are graphical interface for input and output, the Data storage module using DBMS, a Data Mining module for spatial Data Mining Function. The system proposes about spatio-temporal Data and clustering. In this paper, we take an example of Intel lab to show spatial Data Mining Functions.
Thanhvu Nguyen - One of the best experts on this subject based on the ideXlab platform.
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ICINCO-SPSMC - Evolutionary Data Mining Approach to Creating Digital Logic
2010Co-Authors: James F. Smith, Thanhvu NguyenAbstract:Abstract : A Data Mining based procedure for automated reverse engineering has been developed. The Data Mining algorithm for reverse engineering uses a genetic program (GP) as a Data Mining Function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness Function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a Database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness Function for the genetic program. Genetic program based Data Mining is then conducted. This procedure incorporates not only the experts? rules into the fitness Function, but also the information in the Database. The information extracted through this process is the internal design specifications of the sensor. Significant mathematical formalism and experimental results related to GP based Data Mining for reverse engineering will be provided.
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Fuzzy Decision Trees for Planning and Autonomous Control of a Coordinated Team of UAVs
Signal Processing Sensor Fusion and Target Recognition XVI, 2007Co-Authors: James F. Smith, Thanhvu NguyenAbstract:ABSTRACT A fuzzy logic resource manager that enables a collection of unmanned aerial vehicles (UAVs) to automatically cooperate to make meteorological measurements will be discussed. Once in flight no human intervention is required. Planning and real-time control algorithms determine the optimal trajectory and points each UAV will sample, while taking into account the UAVs risk, risk tolerance, reliability, mission priority, fuel limitations, mission cost, and related uncertainties. The control algorithm permits newly obtained information about weather and other events to be introduced to allow the UAVs to be more effective. The appr oach is illustrated by a discussion of the fuzzy decision tree for UAV path assignment and related simulation. The different fuzzy membership Functions on the tree are described in mathematical detail. The different methods by which this tree is obtained are summarized including a method based on using a genetic program as a Data Mining Function. A second fuzzy decision tree that allows the UAVs to automatically collaborate without human intervention is discussed. This tree permits three different types of collaborative behavior between the UAVs. Simulations illustrating how the tree allows the different types of collaboration to be automated are provided. Simulations also show the ability of the control al gorithm to allow UAVs to effectively cooperate to increase the UAV teams likelihood of success. Keywords: resource management, fuzzy logic, planning algorithms, decision support algorithms, cooperative behavior
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Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security - Genetic program based Data Mining to reverse engineer digital logic
Data Mining Intrusion Detection Information Assurance and Data Networks Security 2006, 2006Co-Authors: James F. Smith, Thanhvu NguyenAbstract:A Data Mining based procedure for automated reverse engineering and defect discovery has been developed. The Data Mining algorithm for reverse engineering uses a genetic program (GP) as a Data Mining Function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness Function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a Database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness Function for the genetic program. Genetic program based Data Mining is then conducted. This procedure incorporates not only the experts' rules into the fitness Function, but also the information in the Database. The information extracted through this process is the internal design specifications of the sensor. Uncertainty related to the input-output Database and the expert based rule set can significantly alter the reverse engineering results. Significant experimental and theoretical results related to GP based Data Mining for reverse engineering will be provided. Methods of quantifying uncertainty and its effects will be presented. Finally methods for reducing the uncertainty will be examined.
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Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security - Data-Mining-based automated reverse engineering and defect discovery
Data Mining Intrusion Detection Information Assurance and Data Networks Security 2005, 2005Co-Authors: James F. Smith, Thanhvu NguyenAbstract:A Data Mining based procedure for automated reverse engineering and defect discovery has been developed. The Data Mining algorithm for reverse engineering uses a genetic program (GP) as a Data Mining Function. A GP is an evolutionary algorithm that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a fitness Function. The system to be reverse engineered is typically a sensor that may not be disassembled and for which there are no design documents. The sensor is used to create a Database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness Function for the GP allowing GP based Data Mining. This procedure incorporates not only the experts’ rules into the fitness Function, but also the information in the Database. The information extracted through this process is the internal design specifications of the sensor. These design properties can be used to create a fitness Function for a genetic algorithm, which is in turn used to search for defects in the digital logic design. Significant theoretical and experimental results are provided.
Li Lan - One of the best experts on this subject based on the ideXlab platform.
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Research of Web Mining Technology Based on XML
Networks Security Wireless Communications and Trusted Computing 2009. NSWCTC '09. International Conference on, 2009Co-Authors: Li Lan, Rong Qiao-meiAbstract:Web Data Mining is a new important research field in Data Mining. In this paper, the conception and characteristic of Data Mining based on Web are introduced the process and the general methods of Data Mining based on Web are expatiated. At present many websites are built with HTML, which is difficult to achieve real effective and accurate web Mining. The appearance of XML has brought convenience for it. Based on the research of web Mining, XML is used to transform semi-structured Data to well structured Data, and a model of web Mining system which has basic Data Mining Function and faces multi-Data on the Web is built. At the same time, the problem in Data Mining is analyzed and studied. An example is put forward to prove the solution.
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Application of Data Mining Technology In Electronic Business
Communications Technology, 2007Co-Authors: Li LanAbstract:E-business is becoming a trend,the new knowledge discovered in the information resource related will be of benefit to developing pertinence E-business.Base on C/s architecture, an on-line shopping system with a Data Mining Function is designed. Functions of the main modules are analyzed in detail and some solutions to some practical problems are proposed.
Y J Akhila - One of the best experts on this subject based on the ideXlab platform.
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SD Miner - A Spatial Data Mining System
International Journal of Research, 2014Co-Authors: Y J Akhila, Akshaya Naik, Bhagyashree Hegde, Pooja Shetty, A J Krishna MohanAbstract:nfluenced by the GIS technology, a vast volume of bulk Data have been stored known as spatial Data, using the spatial Data Mining technique. In this paper we propose a new spatial Data Mining known as SD-Miner. It consists of three parts. They are graphical interface for input and output, the Data storage module using DBMS, a Data Mining module for spatial Data Mining Function. The system proposes about spatio-temporal Data and clustering. In this paper, we take an example of Intel lab to show spatial Data Mining Functions.
