LEADER 04392nam 2200721 450 001 9910138899403321 005 20221206183941.0 010 $a1-119-13467-6 024 7 $a10.1002/9781119134671 035 $a(CKB)2430000000040330 035 $a(CaBNVSL)mat07304003 035 $a(IDAMS)0b00006484a80f83 035 $a(IEEE)7304003 035 $a(SSID)ssj0000373594 035 $a(PQKBManifestationID)12145137 035 $a(PQKBTitleCode)TC0000373594 035 $a(PQKBWorkID)10440110 035 $a(PQKB)11102372 035 $a(EXLCZ)992430000000040330 100 $a20151222d2015 uy 101 0 $aeng 135 $aur|n||||||||| 181 $2rdacontent 182 $2isbdmedia 183 $2rdacarrier 200 10$aGuidance for the verification and validation of neural networks /$fLaura L. Pullum, Brian J. Taylor, Majorie A. Darrah 210 1$aHoboken, New Jersey :$cIEEE Computer Society,$dc2007. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2015] 215 $a1 PDF (ix, 133 pages) $cillustrations 225 1 $aEmerging technologies 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-470-08457-X 320 $aIncludes bibliographical references (p. 119-121) and index. 327 $aAreas of consideration for adaptive systems -- Verification and validation of neural networks-guidance -- Recent changes to IEEE std 1012. 330 $aGuidance for the Verification and Validation of Neural Networks is a supplement to the IEEE Standard for Software Verification and Validation, IEEE Std 1012-1998. Born out of a need by the National Aeronautics and Space Administration's safety- and mission-critical research, this book compiles over five years of applied research and development efforts. It is intended to assist the performance of verification and validation (V&V) activities on adaptive software systems, with emphasis given to neural network systems. The book discusses some of the difficulties with trying to assure adaptive systems in general, presents techniques and advice for the V&V practitioner confronted with such a task, and based on a neural network case study, identifies specific tasking and recommendations for the V&V of neural network systems. "As the demand for developing and assuring adaptive systems grows, this guidebook will provide practitioners with the insight and practical steps for verifying and validating neural networks. The work of the authors is a great step forward, offering a level of practical experience and advice for the software developers, assurance personnel, and those performing verification and validation of adaptive systems. This guide makes possible the daunting task of assuring this new technology. NASA is proud to sponsor such a realistic approach to what many might think a very futuristic subject. But adaptive systems with neural networks are here today and as the NASA Manager for Software Assurance and Safety, I believe this work by the authors will be a great resource for the systems we are building today and into tomorrow." -Martha S. Wetherholt, NASA Manager of Software Assurance and Software Safety NASA Headquarters, Office of Safety & Mission Assurance. 410 0$aEmerging technologies 606 $aNeural networks (Computer science) 606 $aComputer programs$xValidation 606 $aComputer programs$xVerification 606 $aNeural networks (Computer science)$xValidation 606 $aComputer programs$xVerification 606 $aComputer programs 606 $aEngineering & Applied Sciences$2HILCC 606 $aComputer Science$2HILCC 615 0$aNeural networks (Computer science) 615 0$aComputer programs$xValidation. 615 0$aComputer programs$xVerification. 615 0$aNeural networks (Computer science)$xValidation 615 0$aComputer programs$xVerification 615 0$aComputer programs 615 7$aEngineering & Applied Sciences 615 7$aComputer Science 676 $a006.32 700 $aPullum$b Laura L.$0953634 701 $aTaylor$b Brian J$0953635 701 $aDarrah$b Majorie A$0953636 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910138899403321 996 $aGuidance for the verification and validation of neural networks$92156295 997 $aUNINA LEADER 11633nam 2200565 450 001 9910559383303321 005 20221116145825.0 010 $a3-030-97269-0 035 $a(MiAaPQ)EBC6949921 035 $a(Au-PeEL)EBL6949921 035 $a(CKB)21479262900041 035 $a(PPN)262167794 035 $a(EXLCZ)9921479262900041 100 $a20221116d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial intelligence in project management and making decisions /$fPedro Y. Pin?ero Pe?rez, Rafael E. Bello Pe?rez and Janusz Kacprzyk, editors 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (423 pages) 225 1 $aStudies in computational intelligence ;$vVolume 1035 311 08$aPrint version: Piñero Pérez, Pedro Y. Artificial Intelligence in Project Management and Making Decisions Cham : Springer International Publishing AG,c2022 9783030972684 320 $aIncludes bibliographical references. 327 $aIntro -- Preface -- Acknowledgments -- Contents -- Part I Linguistic Data Summarization for Decision-Making in Project Management -- 1 Linguistic Data Summarization: A Systematic Review -- 1 Introduction -- 2 Methodology -- 3 Linguistic Data Summarization Review -- 3.1 Evolution and Trends in Protoforms for the Construction of Summaries -- 3.2 Methods or Techniques for the Generation of Linguistic Data Summaries -- 3.3 Main Validation Techniques and Methods Used in the Investigations -- 3.4 Areas of Application of Linguistic Summaries -- 4 Conclusions -- References -- 2 New Linguistic Data Summarization Approach for Prediction Problems in Project Management Applications -- 1 Introduction -- 2 Structure of Linguistic Summaries and Contact Points with Fuzzy Inference Systems -- 3 A New Approach for Inference Based on Linguistic Summaries -- 4 Application in Decision-Making in Project Management -- 4.1 Results of Test 1 Impact of the Use of Different Combinations of T Indicators in the Inference Process -- 4.2 Results of Test 2 Comparison of the Proposal with Other Inference Methods -- 5 Conclusions -- References -- 3 Linguistic Data Summarization with Multilingual Approach -- 1 Introduction -- 2 New Approach for Linguistic Summaries Generation by Using Controller Natural Language -- 2.1 Definition of Controlled Natural Languages for the Construction of Multilingual Linguistic Summaries -- 3 New Algorithms for Generation of Multilingual Linguistic Summaries -- 3.1 LPALDS Algorithm Based on Probabilistic Graphs -- 3.2 Algorithm for the Generation of Linguistic Summaries Based on Rough Sets (RSTLDS) -- 3.3 Algorithm for the Humanization of Linguistic Summaries Using Controlled Natural Languages. -- 4 Analysis of Results and Validation of the Proposed Algorithms -- 4.1 Comparison of the Proposed Algorithms with Others Reported in the Bibliography. 327 $a4.2 Validation of the Algorithms in Their Ability to Generate Summaries Under a Multilingual Approach -- 5 Conclusions -- References -- 4 Project to Improve Offensive Phase Finalization of Futsal Teams by Using Linguistic Data Summarization Techniques -- 1 Introduction -- 2 Discovering Linguistic Summaries Deal with Futsal Team Weaknesses -- 3 Results and Discussion -- 3.1 Variable Goal in the 2018/2019 Seasons -- 3.2 Variable Positive Shots in 2018/2019 Seasons -- 3.3 Static Positional Strategy Plays in 2018/2019 Seasons with Respect to Goal and Positive Shots -- 3.4 Positional Transitions in Motion in 2018/2019 Seasons Regarding Goal and Positive Shots -- 4 Conclusions -- References -- 5 Algorithms for Linguistic Description of Categorical Data -- 1 Introduction -- 2 Method for Generating Composite Linguistic Summaries -- 2.1 Generation of Association Rules -- 2.2 Building Type-I Constituent Summaries -- 2.3 Building Type-II Constituent Summaries -- 2.4 Building the Evidence Composite Relations -- 2.5 Building the Contrast Composite Relations -- 2.6 Building the Emphasis Composite Relations -- 3 Use Case -- 3.1 Design and Implementation -- 3.2 Results and Examples -- 4 Evaluating the Interpretability of Relations -- 4.1 Design -- 4.2 Instrument -- 5 Results and Discussion -- 6 Conclusions -- References -- 6 New Indicators for the Assessment of Linguistic Summaries Considering a Rough Sets Approach -- 1 Introduction -- 2 Traditional Indicators for the Evaluation of Linguistic Summaries -- 2.1 Degree of Truth -- 2.2 Degree of Imprecision T2 -- 2.3 Degree of Coverage T3 -- 2.4 Degree of Appropriateness T4 -- 2.5 Length of a Summary T5 -- 3 New Extensions of T Indicators to Evaluate Linguistic Summaries -- 3.1 Definitions and Notations Used in the Proposed Extensions -- 3.2 Extensions for Calculating the Degree of Truth Te1a. 327 $a3.3 Extensions to Degree of Imprecision -- 3.4 Extension to the Calculation of the Degree of Coverage Te3 -- 3.5 Extension to the Calculation of the Degree of Appropriateness Te4 -- 3.6 Extension to the Evaluation of the Length of Te5 Summaries -- 4 Comparison of Traditional and Extended Indicators -- 4.1 Analysis of the Behavior of the Degree of Truth Indicator and Its Extensions -- 4.2 Analysis of the Behavior of the Degree of Support Indicator and Its Extension -- 4.3 Analysis of the Behavior of the Degree of Appropriateness Indicator and Its Extension -- 4.4 Analysis of the Behavior of the Indicator Length of a Summary and Its Extension -- 4.5 Summary of Comparison of Indicators Regarding the Treatment of Uncertainty -- 5 Conclusions -- References -- Part II Planning and Sustainability of Projects Assisted by Artificial Intelligence -- 7 Constraints Learning Univariate Estimation of Distribution Algorithm on the Multi-mode Project Scheduling Problem -- 1 Introduction -- 2 Modeling the MMRCPSP Optimization Problem -- 2.1 Formalization of the Optimization Problem -- 2.2 Constraints Learning Univariate Marginal Distribution Algorithm (CLUMDA) -- 2.3 Solution Design -- 2.4 Detailed Formalization of the CLUMDA -- 3 Experimental Results and Discussion -- 3.1 "Mean Makespan" Variable -- 3.2 "Number of Times the Optimum Founded" Variable -- 4 Conclusions -- References -- 8 New Methods for Feasibility Analysis of Investment Projects in Uncertain Environments -- 1 Introduction -- 2 Background -- 3 Model for the Feasibility Analysis of Investment Projects in Environments with Uncertainty -- 4 Experimentation -- 4.1 Case Study -- 5 Conclusions -- References -- 9 Sustainability Risk Management for Project-Oriented Organizations -- 1 Introduction -- 2 Procedure -- 2.1 Stage 1. Previous Preparation -- 2.2 Stage 2. Organizational Analysis. 327 $a2.3 Stage 3. Risk Evaluation -- 2.4 Stage 4. Risk Treatment -- 2.5 Stage 5. Monitoring and Continuous Improvement -- 3 Results -- 3.1 User Satisfaction with the Proposed Procedure -- 3.2 Case Study -- 4 Conclusions -- References -- 10 New Extensions of Fuzzy Cognitive Maps for Sequential Multistage Decision-Making Problems: Application in Project Management -- 1 Introduction -- 2 Multistage Sequential Triangular Neutrosophic Cognitive Map (MSTrNCM) -- 2.1 Representation of the Relationships Among Concepts and Map Construction -- 2.2 Map Inference and Activation Function -- 3 Neutrosophic Cognitive Map Based on Linguistic Data Summarization -- 3.1 Representation of the Relationships Among Concepts and Map Construction -- 3.2 Inference Process of NCMLDS -- 4 Validation and Results Analysis -- 4.1 Experiment 1: Analysis of the Algorithms Regarding the Parameter Lambda ? -- 4.2 Experiment 2: Comparison Regarding the Error in Prediction and Precision -- 4.3 Experiment 3: Algorithms Applicability Analysis -- 4.4 Experiment 4: Evaluation of the Efficiency of Algorithms Considering the Indicator "Execution Time" -- 5 Conclusion and Future Work -- References -- 11 A Software Ecosystem for Project Management in BIM Environments Assisted by Artificial Intelligent Techniques -- 1 Introduction -- 2 Brief Analysis of Software Ecosystems -- 3 Architecture of the BusinessRedmine Software Ecosystem -- 4 Results Analysis -- 4.1 Experiment 1: Comparison of the Proposal with Other Tools -- 4.2 Experiment 2: Analysis of the System Implementation Process in Different Scenarios -- 4.3 Experiment 3: Analysis of the Behavior of the Project Evaluation Subsystem -- 5 Conclusions -- References -- Part III Knowledge and Human Resources Management Assisted by Artificial Intelligence -- 12 Team Formation Integrating Various Factors: Model and Solution Approach -- 1 Introduction. 327 $a2 Related Works -- 2.1 Formation of Student Teams -- 2.2 Formation of Experts Teams in Social Networks -- 2.3 Formation of Sports Teams -- 2.4 Formation of Professional Teams -- 2.5 Formation of Software Teams -- 2.6 Formation of Medical Teams -- 3 Multiple Team Formation Model -- 4 Solution Approach to the Multiple Team Formation Model -- 5 Experiments -- 6 Conclusions and Future Works -- References -- 13 A TOPSIS-Based Method for Personnel Selection in Software Projects -- 1 Introduction -- 2 Background on MCDM Process and Methods -- 3 The Proposed TOPSIS-Based Method for Personnel Selection in Software Projects -- 4 Solving a Personnel Selection Problem in a Cuban IT Project -- 5 Conclusions -- References -- 14 Combining Artificial Intelligence and Project Management Techniques in Ecosystem for Training and Innovation -- 1 Introduction -- 2 Proposal for an Ecosystem of Training and Innovation in Project Management -- 3 Analysis of Results and Application of the Program -- 3.1 Analysis of Results in the Application in the Master's Program in Project Management -- 3.2 Analysis of Results in the Development of the BusinessRedmine Ecosystem and Its Application in Different Environments -- 4 Conclusions -- References -- 15 Evaluation of an Accreditation Variable for University Institutions Using 2 Tuple Linguistic Representation Model -- 1 Introduction -- 2 Materials and Methods -- 2.1 Characteristics of the Quality Evaluation Process of Higher Education Institutions in Cuba -- 2.2 The Evaluation of the Quality of HEIs as a Decision-Making Problem -- 3 Results and Discussion -- 3.1 Description and Classification of the Problem -- 3.2 Solution of the Problem by Means of FLINSTONES -- 4 Conclusions -- References -- 16 Ontology-Based Management of the Scientific Activity in Software Development Projects -- 1 Introduction -- 2 Technologies and Tools. 327 $a3 Ontology for the Management of Scientific Activity. 410 0$aStudies in computational intelligence ;$vVolume 1035. 606 $aArtificial intelligence 606 $aProject management$xData processing 606 $aProject management$xDecision making 615 0$aArtificial intelligence. 615 0$aProject management$xData processing. 615 0$aProject management$xDecision making. 676 $a006.3 702 $aPin?ero Pe?rez$b Pedro Y. 702 $aBello Pe?rez$b Rafael E. 702 $aKacprzyk$b Janusz 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910559383303321 996 $aArtificial intelligence in project management and making decisions$92967954 997 $aUNINA