Decision Support System and Automated Negotiations

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Author Bio
Part 1: Decision Support System
Chapter 1: Decision Support System for Agriculture
1.1 Problem Statement
1.2 Motivation
1.3 Grape Production in India
1.4 Decision Support Systems
1.5 Integrated Pest Management
1.6 Integrated Nutrient Management and Smart Irrigation
1.7 Government of India Initiatives for Decision Support to Farmers
Chapter 2: Ontology Development
2.1 Building Ontology from Text
2.1.1 Building Agricultural Ontology
2.2 Ontology Merging
2.3 Use of Image Processing for Pests and Diseases Detection
2.4 Decision Support Systems and Expert Systems
2.5 Nutrition Management
2.6 Ontology Management
2.7 Irrigation Management Systems
Chapter 3: Building Vineyards Knowledge Base
3.1 Introduction
3.1.1 Ontology
3.1.1.1 Upper Ontology
3.1.1.2 Domain Ontology
3.1.2 AGROVOC
3.1.3 Web Ontology Language
3.1.4 Protégé
3.1.5 Jena API
3.2 Ontology Construction from Text
3.2.1 Relationship Extraction
3.2.2 Pest Description Ranking
3.3 IPM Ontology Construction
3.4 Evaluation of Grape-Pest-Management Ontology
3.5 Conclusions
Chapter 4: Knowledge Bases: Making It All Together
4.1 Introduction
4.1.1 Literature Survey
4.2 IPM Ontology Merging with IPMOntoShare
4.3 Implementation and Evaluation
4.4 Conclusions
Chapter 5: PDMGrapes: Forecasting Occurrence of Pests on Grapes
5.1 Introduction
5.1.1 Literature Survey
5.1.2 Using Fuzzy Logic Based Inference Engine
5.1.2.1 Implementation
5.1.2.1.1 jFuzzyLogic
5.1.2.1.2 JSOUP Library
5.1.2.1.3 Adding Rules to Inference Engine
5.1.3 Semantic Web Rule Language
5.1.4 Extending Ontology
5.1.5 Sensor Ontology
5.1.6 Conclusions
5.2 Disease Detection on Grape Crop
5.2.1 Introduction
5.2.2 Literature Survey
5.2.3 Disease Detection Using Image Processing Techniques
5.2.4 Implementation and Analysis
5.2.4.1 OpenCV
5.2.5 Pest and Disease Management Using PDMGrapes
5.2.6 Smartphone App
5.2.6.1 Android Studio
5.2.7 Existing Practice versus Developed Decision Support System
5.2.8 Performance Evaluation of PDMGrapes
Chapter 6: Nutrient Management
6.1 Introduction
6.1.1 Literature Survey
6.2 Methods
6.2.1 Building NM Ontology
6.2.2 Mapping of Concepts from Decision Tree for Ontology Evolution
6.3 Decision Support
6.3.1 Mapping of Concepts from Decision Tree for Rules Formation
6.3.2 Decision Support System
6.4 Evaluation
6.4.1 Quantitative Analysis
6.4.2 Qualitative Analysis
6.5 Conclusions
Chapter 7: Irrigation Management
7.1 Introduction
7.2 Literature Survey
7.3 Methodology
7.3.1 Building Vineyard Ontology
7.3.2 Smart Irrigation Ontology
7.4 Conclusions
Chapter 8: Crop Suitability Recommendation
8.1 Introduction
8.2 Design and Implementation
8.2.1 Crop Recommendation
8.2.1.1 Fertilizer Recommendation
8.3 Performance Evaluation
8.4 Conclusions
Part 2: Automated Negotiation
Chapter 9: Negotiation
9.1 Need of Negotiation Automation
9.2 History of Electronic Negotiation
9.3 Negotiation and Latest Technologies
9.4 Multilateral Negotiations and Related Issues
9.5 Negotiations and Multiple Strategies
9.6 Organization of Part 2
Chapter 10: Literature Survey
10.1 Motivation for the Survey
10.2 Auctions versus Negotiations
10.3 Negotiation Types
10.3.1 Distributive Negotiations – The Fixed Pie
10.3.2 Integrative Negotiations – Everybody Wins Something (Usually)
10.4 Negotiation Tactics
10.5 Existing Systems and Issues
10.5.1 Survey on Prediction Part in E-Negotiation
10.5.2 Survey on Multilateral Negotiations and Issues
10.5.3 Survey on Latest Technologies in E-Negotiation
10.6 Survey on Multiple Strategies
10.6.1 Literature Survey on Strategy Selection
10.6.2 Literature Survey on Deployment Techniques
10.6.3 Literature Survey on Acceptance Model
10.7 Comparison of Different Methods with their Strengths and Limitations
Chapter 11: Problem Statement and Scope
11.1 Problem Definition
11.2 Scope and Purpose
11.3 Constraints
11.4 Feasibility Study
11.5 The Gap and Analysis
11.6 Mathematical Modeling
11.6.1 Mathematical Model for Multiple Strategies
Chapter 12: Methodology
12.1 Design
12.1.1 System Architecture Bilateral E-Negotiation and Opponent’s Behavior Prediction based on Decision Support Systems
12.1.2 System Architecture Automated Multilateral Negotiation with Linear Programming
12.1.3 System Architecture E-Negotiation Using Rule-Based and Case-Based Reasoning (earlier published in www.igi-global.com)
12.1.4 System Architecture Multi Strategy Based E-Negotiation
12.2 System Details
12.2.1 Proposed Negotiation Protocol
12.2.2 Reservation Value
12.2.2.1 Running Negotiation Session
12.2.2.2 E-Negotiation Terminologies (in Multi-Strategy Base)
12.2.3 Generic Framework for Automated Negotiation
12.3 UML Diagrams
12.3.1 Use-Case Diagram
12.3.2 Class Diagram
12.3.3 Activity Diagram
12.3.4 Sequence Diagram
12.3.5 Software and Hardware Requirements
Chapter 13: Results and Analysis
13.1 Negotiation Scenario
13.1.1 Agent Selection
13.1.2 Negotiations
13.2 Prediction Algorithm
13.2.1 Working of Multilateral Model
13.2.2 Working of Multi Strategy Selection Model and Algorithm
13.3 Analysis
13.3.1 For Bilateral System with Opponents Behavior Prediction
13.3.2 Multilateral Negotiations
13.3.3 Negotiation Using RBR and CBR and Improved Memory Utilization and Response Time
13.3.4 Multi-Strategy Selection
Chapter 14: Conclusion
14.1 Future Work (According to Earlier Published Results on Web Source www.ijert.org)
14.2 Limitations of Research
14.3 Concluding Remarks
Bibliography
Index