Advances in Computing and Intelligent Systems: Proceedings of ICACM 2019 (Algorithms for Intelligent Systems)

Preface\nContents\nAbout the Editors\nIntuitionistic Fuzzy Shannon Entropy Weight Based Multi-criteria Decision Model with TOPSIS to Analyze Security Risks and Select Online Transaction Method\n	1 Introduction\n	2 Online Payment Systems\n	3 Fuzzy Set, Intuitionistic Fuzzy Set (IFS)\n		3.1 Entropy, Fuzzy Entropy, and Intuitionistic Fuzzy Entropy\n	4 An Algorithm for Intuitionistic Fuzzy TOPSIS\n	5 Application of IFS-TOPSIS Method in Selecting Online Payment System\n	6 Conclusion\n	References\nFermat Spiral-Based Moth-Flame Optimization Algorithm for Object-Oriented Testing\n	1 Introduction\n	2 Literature Review\n	3 Methodology Used\n		3.1 Moth-Flame Optimization Algorithm\n		3.2 Object-Oriented Benchmarks Used\n	4 Proposed Moth-Flame Optimization Algorithm—A Variant\n		4.1 Mathematical Formulation\n		4.2 Steps Used to Generate Test Paths Using MFO Variant\n	5 Experimental Evaluation of Proposed Variant of MFO\n	6 Conclusion and Future Work\n	References\nA Comparative Study of Information Retrieval Using Machine Learning\n	1 Introduction\n	2 Literature Survey\n	3 Gap Analysis\n	4 Conclusion\n	References\nAdaptive Background Subtraction Using Manual Approach for Static Images\n	1 Introduction\n	2 System Design Approach\n	3 Methods\n	4 Algorithms\n	5 Proposed Algorithm for Manual Selection\n	6 Proposed Algorithm for Automatic Region Selection\n	7 Results\n	8 Evaluation\n	9 Conclusion\n	10 Future Scope\n	References\nTweetsDaily: Categorised News from Twitter\n	1 Introduction\n	2 Related Work\n	3 Proposed Work\n		3.1 Data Collection\n		3.2 Labelling\n		3.3 Feature Extraction\n		3.4 Building Feature Sets\n		3.5 Training of Individual Classifiers\n		3.6 Combining Trained Classifiers to Form an Ensemble Classifier\n	4 User Interface\n	5 Experiments and Results\n		5.1 Testing of Indian News Ensemble Classifier\n		5.2 Testing of Global News Ensemble Classifier\n	6 Conclusion and Future Work\n	References\nCompressing Metaclass Files Through String Optimization\n	1 Introduction\n	2 String Optimization\n	3 Conclusion\n	References\nAn Algorithm to Generate Largest Prime Number\n	1 Introduction\n	2 Literature Survey\n		2.1 Prime Sieves\n		2.2 6k ± 1\n		2.3 William’s Function for Prime Numbers\n		2.4 Fermat’s Little Theorem\n		2.5 Solovay–Strassen Algorithm\n		2.6 Miller–Rabin Algorithm\n		2.7 AKS Algorithm\n		2.8 Improved Basic Method\n	3 Observation Table\n	4 Our Approach\n		4.1 Prime 1’s Algorithm\n		4.2 Reverse Prime Algorithm\n		4.3 Prime Palindromes\n	5 Conclusion\n	6 Future Approach\n	References\nDevelopment of a Discretization Methodology for 2.5D Milling Toolpath Optimization Using Genetic Algorithm\n	1 Introduction\n	2 Work Area Discretization\n	3 Reduction of Discrete Elements\n	4 Generation of Valid Toolpath\n	5 Optimization Problem\n		5.1 Minimization of Cutting Distance\n		5.2 Minimization of Tool Parking Distance\n		5.3 Minimization of Jerk\n		5.4 Minimization of Tool-Change Time\n	6 Optimization Method—Genetic Algorithm (GA)\n		6.1 GA Operators\n		6.2 Fitness Evaluation\n	7 Results and Discussion\n		7.1 Search Space and Computational Time\n		7.2 Selection of GA Parameters\n		7.3 Generation of Toolpaths for Different Objectives\n	8 Conclusions\n	References\nMachine Learning Based Prediction of PM 2.5 Pollution Level in Delhi\n	1 Introduction\n	2 Methodology\n		2.1 Dataset\n		2.2 Approach\n		2.3 Methods\n	3 Results and Conclusion\n	References\nA Comparative Study of Load Balancing Algorithms in a Cloud Environment\n	1 Introduction\n	2 Load Balancing\n		2.1 Benefits of Load Balancing\n		2.2 Categories of Algorithm for Load Balancing\n		2.3 Load Balancing Algorithms\n		2.4 Comparison of Algorithm for Load Balancing\n	3 Conclusion\n	References\nInformation Retrieval from Search Engine Using Particle Swarm Optimization\n	1 Introduction\n	2 Methods and Approach\n	3 Experiments\n	4 Result and Conclusion\n	References\nGenetic Algorithm Based Multi-objective Optimization Framework to Solve Traveling Salesman Problem\n	1 Introduction\n	2 Literature Review\n	3 Objectives of the Research\n		3.1 Multi-objective Optimization of TSP\n		3.2 Genetic Algorithm\n		3.3 Proposed Framework of TSP\n	4 Discussion of Results\n	5 Conclusions\n	References\nDesign of Optimal PID Controller for Varied System Using Teaching–Learning-Based Optimization\n	1 Introduction\n	2 Teaching–Learning-Based Algorithm\n		2.1 Teacher Phase\n		2.2 Learning Phase\n	3 Various Problems\n		3.1 Speed Control of DC Motor System\n		3.2 Automatic Voltage Regulator System\n	4 Result and Discussion\n	5 Conclusion\n	References\nInnovative Review on Artificial Bee Colony Algorithm and Its Variants\n	1 Introduction\n	2 ABC Algorithm\n	3 Extensions of ABC Algorithm\n	4 Modified Approaches in ABC Algorithm\n		4.1 Best-so-Far Artificial Bee Colony Algorithm\n		4.2 Global Best Artificial Bee Colony Algorithm\n		4.3 Memetic Search in Artificial Bee Colony Algorithm\n	5 Applications of ABC Algorithm\n		5.1 Traveling Salesmen Problem\n		5.2 Chart Shading\n		5.3 Bioinformatics Application\n		5.4 Image Processing Applications\n		5.5 Benchmarking Optimization\n		5.6 Clustering\n	6 Experimental Results\n	7 Conclusion\n	References\nMultilinear Regression Model to Predict Correlation Between IT Graduate Attributes for Employability Using R\n	1 Introduction and Literature Survey\n		1.1 Skill\n		1.2 Graduate Skills\n		1.3 Employability Skills\n	2 Data Mining and Its Role\n	3 Solution of the Model\n	4 Measures of Performances\n	5 Conclusions and Future Scope\n	References\nAn Intelligent Journey to Machine Learning Applications in Component-Based Software Engineering\n	1 Introduction\n	2 Machine Learning and Software Engineering—The Future\n	3 Artificial Intelligence and Machine Learning Algorithms in Software Development Life Cycle\n	4 Conclusions\n	References\nEffective Prediction of Type II Diabetes Mellitus Using Data Mining Classifiers and SMOTE\n	1 Introduction\n	2 Related Work\n	3 Class Imbalance Problem\n	4 Materials and Methods\n		4.1 Dataset Used\n		4.2 WEKA\n		4.3 SMOTE (Synthetic Minority Oversampling Technique)\n		4.4 Multi-Layer Perceptron (MLP)\n		4.5 Simple Logistic\n		4.6 Bagging\n		4.7 Support Vector Machine (SVM)\n		4.8 Decision Tree\n		4.9 Statistical Evaluation Metrics\n		4.10 ROC (Receivers Operator Curve)\n		4.11 10-Fold Cross Validations\n	5 Experimentation\n	6 Analysis of Result\n	7 Conclusion\n	References\nA Comprehensive Analysis of Classification Methods for Big Data Stream\n	1 Introduction to Big Data\n	2 Big Data Mining\n		2.1 Big Data Capture and Collection\n		2.2 Storage and Management of Big Data\n		2.3 Analysis of Big Data and Decision Taking\n	3 Classification of Big Data\n	4 WEKA for Big Data\n	5 Experimental Results and Analysis\n	6 Conclusion and Future Scope\n	References\nA Literature Analysis on Privacy Preservation Techniques\n	1 Introduction\n	2 Anonymization Techniques and Associated Risks\n		2.1 Data Anonymization\n		2.2 Anonymization Techniques\n		2.3 K-Anonymization Methods\n	3 Anonymization Algorithms\n		3.1 Datafly Algorithm\n		3.2 Mondrian Algorithm\n		3.3 Comparison of DF and Mondrian\n	4 Conclusion and Future Scope\n	Rеfеrеncеs\nAn Overview of Recommendation System: Methods and Techniques\n	1 Introduction\n	2 Related Work\n	3 Approaches to Recommendation System\n		3.1 Non-personalized Recommendation System\n		3.2 Content-Based Filtering System\n		3.3 Collaborative Filtering System\n		3.4 Knowledge-Based Filtering System\n		3.5 Sentimental Product Recommendation\n		3.6 Group Filtering\n		3.7 Hybrid-Based Filtering System\n	4 Conclusion and Future Work\n	References\nEmpirical Evaluation of Shallow and Deep Classifiers for Rumor Detection\n	1 Introduction\n	2 Literature Survey\n	3 System Architecture\n		3.1 Dataset\n		3.2 Data Pre-processing\n		3.3 Feature Extraction\n		3.4 Shallow Classifiers\n		3.5 Deep Classifiers\n	4 Results and Discussion\n		4.1 Shallow Classifier Performance\n		4.2 Deep Classifier Performance\n	5 Conclusion\n	References\nShort-Term Hydrothermal Scheduling Using Gray Wolf Optimization\n	1 Introduction\n	2 Problem Formulation\n		2.1 Objective Function\n	3 Gray Wolf Optimization\n		3.1 Mathematical Model\n	4 Numerical Results\n		4.1 Robustness\n		4.2 Solution Quantity\n	5 Conclusion\n	References\nFeature Selection Using SEER Data for the Survivability of Ovarian Cancer Patients\n	1 Introduction\n	2 Data Analysis and Preprocessing\n	3 Experiments\n		3.1 Methodology\n		3.2 Feature Selection Algorithms\n	4 Results\n	5 Conclusion\n	References\nMonitoring Logistics Through Blockchain\n	1 Introduction\n	2 Methodology\n		2.1 Functionaries in Blockchain\n		2.2 Types of Blockchain\n		2.3 Features of Blockchain\n	3 Ethereum and Smart Contracts\n		3.1 Ethereum\n		3.2 Smart Contracts\n	4 Design\n		4.1 Smart Contract\n	5 Conclusion\n	References\nAn M/M/2 Heterogeneous Service Markovian Feedback Queuing Model with Reverse Balking, Reneging and Retention of Reneged Customers\n	1 Introduction and Literature Survey\n	2 Mathematical Model Formulation\n	3 Solution of the Model—Probabilistic Measures\n	4 Measures of Performances\n	5 Conclusions and Future Scope\n	References\nInvestigation of Facial Expressions  for Physiological Parameter Measurement\n	1 Introduction\n	2 Preprocessing\n	3 Linear Discriminant Analysis\n	4 Discrete Wavelet Transform (DWT)\n	5 Database\n	6 Experimental Setup and Results\n	7 Conclusion\n	References\nIdentification of Severity of Coronary Artery Disease: A Multiclass Deep Learning Framework\n	1 Introduction\n	2 Data and Methods\n		2.1 Dataset Description\n		2.2 Correlation-Based Feature Subset Selection\n		2.3 Multilayer Perceptron\n		2.4 Proposed Model\n	3 Results\n		3.1 Confusion Matrix\n	4 Conclusion\n	References\nAn Intelligent System to Generate Possible Job List for Freelancers\n	1 Introduction\n	2 Related Work\n	3 Preliminaries\n		3.1 Frequent Item Set Generation Techniques\n		3.2 Multiple Keyword Matching Strategies\n	4 Methodology\n		4.1 Extract Freelancer’s Job-Related Information\n		4.2 Find Out Freelancer’s Frequent Skill Sets\n		4.3 Generate Possible Job Lists\n	5 Experimental Results and Discussion\n		5.1 Experimental Data and Environment Setup\n		5.2 Candidate and Frequent Skill Sets Versus Minimum Support\n		5.3 Run Time (ms) and Required Memory (mb)\n		5.4 Length of Generated Frequent Skill Sets\n		5.5 Skills Considered and Maximum Skills Per Job Versus Number of Generated Frequent Skill Sets\n	6 Conclusion and Future Directions\n	References\nA Novel Image Based Method for Detection and Measurement of Gall Stones\n	1 Introduction\n	2 Existing Method to Diagnose and Evaluate the Gallstones\n	3 Treatment for Gall Stones\n	4 Research Highlights\n	5 Proposed Method\n	6 Results and Discussion\n	7 Conclusion\n	References\nBig Data and Query Optimization Techniques\n	1 Introduction\n	2 Literature Study and Comparative Analysis\n	3 Conclusion\n	References\nCategorization and Classification of Uber Reviews\n	1 Introduction\n	2 Literature Review\n	3 Why Choose UBER\n	4 Data Sources\n		4.1 Twitter Tweets\n		4.2 Uber App Reviews\n	5 Methodology\n		5.1 Data Loading and Data Cleaning\n		5.2 Applying Data Mining Techniques\n		5.3 Classifier Selection\n		5.4 Evaluation Parameters\n		5.5 Knowledge Presentation\n	6 Conclusion and Future Work\n	References\nReview Paper on Novel Approach Improvising Techniques for Image Detection Using Deep Learning\n	1 Introduction\n	2 Problem Overview\n	3 Feature Selection\n		3.1 Filter Methods\n		3.2 Wrapper Methods\n	4 Simultaneous Detection and Segmentation\n	5 Residual Network\n		5.1 Identity Block\n		5.2 Convolution Block\n	6 Conclusion\n	References\nComparative Analysis of Selected Variant of Spider Monkey Optimization Algorithm\n	1 Introduction\n	2 SMO Algorithm\n		2.1 Local Leader Phase (LLP)\n		2.2 Global Leader Phase (GLP)\n		2.3 Global Leader Learning (GLL) Phase\n		2.4 Local Leader Learning (LLL) Phase\n		2.5 Local Leader Decision (LLD) Phase\n	3 Variant of SMO\n		3.1 ESMO\n		3.2 CSMO\n		3.3 ESMO\n	4 Result Analysis\n	5 Conclusion\n	References\nA Novel Method to Access Scientific Data from IRNSS/NaVIC Station Using Cloud Computing\n	1 Introduction\n	2 Service Models\n		2.1 Infrastructure as a Service\n		2.2 Platform as a Service\n		2.3 Software as a Service\n	3 Deployment Models\n		3.1 Public Cloud\n		3.2 Private Cloud\n		3.3 Hybrid Cloud\n	4 Compute Domain\n		4.1 Elastic Cloud Compute (EC2)\n		4.2 Elastic Load Balancer\n		4.3 Auto-scaling\n	5 Storage Domain\n		5.1 Simple Storage Service (S3)\n		5.2 Cloud Front\n		5.3 Elastic Block Storage\n		5.4 Amazon Glacier\n	6 Networking Domain\n		6.1 Virtual Private Cloud (VPC)\n		6.2 Direct Connect\n		6.3 Route53\n	7 Management Domain\n		7.1 CloudWatch\n		7.2 OpsWorks\n		7.3 Trusted Advisor\n	8 Security Domain\n		8.1 Identity and Access Management (IAM)\n		8.2 Key Management Service (KMS)\n	9 Results\n		9.1 Using Services S3 and IAM\n	10 Conclusion\n	References\nLive Class Monitoring Using Machine Learning\n	1 Introduction\n		1.1 Brief History\n		1.2 Current Scenario\n		1.3 Working\n	2 Introduction\n		2.1 How LBPH Works?\n	3 Conclusion and Future Work\n	References\nDeep Learning for Big Data Analytics\n	1 Introduction\n	2 Deep Learning\n		2.1 Deep Belief Network\n		2.2 Stack Auto-Encoder\n		2.3 Recurrent Neural Network\n		2.4 Convolutional Neural Network\n	3 Big Data\n		3.1 Big Data Analytics\n	4 Deep Learning for Big Data Analytics\n	5 Conclusion\n	References\nText-Based Spam Tweets Detection Using Neural Networks\n	1 Introduction\n	2 Literature Survey\n	3 Proposed System\n	4 Results and Discussion\n	5 Conclusion and Future Work\n	References\nPreserving IPR Using Reversible Digital Watermarking\n	1 Introduction\n	2 Literature Review\n	3 Method Proposed\n		3.1 The Proposed Method Will Have the Following Properties\n		3.2 Embedding Algorithm\n		3.3 Extraction Algorithm\n	4 Conclusion and Future Scope\n	References\nAnalysis of Part of Speech Tags in Language Identification of Code-Mixed Text\n	1 Introduction\n	2 Language Identification and Code-Mixed Text\n	3 POS Tag Based Language Identification for Code-Mixed Text\n		3.1 Transliteration\n		3.2 Hindi–English POS Tagging and Language Labelling\n	4 Experiments for Task Evaluation\n	5 Conclusion and Future Scope\n	References\nSentiment Analysis of Smartphone Product Reviews Using Weightage Calculation\n	1 Introduction\n	2 Literature Survey\n	3 Methodology\n	4 Result and Discussion\n		4.1 Experimental Setup\n		4.2 Result\n	5 Conclusion and Future Enhancement\n	References\nPersonal Identity on Blockchain\n	1 Introduction\n		1.1 Objectives\n	2 Proposed System\n		2.1 Creation of Identity Documents\n		2.2 Storing of Documents\n		2.3 Sharing of Documents\n	3 Implementation\n		3.1 Method and Implementation Tools\n	4 Use Cases of Identity Management on Blockchain\n	5 Conclusion\n	References\nInvestigation of Robust Control of Position Control System\n	1 Introduction\n	2 Sun Seeker System (SSS) Modelling\n	3 Control Schemes\n		3.1 PID Controllers\n		3.2 FOPID Controllers\n	4 Optimization of Controller Parameter\n	5 Simulations and Result Discussions\n	6 Conclusions\n	References\nReal-Time Hand Hygiene Dispenser System Using Internet of Things\n	1 Introduction\n	2 Literature Review\n	3 Design and Implementation\n		3.1 Interfacing with Sensor and Display\n		3.2 Interfacing with RFID Reader\n		3.3 Interfacing with Level-Sensing Circuit\n	4 Block Diagram\n	5 Results\n	6 Conclusion\n	References\nModeling the Factors Affecting Crime Against Women: Using ISM Technique\n	1 Introduction\n	2 Past Study\n	3 Proposed Model\n		3.1 Factor Identification Related to the Problem and Contextual Relationship Between Them\n		3.2 Initial and Final Reachability Matrix\n		3.3 Assignment of Levels\n		3.4 Construction of Conical Matrix\n		3.5 Formation of Digraph and Model\n	4 Conclusion and Future Scope\n	References\nImpact of Business Analytics for Smart Education System and Management Functions\n	1 Introduction\n	2 Review of Literature\n	3 Statement of the Problems\n	4 Objectives of the Study\n	5 Hypotheses for the Study\n	6 Limitations of the Study\n	7 Research Methodology\n	8 Data Analysis\n		8.1 Analyzing the Level of Difficulties in Changing the Impact of Business Analytics for Smart Education System and Management Functions\n		8.2 Analyzing the Level of Difficulties in Altering the Mechanism for Business Analytics to Ensure Smart Education System and Management Functions\n		8.3 Analysis Regarding the Challenges Faced by the Professionals While Executing Business Analytics in an Organization\n	9 Conclusions\n	References\nA Review on Security and Privacy Issues in Internet of Things\n	1 Introduction—Internet of Things\n	2 Applications of the Internet of Things in Near Future\n	3 Popular Security Issues with Internet of Thing’s Devices (with Suggested Countermeasures)\n		3.1 Insecure Web Interface\n		3.2 Unsatisfactory Authentication/Authorization\n		3.3 Insecure Network Services\n		3.4 Lack of Transport Encryption\n		3.5 Privacy Issues\n		3.6 Insecure Cloud Interface\n		3.7 Insecure Mobile Interface\n		3.8 Insufficient Security Configurability\n		3.9 Insecure Software/Firmware\n		3.10 Other Security Issues in Internet of Things\n	4 Security and Privacy Issues in Internet of Things Ecosystem\n	5 Challenges in Internet of Things\n	6 Conclusion\n	References\nStudy of Information Retrieval and Machine Learning-Based Software Bug Localization Models\n	1 Introduction\n	2 Software Bug Localization Using IR\n	3 TFIDF-Based Cosine Similarity\n	4 Experimental Setup\n		4.1 Case Studies\n		4.2 Performance Metrics\n	5 Results and Discussion\n	6 Related Work\n	7 Conclusion and Future Work\n	References\nA Review on Diverse Applications of Case-Based Reasoning\n	1 Introduction\n	2 CBR Framework\n	3 The Literature Review\n	4 Conclusion\n	References\nPetri Net Modeling and Analysis of SMART ATM\n	1 Introduction\n	2 Petri net Model\n	3 SMART ATM Petri Net Model Design\n	4 Result and Discussion\n		4.1 Reachability Analysis\n		4.2 Liveness Analysis\n		4.3 Boundedness Analysis\n	5 Conclusion\n	References\nRobot Path Planning Using Differential Evolution\n	1 Introduction\n	2 Overview of DE Algorithm\n		2.1 Mutation\n		2.2 Crossover\n		2.3 Selection\n	3 Robot Path Planning Problem\n	4 Conclusion\n	References\nModified Dragon-Aodv for Efficient Secure Routing\n	1 Introduction\n	2 Aodv Routing Protocol\n	3 Blackhole Attack\n	4 Dragonfly Algorithm\n	5 Dragon-Aodv Algorithm\n	6 DRAGON-AODV Algorithm\n	7 Implementation\n	8 Conclusion\n	References\nBrain Tumor Detection and Classification\n	1 Introduction\n	2 Existing System\n	3 Proposed Methodology\n		3.1 Data Collection\n		3.2 Preprocessing\n		3.3 Segmentation\n		3.4 Feature Extraction\n		3.5 Classification\n	4 Results and Discussions\n	5 Conclusion\n	6 Future Work\n	References\nA Multiple Criteria-Based Context-Aware Recommendation System for Agro-Cloud\n	1 Introduction\n	2 Literature Review\n	3 Proposed Methodology\n		3.1 Proposed Solution\n		3.2 Weightage Calculation Equations\n		3.3 Proposed System Flow\n		3.4 Proposed Algorithm Steps\n	4 Conclusion\n	References\nLatest Trends in Sheet Metal Components and Its Processes—A Literature Review\n	1 Introduction\n		1.1 Sheet Metal Priming Methods\n	2 Development of Literature\n	3 Application of Sheet Metal in Various Fields\n		3.1 Manufacturing Sector\n		3.2 Material Sector\n		3.3 Healthcare Sector\n		3.4 Civil Sector\n		3.5 Design Sector\n	4 Location Wise Recognition in Sheet Metal\n	5 Identified Research Gap and Proposed Research Objectives\n		5.1 Research Gap\n		5.2 Research Objectives\n	6 Conclusions\n	References\nAn Enhance Mechanism for Secure Data Sharing with Identity Management in Cloud Computing\n	1 Introduction\n	2 Literature Review\n	3 Proposed Methodology\n		3.1 Overview\n	4 Conclusion\n	References\nAn Enhance Mechanism to Recognize Shill Bidders in Real-Time Auctioning System\n	1 Introduction\n	2 Literature Review\n	3 Proposed Methodology\n		3.1 Data Cleaning Steps\n		3.2 Proposed System Flow\n		3.3 Algorithm\n		3.4 Token Generation and Verification\n	4 Conclusion\n	References\nAn Target-Based Privacy-Preserving Approach Using Collaborative Filtering and Anonymization Technique\n	1 Introduction\n	2 Literature Review\n	3 Proposed Methodology\n		3.1 Overview\n	4 Conclusion\n	References\nAn Effective Priority-Based Resource Allocation Approach in Cloud Computing\n	1 Introduction\n	2 Literature Review\n	3 Proposed Methodology\n		3.1 Overview\n	4 Conclusion\n	References\nA Comparative Study on CBIR Using Color Features and Different Distance Method\n	1 Introduction\n		1.1 Color Moment\n		1.2 Dominant Color Descriptor\n		1.3 HSV Histogram\n		1.4 Color Statistics\n		1.5 Color String\n		1.6 Color String\n		1.7 Similarity Metrics\n		1.8 Support Vector Machines\n	2 Related Work\n	3 Performance Measures\n		3.1 Data Set\n		3.2 Performance\n	4 Conclusion\n	References\nPerformance Evaluation of Wrapper-Based Feature Selection Techniques for Medical Datasets\n	1 Introduction\n	2 Related Work\n	3 Feature Selection\n		3.1 Filter Methods\n		3.2 Wrapper Methods\n		3.3 Embedded and Hybrid Methods\n	4 Proposed Methodology\n		4.1 Dataset Used\n		4.2 Model Diagram\n	5 Experiment and Results\n	6 Conclusion\n	References\nAuthor Index