Machine Learning for Predictive Analysis

Name: Machine Learning for Predictive Analysis | Proceedings of ICTIS 2020
Brand: Springer
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Proceedings of ICTIS 2020

Amit Joshi Mahdi Khosravy Neeraj Gupta(Editor)

Springer (Publisher)

1st Edition

Published on 22. October 2020

XXIX, 627 pages

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978-981-15-7106-0 (ISBN)

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Amit Joshi is currently the Director of Global Knowledge Research Foundation and also an Entrepreneur Researcher who has completed his Masters and research in the areas of cloud computing and cryptography in medical imaging. Dr. Joshi has an experience of around 10 years in academic and industry in prestigious organizations. Dr. Joshi is an active member of ACM, IEEE, CSI, AMIE, IACSIT, Singapore, IDES, ACEEE, NPA and many other professional societies. Currently, Dr. Joshi is the International Chair of InterYIT at International Federation of Information Processing (IFIP, Austria). He has presented and published more than 50 papers in national and international journals/conferences of IEEE and ACM. Dr. Joshi has also edited more than 40 books which are published by Springer, ACM and other reputed publishers. Dr. Joshi has also organized more than 50 national and international conferences and programs in association with ACM, Springer and IEEE to name a few across different countries including India, UK, Europe, USA, Canada, Thailand, Egypt and many more.

Mahdi Khosravy holds a B.Sc. in Electrical Engineering (bio-electric) from Sahand University of Technology, Tabriz, Iran; an M.Sc. in Biomedical Engineering (bio-electric) from Beheshti University of Medical Studies, Tehran, Iran; and a Ph.D. in the field of Information Technology from the University of the Ryukyus, Okinawa, Japan. He received an award from the Head of the University for his research excellence. In 2010, he joined the University of Information Science and Technology (UIST), Ohrid, Macedonia as an Assistant Professor, and since 2018, he has been a Visiting Associate Professor at the Electrical Engineering Department, Federal University of Juiz de Fora in Brazil, and the Electrical Department at the University of the Ryukyus, Okinawa, Japan. Since November 2019, Dr. Khosravy has been a researcher at Media-integrated Laboratories, University of Osaka, Japan. He is a member of IEEE.

Neeraj Gupta received his Diploma in Environmental and Pollution Control, Civil Engineering in 1999, Bachelor of Engineering (B.E.) in Electrical and Electronics Engineering in 2003, Master of Technology (M.Tech.) in Engineering Systems in 2006, and his Ph.D. in Electrical Engineering from the Indian Institute of Technology (IIT), Kanpur, India, in 2013. He was a Postdoctoral Fellow (Sr. Project Engineer) at the Indian Institute of Technology (IIT) Jodhpur, India, for one year (June 2012-May 2013), and was a member of the faculty at the System Engineering Department of the same institute from 2013 to 2014. He then became an Assistant Professor at the Department of Applied IT Machine Intelligence and Robotics at the University for Information Science and Technology, Ohrid, Macedonia, from 2014 to 2017. Currently, he is an Assistant Professor at the School of Engineering and Computer Science, Oakland University, USA.

Content

Intro
Committees
Technical Program Committee Chairs
Technical Program Committee Members
Organizing Chairs
Organizing Secretary
Conference Secretary
Supporting Chairs
Preface
Contents
Editors and Contributors
A Hybrid Deep Learning Approach for Stock Price Prediction
1 Introduction
2 Model Design and Problem Evaluation
2.1 Input Stage
2.2 Analysis Stage
2.3 Output Stage
3 Experimental Setup
4 Experimental Results
5 Conclusion
References
Detection of Alphanumeric Characters by Connectionist Temporal Classification with Vanilla Beam Search Algorithm and NLP Using MATLAB and Keras
1 Introduction
2 Implementation for the System
2.1 Input Image Capturing from the Live Stream of Raw Camera
2.2 Increasing the Enhancement, Brightness and Contrast of the Captured Image
3 Results
4 Conclusions
References
Multilabel Toxic Comment Classification Using Supervised Machine Learning Algorithms
1 Introduction
2 Related Work
3 Dataset
4 Implementation
4.1 Preprocessing
4.2 Models
5 Analysis
6 Conclusion
7 Future Work
References
Model of Speed Spheroidization of Metals and Alloys Based on Multiprocessor Computing Complexes
1 Introduction
2 Research Problem Analysis
3 The Research Problem Purpose and Statement
4 Statement of the Research's Primary Material
5 Conclusions and Prospects for Further Research
6 Prospects for Further Research
References
Prediction of Sales Using Stacking Classifier
1 Introduction
2 Related Work
3 Proposed Method
3.1 Data Pre-processing
3.2 Merging Data
3.3 Modelling Data
3.4 Forecasting Using Stacking Classifier
4 Results
5 Conclusion
References
How to Use LDA Model to Analyze Patent Information? Taking Ships Integrated Power System as an Example
1 Introduction
2 Data and Methods
3 Analysis of Patent Technology Development
3.1 Language Distribution of Patent Announcement
3.2 Distribution of Patentees and Institutions
3.3 Distribution of DC and MC Classification Number
4 Distribution of Patent Core Technology
5 Conclusion and Discussion
References
A Comparison Study on Various Continuous Integration Tools in Software Development
1 Introduction
2 Continuous Integration
2.1 Elements of Continuous Integration System
3 Different Continuous Integration Tools
3.1 Jenkins
3.2 Travis
3.3 Codeship CI
3.4 Bamboo CI
3.5 TeamCity
3.6 CircleCI
4 Comparison of Continuous Integration Tools
5 Conclusion
References
A Holistic Study on Approaches to Prevent Sexual Harassment on Twitter
1 Introduction
2 Literature Survey
2.1 Wordlist-Based Approaches
2.2 Rule-Based Approaches
2.3 Machine Learning Based Approaches
2.4 Social Network Analysis
3 Research Gap
4 Conclusions
References
Cyber Bullying Detection Based on Twitter Dataset
1 Introduction
2 Proposed Approach
3 Methods
3.1 Data Pre-processing
3.2 Convolutional Neural Network
3.3 Long Short-Term Memory
4 Results
5 Future Work
6 Conclusion
References
Soil pH Prediction Using Machine Learning Classifiers and Color Spaces
1 Introduction
2 Literature Survey
3 Methodology
3.1 Dataset Discussion
3.2 Proposed System
3.3 Color Models and Classification Techniques
4 Results and Discussions
5 Conclusion
References
A Food Recommendation System Based on BMI, BMR, k-NN Algorithm, and a BPNN
1 Introduction
2 Review of Related Literature
3 Importance of BMI and BMR in the Food Recommendation System
4 Application of the k-NN Algorithm
5 Application of the BPNN
6 The Complete Structure of the Food Recommendation System
7 Conclusion
References
Complexity Reduced Bi-channel CNN for Image Classification
1 Introduction
2 Methodology
2.1 Multi-channel Convolutional Neural Network
2.2 Proposed Architecture
3 Experiments and Results
3.1 Software Requirements
3.2 Dataset Collection and Pre-processing
3.3 Simulation and Results
4 Conclusion
References
An Approach to Mitigate the Risk of Customer Churn Using Machine Learning Algorithms
1 Introduction
2 Literature Review
3 Dataset Overview
3.1 Visualization of Dataset
4 Proposed Work
4.1 Data Collection
4.2 Data Preparation
4.3 Prediction
4.4 Data Visualization Tools
5 Architecture
5.1 Support Vector Machine
5.2 Random Forest
5.3 KNN
5.4 Logistic Regression
6 Future Scope
7 Conclusion
References
Frequency Detection and Variation with Smart-Sensor Data Analysis Using Artificial Neural Network and Cloud Computing
1 Introduction
2 Methods
2.1 Artificial Neural Network
2.2 C# Application with ANN
2.3 Hall Effect Sensor Data Input with IoT Implementation
2.4 Transmission of Data Through Lora
2.5 Cloud to Application Interaction
3 Data Set and Simulation Result
3.1 Data Set
3.2 Simulation
4 Conclusion and Future Work
References
Comprehensive Study of Fetal Monitoring Methods for Detection of Fetal Compromise
1 Introduction
1.1 Fetal Monitoring
2 Related Work
2.1 Comparison Between Common Fetal Monitoring Devices
2.2 Study of CTG and Electrocardiogram (ECG) Based Devices for FHR and Uterine Contractions (UC) Accuracy
2.3 Study of CTG Only and CTG and ST Waveform Analysis Methods
2.4 Study of CTG Interpretation Methods
3 Proposed Methodology
4 Conclusion
References
Enhanced Flower Pollination Algorithm for Task Scheduling in Cloud Computing Environment
1 Introduction
2 Concept and Architecture of Cloud Computing
3 Proposed Task Scheduling Model
4 Flower Pollination Algorithm Overview
4.1 Our Modification
5 Experimental Results and Discussion
6 Conclusion
References
Counterfeit Currency Detection Using Supervised Machine Learning Algorithms
1 Introduction
2 Database
3 Performance Measures
3.1 Benchmark Model
4 Pre-processing
5 Training and Testing Data on Machine Learning Algorithms
6 Result and Analysis
7 Conclusion
References
Spam Mail Classification Using Ensemble and Non-Ensemble Machine Learning Algorithms
1 Introduction
2 Data Preprocessing and Feature Extraction
3 Classifier Models
3.1 K-Nearest Neighbor
3.2 Naïve Bayes
3.3 Support Vector Machines
3.4 Ensemble Method Classifiers
4 Optimization of Model Parameters
4.1 K-Nearest Neighbor (NN)
4.2 Naive Bayes
4.3 Support Vector Machines
4.4 Ensembling Classifier with Voting Approach
4.5 Ensembling Classifier with Boosting Approach
4.6 Ensembling Classifier with Bagging Approach
5 Results
6 Discussion and Conclusion
References
On the Desired Properties of Linear Feedback Shift Register (LFSR) Based High-Speed PN-Sequence-Generator
1 Introduction
2 Related Works
2.1 Some Comments on the "Novel Complex PN-Code-Generator"
2.2 Another New High-Speed PN-Generator
3 Mathematical Tools for Representation of Interleaved Structure
3.1 The Interleaved Structure and Methods for Representation and Analysis of LC of the Interleaved Sequences
4 LC of Proposed PN-Generator
4.1 The Big Differences in LC Due to the Nonlinear Structure
5 The Statistic Properties of the Interleaved Sequences
6 Hardware Implementation
7 Conclusion and Further Works
References
Syngas Assessment from Plastic Waste Using Artificial Neural Network-A Review
1 Introduction
2 Plastics Global Production and Statistics
3 Effects of Plastic Waste on Environment
4 Mechanical Recycling
5 Effect of Nanomaterial on Gasification
6 Artificial Neural Network in Gasification
7 Conclusion
References
Applications of Data Mining in Predicting Stock Values
1 Introduction
2 Literature Survey
3 Conclusion
References
Smart Artificial Intelligent-Based Controller for Hydroponic: New Technique for Soilless Plantation
1 Introduction
2 Need of Smart Greenhouse
3 AI-IoT Algorithm for Hydroponic Control Scheme
4 Differential Equation Algorithm for HCS Relaying in Greenhouse
5 Hydroponic Control Scheme
6 Methodology of the System
7 System Design and Realization
8 Results
9 Conclusion
References
Human Action Detection Using Deep Learning
1 Introduction
2 Related Works
3 Literature Survey
4 Existing System
5 Proposed System
6 Future Enhancement
7 Conclusion
References
Automatic Detection of Leaf Disease Using CNN Algorithm
1 Introduction
2 Database Collection
3 Experimental Settings
4 Proposed Methodology
5 Results and Discussion
5.1 Metrics
6 Conclusion
References
Prediction of Emotional Condition Through Dialog Narratives Using Deep Learning Approach
1 Introduction
2 Literature Review
3 Research Approach
3.1 Data Set-IEMOCAP
3.2 Data Preprocessing Phases
4 Proposed Emotion Prediction Model
4.1 Convolution Neural Network (CNN) Model
4.2 Long Short-Term Memory (LSTM) Model
4.3 Conversational Memory Network Model
4.4 Dialogue RNN
5 Results and Discussion
5.1 Comparitive Analysis Among Dialogue RNN and CNN Models
5.2 Comparision Among Dialogue RNN Varients
6 Conclusions
References
Software Requirements Classification and Prioritisation Using Machine Learning
1 Introduction
2 Related Work
2.1 Text Preparation Phase
2.2 Classification of Requirements Methods
2.3 Requirements Prioritisation Methods
2.4 Limitations of Existing Techniques
3 Proposed Work
4 Conclusion
References
Comparison of Hidden Markov Models and the FAST Algorithm for Feature-Aware Knowledge Tracing
1 Introduction
2 Literature Survey
3 Methodology
3.1 Dataset
3.2 Bayesian Knowledge Tracing
3.3 Hidden Markov Models
3.4 Feature-Aware Student Knowledge Tracing (FAST)
4 Comparison Methodology
5 Results
6 Discussion and Conclusion
References
ABCADF: Deploy Artificially Bee Colony Algorithm for Model Transformation Cohesive with Fitness Function of Adaptive Dragonfly Algorithm
1 Introduction
2 Motivation of the Work
2.1 Literature Survey
3 Proposed Bee Colony Algorithm for Effective Model Transformation
3.1 First Module of Proposed ABCADF Algorithm
3.2 Second Module of ABCADF Method
4 Results and Discussions
4.1 Database Description
4.2 Algorithm Used for Comparison
4.3 Comparative Discussion
5 Conclusion
References
Performance Comparison of Markov Chain and LSTM Models for Spectrum Prediction in GSM Bands
1 Introduction
1.1 Cognitive Radio (CR)
1.2 Global System for Mobile (GSM) Communication
1.3 Markov Chains (MC) and LSTM Model
2 Methodology
3 Implementation
3.1 Data Collection
3.2 Data Processing for LSTM
4 Results and Discussions
5 Conclusions
References
Prediction of Network Attacks Using Connection Behavior
1 Introduction
2 Literature Survey
3 Algorithms
3.1 Logistic Regression
3.2 K-Nearest Neighbor (KNN)
3.3 Random Forest
3.4 Naïve Bayes
3.5 Decision Tree
3.6 Support Vector Machine
4 Parameters
5 Performance Measurement of ML
6 Proposed System
6.1 Modules
7 Results
7.1 Dos Attack Prediction
7.2 R2l Attack Prediction
7.3 U2r Attack Prediction
7.4 Probe Attack Prediction
7.5 Overall Attack Prediction
8 Conclusion and Future Scope
References
Multi-Face Recognition Using CNN for Attendance System
1 Introduction
2 Existing System
3 CNN (Convolutional Neural Networks)
4 Proposed System
4.1 Multiple Face Recognition
4.2 Alerting and Result Recording
5 Result Analysis
6 Conclusion and Future Scope
References
Simulating the Concept of Self-Driving Cars Using Deep-Q Learning
1 Introduction
2 Related Work
3 Methodology
3.1 Initial Approach
3.2 Intuition Behind Q Learning
4 System Architecture
4.1 System Overview
4.2 Network Architecture
4.3 Deep Q-Training
5 Results
6 Conclusion
References
Dynamic Cloud Access Security Broker Using Artificial Intelligence
1 Introduction
2 Literature Review
3 Objectives
4 System Architecture
5 Results and Discussions
6 Conclusion
References
A Comparative VHDL Implementation of Advanced Encryption Standard Algorithm on FPGA
1 Introduction
2 Literature Survey
2.1 Types of Cypher
2.2 AES Algorithm
3 Implementation and Results
3.1 Timing Summary for Xilinx Spartan 6
3.2 Timing Summary for Xilinx Virtex 7
4 Conclusion
References
Document Recommendation for Medical Training Using Learning to Rank
1 Introduction
1.1 MedSim
1.2 Paper Outline
2 Learning to Rank
2.1 Classification of LtR Algorithms
2.2 LtR Algorithms
3 Related Work
4 Methodology
4.1 Relevant Questions
4.2 Query
4.3 Feature Vector
4.4 Relevance Score
4.5 Dataset
5 Evaluation
5.1 Experimental Setup
5.2 Evaluation Measure-NDCG@10
5.3 Results
6 Discussion
7 Conclusion and Future Work
References
An Algorithmic Game Theory Approach for the Stable Coalition and Optimum Transmission Cost in D2D Communication
1 Introduction
2 Coalition Formation for Optimum Transmission Cost
2.1 Transmission Cost
2.2 The Condition Required for the Movement of D2D Link from One Coalition to Another
3 Matching Game Formulation
3.1 Preference Profile for D2D Links or Players I
3.2 Preference Profile for Coalition G
4 Case Study
4.1 Keeping the Size of Preference Lists Less or Equal to the Number of Players for Coalition Sets and the Number of the Coalition for a Player Set
4.2 If the Players Set Have Only Two Coalitions with Lower Transmission Cost in Their Preference Profile and the Coalition Set Have Only Three Players in Their Preference Profile
5 Challenge
6 Conclusion
References
A Study of Hybrid Approach for Face Recognition Using Student Database
1 Introduction
2 Face Recognition
3 Proposed System Using Hybrid Approach
4 Method
5 Result Analysis
6 Conclusion
References
Multi-objective Consensus Clustering Framework for Flight Search Recommendation
1 Introduction
2 Problem Formulation
3 Clustering Ensemble Framework
3.1 Weighted Co-association Matrix Based on Confidence
3.2 Mapping Function
4 Experiments and Results
4.1 Dataset Description and Preprocessing
4.2 Experimental Results
5 Conclusions and Discussion
References
Profiling JVM for AI Applications Using Deep Learning Libraries
1 Introduction
2 Enabling technology
2.1 Deep learning
2.2 AI libraries for JVM
2.3 Java Virtual Machine
2.4 VisualVM
3 Parameters description
3.1 CPU usage
3.2 Memory usage
3.3 Garbage collector performance
4 Detailed analysis
4.1 AI applications
4.2 CPU usage
4.3 Memory usage
4.4 GC performance
5 Comparative analysis
6 Conclusion
References
Offline Signature Recognition Using Deep Features
1 Introduction
2 Convolution Neural Network
3 Literature Review
4 Methodology
4.1 Preprocessing
5 AlexNet as Feature Extractor
5.1 Classifier
6 Experimental Results
6.1 Persian Signature Recognition
6.2 English Signature Recognition
6.3 Bengali Signature Recognition
6.4 Hindi Signature Recognition
7 Comparison with Bag of Features
8 Conclusion
References
An Analysis and Comparative Study of Data Deduplication Scheme in Cloud Storage
1 Introduction
2 Literature Survey
3 Observations and Discussions
4 Conclusion
References
Prediction of the Most Productive Crop in a Geographical Area Using Machine Learning
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Data Cleaning and Preprocessing
3.2 Implementation of the Algorithm
3.3 Error and Accuracy
4 Result
5 Discussion
6 Conclusion
References
The Smart Set: A Study on the Factors that Affect the Adoption of Smart Home Technology
1 Introduction
2 Literature Review
2.1 Previous Research on Barriers to Adoption of Smart Home Technology
3 Conceptual Model
4 Hypotheses
4.1 UTAUT2 Model
4.2 Trustworthiness
4.3 Trialability and Observability (D0I)
4.4 Psychological Risk
4.5 Tech-Savvy Attitude
4.6 Energy Conservation
5 Methodology
6 Data Analysis and Results
7 Limitations and Future Research
8 Conclusion
References
New Approach for Multimodal Biometric Recognition
1 Introduction
2 Related Works
3 Proposed Model
3.1 Initial Research
3.2 Fingerprint Authentication Method
3.3 Recognizing Iris
3.4 Fusion of Features of Fingerprint and Iris Features
3.5 Fingerprint Matching Score Calculation
4 Conclusion
References
Survey on Object Detection, Distance Estimation and Navigation Systems for Blind People
1 Introduction
2 Literature Survey
3 Proposed System
3.1 Indoor Navigation
3.2 Outdoor Navigation
3.3 Travel Around City
3.4 Object Detection
3.5 Distance Estimation
3.6 Voice Assistant
4 Conclusion
References
Classroom to Industry: A Pathway of a Student to Be an Engineer
1 Introduction
2 Literature Review
3 Activity Based Learning
3.1 Challenges Noticed Before the Industrial Visit
3.2 Methodology
3.3 Observations and Feedback
4 Project Based Learning
4.1 Case Studies on Project Based Learning
5 Conclusions
References
ML Suite: An Auto Machine Learning Tool
1 Introduction
2 Literature Review
2.1 Auto-WEKA
2.2 Auto-SKLEARN
2.3 Document Search
2.4 Text Summarization Using NLTK
3 Conclusion
References
Survey and Gap Analysis on Event Prediction of English Unstructured Texts
1 Introduction
2 Goals of the Study
3 Literature Survey
3.1 Probabilistic Logic Approach
3.2 Rule-Based Approach
3.3 Machine Learning Approaches
3.4 Evidence Gathering Approaches
4 Technology Survey
4.1 DoWhy by Microsoft
4.2 Tuffy
4.3 IBM Watson
4.4 Event Registry
5 Gap Analysis
6 Conclusion and Future Work
References
Human Action Detection Using Deep Learning Techniques
1 Introduction
2 Related Work
2.1 Logistic Regression
2.2 Kernel SVM
2.3 Decision Tree
2.4 Random Forest
2.5 Linear SVC
2.6 Grid Search
2.7 Gradient Boosted Decision Tree
3 Results and Analysis
3.1 Logistic Regression
3.2 Kernel SVM
3.3 Decision Tree
3.4 Random Forest
3.5 Linear SVC
3.6 Gradient Boosted Decision Tree
3.7 Model Comparisons
4 Conclusion and Further Work
References
Deep Learning Methods and Applications for Precision Agriculture
1 Introduction
2 Overview of Deep Learning
3 Deep Learning Architectures, Frameworks and Datasets
4 Deep Learning Application in Agriculture
4.1 Soil Moisture Prediction
4.2 Yield Estimation
4.3 Leaf Classification
4.4 Disease Detection
4.5 Weed Identification
4.6 Plant Recognition
4.7 Fruit Counting
4.8 Animal Welfare
5 Limitations of Deep Learning
6 Future Usage of Deep Learning Technology for the Advancement in Agriculture Domain
7 Conclusion
References
Object Detection with Convolutional Neural Networks
1 Introduction
2 Object Detection Models
3 Two-Stage Detectors
3.1 Region-Based Convolutional Neural Network (R-CNN)
3.2 Fast R-CNN
3.3 Faster R-CNN
3.4 Mask R-CNN
4 Single-Stage Object Detectors
4.1 You Only Look Once (YOLO)
4.2 Single-Shot Detector (SSD)
5 Benchmark Datasets for Object Detection
6 Applications of Object Detection Models
7 Conclusion
References
Autonomous Vehicle Simulation Using Deep Reinforcement Learning
1 Introduction
2 Algorithms Used
2.1 Imitation Learning
2.2 Deep Deterministic Policy Gradient (DDPG)
3 Methodology
3.1 Imitation Learning Using Udacity Simulator
3.2 Reinforcement Learning Using CARLA Simulator
4 Results
4.1 Imitation Learning
4.2 Reinforcement Learning
5 Conclusion
6 Future Scope
References
Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques
1 Introduction
2 Related works
3 Methodology
3.1 Time series analysis (TSA)
3.2 Machine learning
3.3 Deep learning
4 Data collection and refining
5 Implementation and results
6 Conclusion
7 Challenges and future direction
References
The Detection of Diabetic Retinopathy in Human Eyes Using Convolution Neural Network (CNN)
1 Introduction
2 Literature Review
3 Problem Statement
4 Proposed System
5 Conclusion
References
Breast Cancer Classification Using Machine Learning Algorithms
1 Introduction
2 Literature Review
3 Proposed System
4 Conclusion
References
A Strategic Approach to Enrich Brand Through Artificial Intelligence
1 Introduction
2 Applications of AI in Branding
2.1 Strengthening Consumer Engagement
2.2 Stirring the Brand Planning
2.3 Corporate Strategy
3 Strategic Mapping of Brand Through AI
4 Discussion
References
Real Estate Price's Forecasting Through Predictive Modelling
1 Introduction: Data Mining
2 Data Science and Analytics
2.1 Exploratory Data Analytics (EDA)
2.2 Types of Machine Learning
2.3 Life Cycle of a Data Mining Project
3 Features
3.1 Longitude and Latitude
3.2 OceanProximity
3.3 Population
3.4 TotalRooms, TotalBedrooms and MedianHouseValue
4 Results
5 Conclusion
References
Bitcoin Cost Prediction Using Neural Networks
1 Introduction
1.1 Bitcoin Price
1.2 Declination of Bitcoin
2 Data Pretreatment
2.1 Data Assembly
2.2 Data Normalization
3 Methodology
3.1 Software Used
3.2 Time Series Set
3.3 Division of Data into Training and Testing Sets
3.4 Shifting of the Data into Tensors
3.5 Long Short Term Memory Meantime
3.6 Hyper Parameters
4 Result and Analysis
5 Conclusions
References
A Comprehensive Analysis for Validation of AVISAR Object-Oriented Testing Tool
1 Introduction
2 The Conceptual Design Schema of the AVISAR Framework
3 The Java Component Libraries Used in the AVISAR Framework
3.1 Jenetics Library
3.2 Integer Chromosome
3.3 Java Parser
3.4 Jacoco Code Coverage Tool
4 Validation of the Proposed Tool AVISAR
5 Conclusion
References
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Machine Learning for Predictive Analysis

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