Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 3 | Issue - 1 | march 2021
Published
29 March, 2021
Inferring complex and non-linear dynamic system using the data that is available plays an important role in many areas of work such as physical, social, biological and computer sciences. In order to address these issues, network structure using a number of evolutionary algorithms has been proposed. However, the important criteria like the community structure have been ignored while developing these methodologies. Accordingly, this proposed work is focused on developing a multi-objective network reconstruction based on community structure in order to improve the network construction using ES by boosting their reconstruction performance. This framework that is used to further improve their performance is known as the community-based framework. It is based on multi-objective metaheuristic algorithm that is based on population and can be used as the base optimizer. The original decision space of the community structure is divided using the proposed work. From the solution obtained, an improved solution using reduced decision space is implemented using the multi-objective evolutionary algorithm (MOEA). A test suite is also designed to verify the performance of community based network reconstruction with respect to the complex network issue. In the proposed reconstruction methodology based on community criteria, the MOEAs are incorporated and are used to bind the original version. A noticeable improvement is seen in the experimental results based on the proposed work on 30 reconstruction issues.
KeywordsCommunity service evolutionary algorithm muti-objective optimization complex network reconstruction
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