Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
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Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
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Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
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QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
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Probabilistic Neural Network based Managing Algorithm for Building Automation System
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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 - 4 | Issue - 1 | march 2022
Published
20 January, 2022
The primitive focus of this research work is about the artificial intelligence methods engaged for creating an outlook for flexural strength of High Strength Hybrid Fiber Self Compacted Concrete (HSHFSCC), which is considered to be a special concrete in order to tackle both workability and durability without disturbing the strength of the concrete. It possesses not only the good deformability during fresh state but also put forward high aversion to segregation resulting in superior homogeneity and increase in productivity by altering the period of construction. While incorporating various fibers like glass, steel, carbon, synthetic, and quartz powder in plain concrete, directs in the enhancement of post-cracking, toughness, ductility and limits the detrimental effect of shrinkage. The current work is classified into two stages. 1) Development of HSHFSCC and High Strength Self Compacting Concrete (HSSCC). 2) Engaging different Machine Learning (ML) models to divide the obtained information into Train, Test and Validation followed by 19 types of different ML regression models accompanied with Artificial Neural Network for engaging the function to appropriate the flexural strength of HSHFSCC and HSSCC. The boundary conditions discussed as input includes Setting time, percentage of quartz and river sand. Total 25 number of datasets are used for 5-fold cross validations by adopting MATLAB ML and Deep learning toolkit and Python is adopted to validate the optimized models. The evaluation factors like R-square and Root mean square show a great level of accuracy and reliability of the model.
KeywordsArtificial Neural Networks (ANN) flexural strength HSSFCC HSHFSCC modeling MATLAB
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