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 - 2 | Issue - 2 | june 2020
Published
02 June, 2020
The customer consumption pattern prediction has become one of a significant role in developing the business and taking it to a competitive edge. For forecasting the behaviors of the consumers the paper engages an artificial recurrent neural network architecture the long short-term memory an improvement of recurrent neural network. The mechanism laid out to predict the pattern of the consumption, uses the information's about the consumption of products based on the age and the gender. The information essential are extracted and described with the prefix-span procedure based association rule. Utilizing the information about the day to day products purchase pattern as input a frame work to predict the customer daily essentials was designed, the designed frame was capable enough to learn the dissimilarities across the predicted and the original miscalculation rates. The frame work devised was tested using real life applications and the results observed demonstrated that the proposed LSTM based prediction with the prefix span association rule to acquire the day today consumption details is compatible for forecasting the customer consumption over time accurately.
KeywordsArtificial Recurrent Neural Network Deep Learning Customer Consumption Long Short Term Memory Accuracy Miscalculation Rate
Full Article PDF Download Article PDF