Page 211 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470

IV. PROPOSED RESEARCH MODEL
1. Research Model Overview
The proposed model integrates a CNN-based approach to enhance the efficiency of the VaxHub digital tracking system by
leveraging data analytics for:
 Predictive demand forecasting
 Appointment scheduling optimization
 Identifying vaccination trends
 Detecting anomalies in vaccination data.
2. Proposed CNN Architecture
The CNN model will be used to analyze vaccination data such as:
 Time-series data of vaccinations
 Demographics-based trends
 Appointment adherence patterns
CNN Architecture Layers:
1. Input Layer:
 Input vaccination-related data (e.g., patient records, scheduling logs).
 Data pre-processing (normalization, encoding categorical features).
2. Convolutional Layers:
 Extract patterns related to vaccination demand and inefficiencies.
 Feature maps created from historical vaccination data.
3. Pooling Layers:
 Down-sampling to reduce complexity and focus on key trends.
 Max pooling to capture the most significant patterns.
4. Fully Connected Layers:
 Interpret features to predict vaccination demand and identify inefficiencies.
5. Output Layer:
 Forecast upcoming demand for vaccinations.
 Provide insights for better scheduling and inventory management.
3. Research Workflow
The proposed CNN-based system workflow follows these stages:
A. Data Collection:
 Collect vaccination data from VaxHub (appointment records, patient demographics).
B. Preprocessing:
 Cleaning and structuring data to feed into the CNN model.

C. Model Training:
 Training the CNN nation data.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 201