Page 34 - 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
3. Tailored Care Strategies: Suggests best practice care 2. Automating mundane tasks with AI enabled veterinary
plans informed by past data and species variables. clinics to be more operationally efficient and allowed for
real-time data access.
4. Anomaly Detection: Recognizes abnormal behaviour
deviating from typical health patterns and raises alerts 3. Improved communication between stakeholders which
for interventions in due time. ultimately allows for better care coordination.
E. User Friendly Interface for Decision Making Support 4. More satisfied pet owners through transparency and
Realign the UI to that interaction occurs between the user active health monitoring.
access, which serves veterinarians, pet owners, and farm IV. PERFORMANCE EVALUATION
managers. Features include:
A confusion matrix and classification metrics are computed
1. Visualizing the Dashboard: Graphs, charts, and timelines for performance evaluation.
make the health data interpretation more Here is the method for evaluation metrics:
straightforward.
1. Accuracy: The consistency of the system’s accurate
2. Alerts & Notifications: Provides immediate notifications predictions. It is computed as the ratio of correct
about disease outbreaks, vaccination schedules, and classifications to total possibly categorized cases.
other treatment-related plans.
2. Precision: how many times correct positives predicted
3. Customizable Reports: Users can create reports by the system / how many times positives predicted.
according to their individual needs
3. Recall: A metric of frequency with which the system
F. Research Model Workflow successfully predicts positive instances over all the
The following steps summarize the operational flow of the actual positive instances present.
proposed model:
F1 Score: The F1 score is the most used performance metric
1. Data Collection: Sensors attached to IoT-equipped in evaluation of classification models especially when our
livestock gather and send information about the health model encounters trade-off between precision and recall. It
of the animals. is the harmonic mean of precision (P )and recall (R), which
means that it is a single measure for evaluating the trade-off
2. Data Preprocessing: The gathered data is then cleaned between these two events.
and normalized and then stored in the issued EMR in the
cloud. The F1 Score is calculated using the following formula:
3. Data processing and prediction: The data is processed
using AI-based algorithms to generate predictions and
insights.

4. Decision-making: Veterinarians and related players use Here:
the understanding to get informed decisions concerning
diagnosis, treatment and preventive care
G. Model Evaluation Metrics
We set the following evaluation metrics to assess the
proposed research model:

1. Diagnostic Accuracy: The accuracy of disease detection The F1 Score ranges from 0 to 1, with a higher value
by AI algorithms signifying better model performance. This metric is
particularly useful when we want to know how good is our
2. Latency: Measures how fast alerts and
recommendations are generated. model when precision and recall are equally important and
we want to balance between both.
3. User Satisfaction: Measures ease of use & perceived V.
utility through surveys and feedback. RESULT ANALYSIS
Several metrics and visual analysis was done on the Smart
4. Data Security: Evaluates encryption strength and access Animal Care system performance, accuracy, precision, recall,
restrictions. F1 score, and confusion matrix were extracted and analyzed
These measurements offer a broad perspective on system
H. Anticipated Outcomes
strengths and opportunities for enhancements.
Based on the implementation of the proposed model we can
expect: Confusion Matrix and Metrics Analysis
1. Monitoring of animal diseases and prevention, lowering The confusion matrix generated during the test phase gives
death rates and treatment costs. excellent insight into how the system classified the images.
Below is a summary of the matrix:

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