Page 32 - 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
analytics, these innovations are a paradigm shift in the generation to extracting the actionable insights by analyzing
delivery of veterinary services. These technological it the proper way.
integrations are not only an opportunity for improved The methodology of the proposed system is divided into the
workflow efficiencies and better outcomes for patients, they
following key stages:
also set the stage for a more connected, transparent, and A. Data Collection through IoT Integration
responsive veterinary ecosystem.
Proposed system is heavily reliant on the use of IoT devices
II. RELATED WORK for data collection. Wearable sensors and implantable
This work will develop a crosscutting digital tool platform devices are used to track numerous physiological metrics of
for veterinary application. With the seamless integration of animals, including:
Internet of Things (IoT) devices, cloud-based electronic 1. Vital Signs: Temperature, heart rate, respiratory rate,
medical records (EMRs), artificial intelligence (AI) analytics, and blood oxygen levels.
and an easy-to-use interface, this platform transforms the 2. Activity Metrics: Movement, sleep patterns, and
way we provide animal care. Operation Workflow of the energetic output data.
Evolutionary Pipeline with Data-Centric Approach1 depicts 3. Environmental Parameters: Ambient temperature,
an overview of the pipeline that takes the process from data humidity, and location based tracking for livestock.

These devices stream real-time data to the cloud, allowing C. AI-Powered Analytics for Predictive Insights
veterinarians to remotely monitor an animal’s condition. It utilizes advanced AI algorithms to process and analyze the
This early detection of deviations from normal health data collected. The key functionalities cover:
patterns enables interventions before the disease has a 1. Disease Prediction: Machine learning models sensitive
chance to progress further.
to large datasets can predict the probability of diagnosis
B. Cloud-Based EMR Management with several types of infectious, metabolic, and chronic
Being a system, it relies upon a cloud-based electronic diseases.
medical records (EMR) platform at its heart which ensures
2. Health Threat Alerts: Algorithms detect patterns that
that all animal health data are stored and made accessible in
indicate a risk of going for health injury, generating
a centralized and secure manner. Here are the main features
health threat alerts for taking action in real-time.
of EMR system:
3. Treatment Personalization: Insights driven by AI
 Data Aggregation: Combines IoT device data,
medication diagnostics, and manual input into a single recommend the most effective treatment plans using
database. historical data and evidence-based practices.
4. Behavioural Analysis: AI analyzes behavioural data for
 Role-Based Access Control: Provides privacy and early signs of stress, anxiety, or other psychological
security by only allowing access to users based on their
roles, (veterinarians, pet owners, or researchers). conditions in animals.
D. User-Friendly Dashboard for Decision Support
 Real-Time Updates: Allows records to be updated The platform's user interface is intuitive for a variety of
instantly, like vaccination schedules, treatment plans,
users, from technical veterinarians to pet owners. The
and diagnostic results.
dashboard provides:
 Interoperability: Also integrates with telemedicine
platforms and allows for smooth exchange of data with  Intuitive Visualizations: Graphical representations for
easy analysis of health data like charts, timelines etc.
external diagnostic laboratories.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 22