Page 33 - 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
 User Engagement: Real-time information on significant an operational model is proposed, focusing on overcoming
health activities and reminders about vaccinations and the significant voids in veterinary services. The objective of
future appointments. this model is to organize the collection, analysis, and use of
animal health data in order to improve diagnostics,
 Views that can be customized: Filters data that is specific
treatment planning, and proactive care.
to users and the data to be prioritized
A. Overview of the Research Model
E. Data Preprocessing and Security It integrates several interlocking components, making it a
Before analyzing data, preprocessing steps are taken to closely unified system for the surveillance and management
ascertain its accuracy and quality:
of animal health. It is also built to accommodate different
1. Data Cleaning: Adjusts missing values, duplicates and species, adjust to different clinical scales and enable real-
exceptions appearing in dataset. time decision-making. The proposed system contains major
modules as follows:
2. Normalization: To make data from various devices
consistent. 1. IoT-Based Data Collection
3. Encryption: The service encrypts all data in transit and 2. Cloud-Enabled EMR Management
at rest, ensuring that it cannot be accessed by anyone 3. Predictive Care with AI-Powered Analytics
who shouldn't have it
4. Decisions Support as a User Friendly Interface
F. Workflow Overview
an overview of the system workflow is presenting in detail All modules work independently of each other but are
the interaction of key components: interlinked to function together in coordination.
1. IoT devices collect data, enter manually where required. B. IoT-Based Data Collection
Wearable collars, sensors, and implantable chips are
2. After preproccessing the data is uploaded to the examples of IoT devices that capture real-time health
secured cloud.
metrics from animals. A few of the main features of this
3. To produce insights and predictions, the data is module are:
analyzed by AI models.
 Monitoring vital parameters such as heart rate, body
4. A dashboard displays the results for decisions you can temperature, and respiratory rate.
take.
 Monitoring trends in behaviour including levels of
G. Key Objectives of the Proposed System activity, food intake and sleep pattern.
The system aims to achieve several objectives, including:
 Monitoring environmental variables (such as humidity,
 Better Diagnostic Precision: analyzing data to improve temperature) that could affect livestock wellbeing.
disease detection.
Secure wireless protocols enable these devices to send data
 Preventive Care: Moving from reactive treatments to to the cloud, and by doing so, they make up-to-date
proactive solutions. information available at all times.
 Operational Efficiency: Lessening the volume of C. Cloud-Enabled EMR Management
administrative tasks veterinary staff must face through A cloud-based centralized electronic medical records (EMR)
automation of routine duties. system is the backbone of the model of research. Some key
features of the EMR system include but not limited to:
 Improved Collaboration: Streamlining communication
between veterinarians, pet owners and other involved 1. Data Aggregation: Combines data from IoT devices,
parties diagnostic tests, and manual entries into single
repository.
H. Anticipated Benefits
Expected outcome from implementing presence of platform: 2. Access: Allows veterinarians, pet owners, and
specialists to view data from anywhere and ensures
1. Decreased morbidity and mortality rates via early
continuity of care.
disease detection
3. Data Security: It has encryption and role-based access
2. Increased efficiency for clinical workflows, enabling controls to safeguard sensitive data.
veterinarians to spend more time on direct care.
As an EMR enables weeding out of data, it is also the
3. Better health data transparency and accessibility
precursor for newer form of analytics to take place.
enhancing pet owner satisfaction
D. AI-Powered Analytics for Predictive Care
4. Implemented experience sharing for researchers using
Once the data has been gathered, AI algorithms interpret the
the data.
data to provide actionable insights. There are several
In essence, this proposed system is a promising components in the module for analytics:
advancement in the incorporation of digital technology into 1. Predicting Disease: Early prediction of diseases like
veterinary medicine, responding to the pressing demand for infections, metabolic disorders and behavioural
innovation in this sector.
disorders etc.
III. PROPOSED RESEARCH MODEL
By applying IoT through cloud computing integrated with AI- 2. Health Risk Assessment: Identifies patterns and trends
within the data that may suggest health risks.
based analytics accessible through a user-friendly interface,

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