Diabetic Retinopathy

Our project on diabetic retinopathy sounds incredibly impactful and innovative! Here's a breakdown of the key aspects and benefits of our CNN model for predicting diabetes based on retinal images:

• Purpose and Problem Solving: Traditionally, diagnosing diabetic retinopathy requires expert ophthalmologists to manually examine retinal images, which is time-consuming and expensive. Our CNN model addresses this challenge by automating the diagnostic process, potentially reducing the burden on healthcare professionals and speeding up diagnosis for patients.
• Technology and Methodology: Convolutional Neural Networks (CNNs) are well-suited for image recognition tasks like diagnosing diabetic retinopathy. By training your CNN model with a large dataset of JPEG and PNG retinal images, you achieved an impressive accuracy rate of 92%.

Cattle Industry

In our recent project for the cattle industry, we successfully integrated IoT and machine learning to proactively identify sickness in animals. By deploying IoT devices to monitor various health metrics, we gathered continuous data streams.

These data were analyzed using advanced ML techniques, including Isolation Forest and deep learning models like Autoencoders and LSTMs.

This innovative solution enabled us to detect anomalies and potential illnesses in real-time. Consequently, vendors could identify and address health issues early, minimizing losses and making informed decisions about selling animals before outward signs of sickness appeared.

This project not only enhanced animal welfare but also improved the overall efficiency and profitability of the vendors.

Automotive Industry - Legacy DB Use Case

In response to the challenges faced by the automotive manufacturer with its legacy database system, the decision to transition to IBM's DB2 database management system proved transformative across several critical areas:

• Slow Data Retrieval Times: The legacy system suffered from delays in retrieving production and sales data, impacting operational efficiency. DB2 introduced enhanced indexing and efficient storage management, resulting in a significant 40% reduction in data retrieval times.
• Limited Scalability: The existing system struggled to handle the increasing data volumes from multiple manufacturing plants. DB2's advanced partitioning and clustering capabilities provided seamless scalability.
• Inefficient Data Analysis: Analytical processes were inefficient, leading to delays in decision-making. DB2's advanced analytical functions facilitated real-time data processing and reporting.
• High Maintenance Costs and Downtimes: The legacy system incurred high maintenance costs and experienced frequent downtimes. DB2's high-availability features ensured consistent system performance with a notable 99.95% uptime.