Explore our research and consultancy projects that deliver measurable results.
Description: Developed a machine learning model to predict the necessity of Caesarean Section, supporting clinical decision-making for obstetricians.
Before: Decisions were mostly based on experience and general guidelines.
After: Predictive insights improved patient safety, reduced risks, and enhanced hospital efficiency.
Outcome: Data-driven decision support in maternal healthcare.
Description: Implemented AI-based image analysis to classify medicinal plants using leaf images, supporting research and biodiversity monitoring.
Before: Manual identification of medicinal plants was time-consuming and error-prone.
After: Automated classification enabled faster, more accurate identification and documentation.
Outcome: Enhanced research, conservation, and herbal medicine studies.
Description: Developed a predictive model using patient data to assess the risk of diabetes, facilitating early detection and preventive interventions.
Before: Diabetes risk was often assessed manually, leading to delayed interventions.
After: ML-based predictions helped identify high-risk patients earlier and improve preventive care.
Outcome: Reduced complications through early detection and personalized recommendations.