While ML models are powerful tools for predicting diabetes, their lack of interpretability presents a major challenge for ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random neighborhoods ...

Bhubaneswar: The buffer zone of Similipal Biosphere Reserve is most affected in forest fires with most incidents linked to ...

Traditional authentication methods, such as single-factor passwords, have proven inadequate in combating sophisticated cyber ...

Random Forest consistently outperformed Decision Trees in all three case studies. XGBoost and LightGBM improved model accuracy through advanced boosting techniques.

Polymer research shows that machine learning can optimize tribological performance of epoxy coatings, with GBR outperforming ...

The spread of metastatic brain cancer presents one of the most challenging hurdles in oncology, as it often leads to poor outcomes and limited treatment options. Recent advancements in artificial ...

This study highlights the potential of saliva-based metabolomics and AI in prenatal diagnostics, improving detection rates ...

Developed and evaluated models such as Random Forest, SVM, and Decision Trees. Assessed performance using accuracy, ROC-AUC, confusion matrices, and cross-validation. Used Python with libraries like ...

The proposed system utilized four ML algorithms, namely XGBoost, extra tree, random forest, and LGBM. COVID-19 signs and symptoms were captured using IoMT-based devices for the patient’s diagnosis. An ...

Seamless Real-Time Data Integration for ML Models ⚡🤖 Need real-time data for your ML models? 🚀 Keep them updated & responsive with streaming platforms like Apache Kafka & AWS Kinesis 📡 ...