Applications of AI in Big Data Analysis
- Design dynamic web crawlers.
- Unstructured big data acquisition.
- Batch data processing in Hadoop platform.
- Streaming data processing in SPARK platform.
- Design resource and network aware big data processing models.
- Design efficient speculative task execution models.
- Heterogeneity aware job scheduling analysis.
- Optimal resource provisioning
AI for Cerebrovascular Disease (CVD) Image Data Analysis
- Ischemic stroke severity determination using Deep Learning.
- Localization of Ischemic stroke region using TensorFlow.
- Ischemic stroke volume calculation using DCNN.
- Hematoma volume calculation using DCNN.
- Detect the hemorrhagic stroke region using TensorFlow.
- Carotid artery plaque segmentation using Caffe.
- Correlation of hyperperfusion syndrome big data with biomarker using DL and ML.
AI for Digital Pathological Image Data Analysis
- Histopathological image data analysis using Deep Learning.
- Colorectal cancer (CRC) biomarkers data analysis using Machine Learning.
- Classification of colon tissues using DCNN.
- Segmentation of tissue layers using TensorFlow.
- Determination of tissue structures using DCNN.
AI for Polycystic Kidney Image Data Analysis
- Automatic object detection using Tensorflow.
- Kidney segmentation using Tensorflow.
- Automatic Kidney area and volume calculation using Deep Learning.
- Renal mass classification using DCNN.
- Biomarkers data analysis using Machine Learning.
- Design prognosis models using AI.
IoT Data Processing in Fog/Cloud Platforms
- Resource and Network aware scheduling using AI.
- Virtual Network Embedding (VNE) problem modelling.
- Migration cost optimization in Core computing.
- Fault tolerant problem analysis in FOG Computing.
- Mobility aware resource management in FOG Computing.
- Design low latency IoT big data service models.
- Design intelligent decision making models for Core and Edge Computing platforms.
