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.