A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation.

Implementation of an artificial intelli gence-based medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The deep learning convolutional neural network (DLCNN) was utilized to fuse MRI and CT images with robust edge analysis (REA) properties, which are used to identify the slopes and edges of source images. Then, hybrid fuzzy c-means integrated k-means (HFCMIK) clustering is used to segment the disease affected region from the fused image.

Infrared Monitoring of Oxygenation Process Generated by Robotic Verticalization in Bedridden People.

Bedridden people, especially at home, suffer from diverse pathologies beyond the main one that brings them to a specific position. Long-term cares are suitable at home to avoid congestions within hospital facilities. There are different technologies available to improve such people's conditions in their daily life.

Cloud-based adaptive exon prediction for DNA analysis.

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries.