In the western world,the prostate cancer is major cause of death in males. Magnetic Resonance Imaging (MRI) is widely used for the detection of prostate cancer due to which it is an open area of research. The proposed method uses deep learning framework for the detection of prostate cancer using the concept of Gleason grading of the historical images. A3D convolutional neural network has been used to observe the affected region and predicting the affected region with the help of Epithelial and the Gleason grading network. The proposed model has performed the state-of-art while detecting epithelial and the Gleason score simultaneously. The performance has been measured by considering all the slices of MRI, volumes of MRI with the test fold, and segmenting prostate cancer with help of Endorectal Coil for collecting the images of MRI of the prostate 3D CNN network. Experimentally, it was observed that the proposed deep learning approach has achieved overall specificity of 85% with an accuracy of 87% and sensitivity 89% over the patient-level for the different targeted MRI images of the challenge of the SPIE-AAPM-NCI Prostate dataset.

Grid computing is a prominent tool for assembling and incorporating groups of heterogeneous resources scattered around the world, connected through a network. Since load balancing is a challenging issue in a distributed environment, Grid computing provides an architecture, which is well suited, low-cost, and consistent. For task execution on suitable resources, grid computing provides efficient load balancing and fault tolerance approaches. But, due to the dynamic nature of grid resources, sometimes tasks can’t be completed within given constraints (deadline, cost). To solve these issues, this paper proposes a fault tolerance–based load balancing approach by considering the dynamic nature of resources. First, significant contributions in the field of load balancing are analyzed based on several parameters. Second, a fault tolerance dynamic load balancing model is proposed for task execution based on resource load and fault index value. For fault tolerance, checkpoints are set at various determined intervals to resume tasks at the next possible instance that avoids unnecessary placement of checkpoints. Third, the proposed model is validated and provides improved performance in terms of response time, makespan, and throughput. The model provides up to 12% and 9% performance improvement depending on method of measurement in comparison to other state-of-the-art methods.

Cloud computing consists group of heterogeneous resources scattered around the world connected through the network. Since high performance computing is strongly interlinked with geographically distributed service to interact with each other in wide area network, Cloud computing makes the architecture consistent, low-cost, and well-suited with concurrent services. This paper presents a fault tolerance load balancing technique based on resource load and fault index value. The proposed technique works in two phases: resource selection and task execution. The resource selection phase selects the suitable resource for task execution. A resource with least resource load and fault index value is selected for task execution. Further task execution phase sets checkpoints at various intervals for saving the task state periodically. The checkpoints are set at various intervals based on resource fault index. When a task is executed on a resource, fault index value of selected resource is updated accordingly. This reduces the checkpoint overhead by avoiding unnecessary placements of checkpoints. The proposed model is validated on CloudSim and provides improved performance in terms of response time, makespan, throughput and checkpoint overhead in comparison to other state-of-the-art methods.