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Encryption Algorithms for Cloud Computing and Quantum Blockchain
Published in Thiruselvan Subramanian, Archana Dhyani, Adarsh Kumar, Sukhpal Singh Gill, Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network, 2023
Prabhsharan Kaur, Isha Sharma, Rahul Kumar Singh
Ciphertext Policy Attribute Based Encryption (CP-ABE) with constant cipher text [15] is proposed in this mechanism which uses fixed-size cipher text and calculates bilinear pairing which enhances data transmission and efficiency of the system and minimizes overhead due to space storage. The burden is reduced and risk on a single authority is minimized by inheritance of authorization provided by a hierarchical access control system. Four polynomial-time algorithms are used in CP-ABE. The data file is firstly encrypted by the data owner using a symmetric key Data Encryption Key (DEK) after which DEK is encrypted by utilizing this mechanism using access control policies. Final cipher text is uploaded by the data owner on the cloud server. A symmetric key is required for a user to decrypt and access the data files. The following are the four sub-algorithms:
Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)
Published in Molecular Physics, 2020
Matthew W. Thompson, Justin B. Gilmer, Ray A. Matsumoto, Co D. Quach, Parashara Shamaprasad, Alexander H. Yang, Christopher R. Iacovella, Clare McCabe, Peter T. Cummings
Determining how these guidelines for reproducibility should be – and/or can be – implemented in soft matter simulation is in itself a challenge. For example, simply providing code is not effective if that code is poorly written or not well documented and has subtle issues, such as dependencies within a code (e.g. use of external libraries, especially if they are proprietary/non-free or difficult to obtain/install). These issues may create barriers to proper compilation/installation and hence hamper reproducibility. Similarly, providing a raw data file without defining the structure of it, and/or without appropriate metadata, does little to aid in reproducibility. Since journals largely do not provide mechanisms for sharing code, scripts, and/or data (aside from supplemental material), it is also not clear how such information should best be shared.
Local quantized extrema quinary pattern: a new descriptor for biomedical image indexing and retrieval
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2018
Experiments are conducted on the images collected from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), which is public lung image database of CT scans as been provided by Kascic (2011) and NEMA-CT online image database (2012). The database is separated into 84 cases, each containing around 100–400 Digital Imaging and Communication (DICOM) images and XML data file containing the physicians annotations. Database contains 143 nodules having size range of 3 to 30 mm (to be manually segmented by radiologists). Here, in the experiments, for the rapid processing, Lampert et al. (2013) have provided the LIDC toolbox of which functions are used to convert the CT lung images (512 × 512) from the database into ‘tif’ image related to each slice of the scan. Radiologists detected the locations of nodules which have also been provided. Furthermore, ROIs were annotated manually from each slice from some patients to construct the ROI CT image database. For the experiment, 12 patient cases consisting of 75 nodules (26 benign and 49 malignant) and 229 slices have been selected. The CT scan data acquisition details are given in Table 1. Figure 8 depicts the sample lung nodule images of LIDC-IDRI-CT database (one image from each patient scan).
Local mesh ternary patterns: a new descriptor for MRI and CT biomedical image indexing and retrieval
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2018
Experiments are conducted on the images collected from lung image database consortium and image database resource initiative (LIDC-IDRI), which is public lung image database of CT scans as been provided by Kascic (2011) and NEMA-CT online image database (2012). The database is separated into 84 cases, each containing around 100–400 Digital Imaging and Communication (DICOM) images and an XML data file containing the physicians annotations. Database contains 143 nodules having size range of 3–30 mm (to be manually segmented by radiologists). The CT lung images (512 × 512) from the database are converted into ‘tif’ image format for rapid processing with the use of Lampert’s LIDC 2 image toolbox (online available https://wiki.cancerimagingarchive.net/display/Public/LungImageDatabaseConsortium). Radiologists detected the locations of nodules which have also been provided. Furthermore, ROIs were annotated manually from each slice from some patients to construct the ROI CT image database. For the experiment, 12 patient cases consisting of 75 nodules (26 benign and 49 malignant) and 229 slices have been selected. The CT scan data acquisition details are given in Table 1. Figure 9 depicts the sample lung nodule images of LIDC-IDRI-CT database (one image from each patient scan).