Vikas Tripathi

Academic writing is an integral part of scientific communities. This is a formal style of writing used by researchers and scholars to communicate critical analysis and evidence based arguments. This work showcased a graph-based approach for scraping, extracting, representing and evaluating the available academic writing forgery detection criteria and further enhancing the model by proposing a set of new age criteria. The proposed work is based on knowledge graphs and graph analytics capable of selecting subset of 16 criteria from the available superset of a cent of criterias provided by Bealls, Cabells, Shreshtha, and Think.Check.Submit, Scopus, and other relevant authors. The process for detecting the influencial parameters consists of 04 phases: dataset preparation, knowledge graph representation and making inferences through graph analytics and evaluation of results. The experimental results are then compared to the retraction database that consisting of information about retracted articles. The work enables the construction of an experiential knowledge graph that effectively identifies influential criteria, enhancing this list by incorporating new age criteria into current influential set and concluding in result by successfully detecting the academic predatory behavior.

Health Insurance Portability and Accountability Act (HIPAA) has laid down several ethical and legal obligations on healthcare providers to preserve the privacy and confidentiality of patient information. Digitization in healthcare has been of great help to the medical fraternity and the patients; however, digital-based services in healthcare have also widened the spectrum of vulnerabilities. In this article, we propose a blockchain-based system to trace the data collection, storing, and sharing process for medical records. We present an Architecture of Medical Records Data Accountability and Compliance (MRDACE) which is a decentralized permissioned data sharing platform. The Medical Images and Information of a patient will be under the sole ownership of the patient. Logs of any access made to the Medical Images and Information of a patient will be maintained and cannot be modified, leading to a transparent traceability mechanism. This architecture utilizes the Proof-of-Authority consensus algorithm and further introduces a new automated incentive function to decide upon the access rights of the data requestors. In comparison to the existing state-of-art solutions, it performs with 56.71% less gas since the automated function introduced removes the need for the patient consent to share the health records with the data requestors.

JCM is an open access journal on the science and engineering of communication.