Recent technological advancements have led to the development of new methods for managing organ donation systems, which aim to overcome the limitations of traditional centralized systems. To achieve increased transparency, security, and efficiency in the organ donation process, blockchain technology is being proposed as a replacement for these centralized systems. However, most previous works on organ donation systems have focused on using Ethereum-based blockchain solutions, which offer limited control, a fixed set of consensus protocols, and no support for concurrent executions. In contrast, our work has utilized the Hyperledger Fabric framework to develop a network model of the organ donation system. We have designed and deployed a prototype system with smart contracts using Amazon Managed Blockchain Service. Additionally, we have built a client application that uses the Fabric SDK to interact with the network and perform various actions. To evaluate the performance of our system, we conducted extensive testing using the Hyperledger Caliper benchmarking tool. In our test bench, the system achieved a peak actual send rate of 389.1 transactions per second (TPS) for creating new records and 508.4 TPS for reading records. At a send rate of 800 TPS, the system took an average of 12.16 seconds to serve a request for creating a record and an average of 3.71 seconds to serve a request for reading a record. Future work is required to extend the functionalities of the system and identify potential endorsers and managers for this type of controlled blockchain network.

Digital adoption in India has seen rapid growth in recent years. On another side of this digital and modern country, century-old traditions like dowry still exist. Women often get molested and murdered for dowry. Prompt action from the legal system can reduce such incidents. Digital adoption in the legal system can help reduce the time required for delivering justice to the dowry death victims. Text analytics and an artificial intelligence-based approach can help law professionals to find relevant information from legal cases without going through the tedious and time-consuming process of reading and analyzing them. It will also resolve the issue of raising pending cases in the legal system. The objective of this paper is to find out the major parameters from legal documents and extract meaningful information from them.

In the Indian court system, pending cases have long been a problem. There are more than 4 crore cases outstanding. Manually summarising hundreds of documents is a time-consuming and tedious task for legal stakeholders. Many state-of-the-art models for text summarization have emerged as machine learning has progressed. Domain-independent models don't do well with legal texts, and fine-tuning those models for the Indian Legal System is problematic due to a lack of publicly available datasets. To improve the performance of domain-independent models, the authors have proposed a methodology for normalising legal texts in the Indian context. The authors experimented with two state-of-the-art domain-independent models for legal text summarization, namely BART and PEGASUS. BART and PEGASUS are put through their paces in terms of extractive and abstractive summarization to understand the effectiveness of the text normalisation approach. Summarised texts are evaluated by domain experts on multiple parameters and using ROUGE metrics. It shows the proposed text normalisation approach is effective in legal texts with domain-independent models.