Cheque Truncation System (CTS) is an imagebased cheque clearing framework used in Sri Lanka. This semi manual process has certain limitations and takes up to 3 working days to clear an inter-bank national cheque in Sri Lanka. Faced with the limitations of this system, cheque users and commercial banks must need an efficient and a secured system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. This research portrays an automated solution, which is feasible for any commercial bank in Sri Lanka, to address above-mentioned issues. The proposed system is based on the blockchain where all banks willing to take an interest in this framework must connect the proposed blockchain based system to supply the quicker cheque clearance to its clients. Answers were proposed with a complete framework consisting of four main phases: (i) paper cheque clearing process, (ii) digital cheque issuing and clearing process, (iii) cheque fraud detection process and (iv) cheque transaction securing process. Python along with Flutter framework and Ethereum were the major technologies used for implementing the system. The proposed system is highly scalable as Ethereum provides added integrity to the system. The approach advocates the customer as well as the bank with much simpler and speedier cheque clearing process with increased security. It also contributes with a paper cheque fraud detection system with faster and reliable results. The proposed system provides benefits to the user as well as the bank by addressing the requirement of producing a secure, effective and environment friendly system. Finally, CheckMate permits a consistent stream of cheque clearance operation for the payer and the payee without any mediators.

Infectious viruses are conveyed via respiratory droplets produced by an infected person when they speak, sneeze, or cough. So, to combat virus transmission, the World Health Organization (WHO) has imposed severe regulations such as mandatory face mask use and social segregation in public spaces. The ’Human Tracking and Profiling for Risk Management System (HTPRM)’ is an online application that identifies the risk associated with failing to follow proper health practices. This proposed approach, which is divided into four components, utilizes ’You Only Look Once (YOLOv3)’ to detect face-mask danger, which would be determined based on two factors: wearing the face mask properly and the type of mask (Surgical,k95, homemade, and bare). The second phase is to use OpenCV and SSD-Mobilenet to evaluate the value of a one-meter space(Social Distance) between people. The system recognizes the maximum number of individuals that can be in the vicinity of the specific hall that uses YOLO(V3) and image processing as the third procedure. In the last processing, the system identifies each person’s behavior, classifies it as uncommon or not, and calculates the risk associated with each category. Finally, the system computes the overall risk and generates a warning alarm to notify the user that they are in a dangerous scenario.

Document workflows, which plays a major role in enterprise business process automation, are dynamic and prone to be changed over time. Assuring the provenance of these workflows is important when comes to quality, long term preservation, forensics and regulatory compliance. This research introduces a Provenance Framework for collecting workflow provenance data, storing them in documents as metadata securely and querying stored provenance data. The author uses the concept of making data objects independent and consider provenance data as intrinsic property of the data object, to support long term preservation of documents with provenance and to maintain the link between the data object and its provenance in cloud over time. Provenance data modeling and representation is done according to the W3C PROV Model. XMP framework is used to store and query provenance data as metadata in documents. Document signatures and metadata encryption is used to ensure security of provenance data.

Trust is an indispensable component in mobile ad-hoc networks since the devices involved in the network can be complete strangers to each other. In a situation where there is a need of a suddenly build network and exchange of data, a trust establishment mechanism plays a vital role. In developing a trust implementing system, the traditional methods such as cryptography and centralized certificate based solutions are ill-suited due to the lack of processing power and energy in mobile devices. In this paper, we propose a trust mechanism based on the predictive theories of probability, Markov Chains and Hidden Markov Models.

In the field of medical science, it is required the application of Information Technology to manage the medical knowledge of the expertise personnel. Actually these expertise personnel are mainly the Specialists in various fields. It is found that all the Specialists are not equal in the point of knowledge in diagnosis. It introduces a new issue known as the knowledge gap. The knowledge gap in the sense is the variation of the knowledge of two or more Specialists. In this case the patients (or other people) cannot get the service from them accurately. To get an accurate decision, the patient has to consult two or more Specialists. It is logical that Information Technology will help to minimize the effect of the knowledge gap and also to reduce the knowledge gap of the Specialists. The knowledge should be available on demand. A detailed analysis is provided how it's possible to create better prospects for reduce the knowledge gap and to make available the knowledge on demand.