Supply chain management (SCM) is essential for a company’s faster, efficient, and effective product life cycle. However, the current SCM systems are insufficient to provide product legitimacy, transaction privacy, and security. Therefore, this research proposes a secure SCM system for the authenticity of the products based on the Internet of Things (IoT) and blockchain technology. The IoT-enabled Quick Response (QR) scanner and the blockchain-integrated distributed system will allow all the SCM stakeholders to begin secure and private transactions for their products or services. Resulting, the consumer will receive an authentic and genuine product from the original producer. A lightweight asymmetric key encryption technique, i.e., elliptic curve cryptography (ECC) and Hyperledger Fabric-based blockchain technology with on-chain smart contracts are applied for distributed IoT devices to make the authentication process faster and lighter. Each SCM stakeholder is registered by the service provider and receives corresponding public and private keys, which will be used for the authentication process of the participants and IoT devices. The authenticated QR scanner records all transactions on the blockchain. Consequently, there will be no human intervention for the SCM transactions. The security and scalability analysis demonstrates that the proposed system is more secure and robust than other state-of-the-art techniques.

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Now-a-days Diabetes is an alarming issue all over the world. In Bangladesh, many individuals are affected by it. Due to overpopulation and lack of proper education, it is very difficult to provide sufficient care for diabetes patients. This paper presents a system that can detect if anyone has diabetes. A machine learning algorithm, KNN is used on a supervised dataset to detect diabetes. It also shows nearby doctor chambers through location tracking. The dataset was collected from different hospitals of Bangladesh. Due to security reason, we cannot disclose the names of the institutions from where we collected all the data. However, supervised training in such situation shows a great accuracy although no such work has found for Bangladesh region. This paper studies two different algorithms on the dataset. These are, KNN and K-means. Between them, the proposed approach achieves 99.78% accuracy, which is so far the best for detecting diabetes. We have used total number of 6219 data of different diabetes affected patients. Through this system, one can easily know if he has diabetes by giving test reports and consult with nearest certified doctors with location tracking. Resulting in saving time and money used to detect diabetes and to find preferable doctors as one do not have to go to doctors the very first time and see his health condition sitting right at their home.

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