The wide use of supply chain management systems in various business sectors encouraged researchers and those who were concerned to explore and employ efficient technologies to improve such systems. The integration of blockchain into supply chains has proved its effectiveness at increasing the customer’s trust level, as well as many other features, such as traceability, immutability, provenance awareness, etc. Moreover, the use of private permissioned blockchain networks, for instance Hyperledger Fabric (HLF), not only leverages the level of confidence, but also increases the speed of transaction execution. In this paper, an exploratory detailed study on Hyperledger Fabric framework is conducted. The study focused on the HLF network design, the consensus algorithms used in HLF, the HLF smart contracts and the transaction flow stages. Moreover, a number of illustrative case studies that used HLF into their networks designed for food supply chain management systems have been introduced. The basic design components in each of the applications are reviewed as well as the main goals and desired outcomes.

In this paper, a new Chaotic Map with Block Chaining (CMBC) cryptosystem for image encryption is proposed. It is a simple block cipher based on logistic chaotic maps and cipher block chaining (CBC). The new system utilizes simplicity of implementation, high quality, and enhanced security by the combined properties of chaos and CBC cipher. Implementation of the proposed technique has been realized for experimental purposes, and tests have been carried out with detailed analysis, demonstrating its high security. Results confirm that the scheme is unbreakable with reference to many of the well-known attacks. Comparative study with other algorithms indicates the superiority of CMBC security with slight increase in encryption time.

In this paper, we present an efficient content based image retrieval system that uses texture and color as visual features to describe the image and its segmented regions. Our contribution is of three directions. First, we use Gabor filters to extract texture features from the whole image or arbitrary shaped regions extracted from it after segmentation. Second, to speed up retrieval, the database images are segmented and the extracted regions are clustered according to their feature vectors using Self Organizing Map (SOM). This process is performed offline before query processing; therefore to answer a query, our system does not need to search the entire database images. Third, to further increase the retrieval accuracy of our system, we combine the region features with global features to obtain a more efficient system.
The experimental evaluation of the system is based on a 1000 COREL color image database. From experimentation, it is evident that our system performs significantly better and faster compared with other existing systems. We provide a comparison between retrieval results based on features extracted from the whole image, and features extracted from image regions. The results demonstrate that a combination of global and region based approaches gives better retrieval results for almost all semantic classes.