The web contains lots of information that gets updated every second. Searching for a relevant document from the web needs an efficient scrutinization. As the user’s need varies based on location, intention and purpose the retrieval of an efficient response is a challenge. To address this challenge an informationretrieval technique has been put forth along with the advent of the machine learning and deep learning models. We have proposed a QeCSO algorithm to perform an efficient retrieval of relevant response. The AttentionbasedBi-directional LSTM (ATT-BLSTM) helps to improve the retrieval of the relevant document based on its feature that correlates the semantics between the query and the content. On further expanding the query we can observe a steep improvement in retrieving the response. To perform this, the output from ATT-BLSTM is given as an input to the meta-heuristic algorithm called cuckoo search. It helps us to retrieve the exact term to expand the query and make our search to move closer to an optimal solution. The performance of our approach iscompared to those of other models based on the evaluation metrics such as accuracy and F-measure. It is evaluated by applying the model over the SQUAD 1.1 dataset. By analyzing the results it is verified that our proposed algorithm achieves an accuracy of 95.8% for an efficient information retrieval of relevant response with an increase in F-measure.

For the last decade, businesses and industries have been facing difficulties in tracking their products. During the supply chain process, the attackers add their counterfeit products into the market along with legitimate products. With the increase in the number of counterfeits entering the open market, it is hard todiscover the source of the counterfeit product. Most of the existing anti-counterfeit solutions are centralized. However, current solutions lack in anti-counterfeiting, decentralization, auditing, transparency, traceability, and tag cloning. To mitigate these issues, we proposed a Blockchain-based Anonymous Anti-Counterfeit (BA2C) supply chain framework that exploits Radio Frequency Identification (RFID) and blockchain technologies. The proposed model streamlined all the transactions through Ethereum, a blockchain-based platform with the support of Proof of Authority (PoA) consensus. Furthermore, the confidentiality of transactions is gained by incorporating zk-SNARKS on the Ethereum blockchain. In this work, the proposed model was tested in terms oftransaction cost, latency, and throughput. Further, it was compared with the existing system and was found to perform better transactions in the supply chain management in terms of lower cost and higher scalability. This work concluded that the proposed model was more appropriate to evaluate the critical issues of the supply chain.