Archimedes Optimization with Enhanced Deep Learning based Recommendation System for Drug Supply Chain Management

Rathor, Ketan; Chandre, Shanker; Thillaivanan, Alagu; Raju, M Naga; Sikka, V.K.; Singh, Kamlesh

doi:10.1109/icstsn57873.2023.10151666

Cited by 11 publications

References 15 publications

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Intelligent privacy‐preserving data management framework for medicine supply chain system

Hathaliya,

Tanwar

2024

Security and Privacy

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In today's era, the pharmaceutical industry has integrated blockchain to secure the sensitive information of medicines, wherein public and private blockchains are used to preserve the security and privacy of the medicine supply chain data. However, conventional blockchains often limit scalability due to limited storage. Moreover, blockchain has loopholes; for example, it is not able to prove the validity of the data prior to being stored in the blockchain, which leads to fake data being added to the blockchain. As a result, it causes an issue of data provenance. Motivated by this, the proposed framework incorporated artificial intelligence (AI) algorithms to enhance the efficiency of the medicine supply chain data. The proposed framework integrated machine learning (ML) and blockchain, where ML classifies the valid and invalid data of the medicine supply chain, whereas blockchain stores only valid data and maintains its security and privacy. This identification helps the blockchain to verify medicine supply chain data before adding it to the blockchain. Additionally, we employed an InterPlanetary file system (IPFS) that saves medicine supply chain data and computes its hash to offer decentralized storage. Further, this hash data is stored on a private Hyperledger Fabric blockchain, which requires minimal storage instead of storing an entire large file. This minimal storage optimizes the process of data storage and retrieval in the Hyperledger Fabric blockchain, which enhances the scalability of the proposed framework. Finally, the result of the proposed framework is assessed in two phases: ML and blockchain, wherein the ML model's performance is measured by statistical measures and the blockchain‐based result is assessed using several performance parameters such as throughput is around (618 transactions per second), latency (0.12 s), response time (11 s) and data rate (282 Mbps).

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Intelligent privacy‐preserving data management framework for medicine supply chain system

Hathaliya,

Tanwar

2024

Security and Privacy

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Emerging Threats in Cybersecurity : A Deep Analysis of Modern Attack

Ashish Dewakar Pandey,

Shakil Saiyad

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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This paper delves into the evolving landscape of cybersecurity threats, focusing on the latest attack vectors and techniques employed by malicious actors. With the rapid advancement of technology and increasing connectivity, the cybersecurity landscape is continuously evolving, presenting new challenges and threats to organizations and individuals alike. The analysis covers various modern attack methods, including but not limited to, ransomware, phishing, advanced persistent threats (APTs), and supply chain attacks. Each of these attack vectors is examined in detail, highlighting their characteristics, impact, and potential mitigation strategies. Furthermore, the paper discusses the role of emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT) in shaping the cybersecurity threat landscape. While these technologies offer numerous benefits, they also introduce new vulnerabilities that can be exploited by cybercriminals.

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International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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In the pursuit of precise forecasts in machine learning-based breast cancer categorization, a plethora of algorithms and optimizers have been explored. Convolutional Neural Networks (CNNs) have emerged as a prominent choice, excelling in discerning hierarchical representations in image data. This attribute renders them apt for tasks such as detecting malignant lesions in mammograms. Furthermore, the adaptability of CNN architectures enables customization tailored to specific datasets and objectives, enhancing early detection and treatment strategies. Despite the efficacy of screening mammography, the persistence of false positives and negatives poses challenges. Computer-Aided Design (CAD) software has shown promise, albeit early systems exhibited limited improvements. Recent strides in deep learning offer optimism for heightened accuracy, with studies demonstrating comparable performance to radiologists. Nonetheless, the detection of sub-clinical cancer remains arduous, primarily due to small tumor sizes. The amalgamation of fully annotated datasets with larger ones lacking Region of Interest (ROI) annotations is pivotal for training robust deep learning models. This review delves into recent high-throughput analyses of breast cancers, elucidating their implications for refining classification methodologies through deep learning. Furthermore, this research facilitates the prediction of whether cancer is benign or malignant, fostering advancements in diagnostic accuracy and patient care.

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Archimedes Optimization with Enhanced Deep Learning based Recommendation System for Drug Supply Chain Management

Cited by 11 publications

References 15 publications

Intelligent privacy‐preserving data management framework for medicine supply chain system

Intelligent privacy‐preserving data management framework for medicine supply chain system

Emerging Threats in Cybersecurity : A Deep Analysis of Modern Attack

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

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