A blockchain and deep neural networks-based secure framework for enhanced crop protection

Hassija, Vikas; Batra, Siddharth; Chamola, Vinay; Anand, Tanmay; Goyal, Poonam; Goyal, Navneet; Guizani, Mohsen

doi:10.1016/j.adhoc.2021.102537

Cited by 27 publications

(9 citation statements)

References 17 publications

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“…Smart contracts can be used to automate certain supply chain processes, such as tracking and verifying environmental information, which could improve efficiency and reduce the risk of errors. However, cryptocurrency and blockchain technologies also raise new security and privacy concerns that must be carefully considered [95]. While several factors and challenges must be addressed when managing supply chain environmental information, many promising strategies and technologies can help organizations achieve their sustainability goals.…”

Section: Discussionmentioning

confidence: 99%

Revealing the Barriers of Blockchain Technology for Supply Chain Transparency and Sustainability in the Construction Industry: An Application of Pythagorean FAHP Methods

et al. 2023

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Blockchain technology has emerged as a promising solution to enhance supply chain transparency and sustainability in the construction industry. However, the widespread adoption of blockchain faces several barriers that need to be identified and understood. The construction industry faces significant challenges regarding supply chain transparency and sustainability. Current practices lack visibility, leading to difficulties in tracing material origins, tracking movement, and ensuring compliance. To fill this gap, this study employed a three-phase approach. In the first phase, a comprehensive literature review identified 37 potential barriers. Subsequently, expert discussions were held to refine the list, ultimately selecting 15 barriers of utmost importance. In the second phase, data were collected from 17 experts representing academia and industry. Finally, in the last phase, the collected data were analyzed using the Pythagorean fuzzy analytical hierarchical process (AHP) methodology. The findings revealed that the “transparency range” category was the most critical barrier, closely followed by “inadequate access to institutional finance”. Surprisingly, the study identified the “security environment” as the most significant barrier. These results offer construction companies, policymakers, and other industry stakeholders a comprehensive understanding of blockchain adoption’s challenges. With this knowledge, stakeholders can design effective strategies and policies to address these barriers. Moreover, the research highlights the importance of considering uncertainty in decision making when assessing technology adoption, making the findings applicable beyond the construction industry.

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Section: Discussionmentioning

confidence: 99%

Revealing the Barriers of Blockchain Technology for Supply Chain Transparency and Sustainability in the Construction Industry: An Application of Pythagorean FAHP Methods

et al. 2023

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“…Since the early 1980s, research scholars have been seeking to expand the scope of AI by incorporating the explainability factor and enhancing trust among end-users. With XAI now being recognised as a necessity rather than just a choice, explanation methods have come a long way, from textual formats to visual aids [29,30]. However, to ensure that the concept of XAI is fully grasped, we put forward in the following section the underlying concepts that will serve as a solid baseline for comprehensive and up-to-date navigation of this rapidly growing area of research.…”

Section: Fundamental Concepts Of Xaimentioning

confidence: 99%

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Hassija,

Chamola,

Mahapatra

et al. 2023

Cogn Comput

Self Cite

127

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Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy.

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“…The number of hidden layers varies from one to three in our case for conducting the performance evaluation. The next step is to shrink the sampling dataset in order to better localize the feature that has to be extracted [53]. The pooling techniques are used to achieve this.…”

Section: Proposed Systemmentioning

confidence: 99%

“…These two algorithms have a common characteristic: they both maintain a constant learning rate across all parameters. Adam sees the value in AdaGrad and RMSProp [53]. To fine-tune the weights of the neurons, we compute the gradient of the loss function and apply gradient descent optimization.…”

Section: Optimization and Tuningmentioning

confidence: 99%

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

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A blockchain and deep neural networks-based secure framework for enhanced crop protection

Cited by 27 publications

References 17 publications

Revealing the Barriers of Blockchain Technology for Supply Chain Transparency and Sustainability in the Construction Industry: An Application of Pythagorean FAHP Methods

Revealing the Barriers of Blockchain Technology for Supply Chain Transparency and Sustainability in the Construction Industry: An Application of Pythagorean FAHP Methods

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

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