Crop Yield Prediction Using Spatio Temporal CNN and Multimodal Remote Sensing

Arunarani, AR.; Selvanayaki, S.; Saleh Al Ansari, Mohammed; Ala Walid, Md. Abul; Devireddy, Nagamalleswari; Keerthi, Murugesan Mohana

doi:10.1109/icecaa58104.2023.10212267

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The SRU is designed to provide expressive recurrence and enable highly parallelized implementation, and can facilitate the training of deep models. Based on the deep learning framework of the SRU, a TSFFAM model was constructed that utilizes an attention mechanism to predict buoy information [35][36][37][38][39][40][41][42], effectively avoiding the problems of traditional LSTM models such as time-consuming prediction and difficulty in training. Due to the limitations of observational data, the understanding of the 3D Pacific circulation in the oceanographic community has long focused on surface circulation, while significant knowledge gaps exist regarding the structure, variability, and links to climate change of flows in the deep subsurface mesosphere.…”

Section: Introductionmentioning

confidence: 99%

Argo Buoy Trajectory Prediction: Multi-Scale Ocean Driving Factors and Time–Space Attention Mechanism

Ning,

Zhang,

Zhang

et al. 2024

JMSE

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The Array for Real-time Geostrophic Oceanography (Argo) program provides valuable data for maritime research and rescue operations. This paper is based on Argo historical and satellite observations, and inverted sea surface and submarine drift trajectories. A neural network method was developed to predict the position of Argo buoys, improving target tracking and emergency support capabilities. Based on a deep learning framework using a Simple Recurrent Unit (SRU), a new Time–Space Feature Fusion Method based on an Attention Mechanism (TSFFAM) model was constructed. The TSFFAM mechanism can predict the target trajectory more accurately, avoiding the disadvantages of traditional Long Short-Term Memory (LSTM) models, which are time consuming and difficult to train. The TSFFAM model is able to better capture multi-scale ocean factors, leading to more accurate and efficient buoy trajectory predictions. In addition, it aims to shed light on the mechanism of the joint multi-element and multi-scale effects of laminar and surface currents on multi-scale ocean factors, thereby deepening our understanding of the multi-element and multi-scale interactions in different spatio-temporal regimes of the ocean. Experimental verification was conducted in the Pacific Ocean using buoy trajectory data, and the experimental results showed that the buoy trajectory prediction models proposed in this paper can achieve high prediction accuracy, with the TSFFAM model improving the accuracy rate by approximately 20%. This research holds significant practical value for the field of maritime studies, precise rescue operations, and efficient target tracking.

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Section: Introductionmentioning

confidence: 99%

Argo Buoy Trajectory Prediction: Multi-Scale Ocean Driving Factors and Time–Space Attention Mechanism

Ning,

Zhang,

Zhang

et al. 2024

JMSE

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“…The aforementioned technology is employed for the purpose of disseminating and retaining data in a decentralised way through a network of peers [11]. In contemporary times, the utilisation of blockchain technology has proven to be highly impactful in facilitating secure and efficient financial transactions [12]. Additionally, it has the potential to serve as a facilitator in various other domains.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Preventions of Attacks on IOT Using Block Chain: Review

Anjum

2023

J. Adv. Zool.

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The increasing acceptance and integration of the Internet of Things (IoT) has made it a prominent element in our everyday existence. Regrettably, a significant level of vulnerability is present in Internet of Things (IoT) devices, which might potentially be abused by malicious actors. The predominant source of security vulnerabilities in IoT systems originates from their centralized architecture. The lack of adequate authentication and access control systems for managing access to information generated by Internet of Things (IoT) devices is a significant concern. Consequently, the issue of verifying the identification of the equipment or communication node arises. The decentralized nature of Blockchain serves as a viable alternative for ensuring secure operations inside a trustless environment. Extensive research has been conducted in the domain of the convergence of Internet of Things (IoT) and Blockchain, yielding notable progress in addressing several significant challenges encountered in the IoT realm. This study investigates the challenges and vulnerabilities associated with the Internet of Things (IoT), as well as explores the potential benefits of integrating Blockchain technology.

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