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Precision Agriculture System Using Big data Analytics in Predicting Yield by Controlling Plant Disease and Irrigation Scheduling 1. ***, Research Scholar, Department of data Science and Business System, school of computing, Srm Institute of Science and Technology,Ktr

SSRN

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SSRN 2022年

作者： Vasudevan, N. Karthick, T. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Chennai India

The agricultural sector is one of India's most important and major endeavors, and it is also critical to the country's economic development. Agriculture is one of the most important things that contributes to humanity's pleasure and welfare, as well as serving as a deterministic element in a nation's economic well-being. Smart agriculture, or the application of information technology in agriculture, attempts to provide precise disease management, irrigation and yield prediction in crop production. We have implemented Big data Analytics and Internet of Things in the agricultural field, which is nothing but Precision Agriculture. This will extensively impact on the crop yield positively. Precision agriculture is utilizing IoT and Big data to enhance crop quality, therefore increasing total output rates. In this paper we have implemented the N stage – CNN (Convolutional Neural Networks) using the Smart Agriculture dataset, using parameters such as temperature, humidity, rainfall, pH and solar irradiance. In this paper, irrigation scheduling is performed using machine learning techniques and plant disease detection using DoubleGAN method. As we have taken into consideration almost all required parameters for plant growth with irrigation scheduling and plant disease detection, the yield prediction is precise. The principle aim of this work is to come up with N stage CNN model that will increase the yield irrespective of the type of crop and thereby largely contributing to the agricultural sector, which will in turn increase food production in the coming years. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Analysis of the SEIR Model for Measles Spread with the Influence of Vaccination

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IAENG International Journal of Applied Mathematics 2025年第3期55卷 626-635页

作者： Hamzah, Joseph Leonardo Marvin Fadhilah, Helisyah Nur Syaifullah, Rindra Department of Data Science School of Computing Telkom University Surabaya Indonesia

In this paper, we analyze the impact of vaccination on the dynamics of measles transmission using the SEIR mathematical model. We demonstrate that high vaccination coverage significantly reduces disease transmission and establishes herd immunity, thereby protecting both vaccinated and unvaccinated populations. We observe that low to moderate vaccination levels result in an increase in the reproduction number, leading to periodic outbreaks, especially in communities with low vaccine uptake. Our analysis identifies both adult and child vaccination rates as critical factors in stabilizing disease dynamics. In the absence of vaccination, we observe uncontrolled outbreaks that pose significant risks to public health. These findings underscore the importance of implementing robust vaccination programs to prevent measles outbreaks and maintain public health stability. © (2025), (International Association of Engineers). All rights reserved.

关键词： Vaccines

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ArtProtect: Blockchain and NFC-based anti-counterfeit system for physical art

IET Blockchain

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IET Blockchain 2024年第4期4卷 423-442页

作者： Tantowibowo, Cindy Handoko Yau, Wei-Chuen School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia

Counterfeit artwork presents a significant risk to copyright holders and the economy. Without expertise in art, it is not straightforward to distinguish an artwork counterfeit from a genuine piece. This work designs and implements a reliable solution that enables users with near-field communication-enabled Android smartphones to verify artwork authenticity by accessing its respective certificate of authenticity stored in the Ethereum blockchain. To represent a physical artwork, the artwork image and metadata are stored in an inter-planetary file system and minted as an ERC721 non-fungible token to a smart contract deployed in Ethereum Rinkeby Testnet. The mobile app ArtProtect was developed to generate certificate of authenticity based on each non-fungible token fetched through OpenSea Testnet API. Users access this information by scanning an near-field communication tag embedded into the artwork. The content of the tag is signed by the respective artwork's artist and responsible agent by using Ethereum signing with their Ethereum wallet accounts. Through testing and analysis, the implemented work is secure, tamper-evident, usable, flexible, and inexpensive to be applied to a real-world scenario. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Computer Science Undergraduate Programs in Australia

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ACM Inroads 2024年第1期15卷 18-26页

作者： Hamilton, Margaret Hol, Ana Richardson, Joan McGovern, Jim School of Computing Technologies RMIT University Australia School of Computer Data and Mathematical Sciences Western Sydney University Australia

Australian CS programs generally provide good problem-solving skills, depth in the foundations of computing theory, hardware and software operating environments and technologies, and complexity, together with a strong focus on programming and software development. Increasingly, programs are focusing on addressing the emerging areas of Artificial Intelligence, Cyber Security and data Science. Australian CS graduates should be in demand in a growing, and increasingly digitized economy. Greater focus on application with industry tools and frameworks, exposure to other disciplines and ways of working, recognition of diversity and development of non-technical competency areas of working may be the best avenues to enhance the capabilities and career prospects of CS graduates. There are challenges in developing more diverse graduates, in funding more effective learning environments, and in building awareness of CS in the community, particularly those that influence the choices of potential students. Overall, the Australian CS discipline is well placed to underpin the developing knowledge economy. © 2024 Copyright held by owner/author(s)

关键词： Computer aided instruction

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Survey on age-related macular degeneration detection in OCT image

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International Journal of Computers and Applications 2024年第2期46卷 101-112页

作者： Deepti, S. Renu Karthik, P.C. Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology Kattankulathur India

Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis and monitoring purposes. In this work, 50 papers are reviewed to examine various approaches and their performance metrics. This paper reviewed various existing works related to AMD detection using different imaging modalities for quality images with high resolution. The investigation of case studies is analyzed on the basis of data collected from various patients undergoing several treatments and scans obtained from various imaging modalities. Furthermore, the analysis extends to the works that used different AI techniques, such as Deep Learning (DL), Machine Learning (ML), and Unsupervised Framework, for the detection of AMD-related diseases. Also, the analysis of different performance measures is done that shows the variations in terms of performance metrics such as accuracy, sensitivity, specificity, and so on. The outcome of this deep analysis shows that the system with the DL model reaches its utmost performance in AMD detection. Finally, gaps and challenges that are still lagging with this research area are summarized, which motivates the researchers to take away the work in an effective way. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diagnosis

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models 38

An In-depth Investigation of Sparse Rate Reduction in Transf...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Yunzhe Zou, Difan Xu, Dong School of Computing and Data Science The University of Hong Kong Hong Kong School of Computing and Data Science Institute of Data Science The University of Hong Kong Hong Kong

Deep neural networks have long been criticized for being black-box. To unveil the inner workings of modern neural architectures, a recent work [45] proposed an information-theoretic objective function called Sparse Rate Reduction (SRR) and interpreted its unrolled optimization as a Transformer-like model called Coding Rate Reduction Transformer (CRATE). However, the focus of the study was primarily on the basic implementation, and whether this objective is optimized in practice and its causal relationship to generalization remain elusive. Going beyond this study, we derive different implementations by analyzing layer-wise behaviors of CRATE, both theoretically and empirically. To reveal the predictive power of SRR on generalization, we collect a set of model variants induced by varied implementations and hyperparameters and evaluate SRR as a complexity measure based on its correlation with generalization. Surprisingly, we find out that SRR has a positive correlation coefficient and outperforms other baseline measures, such as path-norm and sharpness-based ones. Furthermore, we show that generalization can be improved using SRR as regularization on benchmark image classification datasets. We hope this paper can shed light on leveraging SRR to design principled models and study their generalization ability. © 2024 Neural information processing systems foundation. All rights reserved.

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Scalable Blockchain Technology for Tracking the Provenance of the Agri-Food

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Computers, Materials & Continua 2023年第5期75卷 3339-3358页

作者： B.Subashini D.Hemavathi Department of Data Science and Business Systems School of ComputingSRM ISTKattankulathurTamilnadu603203India

Due to an increase in agricultural mislabeling and careless handling of non-perishable foods in recent years,consumers have been calling for the food sector to be more *** to information dispersion between divisions and the propensity to record inaccurate data,current traceability solutions typically fail to provide reliable farm-to-fork histories of *** threemost enticing characteristics of blockchain technology are openness,integrity,and traceability,which make it a potentially crucial tool for guaranteeing the integrity and correctness of *** this paper,we suggest a permissioned blockchain system run by organizations,such as regulatory bodies,to promote the origin-tracking of shelf-stable agricultural *** propose a four-tiered architecture,parallel side chains,Zero-Knowledge Proofs(ZKPs),and Interplanetary File Systems(IPFS).These ensure that information about where an item came from is shared,those commercial competitors cannot get to it,those big storage problems are handled,and the system can be scaled to handle many transactions at *** solution maintains the confidentiality of all transaction flows when provenance data is queried utilizing smart contracts and a consumer-grade reliance *** simulation testing using Ethereum Rinkeby and Polygon demonstrates reduced execution time,latency,and throughput overheads.

关键词： Blockchain IPFS sidechain supply chain management traceability

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Precise control of page cache for containers

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Frontiers of Computer Science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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UCat: heterogeneous memory management for unikernels

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Frontiers of Computer Science 2023年第1期17卷 51-61页

作者： Chong TIAN Haikun LIU Xiaofei LIAO Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing LabSchool of Computing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address *** this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel--OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.

关键词： unikernel virtualization non-volatile memory heterogeneous memory large page slab allocation

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