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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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Multi-Beam Management and Resource Allocation for LEO Satellite-Assisted IoT Networks

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IEEE Internet of Things Journal 2025年第12期12卷 19443-19458页

作者： Kim, Donghyeon Jung, Haejoon Lee, In-Ho Niyato, Dusit Gyeonggido Anseong Korea Republic of College of Computing and Data Science Singapore

Multi-beam low-Earth orbit (LEO) satellite communication is a promising solution for providing high data rate and wide area coverage. Therefore, satellite communication is introduced into Internet of Things (IoT) networks to support large-scale connectivity. In the satellite communication system, multi-beam management and power control are challenging issues because inter-beam interference severely affects system performance and power consumption influences the battery life of the satellite. Thus, traditional LEO satellite systems mainly focus on minimizing a capacity-demand gap to develop an effective power reduction algorithm. In contrast to this approach, in this paper, we present a theoretical analysis of the optimal conditions for minimizing the transmit power of the satellite while satisfying the traffic demands of users in multi-beam LEO satellite-assisted IoT networks. Based on this analysis, we propose algorithms for user-beam association, beam pattern selection, timeslot scheduling, and power allocation to minimize the transmit power while satisfying the traffic demands. In addition, we provide low-complexity algorithms for power minimization to reduce the computational complexity. Simulation results demonstrate that the proposed methods outperform the conventional schemes in terms of power consumption, capacity-demand gap, and computational complexity. © 2025 IEEE. All rights reserved,

关键词： Internet of things

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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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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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Acoustic XR Technology Merging Real and Virtual Sounds

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NTT Technical Review 2024年第6期22卷 30-34页

作者： Noguchi, Kenichi Chiba, Hironobu Kako, Tatsuya Kozuka, Shihori Kurokawa, Yoshiaki Watanabe, Yuki Nakayama, Akira Ultra-Reality Computing Group NTT Computer and Data Science Laboratories

With the spread of open-ear earphones that do not cover the ear, new listening experiences are being proposed that combines real ambient sounds with virtual sounds heard from earphones. At NTT, we call this merging of the real and virtual sounds through open-ear earphones "acoustic XR (extended reality) technology," which we are now developing with a view to actual services. In this article, we describe this technology with a focus on actual trials and touch upon future developments. © 2024 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Merging

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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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FedCGAT: A federated demand prediction method for shared bicycles based on contrastive graph attention mechanism

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Journal of Intelligent and Fuzzy Systems 2025年第3期48卷 329-344页

作者： Zhu, Yichang Chen, Leiming Zhang, Weishan Zhang, Baoyu Dong, Cihao Qiu, Jiakang China Shandong Data Open Innovative Application Laboratory Qingdao China School of Computing and Data Science Nanyang Technological University Singapore

Demand prediction for shared bicycles based on historical trip data helps bicycle management organizations plan the scheduling of bicycles. However, the current prediction methods have the following problems: (1) the bicycle trip station data hides the temporal and spatial information, which is crucial for demand prediction, and the traditional methods cannot effectively use that information. (2) each bicycle organization manages its region, and local region data cannot accurately predict the demand of the whole region. Due to the privacy trip data, organizations cannot share raw data directly, which makes it a challenge to achieve federated multi-participant analyses. To address these issues, we propose a federated learning framework for demand prediction of shared bicycles (FedCGAT). Firstly, we propose a spatio-temporal graph neural network based on an attention mechanism for feature modeling of data. Meanwhile, we propose a graph data augmentation method to eliminate noisy data and capture spatial correlations. Then, we construct an auxiliary task based on contrastive learning to assist model training, which can learn the data features fully. Finally, we conducted experiments on two real-world bicycle datasets. The experiments demonstrate that FedCCAT achieves high prediction accuracy while preserving data privacy. Compared to the best-performing baseline model on both datasets, our model achieves reductions in MAPE values of 1.67% and 1.94%, respectively. © 2025 – IOS Press. All rights reserved.

关键词： Contrastive Learning

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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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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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