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data Extraction Techniques for Analysing Agriculture Soil Factors

Data Extraction Techniques for Analysing Agriculture Soil Fa...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Alabdeli, Haider Srinivas, Varanasi Kumari, M. Sirin Keerthana, S. Selvakumar, S. Kaliappan, S. College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Telangana Hyderabad India Cmr College of Engineering & Technology Department of Ece Telanagana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti626115 India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331527495

An essential task in supplying the world's food requirement is soil analysis. It is the backbone of agriculture, particularly in developing nations like India, so if data mining methods are used to the fields, specifically to the soils, the pledge-making scenario may be altered, improving cultivations in the process. A significant portion of the decisions made about the majority of problems pertaining to the sector of agriculture include soil analysis. In addition to highlighting several data mining methods and the corresponding work done by various authors in the context of soil analysis, the primary emphasis of this study is on the function that data mining plays in soil analysis in the agricultural area. These data mining methods are quite modern in the soil analysis domain. The study's advancedness lies in its use of sophisticated data mining tools to better understand the characteristics of the soil, its nutrient content, and possible production results. Improving soil management techniques is the primary goal, since it has a direct impact on the agricultural system's sustainability and production. This research makes use of extensive data from soil samples collected from various agricultural locations. Consequently, a number of data mining methods, including cluster analysis, principal component analysis (PCA), and decision trees, will be used in the present study to discover patterns and relationships between crop performance and soil composition. Important soil characteristics, such as pH, the amount of organic matter, nitrogen, and potassium, all affect crop health and productivity. The objective of this study is to identify' soil clusters,' or groups of related prevalent conditions that either support or constrain agricultural output. Additionally, PCA gave me insight into the relevance of soil characteristics that have an impact on crop performance, making them very helpful for focused interventions aimed at improving the soil. It constructed the decision

关键词： Decision making

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Exploring Pricing, Trading, and Protection in the Big data Market

Exploring Pricing, Trading, and Protection in the Big Data M...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Hassan, Muntather M. Ramupriya, G. Gokul, D. Muthubalaji, S. Bharathi, Palathirthapu Suganya, C. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti626115 India Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Cmr College of Engineering & Technology Department of Eee Telangana Hydrabad India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India M.Kumarasamy College of Engineering Mba department Karur India

ISBN: (纸本)9798331527495

Big data is regarded as the secret to releasing the following massive surges of economic development. In light of a variety of new apps and platforms that are integrated into our everyday routines, such as smartphones and tablets, social access networks, and Website of Things-based automated devices, the quantity of data being gathered worldwide has been growing exponentially (smart grid, smart transportation, smart cities, etc.). How to effectively use the data has become a crucial problem due to the overall rise of data. This necessitates the creation of a big data market that facilitates effective data sharing. data proprietors and users can interact with each other, share, and increase the usefulness of data by moving it into the smartphone industry as a sort of product. The creation of a marketing server and going to trade ploys that maximize the people's benefits of income to buy while maintaining high - quality and anonymity, as well as measures to prevent the decided to trade data from it being resold in order to preserve the software's value, are barriers that must be overcome in allowing such a lead to customer satisfaction for data making trades. In this article, we perform a thorough analysis of the data lifetime and data collection. To be more precise, we first investigate a broad range of payment systems, classify them into various categories, and carry out a thorough analysis of the benefits and drawbacks of these modeling techniques. The creation of data sharing systems and methods that enable effective, safe, and private information data trading is the next area of emphasis. We examine information protection identification systems and managing digital rights systems in our final section. data security issues throughout the lifetime of data sharing are described using computer cryptography, timestamping, and other techniques. © 2025 IEEE.

关键词： User experience

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Research on Big data processing Based on Machine Learning in Market Trend Prediction Model

Research on Big Data Processing Based on Machine Learning in...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International conference on

作者： Tao Teng Shandong Technology and Business University Yantai China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes a new Gradient Boosting Component-decision (GBC-D) model, which combines the advantages of Markov Decision process (MDP), gradient lifting tree (GBT) and principal Component analysis (PCA). Applied to big data processing is based on machine learning in market trend prediction model research. The GBC-D model uses PCA for data reduction and feature extraction, which reduces the computation and preserves the main features of the data. MDP is used to describe the market state transition process and maximize the expected cumulative return by optimizing the decision sequence. GBT is introduced as a forecasting model to gradually improve the forecasting performance to predict market trends more accurately. Combining the advantages of the three methods, the model can effectively deal with the complexity and uncertainty in big data and improve the accuracy and stability of market trend prediction. In the experimental part, we compared the BGC-D with the traditional prediction model. The results show that GBC-D can reduce the prediction error in the complex and changeable market environment, capture the market dynamics more accurately, and improve the overall performance of the system. This fully validates the feasibility and effectiveness of GBC-D architecture in big data processing market trend prediction.

关键词： Computational modeling Predictive models Big data Market research Feature extraction Boosting data models Forecasting Principal component analysis Load modeling

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analysis of key factors affecting chili growth and research on predictive data model in the context of smart farms

Analysis of key factors affecting chili growth and research ...

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2024 International conference on Computer Application and Information Security, ICCAIS 2024

作者： Wu, Junde Guo, Tao College of Agriculture and Bioengineering Longdong University Gansu Qingyang 745000 China College of Mathematics and Information Engineering Longdong University Gansu Qingyang 745000 China

ISBN: (纸本)9781510689312

In the current era of booming smart agriculture, chili cultivation is transitioning from traditional models to intelligent approaches. This study deeply analyzed numerous key factors influencing chili growth, including soil conditions, sunlight duration, temperature range, humidity levels, fertilization strategies, pest and disease control, and variety selection. Through detailed analysis of these factors, their important roles and interrelationships in the chili growth process were clearly defined. A regulatory factor calculation formula was constructed, accurately quantifying the impact of each factor on chili growth and providing a strong basis for scientifically evaluating the growth status of chili. Based on the above research findings, a chili growth prediction data model was established using the multiple linear regression analysis method, providing scientific and reliable decision-making references for chili growers. Thus, it realizes the fine-grained management of chili cultivation, improves the yield and quality of chili, reduces cultivation risks, and vigorously promotes the vigorous development of the chili industry in an intelligent and scientific direction. Although the current research has achieved certain stage results, the chili growth process is complex and variable, and there are still many aspects that need to be further explored and improved. Future research needs to deeply study the synergistic mechanism of factors, optimize the data model, strengthen the transformation of research results, and address the impact of climate change. © The Authors.

关键词： Chili growth key factors prediction model smart agriculture

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Conceptual Framework for Integrating Building Information Modelling (BIM) with Pavement Management System (PMS) 20th

Conceptual Framework for Integrating Building Information Mo...

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20th International conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Utami, Retno Sandoval, Carlos Osorio Thom, Nick Department of Civil Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Department of Civil Engineering. Politeknik Negeri Bandung Indonesia

ISBN: (纸本)9783031842078

In developing countries, pavement management systems (PMS) face limitations such as insufficient data, budget constraints, and inadequate analysis tools. To address those limitations, simplified PMS are needed to increase efficiency and effectiveness in managing pavement assets. Meanwhile, Building Information Modelling (BIM) is a promising technology that enables collaborative work, process automation, and centralised asset information sharing. However, current BIM implementation for PMS remains scattered across different steps of the pavement management process. A unified mechanism is required to seamlessly integrate all PMS elements into the BIM workflow. This article proposes a conceptual framework to integrate BIM with PMS. It integrates three key modules: (1) an As-Is model for capturing real-world pavement conditions, (2) a prediction model for automated performance analytics and deterioration forecasting, and (3) a decision-making engine to optimise recommended maintenance based on constraints. The unified mechanism enables visualisation, analytics, and data management to increase the efficiency and effectiveness of pavement management. The next step for this research is to implement and validate the framework through a real-world case study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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MLP-Based Adaptive Sampling and Optimization of Laser-Ion Acceleration with Ultra-Short Laser Pulses 15th

MLP-Based Adaptive Sampling and Optimization of Laser-Ion ...

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15th International conference on Parallel processing and Applied Mathematics, PPAM 2024

作者： Miethlinger, Thomas Bussmann, Michael Schramm, Ulrich Kluge, Thomas Helmholtz -Zentrum Dresden-Rossendorf Dresden01328 Germany Technische Universität Dresden Dresden01069 Germany

ISBN: (纸本)9783031857027

The field of laser-ion acceleration faces significant challenges in handling high-dimensional, computationally intensive problems, often constrained by budgets and available computational power. Reliably achieving high ion energies with current laser technologies through ultra-high intensity pulses in near-critical to overdense plasmas remains difficult, necessitating detailed, costly simulations to explore various acceleration mechanisms and optimize outcomes. Due to the high computational cost of these simulations, there is a need for precise, scalable, and efficient adaptive sampling methods that balance exploring new mechanisms while exploiting known parameter dependencies and that can operate in high-performance computing environments. In this work, we propose and investigate a scalable adaptive sampling approach that intrinsically supports parallel processing. We apply this method to data obtained from particle-in-cell simulations using multilayer perceptrons (MLPs), chosen for their flexibility in modeling complex dependencies. We benchmark our results against Bayesian optimization, and we highlight the limitations of MLPs when accounting for the intrinsic uncertainty present in the acceleration process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Identifying EEG-based Functional Networks for Whole-Brain Models 11

Identifying EEG-based Functional Networks for Whole-Brain Mo...

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11th Vienna International conference on Mathematical Modelling, MATHMOD 2025

作者： Edthofer, Alexander Korner, Andreas Institute of Analysis and Scientific Computing TU Wien Wiedner Hauptstraße 8-10 Vienna1040 Austria

Brain activity differs according to the state of consciousness. Whole-brain models, typically based on functional magnetic resonance imaging (fMRI) data, provide valuable insight into these changes by utilizing structural connectivity and differential equations to model the functional connectivity between brain regions. The goal of this work is to adapt fMRI-based functional connectivity models to electroencephalography data. A key step in this process is to determine the number of clusters and to estimate the coupling parameters. We turn to amplitude envelope correlation, a time-domain measure, to better match functional connectivity patterns observed in fMRI. By analyzing 64-channel electroencephalogram data from 25 male subjects over the age of 60 from the AlphaMax study, we investigate wakefulness and the transition to unconsciousness under anesthesia. Using A;-means clustering, we identify optimal brain network configurations, focusing on whether they match known fMRI-based networks. Clustering is evaluated using the Calinski-Harabasz criterion for different thresholds and numbers of cluster. The results show that two clusters are predominantly optimal for both awake and the mixed half awake, half unconscious scenario. Misplaced electrodes are mainly found in parietal regions. Since we determined the number of differential equations, this work lays the foundation for further development of electroencephalography-based whole-brain models that can track functional connectivity changes during anesthesia. Copyright © 2025 The Authors.

关键词： Electroencephalography

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Effect of Internal Structure Degradation on the Vibration Characteristics of Transformer Outer Box Wall During Aging 16th

Effect of Internal Structure Degradation on the Vibration Ch...

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16th International conference on Modelling, Identification and control, ICMIC 2024

作者： Sun, Lu Liu, Hongliang Tian, Yuan Chen, Guanyu Xu, Fusheng Electric Power Research Institute State Grid Hebei Electric Power Co. Ltd. Shijiazhuang China No. 619 Yonghua North Street Baoding China

ISBN: (纸本)9789819617760

Transformer vibration analysis yields critical information with practical implications. Examining vibration variations throughout the aging process of transformers contributes significantly to understanding the rules governing these changes and assessing internal structural aging. The interplay between mechanical vibration, thermal aging, and the resistance of transformer cores and windings fosters a progressive degradation of the internal structure. However, the relationship between this internal deterioration and changes in the external casing’s wall vibrations remains elucidated. In order to study the synergistic effect of mechanical-thermal aging on the deterioration of the internal structure performance of the transformer, this paper designed an experimental platform for the aging of the internal structure of the transformer and collected the full-cycle vibration data of the internal pressure data and the vibration of the outer case wall of the transformer through continuous overloading operation for 60 days, and carried out vibration entropy, specific gravity of the fundamental frequency, and comprehensive correlation analyses on the data. The study shows that the internal structure pressure fluctuates and decreases during thermal aging, and the two indicators, vibration entropy, and fundamental frequency specific gravity, have opposite trends. This study can provide a reference role for analyzing the deteriorated vibration characteristics under the combined effect of machine-heat aging and diagnosing the location of the deployment points. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vibration analysis

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The Good, the Bad and the Ugly: Investigating the Effectiveness of Graph Deep Neural Networks for Anomaly Detection in Industrial control Systems 7th

The Good, the Bad and the Ugly: Investigating the Effectiven...

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7th IFIP WG 5.5 International Cross-Domain conference on Internet of Things

作者： Nahalka, Martin Cook, Marco M. Pezaros, Dimitrios Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9783031819025;9783031819001;9783031818998

Industrial control Systems (ICS) are paramount to the efficient operation of Critical National Infrastructure (CNI) ranging from electricity generation and distribution to manufacturing. However, the growing convergence of ICS with Information Technology (IT) systems renders CNI vulnerable to a range of cyber threats. Graph neural networks are being increasingly used for anomaly detection by adding granularity to the detection process. In this paper, we present a comparative study of graph-based deep learning models for ICS anomaly detection. Through the evaluation of four models using three multivariate industrial datasets, we aim to discern the effectiveness of prediction and reconstruction-based graph models in the ICS domain. We investigate data reduction techniques to minimise features needed to represent the window size and examine the representation of sliding window in terms of feature size for time-series analysis. Additionally, we assess the impact of the length of a context window on anomaly detection performance. Our results show that using feature reduction techniques on a longer context window produces better results while having the computational advantages of a shorter window size. Graph autoencoder is the most resilient to feature size reduction by maintaining similar F1 and AUC-PR score regardless of the number of features used to represent a context window. The results also provide insight to the suitability of graph-based models in this domain and offer recommendations for their optimal usage, paving the way for enhanced security and resilience in ICS.

关键词： Anomaly detection Industrial control Systems Deep Learning Graph Neural Networks Machine Learning

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Impact of Stem Cell Therapy in Leprosy Pathogenesis Through Mathematical Study: An Impulsive control Based Treatment Design 2nd

Impact of Stem Cell Therapy in Leprosy Pathogenesis Throug...

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2nd International conference on Mathematical analysis and Applications in modeling, ICMAAM 2023

作者： Mukherjee, Satyajit Ghosh, Tushar Ghosh, Salil Centre for Mathematical Biology and Ecology Department of Mathematics Jadavpur University Kolkata India

ISBN: (纸本)9789819791934

Leprosy, characterized by dermatological manifestations and peripheral nervous system impairment due to Mycobacterium leprae infection in Schwann cells, poses persistent challenges in treatment efficacy. Stem cell therapy (SCT), an innovative field in regenerative medicine, holds immense promise for addressing these challenges. Stem cells, distinguished by their unique ability to differentiate into specialized cell types and self-renew, offer a valuable resource for tissue regeneration and healing. In this context, we have built a nonlinear ODE-based four dimensional mathematical model. This research article primarily aims to investigate the dynamic changes and interrelations of the cellular populations within the system during the progression of leprosy. The main focus throughout the research article is to predict the dissemination process of the disease following the transplantation of SCT. Specifically, our goal is to comprehend the disease dynamics involving M. leprae and Schwann cells due to the incorporation of stem cells through an impulsive manner. All analytical outcomes have been verified through numerical simulations and compared with existing clinical findings, ensuring the reliability and applicability of our novel research findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stem cells

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