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Overview of the application of agriculture big data research

Overview of the application of agriculture big data research

引用

AIIPCC 2022;The Third international conference on Artificial intelligence, Information Processing and Cloud computing

作者： Dahui Hu Zhiguo Du

The processing and analysis results based on big data provide reliable decision support for decision makers and users. With the further development of agricultural informatization, big data technology has been gradually applied to agricultural planning, production, management and marketing of agricultural products. Firstly, the problems to be solved in the application of big data in agriculture are put forward. Then, the main contents and difficulties of data acquisition, data analysis and processing, data application and service in the application of big data in agriculture are analysed. Finally, the problems in the application of big data in agriculture are proposed and the application of big data in agriculture is prospected.

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Task Allocation in Containerized Cloud computing Environment

Task Allocation in Containerized Cloud Computing Environment

引用

2022 international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2022

作者： Akram Khan, Md Kumar Mishra, Sambit Kumari, Anisha Sahoo, Bibhudatta National Institute of Technology Rourkela India Bit Sindri Dhanbad India Srm University AP India

ISBN: (纸本)9781665461092

Containerization technology makes use of operating system-level virtualization to pack application that runs with required libraries and is isolated from other processes on the same host. The lightweight easy deployment of containers made them popular at many data centers. It has captured the market of virtual machines and emerged as lightweight technology that offers better microservices support. Many organizations are widely deploying container technology for handling their diverse and unexpected workload derived from modern applications such as Edge/ Fog computing, big data, and IoT in either proprietary clusters or public, private cloud data centers. In the cloud computing environment, scheduling plays a pivotal role. In the same way in container technology, scheduling also plays a critical role in achieving the optimum utilization of available resources. Designing an efficient scheduler is itself a challenging task. The challenges arise from various aspects like the diversity of computing resources and maintaining fairness among numerous tenants, sharing resources with each other as per their requirements, unexpected variation in resource demands and heterogeneity of jobs, etc. This survey provides a multi-perspective overview of container scheduling. Here, we have organized the container scheduling problem into four categories based on the type of optimization algorithm applied to get the linear programming Modeling, heuristic, meta-heuristic, machine learning, and artificial intelligence-based mathematical model. In the previous research work has been done on either Virtual machine placements to Physical Machines or Container instances to Physical machines. This leads to either underutilized PMs or over-utilized PMs. But in this paper, we try to combine both virtualization technology Containers as well as VMs. The primary aim is to optimize resource utilization in terms of CPU time. in this paper, we proposed a meta-heuristics algorithm named Sorted Task-base

关键词： Containers

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13th TPC Technology conference on Performance Evaluation and Benchmarking, TPCTC 2021 held in conjunction with 44th international conference on Very Large data Bases, VLDB 2021

13th TPC Technology Conference on Performance Evaluation and...

引用

13th TPC Technology conference on Performance Evaluation and Benchmarking, TPCTC 2021 held in conjunction with 44th international conference on Very Large data Bases, VLDB 2021

ISBN: (纸本)9783030944360

The proceedings contain 9 papers. The special focus in this conference is on Performance Evaluation and Benchmarking. The topics include: EvoBench: Benchmarking Schema Evolution in NoSQL;Everyone is a Winner: Interpreting MLPerf Inference Benchmark Results;CH2: A Hybrid Operational/Analytical Processing Benchmark for NoSQL;Orchestrating DBMS Benchmarking in the Cloud with Kubernetes;a Survey of big data, High Performance computing, and Machine Learning Benchmarks;tell-Tale Tail Latencies: Pitfalls and Perils in database Benchmarking;Quantifying Cloud data Analytic Platform Scalability with Extended TPC-DS Benchmark.

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Evidence-Based Process in Research Involving Machine Learning Algorithms 9

Evidence-Based Process in Research Involving Machine Learnin...

引用

9th IEEE international conference on Behavioural and Social computing, BESC 2022

作者： Lukmanjaya, Wilson Kurnia Islam, Md Rafiqul Xu, Guandong NSW Australia

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

The current state of research involving the application of machine learning (ML) algorithms on various topics that directly impact human beings does not sufficiently focus on identifying and filling in research gaps. Evidence is lacking that the maximum accuracy showcased in multiple research papers is the best accuracy possible. However, it is vital to fill in research gaps to be cost-effective and actionable in the big picture. This study proposes a guideline that can be useful for future work involving ML algorithms on high-risk topics to fill in research gaps as much as possible for a particular problem. In this study, 10 different models were conducted with 12.7 million different parameter combinations to create this guideline. The results from the experiments demonstrated experimentation with different algorithms and parameters is crucial in research to deduct which algorithm performs well. Exposure to accurate and inaccurate models can assist researchers and relevant professionals in highlighting evidence-based methods that might contribute to improved findings in various areas. It suggests what works and does not and which task is the most appropriate to fill research gaps. It is also important to consider the guideline produced in this study as inspiration. More research is necessary to improve the guideline. © 2022 IEEE.

关键词： data mining

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Multi-view Document Clustering with Joint Contrastive Learning 1

引用

11th CCF international conference on Natural Language Processing and Chinese computing (NLPCC)

作者： Bai, Ruina Huang, Ruizhang Qin, Yongbin Chen, Yanping Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China

ISBN: (数字)9783031171208

ISBN: (纸本)9783031171208;9783031171192

Multi-view document clustering, which learns common representations from multiple views to achieve consistent partition, has emerged lots of increasing work. Though promising performance has been demonstrated in various applications, their view representations are learned with no consideration of achieving a consistent clustering partition. In this paper, we propose a Multi-view document Clustering model with Joint Contrastive learning (MCJC) to address the aforementioned issue. Our model learns the view representations with a joint contrastive learning module by introducing a task-specific objective so that it can effectively achieve consistency both in cluster-wise and featurewise hidden spaces. Meanwhile, in the clustering module, we collect the view-level cluster agreement and document-level clustering partition to refine the contrastive learning and obtain document assignments. As a result, the proposed model can use a joint contrastive module to learn clustering-friendly representation and through multi-level clustering to achieve better clustering performance. Extensive experiments on real datasets demonstrate that our model achieves state-of-the-art clustering effectiveness.

关键词： Multi-view document clustering Contrastive learning Deep clustering

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Model of the Influence of Students’ Participation on Learning Effects Using Educational Artificial intelligence

Model of the Influence of Students’ Participation on Learni...

引用

IEEE international conference on Cloud computing and big data Analysis (ICCCBDA)

作者： Hui Mao Zheng Fang Zhengwei Xia School of Information Engineering Wuhan Business University Wuhan China School of Information Management Wuhan University Information Center Wuhan University Wuhan China

Educational Artificial intelligence is driving broader and deeper changes in higher education. This paper adopts the teaching process data of four classes of Computer System in the 2022-2023-1 semester of school of Information Engineering, Wuhan Business University, using machine learning methods, through the analysis of students’ participation data, to explore and establish an analysis and prediction model of the influence of students' participation in teaching on their final examination results. After the model approximation and comparison between multiple linear regression and polynomial regression, the better polynomial linear regression fitting influence model is obtained, while the linear regression intuitively gives the weight of four variables. The model developed in this paper helps to quantify the effect of students' participation in the teaching process on their final exam results and can predict their final exam results through their teaching participation data, also can help teachers to make real-time adjustment of teaching methods in the teaching process, in order to achieve better learning effects.

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Management Information System based on Artificial intelligence Technology 5

Management Information System based on Artificial Intelligen...

引用

5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Pelleti, Swarupa Bains, Sahil Bansal, Atul Muda, Iskandar Chowdhary, Harish Mahajan, Yashodhan Koneru Lakshmaiah Education India Guru Nanak Dev University India University of Bahrain College of Business Administration Bahrain Universitas Sumatera Utara Sumatra Indonesia National Forensic Sciences University Delhi India Brihan Maharashtra College of Commerce Maharashtra India

ISBN: (纸本)9798350398267

Personalized suggestions, such as those based on a user's search history, purchase history, or other online activity, are a common use of artificial intelligence. AI is used a lot in commerce to do things like optimize products, plan inventory, handle logistics, and more. In order to deal with the problems caused by cloud computing, big data, and AI, developers of modern information systems are adding AI to the platform for managing information. Natural language, intelligent processing, and established procedures are all components of artificial intelligence, a subfield of computer science. Since the inception of AI, there has been continuous development in both the underlying technology and the range of potential uses. The purpose of this essay is twofold: first, to investigate how AI may be useful in the management information system of the container transport industry, and second, to offer suggestions for how that system might be enhanced. K-means clustering is an approach that is proposed in this work. The efforts made in building the terminal management system have substantially benefited the fields of automation, intelligent planning, and integrated management. A lot of work has gone into improving the terminal management system, and it has paid off in the form of significant advances in areas like automation, intelligent planning, and integrated management. Mulesoft Anypoint, which relies heavily on the Navic information platform and GPS for its Internet connectivity, is also being implemented at the same time. Therefore, the CCR approach is employed to operate the multimodal transport virtual container while analyzing its performance under coproduction's management. When sent in containers, as much as 98.5 % of goods arrive in one piece. © 2022 IEEE.

关键词： K-means clustering

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Thermal conductivity prediction with Fully Connected Neural Network 3

Thermal conductivity prediction with Fully Connected Neural ...

引用

3rd international conference on Artificial intelligence, Information Processing and Cloud computing, AIIPCC 2022

作者： Zhang, Xingxing Li, Shaobo Yu, Liya Dong, Rongzhi Song, Qisong Wang, Zhongyu State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China Department of Computer Science and Engineering University of South Carolina SC United States Guizhou University Guiyang China

ISBN: (纸本)9783800759330

In this paper a Fully Connected Neural Network (FCNN) model is developed for the prediction of the material thermal conductivity. The traditional theoretical experimental research and computational simulation could not meet the needs of scientists' exploration of new materials. In recent years, data-driven machine learning algorithm has been used in material screening and performance prediction. Thermoelectric materials have become a hot topic in the field of materials research because of their ability to realize the interactive conversion of electric energy and thermal energy, small size, light weight, long service life and environmental friendliness. The hypothesis verification method based on a large number of experimental observations and the method of system calculation using the first principle are difficult to meet the requirements for rapid discovery of new materials in the current era due to the long research period. Therefore, the use of machine learning algorithms to assist in the development of new thermoelectric materials is of great significance. In this paper, a material thermal conductivity prediction model based on Fully Connected Neural Network (FCNN) is established, which is applied to the thermal conductivity dataset in the Materials Project database. The model is evaluated by ten-fold cross-verification, and good prediction results are obtained. The mapping model between descriptive and thermal conductivity of materials is established by using machine learning algorithm, which can be used for large-scale material screening and guiding experimental research. © VDE VERLAG GMBH.

关键词： Forecasting

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Analysis of Non-Fungible Token Pricing Factors with Machine Learning

Analysis of Non-Fungible Token Pricing Factors with Machine ...

引用

2022 IEEE international conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ho, Kin-Hon Hou, Yun Chan, Tse-Tin Pan, Haoyuan The Hang Seng University of Hong Kong Department of Computing Hong Kong Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs. © 2022 IEEE.

关键词： Machine learning

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Application Research of Cloud computing Technology in Computer data Processing

Application Research of Cloud Computing Technology in Comput...

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CAIBDA 2022;2nd international conference on Artificial intelligence, big data and Algorithms

作者： Shuang Han

In the context of the era of big data, it not only creates a lot of opportunities for the development of modern society but also poses more challenges. If the society wants to develop better, it must take good technical means to process computer data to accurately mine the contained value. The application of cloud computing technology can achieve this purpose. Based on this, this paper analyzes the correlation between cloud computing technology and computer data processing through an overview of cloud computing technology, and then analyzes the cloud computing process based, so as to provide support for better application of cloud computing technology.

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