To build an agricultural product network marketing system in the era of e-commerce, it is necessary for agricultural product business enterprises or farmers to recognize the benefits of using e-commerce to market agricultural products and face up to its influencing factors and to build a support system, application system, and guarantee system with the support of the government to promote agricultural product e-commerce marketing to obtain healthy development. In this study, we study the construction mode of e-commerce agricultural product online marketing system based on the end of blockchain and improved genetic algorithm. This study adopts the design idea of coalition chain and proposes a multichain agricultural product trading information blockchain application technology including agricultural product trading information chain, user information chain, and agricultural product information chain. The agricultural product information chain provides detailed information of agricultural products and ensures the traceability and non-tamperability of the information. It automatically divides the profits of transactions through smart contracts to improve execution efficiency and reduce transaction costs and finally establishes a transparent, efficient, and applicable blockchain architecture for agricultural product transactions. 

Construction Model of E-Commerce Agricultural Product Online Marketing System Based on Blockchain and Improved Genetic Algorithm

Cloud computing is a new type of business computing model. It is connected through the network and can obtain various applications, data and IT services. The core of cloud computing is task scheduling, and the application of genetic algorithm (GA) in cloud computing task scheduling is also a hot topic in recent years. In this paper, the "three-stage selection method" and the genetic strategy of "totaldivision-total" are put forward to improve genetic algorithm. Using simulation experiments in cloud computing simulation software named Cloudsim, the experimental results show that comparing with the simple genetic algorithm (SGA), the improved genetic algorithm (IGA) is better than the simple genetic algorithm on completion time, and it is an effective task scheduling algorithm in cloud computing environment.

Cloud Task Scheduling Algorithm based on Improved Genetic Algorithm

IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-sensitive manner. However, to address the rising need for real-time information processing and decision modeling, task allocation among dispersed Edge Computing nodes has been a major challenge. State-of-the-art task allocation techniques such as Min–Max, Minimum Completion time, and Round Robin perform task allocation, butv several limitations persist including large energy consumption, delay, and error rate. Henceforth, the current work provides a Quantum Computing-inspired optimization technique for efficient task allocation in an Edge Computing environment for real-time IoT applications. Furthermore, the QC-Neural Network Model is employed for predicting optimal computing nodes for delivering real-time services. To acquire the performance enhancement, simulations were performed by employing 6, 10, 14, and 20 Edge nodes at different times to schedule more than 600 heterogeneous tasks. Empirical results show that an average improvement of 5.02% was registered for prediction efficiency. Similarly, the error reduction of 2.03% was acquired in comparison to state-of-the-art techniques.

/pdf/quantum-inspired-task-optimization-for-iot-edge-fog-1csw9ym8.pdf

Quantum Inspired Task Optimization for IoT Edge Fog Computing Environment

With the development of the mobile Internet of Things, people have higher and higher requirements for services, and how to provide high-quality services that meet the needs of users has also become...

/pdf/mobile-internet-of-things-dynamic-grid-qos-service-matching-2xuf5wi992.pdf

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Due to the development of communication device technology and the need to use up-to-date infrastructure ready to respond quickly and in a timely manner to computational needs, the competition for the use of processing resources is increasing nowadays. The scheduling tasks in the cloud computing environment have been remained a challenge to access a quick and efficient solution. In this paper, the aim is to present a new tactic for allocating the available processing resources based on the artificial bee colony (ABC) algorithm and cellular automata for solving the task scheduling problem in the cloud computing network. The results show the performance of the proposed method is better than its counterparts.

Multi-objective tasks scheduling using artificial bee colony algorithm based on cellular automata in cloud computing environment

\n\nMin-min and max-min algorithms were combined on the basis of the traditional\ngenetic algorithm to make up for its shortcomings.\n\n\n\nIn this paper, a new cloud computing task-scheduling algorithm that introduces min-min\nand max-min algorithms to generate initialization population, selects task completion time and load\nbalancing as double fitness functions, and improves the quality of initialization population, algorithm\nsearchability and convergence speed, was proposed.\n\n\n\nThe simulation results proved that the cloud computing task-scheduling algorithm was superior\nto and more effective than the traditional genetic algorithm.\n\n\n\nThe paper proposes the possibility of the fusion of the two quadratively improved algorithms\nand completes the preliminary fusion of the algorithm, but the simulation results of the new\nalgorithm are not ideal and need to be further studied.\n

Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

G.E. Weiqing

Author Tools

Chat about Author

Papers

Task-scheduling Algorithm based on Improved Genetic Algorithm in Cloud Computing Environment