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Blockchain based tomato supply chain management 2

Blockchain based tomato supply chain management

引用

2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and automation, ICAECA 2023

作者： Sudha, V. Aswini, D. Akiladevi, R. Government Polytechnic College Computer Engineering Coimbatore India Kumaraguru College of Technology Computer Science and Engineering Coimbatore India Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350306811

Supply chain in agriculture field is very important as the cultivated goods are sensitive for time and temperature. Most of the time, middlemen act as the price-fixing authorities. The farmer's profit declines as the number of people increases. Additionally, there is no provision for tracking the status of the farming goods throughout transportation. Tomatoes are one of the many products that are time, temperature, and container sensitive. These parameters could be tracked by a centralized system, which would be more advantageous. Blockchain is a distributed database providing tamper proof storage of data. The management of the tomato supply chain based on blockchain is suggested in this research. Customers can use this method to learn how the produce is grown and how the shipping process works. Here, the farmer uses the smart contract to periodically update the blockchain with the crop status. The farmer grows it and sells it once it is ripe for cultivation. As a result, the proposed system enables the customer to be fully informed about the crop and commodities. Additionally, it helps the farmer keep track of their products and maximize their yield's profit. © 2023 IEEE.

关键词： Smart contract

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Drone and computer Vision-Based Detection of Aleurothrixus Floccosus in Citrus Crosp 26

Drone and Computer Vision-Based Detection of Aleurothrixus F...

引用

2024 IEEE international conference on automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Vergara, Barbara Toledo, Karel Fernandez-Campusano, Christian Santamaria, Alejandro Rivero University of Santiago of Chile Department of Electrical Engineering Santiago Chile University of Lille Cnrs Umr 8523 PhLAM - Physique des Lasers Atomes et Molécules LilleF-59000 France

ISBN: (纸本)9798350378115

The paradigm of intelligent agriculture has significantly impacted the way in which crops are cared for and maintained. As an alternative, the use of unmanned aerial vehicles (UAVs), or drones, for the purposes of fertilizer application and the prevention of crop pest spread has been proposed. We put forth a solution based on UAVs equipped with computer vision algorithms for the early detection of plants infested with aleurothrixus floccosus in citrus crops. The drone is equipped with a high-definition camera that records the affected plantation, and this video is shared via streaming with a personal computer (PC) that executes the computer vision algorithm to determine the presence or absence of the pest. We selected the open-source real-time object detection and image segmentation model, You Only Look Once (YOLO) version 10s, as a preliminary approach. The numerical results demonstrate that the average precision is 73 % during the training stage and reaches up to 76 % during the operation stage. © 2024 IEEE.

关键词： Drones

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Attention-based U-Net Denoising Network

Attention-based U-Net Denoising Network

引用

2023 IEEE international conference on Sensors, Electronics and computer engineering, ICSECE 2023

作者： Zhu, Diyang University of Science and Technology Beijing School of Automation Beijing China

ISBN: (纸本)9798350313734

Due of its exceptional denoising performance, the denoising technique based on deep convolutional neural networks has attracted a lot of interest. This paper proposes a new Attention-based U-Net to increase the efficacy and practicability of denoising methods. We combine channel attention and spatial attention to better focus on important information in the input image, and apply residual learning and batch normalization to improve network performance. In addition, the quantitative indicators and visual quality evaluations of multiple algorithms on synthetic noise datasets confirm the model's superiority. © 2023 IEEE.

关键词： Image denoising

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Advanced Fire Detection and Extinguishing Rover with YOLOv8

Advanced Fire Detection and Extinguishing Rover with YOLOv8

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Shah, Aaryan Kumar Yadav, Anil Awal, Sanika Kashyap, Deekshit Pinge, Ninad Kaushik, Shreya Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh Sehore India

ISBN: (纸本)9798331529833

The project 'Advanced Fire Detection and Extinguishing Rover utilizing YOLOv8' introduces a cutting-edge, fire-detection and suppression system that uses deep learning and robotic automation to improve safety. The system uses the YOLOv8 object identification model, which was trained on several fire-related datasets, to effectively identify fire dangers in real-time using live video feeds processed using Python and OpenCV. When the system detects a fire with a high confidence score, it labels the impacted regions and sends the rover driven by the remote controlled and Arduino microcontroller. The rover's servo motor independently directs an inbuilt fire extinguisher to extinguish flames. Bluetooth allows remote monitoring and control, ensuring that everything runs smoothly and securely. This combination of artificial intelligence, computer vision, and robotics provides a revolutionary method for proactive fire-risk mitigation that aims to reduce damage, improve safety, and save lives. © 2025 IEEE.

关键词： Fire extinguishers

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Implementation of Path Planning Algorithm for AGV in Smart Warehouse 11

Implementation of Path Planning Algorithm for AGV in Smart W...

引用

11th international conference on Information Technology, computer and Electrical engineering, ICITACEE 2024

作者： Davin, Arsenius Sofwan, Aghus Setiawan, Iwan Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350389289

The inefficiency of traditional warehouse systems can lead to a 20% decrease in revenue and a 30% reduction in storage capacity within five years. Therefore, this study aims to design an AGV (Automated Guided Vehicle) capable of picking up and storing assets in the warehouse. The design focuses on enabling the AGV to choose the most optimal route. To achieve this, the method used in this research involves creating a warehouse map model using graph theory and employing the A∗ and D∗ algorithms for route selection. The heuristic value in both algorithms is calculated using Euclidean distance in addition to AGV turning costs. The findings demonstrate that, within the confines of the developed warehouse model, the D∗ algorithm perform slightly better than A∗ algorithm by approximately 1.02%. These results offer valuable insights into the efficacy of the D∗ algorithm in conjunction with graph theory mapping for warehouse navigation. By revealing the slight performance advantage of D∗ over A∗ in a static warehouse environment, this research contributes valuable insights for Industry 4.0 implementation. These findings can inform the selection of optimal pathfinding algorithms in warehouse automation, ultimately leading to improved efficiency and competitiveness within Indonesia's logistics sector. © 2024 IEEE.

关键词： Motion planning

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Integrating AI and computer Vision for Creating a Virtual Mouse 2

Integrating AI and Computer Vision for Creating a Virtual Mo...

引用

2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and automation, ICAECA 2023

作者： Senthilkumar, V. Sheela, J. Angeline Jayashree, N. Radha, B. Saranya, P. Devasenan, M. Kumaraguru College of Technology Coimbatore Computer Science & Engineering India Coimbatore Institute of Engineering and Technology Information Technology Coimbatore India Sns College of Technology Biomedical Engineering Coimbatore India Dr.NGP Institute of Technology Dept of Mathematics Coimbatore India Dr.NGP Institute of Technology Electronics & Communication Engineering Coimbatore India

ISBN: (纸本)9798350306811

Nowadays innovation turns into much more available with the assistance of remote innovation. Even though there are numerous innovations still there are few slacks for individuals who are contrastingly abled or individuals who cannot access the mouse. Limited answers had been made to solve this dispute;however, they have limitations like making use of freelance sensors which cost excessive and require excellent computer systems with excessive specifications. To get better from the existing system, The proposed mechanism entitled the computer- generated Mouse for individuals with motor disabilities recommends the digital mouse in fondness to the usage of a regular PC mouse. The prime goal of this paper is to boost the interplay among computer systems and individuals (HCI). The proposed mechanism examines the face and it has been surprisingly skilled via the iBUG-300W dataset which makes the machine an applicable answer for all circumstances. As Machine Learning algorithms are getting eminent to clear up tough and exciting real-global troubles around us. The experimental output recommends that the proposed mechanism has generous overall performance in conjunction with applicable responsiveness. © 2023 IEEE.

关键词： computer vision

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Research on Clothing Pattern Recognition and Classification Algorithm Based on Deep Learning 2

Research on Clothing Pattern Recognition and Classification ...

引用

2nd IEEE international conference on Electrical, automation and computer engineering, ICEACE 2024

作者： Xu, Yingkai Peng, Jun Li, Yijin Liu, Sili Huang, Lei Xi'an Polytechnic University College of Clothing and Art Design Xi'an China Lanzhou Anning District Kindergarten Lanzhou China Soochow University School of Art Suzhou China Jilin Teachers College of Engineering Technology Changchun China

ISBN: (纸本)9798350368208

This study focuses on the deep learning-oriented fashion pattern recognition and classification rules, and enhances the automatic management efficiency of fashion patterns by building an efficient recognition architecture. This paper uses convolutional neural network (CNN) as the pivot model to capture the deep characteristics of fashion patterns. Generative adversarial network (GAN) is simultaneously introduced to produce many high-quality training examples, which significantly alleviates the problem of data scarcity. In addition, this paper adopts transfer learning techniques to transplant pre-trained models to fashion pattern classification tasks, aiming to accelerate model maturation and improve classification accuracy. In the model simulation stage, this paper conducts empirical tests on several public fashion pattern sets. The test results show that the proposed algorithm surpasses existing methods in core indicators such as precision, recall rate and F1 value. Specifically, the accuracy of the CNN model on the data set reached 93.5%, and after integrating the samples generated by GAN, the model accuracy jumped to 96.1%. At the same time, with the help of transfer learning, the model maturation time is reduced by about 40%. In summary, the fashion pattern recognition and classification algorithm based on deep learning presented in this study shows significant advantages in both performance and efficiency, indicating its potential application value in the field of intelligent fashion design and manufacturing. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of global mean temperature based on SARIMA combined model 4

Prediction of global mean temperature based on SARIMA combin...

引用

4rth international conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Li, Dongliang Chu, Chengxu Gao, Huihui Yang, Chenming Liu, Xin Huang, Zhenwu School of Humanities and Law Southwest Forestry University Kunming650224 China School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian116034 China Data Science and Intelligent Media School Communication University of China Beijing100024 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781510681637

With the continuous development of human society, the global warming caused by greenhouse gas emissions is becoming more and more serious. In order to better cope with global warming, this paper uses the SARIMA model to predict global temperature. Then, SARIMA (1,1,1) (1,1,1) model is established to forecast the temperature. It is found that the temperature in 2050 and 2100 will not reach 20℃, and the global average temperature is expected to reach 20℃ in about 2180. Through model evaluation, it is found that the R-square of SARIMA model is 0.87. © 2024 SPIE.

关键词： Global warming

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Application of Multidimensional Information in Predicting Blue Side Attack Actions: A Matching Degree Calculation Method 2

Application of Multidimensional Information in Predicting Bl...

引用

2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： Chen, Qia Huang, Yanyan Xia, Yuqi School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350308341

In response to the problem of difficulty in accurately predicting the intention of blue troops to attack targets on the land battlefield, this paper proposes a prediction method based on matching degree, aiming to predict the intention of blue troops to attack. This article adopts a comprehensive analysis method based on multidimensional information to analyze the matching degree between the blue attacking force and the possible targets of the red side. Firstly, integrate the features of the blue attacking force obtained from the battlefield with the feature information of the red potential target to form a panel of data. Next, the position matching degree, marching angle matching degree, and force target matching degree between the troops and the target are calculated separately, in order to establish the matching degree relationship between the blue attacking troops and the possible attacked targets of the red side, and then use it to speculate on the probability of the blue army implementing the attack, thereby achieving the prediction of the target of the blue attacking action. Finally, an example analysis shows that this matching degree-based prediction method can more comprehensively consider the fusion of multi-dimensional intelligence information, which helps to improve the reliability and effectiveness of target attack prediction. © 2023 IEEE.

关键词： Forecasting

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Machine Learning Approaches to Improve Visibility in Global Supply Chains 2

Machine Learning Approaches to Improve Visibility in Global ...

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2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Hassan, Muntather Muhsin Chintalapudi, S. Rao Anuradha, Kodali Sharma, Himanshu Theresa Cenate, C.F. Rajaram, Gnanajeyaraman The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq CMR Technical Campus Telangana Hyderabad India GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of EEE Tamil Nadu Chennai600073 India Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Literature review is the main aspect of this research where special attention is given to learning the ways of applying machine learning to enhancing transparency of supply chains all over the world. This is achieved with the help of big data to blend a classical model with several features of the improved ML tool of demand forecasting, anomaly detection, or better route optimization. Data is the whole information collected for analyzing while pre processing means the step through which data is passed before analysis. Model development refers to the process of developing a model which will be followed in practice while implementation refers to the act of practicing what was envisaged in the development of the model. Last but not least, control is the last step in the model implementation process where the outcome of the model is critically determined. The outcome of the research has presented that reliability and effectiveness has been established to achieve an enhanced level of predictability in terms of hit ratio, delivery performance, inventory and overall organizational parameters. It reveals that, if helms properly, then the ML has the capability to increase the effectiveness and visibility of the global supply chain - which is a vital element of any business.. Learning, knowledge creation cannot be a one time process and hence needs continuous application where the stakeholders are the active participants. This study provides informed guidance on the factors that can be applied in integrating ML in supply management. © 2024 IEEE.

关键词： Adversarial machine learning

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