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An Investigation of the Utilizations of Machine Learning Methods in Precision Agriculture 2

An Investigation of the Utilizations of Machine Learning Met...

引用

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

作者： Bavana, Hema Sree Kummara, Susmitha Krishna Pranutha, Somaraju Sasi Vijayalakshmi, Vasantha Farooqui, Nafees Akhter Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeshwaram India

ISBN: (纸本)9798350368109

A revolution in farming is taking place all over the world thanks to precision agriculture, which makes use of machine learning (ML) and the Internet of Things (IoT). The combination of machine learning algorithms and Internet of Things sensors makes it possible to make decisions based on data in a variety of agricultural sectors, including crop management and soil analysis. Machine learning is able to provide predictions about soil factors, improve agricultural yields, and assist in the detection of diseases and weeds, as well as reduce resource waste and increase productivity. The implementation of this revolutionary strategy addresses issues such as the depletion of resources and the lack of food security, so becoming farming more effective and sustainable for the future. © 2024 IEEE.

关键词： Crop Management Crop Yields Data-driven Decision-Making Disease Detection Internet of Things (IoT) Machine learning (ML) Precision agriculture Resource Optimization Soil analysis Weed Detection

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A Raspberry pi based smart attendance system

A Raspberry pi based smart attendance system

引用

2024 IEEE international conference on automation and Computation, AUTOCOM 2024

作者： Chauhan, Rahul Negi, Sachin Kumar, Akshit Devliyal, Swati Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350382723

Maintaining constant student attendance is an important administrative challenge for today's colleges and universities since it has an immediate impact on academic achievement and may result in disciplinary action. The old, time-consuming approach of manually recording attendance using paper-based forms given by staff members takes a long time, especially in bigger classrooms. The paper discusses an innovative fingerprint-based student attendance tracking system. This system collects students' fingerprints using a specialised module, matches them to validate attendance, and automatically labels students as present for a lecture. It also keeps a log file to track every entry by subject and creates detailed attendance records. Also, the research explains the system's design, that uses GSM technology for improved communication and data management, effectively removing the need for physical attendance materials and manual record- keeping personnel while providing an easier answer to the ongoing challenges of attendance tracking in schools and colleges. © 2024 IEEE.

关键词： Students

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

引用

9th international conference on Information Technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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Dijkstra and A* algorithms in Automated Vehicle Driving

Dijkstra and A* algorithms in Automated Vehicle Driving

引用

2023 international conference on Mechatronic engineering and Artificial Intelligence, MEAI 2023

作者： Lin, Yingpeng Chang'an Dublin International Transportation College Chang'an University Xian China

ISBN: (纸本)9781510674608

Path planning technology is one of the current hotspots of research in many intelligent technology fields, which has a broad application prospect and research value. With the rapid improvement of computer arithmetic power and the widespread popularization of automation, automatic driving has gradually become a hot topic of people's attention. Among them, path planning algorithms play a central role in the field of automatic driving. In this paper, firstly, it will summarize and introduce the development history and basic principles of the two basic path algorithms, Dijkstra algorithm and A* algorithm, list the advantages and disadvantages of these two path planning methods based on graph search and the application scenarios, and then analyze and compare Dijkstra algorithm and A* algorithm, discuss their advantages and disadvantages in the field of intelligent driving, and finally make an outlook of future development directions of the path planning technology in the field of intelligent driving. The research in this paper will provide certain reference value to the researchers of path planning algorithms for intelligent vehicles. It will be of great value to the research and application of new path planning algorithms based on Dijkstra and A* algorithms in the future. © 2024 SPIE.

关键词： Motion planning

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Design and implementation of vehicle type recognition system based on language large model 6

Design and implementation of vehicle type recognition system...

引用

6th international conference on Internet of Things, automation and Artificial Intelligence, IoTAAI 2024

作者： Liu, Xiang Zhang, Yikuan Software Engineering Institute of GuangZhou Computer Science Department Guangzhou China Software Engineering Institute of GuangZhou Department of Electronics Guangzhou China

ISBN: (纸本)9798350386974

Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by Baidu Qianfan large-scale platform is used to realize the auxiliary development of vehicle type recognition. With the support of large model, the training and testing model of vehicle type recognition is constructed by using deep learning method, and the fine-grained vehicle image classification and recognition system is realized. At present, the ResNet50 Enhance MEAL2 model is used for classification recognition, supporting 1748 types of vehicle classification and recognition. In the Car-Dataset of vehicle data, the Accuracy of the training set is about 95.9%, and the Accuracy of the test set is about 84.3%. The backbone network can support Googlenet. ResNet [18, 34, 50], Inception-v3, Mobilenet-v2 and other common models. The use of language large model in this project improves the efficiency of development. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Forest Safety: Sensor-Assisted Detection and Prevention of Wildfires 8

Enhancing Forest Safety: Sensor-Assisted Detection and Preve...

引用

8th IEEE international conference on Computing, Communication, Control and automation, ICCUBEA 2024

作者： Bhoir, Akshada Ghone, Gayatri Chavhan, Pranali Khedkar, Vijayshri Vishwakarma Institute of Information and Technology Pune Computer Engineering Department Pune India Symbiosis Institute of Technology Pune Pune India

ISBN: (纸本)9798350391770

This paper presents integrated forest fire detection and prevention system leveraging advanced sensor technologies, satellite monitoring, and UAVs. Data analytics and machine learning enhance accuracy, minimizing false alarms. Efficient communication with emergency services, public awareness campaigns, and community involvement are integral. The system aims to provide early detection, rapid response, and effective mitigation of wildfires, ensuring the safety of ecosystems and human communities. They cost more in India. In this paper we have discussed the effects of forest fires on India's biodiversity, forest species, climate changes and soil properties along with the different approaches the Indian government has taken to prevent and control forest fires. The forest fires economic impact is estimated to be over INR 1101 crore/year. By observing this we try to put light on this topic. © 2024 IEEE.

关键词： Premixed flames

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Updating and Refactoring Library Modules in IEC 61499 22

Updating and Refactoring Library Modules in IEC 61499

引用

22nd IEEE international conference on Industrial Informatics, INDIN 2024

作者： Oberlehner, Michael Zoitl, Alois CDL VaSiCS Austria LIT CPS Lab Johannes Kepler University Linz Austria

ISBN: (纸本)9798331527471

This paper proposes a novel library management system tailored for Cyber-Physical Production Systems (CPPSs) using the IEC 61499 standard. Focusing on software maintenance challenges in CPPSs, we introduce efficient updating and refactoring operations for modularized type libraries. Our approach addresses the unique requirements of graphical development environments, enhancing the longevity and efficiency of software maintenance. The workflow of creating, maintaining, and updating library modules is outlined, along with conflict detection, error visualization, and potential refactoring/update operations. Additionally, we illustrate the architectural concept implemented as a prototype in an IDE. The implemented library management system demonstrates its practical application through a case study. This research contributes to the ongoing development of robust software maintenance practices in industrial automation systems. © 2024 IEEE.

关键词： computer software maintenance

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AFRLNet: Adaptive-context Feature Representation Learning for Facial Expressions recognition via Transformers 4

AFRLNet: Adaptive-context Feature Representation Learning fo...

引用

4th international conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Liu, Xionghua He, Lulu Liu, Tingting Liu, Hai He, Dingxin Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Wuhan Technology and Business University School of Computer Science and Automation Wuhan China

ISBN: (纸本)9798331534622

Facial expression recognition (FER) is a critical task in intelligent educational technologies, enabling adaptive learning environments to respond to learners' emotional states and support more personalized instruction. This article introduces AFRLNet, a novel FER framework that integrates convolutional neural networks (CNNs) and transformer architectures to enhance the identification and interpretation of student facial expressions. The proposed framework includes two key components: ContextAware Local Feature Learning (CALL) and Adaptive Feature Relationship Induction (AFRI). CALL leverages attention mechanisms to emphasize symmetrical and contextually meaningful facial regions relevant to students' affective responses, while AFRI employs a Transformer-based approach to capture global features and model long-range dependencies, thus addressing challenges such as facial occlusions or varied head poses commonly found in classroom or remote learning settings. Extensive evaluations on the RAF-DB and KDEF datasets demonstrate that AFRLNet achieves state-of-the-art accuracy of 89.44% and 94.76%, respectively, surpassing existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications 1

Exploring the Role of Internet of Things in Wireless Sensor ...

引用

1st international conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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Optimization of SVM Based on IPSO Algorithm for Tool Life Prediction 6

Optimization of SVM Based on IPSO Algorithm for Tool Life Pr...

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6th international conference on computer Network, Electronic and automation, ICCNEA 2023

作者： Gao, Xianghong Cao, Yan Xi'an Technological University School of Mechanical and Electrical Engineering Xi'an China Xi'an Technological University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350305388

As the basic parts of workshop processing, the life of the tool will affect the processing efficiency and processing quality, and the life of the tool will be affected by a variety of uncertain factors in the processing process, which makes it difficult to accurately predict. In order to improve the prediction accuracy, an improved IPSO algorithm was proposed to optimize the penalty factor c and kernel parameter g of SVM based on particle swarm optimization (PSO). Firstly, the influencing parameters of tool life are analyzed and the experimental data are extracted, and then the data is brought into the IPSO-SVM model to obtain the predicted data. IPSO algorithm can realize the adaptive change of inertia weight and introduce asynchronous learning formula to dynamically adjust the learning factor, which can effectively improve the prediction accuracy of tool life. Compared with the traditional PSO-SVM prediction model, the average relative error and average absolute error of IPSO-SVM are reduced by 5.275% and 5.006% respectively, which indicates that the SVM model optimized by IPSO algorithm has higher prediction accuracy. © 2023 IEEE.

关键词： Forecasting

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