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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

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AYURA – AYURVEDIC DIET APP 2

AYURA – AYURVEDIC DIET APP

引用

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

作者： Shanmathi, M. Arage, Bhavika Premchand Praveena, R. Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

Ayurveda, a system of complementary medicine that has been used for centuries in India, aids in the early treatment of illnesses. Ayurvedic practitioners hold that space, air, fire, water, and earth are the five fundamental components that make up the universe. Three life forces or energies known as doshas are created when these unite in the human body. It manages how a body functions. They are the Pitta (fire and water), Vata (space and air), and Kapha doshas (water and earth). One can identify the dosha using an online programme and can also recommend foods to balance it. The physiological (physical traits, looks, and digestion) and psychological factors will be considered by the application to identify the dosha imbalance. © 2023 IEEE.

关键词： Earth (planet)

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YOLOv5-Based Mask Wearing Status Detection

YOLOv5-Based Mask Wearing Status Detection

引用

2023 IEEE international conference on Electrical, automation and computer Engineering, ICEACE 2023

作者： Du, Feixiang Chen, Shuowen Lin, Yu Yu, Xinran Wang, Wei Hu, Chao School of Electrical Engineering Tongling University Tongling China School of Mathematics and Computer Science Tongling University Tongling China

ISBN: (纸本)9798350309614

Face mask wearing detection technology can effectively supervise and control people wearing masks. The traditional mask detection uses only binary classification labels for training, which has low precision and recall. The mask wearing classification method is redefined, and a mask wearing detection method based on YOLOv5 is proposed. The model algorithm learns finer facial features by processing 24000 collected images with corresponding classification labels. Compared with the traditional two-classification method of mask wearing status, the YOLOv5 mask wearing detection method with multi-classification labels achieves 93.4% detection precision and 97% recall rate, which are improved to 3.1% and 2%, respectively, indicating that the new multi-classification labels of mask wearing can effectively improve the performance of mask detection. © 2023 IEEE.

关键词： mask wearing multi-classification labels object detection YOLOv5

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A Novel Advanced Algorithm in automation of Food and Health Monitoring System using IOT 8

A Novel Advanced Algorithm in Automation of Food and Health ...

引用

8th IEEE international conference for Convergence in technology, I2CT 2023

作者： Sampath Kumar, Pattem. Vijayasree, J. Saikumar, K. Ayad Alkhafaji, Mohammed Obaid, Ahmed J. Ali Zearah, Sajad Malla Reddy Institute of Engineering & Technology Hyderabad India Department of Electronic and Communication India Koneru Lakshmaiah Education Foundation Hyderabad Dept of ECE R.V.S Nagar Moinabad-Chilkur Rd near AP Police Academy Telangana Aziznagar India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq University of Kufa Faculty of Computer Science and Mathematics Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq

ISBN: (纸本)9798350334012

Food is one of the essential factors in human growth and development. The recent results of the internet of things and robots have already shown the beginnings of a new food monitorization. automation in the restaurants and food sector did not originate technically, so humanoid-related IoT platforms are necessary. In this research, an advanced monitoring algorithm for the automation of food & health system performs using the (IoT). The suggested system develop using food quality sensing devices. Each detector was equipped with the above qualities and connected using breadboards and relay boards. There is tremendous fear and motivation in the thought that there may not be enough food to go around as the human population expands exponentially. To construct a monitoring system that can identify the presence of people, gases, and temperature differences, we employed website creation in combination with the (IoT). The designed application has attained a sensitivity of 97.53%, an accuracy of 98.83%, a recall of 97.98%, and a detection rate of 98.91%. The implemented technology is beneficial to researchers and society. The proposed IoT-based automation of food & health monitoring system compete with present technology and outperforms the methodology. © 2023 IEEE.

关键词： Internet of things

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On the Nature of Data in RPA Bots 21st

On the Nature of Data in RPA Bots

引用

21st international conference on Service-Oriented Computing (ICSOC)

作者： Voelker, Maximilian Weske, Mathias Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783031484230;9783031484247

Robotic process automation (RPA) automates tasks traditionally performed by employees, reducing repetitive and error-prone work. While RPA bot models are based on graphical notations, data, a key component of RPA, is often not explicitly represented, making it difficult to understand how data contributes to the automation. This paper explores the role of data in RPA, extends the ontology of RPA operations by data aspects, and proposes a visualization of data in RPA bot models, promoting the design of more comprehensible RPA bot services and enabling different bot analysis techniques.

关键词： Robotic Process automation Ontology Modeling Data

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Performance Development of Industrial automation with Multi Processing Controller 2

Performance Development of Industrial Automation with Multi ...

引用

2nd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Karunakaran, V. Shashikala, H.K. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The industrial automation with multi processing controller is a technology that has revolutionized the way that industrial processes are managed. It has enabled the automation of complex and varied processes, allowing factories to become more efficient and productive. Multi processing controllers are powerful computers that are used to control and monitor a wide range of industrial processes. They are typically connected to a network of sensors and actuators that measure and control the physical environment. By connecting these components to a powerful computer, a multi-processing controller can be used to quickly and accurately adjust the physical environment in order to achieve desired results. A multi-processing controller can be used to control the temperature, pressure, and flow of a production line. By adjusting the temperature and pressure of a certain process, the controller can ensure that the material reaches the desired state. This can be done with a simple push of a button, eliminating the need for manual labor. Multi processing controllers also allow for the automation of complex tasks, such as the management of an entire production line. By connecting different components to the controller, it can be programmed to coordinate the entire production line, ensuring that the correct materials and processes are used at the right time. This eliminates the need for manual labor, as well as reduces the time and cost associated with production. © 2023 IEEE.

关键词： Controllers

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VulPin: Finer-Grained Slicing for Pinpointing Vulnerability in Binary Programs: Precision in Vulnerability Detection: Advanced Slicing Techniques for Binary Program Analysis 7

VulPin: Finer-Grained Slicing for Pinpointing Vulnerability ...

引用

7th international conference on computer science and Artificial Intelligence, CSAI 2023

作者： Chukkol, Abdulrahman Hamman Adama Luo, Senlin Yunusa, Haruna Yusuf, Abdulganiyu Abdu Mohammed, Abulfatahi School of Information and Electronics Beijing Institute of Technology Beijing China Information System and Security and Countermeasures Experimental Center Beijing Institute of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Beijing Institute of Technology Beijing China School of Management and Information Technology Modibbo Adama University Yola Nigeria

ISBN: (纸本)9798400708688

Identifying vulnerabilities in binary code with precision is a critical area of study in the field of software security. Most existing techniques fail to pinpoint the vulnerability's exact location, due to the extensive labelled data requirement and lack of targeted approach to improve on the problematic code. In this paper, we introduce VulPin, a system specifically designed to pinpoint vulnerabilities in binary code. This framework uses a hierarchical attention network that analyze program slices along with a vulnerability position matrix. Experimental outcomes indicate that our framework not only outperforms existing approaches but also pinpoints vulnerabilities more rapidly with a high Accuracy and a very low rate of false positives. © 2023 ACM.

关键词： Binary codes

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Gameplay automation

Gameplay Automation

引用

2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance technology conference, ATCON-1 2023

作者： Aasheesh, Shaik Mahammad Nithin Sri Ram Reddy, K.N. Yellani, Manideep Divya Prabha, N.K. Hegde, Soumya Shridhar Pes University Department of Computer Science Bangalore India

ISBN: (纸本)9781665456272

Reinforcement Learning has performed remarkably well in various games and has the potential to surpass human level gameplay. Although it does good in turn-based games, complex game genres like fighting or much more complex 3D shooters are still a challenge. The created ML model can teach itself how to play a specific game so that it can be used to test the games for completeness, bugs, and irregularities. The model is also used to find out if there are any ways to speed run games by exploiting certain in-game mechanisms or to check if any characters or abilities are overpowered. Models were created for a few games to identify how well these AI models can perform and see what kind of differences were required to switch between the games. © 2023 IEEE.

关键词： Reinforcement learning

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Development of a System for Detecting Problematic Areas of Agricultural Fields by UAV Photos 5

Development of a System for Detecting Problematic Areas of A...

引用

5th international conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2023

作者： Poleshchenko, Dmitriy Petrov, Vladislav Mikhailov, Ilia Shulik, Maksim Gubkin Branch of NUST MISIS Ore-Mining Gubkin Russia National University of Science and Technology MISIS Stary Oskol Technological Institute N.a. Automated and Information Control Systems Department Stary Oskol Russia

ISBN: (纸本)9798350309126

In this paper, the developed system for detecting problematic areas of agricultural fields by using computer vision in unmanned aerial vehicle (UAV) photography was investigated. The structures of the neural networks of the YOLOv5 and YOLOv8 family were considered to find a solution to the task of detecting problem areas. The application of the developed software will reduce labour and time spent on image analysis, which in turn will reduce the response time in detecting problem areas in agricultural fields to achieve higher yields. © 2023 IEEE.

关键词： Antennas

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Cultural Heritage 3D Object Management with Integrated automation Workflows 15th

Cultural Heritage 3D Object Management with Integrated Auto...

引用

15th international conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Orzechowski, Michal Opiola, Lukasz Martínez, Ignacio Lamata Ioannides, Marinos Panayiotou, Panayiotis N. Slota, Renata G. Dutka, Lukasz Kitowski, Jacek Academic Computer Centre Cyfronet AGH Krakow Poland Faculty of Computer Science AGH University of Krakow Krakow Poland EGI Foundation Amsterdam Netherlands Digital Heritage Lab Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031856969

The complexity of high-quality 3D digitised cultural heritage objects creates challenges for existing data management systems as they need to develop metadata management and processing capabilities to provide semantic insight into the interconnectivity of data that constitute cultural heritage objects. To address these challenges, we propose a data and metadata management system, together with the federated authentication and authorisation mechanism, and an integrated system for designing and executing automated workflows that facilitate the processing of both data and metadata. The solution is evaluated with a 3D digitised cultural object of Lambousa Fishing Boat and presents the complete process from data upload to publication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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Electricity Load Forecasting Based on CNN-LSTM

Electricity Load Forecasting Based on CNN-LSTM

引用

2023 IEEE international conference on Electrical, automation and computer Engineering, ICEACE 2023

作者： Zhang, Jianpeng Zhu, Zhehao Yang, Yuming Heilongjiang University Harbin China Beijing Normal University Beijing China Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350309614

With the popularization of smart grid construction, how to predictively provide high-quality long-term stable power supply has become an urgent problem to be solved for the current conflict between supply and demand. Therefore, it puts forward extremely high requirements on how to accurately predict the power load. At the same time, the influencing factors of power load tend to be diversified, resulting in the traditional nonlinear prediction model cannot objectively reflect the actual power load. For this reason, this paper adopts deep learning related theories, introduces holiday influencing factors and temperature influencing factors, calls PyCharm platform for Python programming to compare BP model, LSTM model, BiLSTM model. By doing this, we find that the LSTM model has the best effect, and then adopt convolutional neural narrow network (CNN) and long short-term memory (LSTM) were used to predict the power load data. The results show that the prediction accuracy of CNN-LSTM is 98.94%, and the method provides a new idea for the convenient prediction of power load, and also has positive significance for the multi-factor prediction of power load. © 2023 IEEE.

关键词： CNN-LSTM deep learning load forecasting

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