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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40711 条记录，以下是4501-4510 订阅

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Simulation of the Design of a Compressed Air Launch System Based on AUTODYN 3

Simulation of the Design of a Compressed Air Launch System B...

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3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Li, Junchen Sun, Yanfu Zhu, Jianyu Equipment Engineering College Shenyang Ligong University Liaoning Province Shenyang110159 China

ISBN: (纸本)9781510664814

With the development background of compressed air launcher for single weapon cartridge, the compressed air launch system and internal ballistic calculation simulation model design with different drain diameter is established by using fluid-solid coupling AUTODYN software, relying on the theory related to the effective energy of compressed air, the mathematical model built is calculated and simulated. The simulation results show that: at 70mm drain diameter, the acceleration obtained by the launcher is 8.2m/s2and reaches the maximum value, the initial velocity of the launcher, which meets the requirements of weapon combat performance. The application of cold energy compressed air in the field of man-portable weapons firing has some feasibility. © 2023 SPIE.

关键词： Compressed air

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An Efficient Wireless Sensor Network based Intrusion Detection System 2

An Efficient Wireless Sensor Network based Intrusion Detecti...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rajassekharan, Dinesh Ramachandran, V. Sharma, Dilip Kumar Palanisamy, Thirumoorthy Myilsam, Y. Varatharaj Poonia, Ramesh Chandra Peninsula College Faculty of Computer Science Malaysia Gitam School of Computing Gitam Deemed to Be University Department of Computer Science and Engineering Bengaluru561203 India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India V.S.B College of Engineering Department of Electrical and Electronics Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Uttar Pradesh Ghaziabad201003 India

ISBN: (纸本)9798350369960

Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%. © 2023 IEEE.

关键词： IDS machine learning responsive mechanism Spectral clustering WSN

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IoT-based Patient Monitoring System through Online Cloud and ECG Sensor

IoT-based Patient Monitoring System through Online Cloud and...

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2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Amir-Ul-Haque Bhuiyan, T.M. Ahmed, Shakhir Salahin, Md Pollab, Md Jui, Jannatun Nyeem Ahmmed, Md Tanvir Rahman, Anichur Wadud, Md. Anwar Hussen Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Bangladesh Bangladesh University of Business and Technology Department of Computer Science and Engineering Bangladesh Constituent Institute of University of Dhaka Bangladesh

ISBN: (纸本)9798350316001

The Internet of Things (IoT) is revolutionizing healthcare by enabling remote patient monitoring. An IoT-based patient monitoring system is the main focus of this study that uses an electrocardiogram (ECG) sensor, a wire thermometer sensor, and a DHT11 sensor. The objective of the system is to continuously monitor the ECG signals of patients, monitor patients' body temperature and patients' room temperature and humidity, and transmit them wirelessly to a cloud-based server for real-time analysis. The proposed system comprises an ECG sensor, a one-wire thermometer sensor, a DHT11 sensor, a microcontroller, and a wireless communication module. The ECG sensor captures the electrical signals generated by the heart and converts them into digital signals. The DS18B20 sensor measures patients' body temperature, and the DHT11 sensor monitors room temperature and humidity. The microcontroller processes the digital signals and transmits them to the cloud-based server through the wireless communication module. The smartphone Apps take patients' records from the cloud, analyze the ECG signals, and generate necessary suggestions if any abnormalities are detected. © 2023 IEEE.

关键词： Electrocardiograms

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Web Page Prediction Model using Machine learning Approaches: A Review

Web Page Prediction Model using Machine Learning Approaches:...

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2023 international conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Omosebi, Paul Adeoye Adewole, Adetunji Philip Sennaike, Oladipupo Trinity University Yaba Computer and Information Sciences Department Lagos Nigeria University of Lagos Computer Sciences Department Akoka Lagos Nigeria

ISBN: (纸本)9798350324785

Webpage classification and prefetching are essential techniques to reduce internet access latency. However, insufficient server resources and network bandwidth can lead to inefficient usage when the majority of the prefetched web pages are not visited during subsequent visits. To address this issue, accurate prediction during prefetching is crucial. Existing research has shown that usage data and user navigation can be tracked using methods like Markov models to predict the most popular web paths, leading to more accurate classification and prefetching of web pages. However, accurately classifying web pages remains a challenging task due to the uncontrolled nature of web content, and the need to classify hypertext that is linked through HTML tags and URLs. This study presents several methods to improve the accuracy of machine learning algorithms for classifying web pages. By combining different results, we show how our approach can enable increase in the performance of classification of web page and prefetching. Our findings can lead to the development of more efficient and accurate webpage classification and prefetching algorithms, resulting in reduced internet access latency, which is of great relevance to internet users and providers alike. © 2023 IEEE.

关键词： Forecasting

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Liver Cancer Diagnosis with Lightweight Federated learning Using Identically Distributed Images 12

Liver Cancer Diagnosis with Lightweight Federated Learning U...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Trivedi, Naresh Kumar Shukla, Sunil Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gautam, Vinay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350369861

A major obstacle for cancer research is the prediction of liver cancer progression. This research looked at models that can predict how Hepatocellular carcinoma (HCC) would develop. As medical science has advanced, records of people with liver cancer at different stages have been collected, and the illness remains the top cause of cancer-related mortality. Keeping private information safe, this study presents a streamlined architecture for federated deep learning to categories liver cancer illnesses. This system uses a distributed client-server architecture to safeguard client data and IID for validating federated deep learning models. The framework was tested with a single client and numerous clients in both conventional and federated learning contexts. We compared several pre-trained models and ultimately settled on AlexNet as our standard because of its exceptionally high accuracy of 98.53 percent. Then, federal learning (FL) was used to assess IID datasets. Due to its enhanced capabilities, the framework is the best choice for diagnosing and classifying liver cancer at an early stage. © 2023 IEEE.

关键词： Diagnosis

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Automatic detection of green litchi based on YOLOV3, YOLOV4, YOLOV5-S and YOLOV5-X deep learning algorithm 4

Automatic detection of green litchi based on YOLOV3, YOLOV4,...

引用

4th international conference on computer engineering and Application, ICCEA 2023

作者： Yang, Maoling Wang, Chenglin College of Intelligent Manufacturing Engineering Chongqing University of Arts and Sciences Chongqing China Kunming University of Science and Technology Faculty of Modern Agricultural Engineering Kunming China

ISBN: (纸本)9798350347548

The accurate detection of green litchi is one of the important means of litchi plantation, and plays an important role in disease and insect protection and yield estimation. Selecting models that already exist on the market is not only convenient and fast, but also saves costs. In this experiment, YOLOV3, YOLOV 4, YOLOV5-S and YOLOV5-X of YOLO series were used to detect green litchi in the natural environment, and the most suitable model for detection was obtained through comparison and analysis. The experimental results show that the model detection effect of YOLOV5 is the best, and the detection speed of YOLOV5-S model is the fastest. The average accuracy of YOLOV5-X is 0.998, the average accuracy of FPS is 11.13, the average accuracy of YOLOV5-S is 0.993, and the average accuracy of FPS is 47.16. To sum up, YOLOV5-S model is more suitable for detecting green litchi in natural environment because of its fast speed and relatively high accuracy. © 2023 IEEE.

关键词： learning algorithms

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WBC Subtype Detection using Deep learning with Optimizing Hyperparameters by Genetic Algorithm

WBC Subtype Detection using Deep Learning with Optimizing Hy...

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2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Shuvo, Mehedi Hasan Rahman, Md. Tahamidur Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350316001

The human body's defense mechanism, the immune system, heavily relies on white blood cells (WBCs). These cells come in five subtypes: Eosinophil, Basophil, Neutrophils, Monocyte, and Lymphocyte. Each subtype plays a vital role in the diagnosis of hematological diseases, such as leukemia, as their presence or absence can indicate the presence of a disease. In the past, hematologists have manually examined blood smears under a microscope to detect the subtype of WBCs. However, this method is both time-consuming and prone to errors. This study proposes a new approach, which uses a deep learning-based convolutional neural network (CNN) architecture in combination with an evolution-based algorithm (genetic algorithm) to detect WBC subtypes with greater accuracy. A large dataset of 12,000 images was used for training and analysis. The proposed model, which utilizes the You Only Look Once version 5 (YOLOv5) for image localization and segmentation, was able to accurately localize WBCs with an IoU score of 94% for more than 80% of images and detect the subtypes with an accuracy of 98%. This study aims to overcome the limitations of previous research by providing a more efficient and accurate method for detecting WBC subtypes. © 2023 IEEE.

关键词： Genetic algorithms

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Optimal Water Utilization in the State of Odisha using Precision Agriculture 3

Optimal Water Utilization in the State of Odisha using Preci...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Mohanty, Sibani Singh, Dhanpratap Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Lovely Professionl University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

Agriculture is the important and foremost role player in the life of human beings and thereby increases the economic growth of the country. A great headache and a challenging task of every country head is the increase in population which in turn affects the climatic conditions and less agricultural lands. Resources are also in deficit condition. Food requirement is increasing and due to lack of less production and supply of food, hunger conditions are increasing. To get relief from the said crisis, Precision agriculture plays an important role which is a new way of attaining sustainable development goals. It is otherwise known as Smart Farming. In India, few states have already started implementing this new technology. Proper training had been given to farmers. Water plays an important role in agriculture. Farmers used to water the crops in spite of high humidity in air and soil, which leads to low growth and death of crops and also leads to high yield. Precision Agriculture will take care of it. Machine learning technology drives the smart farming to help farmers in high growth and high yield. Soil Sampling needs to be done to study the parameters of soil properly and along with that Climatic parameters. Among many ML algorithms Random forest performance is best with 98.5 accuracy proposed by Kelasivi © 2023 IEEE.

关键词： Machine learning

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RSSI and Machine learning-Based Indoor Localization Systems for Smart Cities 7

RSSI and Machine Learning-Based Indoor Localization Systems ...

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7th SLAAI - international conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Rathnayake, R.M.M.R. Maduranga, M.W.P. Dissanayake, Maheshi B. General Sir John Kotelawala Defence University Department of Computer Engineering Ratmalana Sri Lanka University of Peradeniya Department of Electrical and Electronics Engineering Kandy Sri Lanka

ISBN: (纸本)9798350319262

Widespread use of the Internet of Things (IoT) is being made in a number of application areas to save costs and improve performance. These types of applications also include services that are dependent on localization. When a sensor node is localized, an IoT network may use that information to identify it. A Received Signal Strength Indicator (RSSI), which gauges the intensity of the signal received from the sensor nodes, is typically used in conjunction with supervised Machine learning (ML) algorithms in localization methods. In this study, a unique indoor and outdoor localization and navigation technique is presented that makes use of LoRA nodes and a highly accurate machine learning model. The suggested method has cutting-edge features and shows considerable gains in accuracy and dependability. These discoveries offer potential answers for practical applications and boost localization technology. © 2023 IEEE.

关键词： Sensor nodes

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Co-creation Lab and Its Liminal Pedagogy as an Innovation Tool for Shaping a Local and Global Future 4th

Co-creation Lab and Its Liminal Pedagogy as an Innovation To...

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4th EAI international conference on Technology, Innovation, Entrepreneurship and Education, TIE 2023

作者： Rodriguez Morales, Delma Pre-doctoral Fellow at the International University of La Rioja La Rioja Logroño Spain

ISBN: (纸本)9783031593826

The article presents the co-creative process carried out in a distributed laboratory on the Internet, crossing continents and different cultures to generate something new in networking. From a transdisciplinary approach of art, science, technology and society, the outcomes, lessons learned and main prospective features for further research and intervention of the project in e-learning, e-culture, multilingual platform, etc. are evidenced. The text covers the first edition of the Co-creation Lab 2019 and its process to date. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： E-learning

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