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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8138 条记录，以下是861-870 订阅

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Early Identification of Cancer Blood Disorder Using Deep Convolutional Neural networks 5

Early Identification of Cancer Blood Disorder Using Deep Con...

引用

5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sujarani, Pulla Yogeshwari, M. Kalaiselvi, K. Chennai600117 India Chennai600117 India Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Chennai602105 India

ISBN: (纸本)9798400709418

A cancer blood disorder can be dangerous if not detected in time. It causes aberrant white blood cell production in the blood by the bone marrow. Using image processing of microscopic images of the blood, it may be quickly diagnosed. Deep learning techniques are a practical approach to cancer blood disorders in early diagnosis. In this study, we have proposed a novel method to identify cancer blood disorders in the early stage using the deep convolutional neural network (DCNN). Using filtering techniques, the microscopic images are first preprocessed. the 2D Adaptive Anisotropic Diffusion Filter (2DAADF) technique is used for image filtering to eliminate noise from the input images. Feature extraction is carried out utilizing the Grey Level Co-Occurrence Matrix (GLCM) from the filtered images to increase the identification accuracy. Finally, the proposed DCNN classifier is used to detect the cancer blood disorder. In comparison to the current approaches, the proposed methodology attained the maximum accuracy of 97%. According to the results, the proposed method can identify blood cancer with high accuracy and may help with its diagnosis and treatment in the early stages. © 2023 ACM.

关键词： Convolutional neural networks

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Abstractive Text Summarization for Tamil Language Using m-T5 7th

Abstractive Text Summarization for Tamil Language Using m-T5

引用

7th international conference on Inventive Systems and Control, ICISC 2023

作者： Saraswathi, C. Prinitha, V. Briskilal, J. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu 603203 India

ISBN: (纸本)9789819916238

Summarization is the act of reducing a long form of text to a shorter version by lowering the length of the original text while retaining and keeping the summary’s core and the informational parts which conveys the content’s meaning. Summarizing lengthy papers by hand is difficult and prone to inaccuracy. the exponential rise of information and data has made automatic text summarization crucial. It leads to a summary generated by a summarizing system that enables users to understand the document’s contents. Abstractive and extractive summarization are the two categories into which summarization falls. We can see that there are not many automatic text summarizers available for Indian languages. Our main goal in this direction is to create an automatic abstractive text summarizer for Tamil using the m-T5 transformer model. We have also used manual dataset of Tamil Kid’s stories for training and testing. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Text processing

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Feasibility Study for the Development of a Diagnostic and Prognostic System on a High-Speed Rotating Cutter 5th

Feasibility Study for the Development of a Diagnostic and Pr...

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5th international conference of international-Federation-for-the-Promotion-of-Mechanism-and-Machine-Science Italy (IFToMM Italy)

作者： Viale, Luca Daga, Alessandro Paolo Garibaldi, Luigi Caronia, Salvatore Ronchi, Ilaria Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Tecnau SRL Ivrea Italy

ISBN: (纸本)9783031645716;9783031645693;9783031645686

the integration of artificial intelligence and advanced machine learning techniques has radically changed the safety and reliability of industrial systems. this innovative paradigm led to the widespread adoption of condition-based maintenance strategies, with vibration monitoring emerging as a milestone technique. this study, conducted in collaboration with Tecnau SRL, investigates the feasibility of implementing a diagnostic and prognostic system for their "Revolution 50" series apparatus. this work explores machinery behaviour through endurance tests to lay the foundation for the future development of anomaly detection and machinery health classification. Experimental tests facilitate continuous monitoring under various operating conditions to potentially conceive real-time industrial diagnostic systems. Endurance tests reveal promising results, showing the potential for accurate recognition of the machine state of health. Multi-scale signal analysis highlights the significance and the detection of transient and steady-state phases, improving the effectiveness of potential real-time monitoring strategies. Future research directions include further industrial development of real-time monitoring systems, optimization of classification models, and exploration of cost-effective sensor selection and acquisition systems.

关键词： machine diagnostics machine prognostics condition-based maintenance vibration monitoring signal processing multi-scale analysis

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Deployment of Self-Powered Structural Health Monitoring System for Highway Bridges 5th

Deployment of Self-Powered Structural Health Monitoring Syst...

引用

5th international conference on Civil Engineering and Architecture, ICCEA 2022

作者： Le, Hoang Minh Ngo Fukada, Saiji Graduate School of Natural Science and Technology Kanazawa University Ishikawa Japan Faculty of Geosciences and Civil Engineering Kanazawa University Ishikawa Japan

ISBN: (纸本)9789819940486

the development of the wireless sensor network has provided an effective tool for building a structural health monitoring system that can monitor the condition of civil structures and infrastructure in real time. However, the power supply for data acquisition components and wireless data communication modules becomes a challenge when the traditional power supply—the electrochemical battery—is limited to short-time operation and the wireless data communication modules must operate with a high frequency to meet the terms "in real time". this study first proposes a self-powered structural health monitoring system using a magnetostrictive vibration energy harvester. A field test was conducted to evaluate the performance of three wireless data communication technologies: IM920, long-range (LoRa), and Sigfox. According to the observation results, Sigfox is the most appropriate wireless data communication technology for the proposed self-powered structural health monitoring system with inexpensive, reliable, and low power consumption advantages. Subsequently, two long-term energy harvesting tests were conducted using the magnetostrictive vibration energy harvester to evaluate the device’s performance. Finally, the proposed self-powered structural health monitoring system was deployed at an actual highway bridge and operated stably with the data frequency transmitted four times a day. these results proved the potential for developing a real-time self-powered structural health monitoring system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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2022 5th international conference on Circuits, Systems and Simulation, ICCSS 2022

2022 5th International Conference on Circuits, Systems and S...

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5th international conference on Circuits, Systems and Simulation, ICCSS 2022

ISBN: (纸本)9781665481199

the proceedings contain 38 papers. the topics discussed include: a high linearity and low load regulation LDO with SATEC and TIR compensation;a cross-shaped vertical vacuum channel transistor;TCAD simulation research of the single event burnout and hardening in power LDMOS transistors;fault injection model of SRAM memory circuit based on hybrid modeling;a simplified flop MTBF extraction methodology;research on battery state identification algorithm based on equivalent model;a new approach to increase the operating frequency and the duty-cycle range of high-speed level shifter in 600V gate driver;fuzzy inference model for recognition of single-phase-to-ground fault caused by insulation deterioration in medium-voltage distribution networks;research on comprehensive evaluation and prediction of power quality in low voltage area;a wide-input-range multi-phase clock generator design for CMOS image sensors;a pseudo-differential high-speed sensing scheme for phase-change memory;and hand gesture recognition system using the dynamic vision sensor.

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Emerging 5G, IoT and WSN: Opportunities, Challenges and Remedies

Emerging 5G, IoT and WSN: Opportunities, Challenges and Reme...

引用

international conference on Web Research (ICWR)

作者： Mohammad Reza Saadatmand Mohsen Ghotbi Technical & Network Operation Department Information Technology Business Intelligence MCI Tehran Iran Research & Development Department MCI Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

the Convergence of 5th generation (5G) technology with the Internet of things (IoT) and Wireless sensor networks (WSNs) will be investigated in this paper. this convergence of these concepts will bring many benefits to the quality of life in communities by increasing the number of connected devices more safely and resulting in higher data rates. However, like any other uprising, it will have challenges, concerns, and bottlenecks. We offer resolutions and remedies to make applying the above three elements more feasible, accessible, and tangible. Future research directions for industries, academia, and policymakers will also be suggested.

关键词： Wireless communication Industries Wireless sensor networks Quantum computing 5G mobile communication Merging Ultra reliable low latency communication Internet of things Sustainable development Convergence

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Training Neural networks Based on PSO Algorithm and BP Algorithm

Training Neural Networks Based on PSO Algorithm and BP Algor...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Darong Gao The Electrical Engineering College Northwest Minzu University Lanzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Signal processing has made extensive use of neural networks. this study examines neural network training using the PSO and BP algorithms. To train the neural network, the PSO algorithm alone, the BP algorithm alone, and the PSO algorithm combined with BP algorithm are examined for the same signal. It is determined that the PSO algorithm in conjunction with the BP algorithm can enhance the network's learning efficiency and accelerate its convergence.

关键词： Training Software algorithms Neural networks Signal processing algorithms Signal processing Approximation algorithms Software Power electronics MATLAB Optimization

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Fire Detection Using Image processing in Industries

Fire Detection Using Image Processing in Industries

引用

Intelligent Communication Technologies and Virtual Mobile networks (ICICV), international conference on

作者： V Kumaresan A Abdul Fashin M Aheilan F Azhageshwar L Sujay Department of Electrical and Electronics Engineering Kongu Engineering College Erode India

Nowadays, fire in the workplace causes significant damage because it is not used early on due to a lack of awareness. As a result, all machines in the industry could be damaged. the most prevalent causes of fire include static electricity, overheating, error by humans, external factors, and mechanical failure. this study addresses concerns with existing solutions by incorporating functionality such as alert and detection. To get real-time detection results, the fire detection unit was created using PythonCV and an image processing model. To detect fire images, an image pre-processing technique called the Haar Cascade Classifier was used. the trained model was tested and compared with other cutting-edge methods. A big fire scale and negative photos of fire in various indoor and outdoor environments were used. the video camera has no specific requirements. the camera will keep monitoring the situation in the room as long as the device is powered ON. If the camera detects a fire, the image is evaluated using an image processing model, the buzzer sounds, and an alarm message is delivered to the user's smartphone via Email.

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Bird Sound Identification System using Deep Learning 5

Bird Sound Identification System using Deep Learning

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5th international conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Indumathi, C.P. Diviyalakshmi, K.R. Mahalakshmi, R. Dept.of.CSE Anna University BIT Campus Tiruchirappalli India

this paper identifies the model for classifying the bird sounds using the Convolutional Neural Network (CNN) algorithm. CNN are considered as one of the powerful toolkits developed in the machine learning and that have shown to be more efficient particularly in the field of image processing and sound recognition. the two-step verification process which will participating in the creation of datasets and neural network input. this system uses the collection of labelled and categorized recordings. Spectrograms are generated from the downloaded data which is used as the input for the neural network. the experiments are conducted and it will match with the different configurations, which including the number of bird species and Spectrograms. As a result, we can identify the bird species from its audio recordings. By identifying the bird species on a large scale, it provides a vital information about our ecosystems and biodiversity. the changes in the ecological environment can be reflected by the quantity and composition of bird species. It is one of the major challenges which leads to recognition of many bird species based on their audio. In modern years, the deep learning techniques namely convolutional neural networks (CNN) have played a major role in many areas of science and also successfully provided efficient methodologies, such as pattern recognition, object detection in images, and classification of patterns in audios and speech recognition. © 2024 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Transformer and Long short term memory networks for long sequence time sequence forecasting problem 5

Transformer and Long short term memory networks for long seq...

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Fang, Wei School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9781510662520

the long sequence time-sequence forecasting problem attracts a lot of organizations. Many prediction application scenes are about long sequence time-sequence forecasting problems. Under such circumstances, many researchers have tried to solve these problems by employing some models that have proved efficient in the Natural Language processing field, like long short term memory networks and Transformers, etc. And there are a lot of improvements based on the primary recurrent neural network, and Transformer. Recently, a model called informer which is made for the LSTF was proposed. this model claimed that it improves prediction performance on the long sequence time-series forecasting problem. But in the later experiments, more and more researchers found that informers still can not handle all the long sequence time-sequence forecasting problems. this paper is going to look at how datasets effect the performance of different models. the experiment is carried out on the Bitcoin dataset with four features and one output. the result shows that the Informer (transformer-like model) cannot always perform well so that sometimes choosing models with simple architecture may gain better results. © 2023 SPIE.

关键词： Forecasting

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