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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20255 条记录，以下是4561-4570 订阅

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Brain Tumor Segmentation Using Convolutional Neural Network in MRI Images Based on Deep learning 3

Brain Tumor Segmentation Using Convolutional Neural Network ...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Praveen, M. Lalithraju, A.N. Mugilan, P. Bharadwaja, Munjuru Saveetha Engineering College Department of ECE Chennai India

ISBN: (纸本)9781665494007

The brain is the master organ of the human body, responsible for every function of the organism. Brain tumors are one of several common types of disorders that can affect the brain. Tumors refer to the unchecked expansion of malignant cells in any region of a human body. To provide the best care possible, a prompt and correct diagnosis is essential. To distinguish between various types of brain tumors, MRIs are the most often utilised medical imaging modality. Compared to other types of medical imaging, MRIs offer more appealing visual analysis and greater flexibility. Computer aided diagnosis systems that are powered by artificial intelligence can accurately and thoroughly read and analyse even the most intricate medical data. One of AI's most exciting new subfields, deep learning, is developing answers for problems in many different areas. Convolutional Neural Networks (CNNs) are a type of deep neural network used in deep learning for the purpose of analyzing and understanding visual data. To classify brain abnormalities, the primary goal of this study is to create a convolution neural network and train it from scratch using brain tumors as a case study. The suggested CNN model for brain abnormality classification is constructed with a stack of convolution and pooling layers, an activation function, and batch normalization to carry out feature learning and vi classification. In order to extract these traits, the proposed model. © 2023 IEEE.

关键词： Tumors

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Deep learning Approaches for Web Page Classification in Focused Crawling 3

Deep Learning Approaches for Web Page Classification in Focu...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Al-Fatlawy, Mohammed Hussien Kumhar, Bhupchand Rajeswari, M. Sundaram, N. Kalyana Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies University Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

Web page classification plays a crucial role in focused crawling, where the goal is to selectively retrieve relevant pages based on specific topics or domains of interest. Deep learning approaches have gained significant attention in recent years due to their ability to extract high-level representations from raw data. This paper presents an overview of deep learning techniques used for web page classification in focused crawling. We discuss various deep learning architectures, training strategies, and evaluation metrics employed in this context. Furthermore, we provide insights into the challenges and future directions in this rapidly evolving field. © 2023 IEEE.

关键词： Deep learning focused crawling natural language processing neural networks web page classification

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Research on Real-time Monitoring System of Multi-scenario Health data Based on Internet of Things 3

Research on Real-time Monitoring System of Multi-scenario He...

引用

3rd international conference on Digital Society and intelligent Systems, DSInS 2023

作者： Wang, Hongyang Huo, Jiuyuan Hu, Jun Wang, Tingjuan School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China Gansu YiWangKeJi Network Technology Co. LTD Lanzhou730070 China

ISBN: (纸本)9798350331387

para>With the continuous development of society and people's growing concern for health, health monitoring technology has become an important part of our lives. For this reason, this study designs and implements a multi-scene real-time health data monitoring system based on the Internet of Things. The system adopts innovative technologies such as smart bracelet, Bluetooth gateway and message queue telemetry transfer (MQTT) to collect and transmit health data in real time. In addition, users can adjust the parameters of the data collection terminal through the remote control function. Through the web interface, users are able to conveniently view their health data in real time. The experimental results show that the data collected by this monitoring system has high accuracy, multi-scene adaptability and real-time performance. Meanwhile, the system is easy to operate and understand, providing users with a convenient experience. This will bring important technological innovation and practical application value to the field of health monitoring, providing people with more convenient and efficient health monitoring services. Future research directions can include optimising system performance, expanding application scenarios, etc., which will help promote the further development of health monitoring technology and protect people's health. © 2023 IEEE.

关键词： Internet of things

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Research on Hybrid Remaining Usable Life Prediction Method Based on Battery Aging Model and data-Driven 3rd

Research on Hybrid Remaining Usable Life Prediction Method B...

引用

3rd international conference on Machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Zhang, Yu Xu, Zhenyu Li, Dongqi Han, Shunjie Changchun University of Technology Changchun130012 China

ISBN: (纸本)9789819624676

Reliable prediction of the remaining useful life (RUL) of the battery is capable of providing a critical reference decision for users to use and replace batteries. Although there are many methods for battery RUL prediction, these methods suffer from insufficient training, ignoring local regeneration of capacity degradation curves, complex structure and a wide range of optimization seeking parameters, so this paper proposes a hybrid battery RUL prediction method. In the data processing stage, adaptive noise complete ensemble empirical modal decomposition (CEEMDAN) decomposes the battery capacity sequence, then calculates the fuzzy entropy value of each component and reconstructs the components with similar entropy values. In the model training stage, two data and model class methods, particle filtering to update the battery aging model parameters and Gaussian process regression (GPR) to fit the reconstructed components, are used respectively to further learn the long-term and local fluctuation parts of the battery capacity series. In the prediction stage, the prediction results of the two components are combined to obtain the predicted value of the future capacity of the battery and the uncertainty quantification of its RUL. Finally, the proposed hybrid model is validated on a battery dataset at different temperatures and experimental conditions, as a result, the RUL prediction method is shown to possess favorable prediction accuracy, good stability, and reliable uncertainty management capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Research on Audio Recognition and Optimization Processing based on Deep learning 3

Research on Audio Recognition and Optimization Processing ba...

引用

3rd international conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Hang, Chuxin Zhuang, Mandan Bai, Tongyuan Sun, Kang Macau University of Science And Technology Faculty of Information Technology 999078 China Beijing Institute of Technology Department of Mathematics And Civil Engineering Guangdong Zhuhai519088 China

ISBN: (纸本)9781665479974

In order to study the audio recognition technology, this paper used the waveform and spectrum of 7434 audio data to know the different kinds of audio data in the part of time and frequncy. Then, MFCC and Chroma features were extracted, and construct CNN model to classify the audio features. Finally, it is concluded that the efficiency of MFCC feature matrix is much higher than that of Chroma feature matrix, and the recognition accuracy of CNN model is 87.72%. To improve the accuracy between audio categories, this paper uses Nonnegative Matrix Factorization (NMF) to optimize audio data. Based on the optimized audio CNN model, the accuracy is improved to 90.88%, and the distinction between processed and unprocessed audio data is effectively improved. © 2022 IEEE.

关键词： Deep learning

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Research on autonomous intelligent inspection technology of substation based on image recognition and computer vision 3

Research on autonomous intelligent inspection technology of ...

引用

3rd international conference on Electronics, Electrical and Information engineering, ICEEIE 2023

作者： Fang, Mengran Li, Jingya Cai, Li Wang, Zheng State Grid Information and Communication Industry Group Co. Ltd. Beijing Branch Beijing China

ISBN: (纸本)9781510671423

Substation is the core hub of power grid transmission, which provides the necessary guarantee for the safe and stable operation of substation and even the whole power grid. There are many kinds of electrical equipment in substation, and the distribution is dense. Different equipment shows obvious differences in appearance and state. When inspecting these electrical equipment, we can not only use traditional manual inspection methods, but also rely on artificial intelligence technology and big data technology to build an intelligent inspection robot, and realize the recognition of image information through image recognition technology. In this article, the application of intelligent inspection robot in autonomous intelligent inspection of substation is studied, and the path planning model of inspection robot is constructed by combining image recognition and computer vision technology. The experimental results show that the path planning algorithm of inspection robot in this article has small error and excellent accuracy, which can reach more than 98% after many iterations. The algorithm realizes the stable, reliable and high-precision inspection operation of the substation inspection robot. © 2023 SPIE.

关键词： Computer vision

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The Use of Big data Platform Plays in Building a New Model of English Teaching in Universities 3rd

The Use of Big Data Platform Plays in Building a New Model o...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Liu, Ning Wang, Xiaohu Guangzhou City Construction College Guangzhou510925 China

ISBN: (纸本)9783031631320

With the rapid development and application of Big data technology, using Big data platform to build a new model of college English teaching has become a possible direction. This paper mainly expounds the implementation method and effect possibility of this new model from two aspects of building a Big data platform and building a new model of college English teaching. First of all, building a Big data platform is a prerequisite for realizing the new model of college English teaching. The Big data platform needs to include student information, curriculum information, teaching resources, evaluation data and other data, which need to be classified and integrated to facilitate subsequent management and analysis. Secondly, building a new model of college English teaching needs to be supported by the Big data platform and adopt methods based on data analysis and students’ personalized learning. This new model can give full play to the advantages of the Big data platform and achieve the goals of standardization of English teaching, sharing of teaching resources, personalized learning and comprehensive evaluation of students. Through this new model, English teaching in universities will be more scientific, advanced, flexible, and able to meet the needs of different student groups, improving the quality and efficiency of English education. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Big data

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ClipXpert: automated Clip Mining from Video data for High-Demand Content

ClipXpert: Automated Clip Mining from Video Data for High-De...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Rajdeep Chatterjee Sudip Chakrabarty Pappu Bishwas School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

ClipXpert is a new system that automates the extraction of relevant clips from YouTube videos using user-defined keywords or frequently used word or comment analysis. The system uses advanced transcription models to search a pre-existing database for lines associated with the provided keywords. If the database doesn’t contain the keywords, it dynamically identifies and stores relevant content for future use. ClipXpert also automates identifying frequently used nouns, adjectives, and adverbs to generate focused content highlights without user intervention. It also performs sentiment analysis on YouTube comments to understand audience engagement and reception. ClipXpert enhances video content extraction efficiency and accuracy, catering to the growing demand for targeted, high-value clips in the digital media landscape.

关键词： Sentiment analysis Video on demand Accuracy Machine learning Media Real-time systems Web sites data mining intelligent systems Videos

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Bitcoin Investment Classifier using Machine learning and Sentimental Analysis 3

Bitcoin Investment Classifier using Machine Learning and Sen...

引用

3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Shah, Jash Jagani, Neelay Raval, Rudresh Sachala, Harsh Divekar, Ravindra K J Somaiya College of Engineering Department of Information Technology Mumbai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Cryptocurrency is an intangible asset that has emerged due to the advancement in the fin-tech space and has led to many research opportunities. Cryptocurrency prices are subject to significant volatility, and predicting trends is very difficult. Around the world, there are hundreds of cryptocurrencies that are in daily use for purchasing goods and services or investing. Although Machine learning models have shown satisfactory results in predicting cryptocurrency prices, it is necessary to consider the public sentiment regarding a cryptocurrency. Public opinion about crypto is most prevalent on Twitter. Therefore, we aim to classify Bitcoin investing better by combining Machine learning Algorithm LSTM with Sentimental Analysis, which an investor can use to decide whether to buy a cryptocurrency or not. © 2022 IEEE.

关键词： Sentiment analysis

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Safety Helmet and Mask Detection at Construction Site Based on Deep learning 3

Safety Helmet and Mask Detection at Construction Site Based ...

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Zhang, Zhen Tang, Yifei Yang, Yannan Yan, Chen Nanjing University of Aeronautics and Astronautics College of Physics Jiangsu China Jiangsu Wei Xin Engineering Consultants Ltd Jiangsu China

ISBN: (纸本)9781665490788

To reduce safety hazards and effectively detect the wearing of helmets and masks by construction workers in complex scenarios, an improved YOLOv4-based detection algorithm called YOLOv4-HelMask was proposed in this paper. It includes dataset construction and data augmentation, k-means algorithm-based check frame selection, PANet-based structure separation category prediction, and location prediction to enhance feature extraction. The convergence of the model was optimized using a cosine annealing controller, and the generalization of the model was enhanced using a label smoothing technique. Experimental results demonstrate that the proposed YOLOv4-HelMask algorithm achieves a mAP value of 95.51% on the self-built dataset, which is 21.99%, 5.89%, 2.42%, and 5.07% higher than the current representative Faster R-CNN, SSD, YOLOv5, and YOLOv4 algorithms, respectively. The proposed algorithm can meet the needs of real-time detection. © 2023 IEEE.

关键词： Deep learning

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