Due to the diversity and heterogeneity of electrical equipment, the operation monitoring systems for power transformers are undergoing diversified development. By utilizing machinelearning methods, this study focuses...
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Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preproces...
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The proceedings contain 376 papers. The topics discussed include: analysis of badminton motion trajectory algorithm based on neural network;intelligent insurance actuarial model under machinelearning and data mining;...
ISBN:
(纸本)9798350360240
The proceedings contain 376 papers. The topics discussed include: analysis of badminton motion trajectory algorithm based on neural network;intelligent insurance actuarial model under machinelearning and data mining;research on real-time data transmission andsignalprocessing system of CIM practical training teaching platform based on 5G network;design of systematic financial risk warning system based on integrated classification algorithm;development of a multi-objective optimization framework for submersible localization and search operations;improved thermal dome structure optimization design based on neural network algorithm;transmission line inspection image intelligent diagnosis system;and application of computer artificial intelligence infrared image processing technology in strength detection of building steel structure.
The proceedings contain 241 papers. The topics discussed include: image processing based on inter-frame color feature matching for visible light positioning;research on short-term load forecasting method based on ANFI...
ISBN:
(纸本)9798350368888
The proceedings contain 241 papers. The topics discussed include: image processing based on inter-frame color feature matching for visible light positioning;research on short-term load forecasting method based on ANFIS-BPNN-LSSVM;multi-scale complex terrain wind power output prediction based on multi-feature refinement fusion;research on multi-feature triplet attention based sharpness-aware classification network;optimization and application of the search algorithm for the efficacy of airfield blockade by penetration submunitions;a deep learning approach for rectifying fisheye image;lightweight image deraining network based on dilated depthwise separable convolution and enhanced channel attention;study on the combination of S-growth curves based on ultra-wideband displacement ranging for predicting landslides;and multi-agent reinforcement learning based on cross task information sharing.
This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The chal...
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ISBN:
(纸本)9798350344868;9798350344851
This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new approaches for estimating the intelligibility of hearing aid signals that can be used in future hearing aid algorithm development. It extends an earlier round (CPC1, 2022) in a number of critical directions, including a larger dataset coming from new speech intelligibility listening experiments, a greater degree of variability in the test materials, and a design that requires prediction systems to generalise to unseen algorithms and listeners. This paper provides a full description of the new publicly available CPC2 dataset, the CPC2 challenge design, and the baseline systems. The challenge attracted 12 systems from 9 research teams. The systems are reviewed, their performance is analysed and conclusions are presented, with reference to the progress made since the earlier CPC1 challenge. In particular, it is seen how reference-free, non-intrusive systems based on pre-trained large acoustic models can perform well in this context.
On the basis of the difficulty in determining the feature point in the signalprocessing of gas ultrasonic flowmeter,a variable threshold based zero-crossing detection signalprocessing method is proposed to determine...
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Hate speech in the social media raises a fundamental concern of social integration and quality individual life. This paper aims at presenting the key importance of hate speech detection in the Telugu language which is...
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This paper mainly focuses on the evaluation and prediction of sports competition results, and uses the methods of feature extraction, CRITIC weighted analysis andmachinelearning algorithm to discuss and analyze. Fir...
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Heart diseases are a global leading cause of death, affecting nations universally. Early detection andmachinelearning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucia...
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Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG record...
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ISBN:
(纸本)9798331540661;9798331540678
Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. machinelearning has emerged as an asset in EEG signalprocessing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machinelearning and EEG signalprocessing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machinelearning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machinelearning and EEG signalprocessing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.
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