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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是111-120 订阅

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A Modular Deep Denoising Autoencoder for speech enhancement 3

A Modular Deep Denoising Autoencoder for speech enhancement

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3rd international conference for Emerging Technology, INCET 2022

作者： Al-Taai, Raghad Yaseen Lazim Wu, Xiaojun School of Computer Science Ministry of Education Shaanxi Normal University Snnu Key Laboratory of Modern Teaching Technology Xi'an China

ISBN: (纸本)9781665494991

Recent researches have proven that deep denoising autoencoder is an effective method for noise reduction and speech enhancement, and can provide better performance than several existing methods. However, training deep denoising autoencoder has proven to be difficult computationally. The goal of this study is to develop a modular approach for training deep denoising autoencoders as a set of classifiers based on collaborative learning. Each classifier is a multilayer deep denoising autoencoder network and specialized for a particular enhancement task and handles a subset of the complete training dataset. The approach performance was assessed using a perceptual evaluation of speech quality and the segmental signal-to-noise ratio. We have trained two individual DDAEs with three and five hidden layers respectively for comparison purposes. We have also compared our proposed model with the traditional spectral subtraction and, log MMSE methods. The results showed that DDAE with three and five hidden layers was sufficient (deeper is better), while the proposed approach provided higher intelligibility results and was more suitable for high-quality cases. © 2022 IEEE.

关键词： Speech enhancement

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Identification of Skin Diseases using a Novel Deep CNN 3

Identification of Skin Diseases using a Novel Deep CNN

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Nagaraj, P. Muneeswaran, V. Krishna, K. Jaya Reddy, K. Yerriswamy Morries, J. Rock Kumar, G. Pavan Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Germany Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Germany

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Skin disorders are the most frequent health problems that people face around the world, and they come with a hefty price tag. Chickenpox, impetigo, infectious erythema, scabies, and skin warts are chronic and incurable skin disorders that cause severe morbidity in the form of physical discomfort. The technique of image processing is used in our procedure of identifying skin problems. Because this approach is based on Deep Convolutional Neural Network Learning, extremely precise results can be obtained by employing these techniques. The patient submits a photo of the affected area of the skin to the prototype as an input. In this photograph, the Deep Learning technique is employed, and as a result, the discovered skin disease is displayed as the output. © 2022 IEEE.

关键词： Convolutional neural networks

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Speech Enhancement Algorithms for Wind Noise Reduction 3

Speech Enhancement Algorithms for Wind Noise Reduction

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Rakshitha, A.H. Rs Chikkamath, Satish Desai, Shrinivas D Kle Technological University School of Electronics and Communication Hubballi580031 India Kle Technological University School of Computer Science Hubballi580031 India

ISBN: (纸本)9781665494991

Speech is the most convenient communication mode. Speech signal, while transmission degrades due to various factors such as white noise addition, noise from the surrounding environment, etc. Wind noise gets added in mobile phones and corrupts speech signals leading to listener's fatigue. Speech enhancement is a research area focusing on improving the quality and intelligibility of degraded speech signals. Wind noise, being non-stationary, is difficult to remove. This work focuses on reducing the effect of wind noise using various speech enhancement algorithms such as Multi-band Spectral Subtraction, Minimum Mean Square Error Short-Time Spectral Amplitude (MMSE-STSA), MMSE log STSA and Wiener filtering. The experiments are conducted considering wind noise alone and in combination with white noise. The performance of these speech enhancement algorithms on wind noise is evaluated using measures such as overall SNR, segmental SNR, Perceptual Evaluation of Speech Quality (PESQ), Itakura Saito Spectral distance and Short-Time Objective Intelligibility (STOI). © 2022 IEEE.

关键词： Speech enhancement

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Study on the difference of brain visual information processing between Parkinson's disease patients with hallucinations and healthy people based on EEG 3

Study on the difference of brain visual information processi...

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3rd international conference on computer science and Communication Technology, ICCSCT 2022

作者： Qu, Liangcheng Liu, Chuan Yin, Kuiyin Zhang, Li Shen, Bo Nanjing Research Institute of Electronic Techology Jiangsu Najing China Department of Geriatrics Affiliated Brain Hospital of Nanjing Medical University Jiangsu Najing China

ISBN: (纸本)9781510661240

computer vision is a mode task that uses computer to learn from training data and apply the learned experience to specific task. When patient experiences hallucinations, brain will call the memory stored in the hippocampus and process through specific pattern, and brain visual terminal will receive the image that does not exist in the real world. Patients with Parkinson's disease (PD) for more than 10 years are usually accompanied by hallucinations, which leads to changes in visual information processing and information perception pathway in brain. Electroencephalogram (EEG) is a spontaneous electrophysiological signal of the brain, which can reflect the working mechanism of the brain. In order to explore the changes of brain information perception pathway in PD, we recruited 5 PD patients with hallucinations, 5 healthy elderly and 5 healthy young subjects to participate visual classification experiment. The EEG collected in the experiment was analyzed by spectrum and brain network. The analysis found that the Delta, theta and alpha power of frontal lobe and occipital lobe in PD patients increased, the beta and gamma power decreased, and the brain fell into the internal circulation, the interaction between brain regions decreased. The lack of interaction may be one of the main causes of hallucinations. © 2022 SPIE.

关键词： Brain

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Systematic Literature Review on Application of Artificial Intelligence in Cancer Detection Using Image processing 3

Systematic Literature Review on Application of Artificial In...

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3rd international conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Chandra, Giovanni Irisha, Kevina Diva Vica, Vivianit Indyca Suri, Puti Andam Syahputra, Muhammad Edo School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Having a low survival rate, cancer has become one of the deadliest diseases with complicated prognosis. In this matter, Artificial Intelligence (AI) is frequently used to aid humans due to the difficulties of cancer identification. Specifically, the use of AI in cancer detection and treatment uses two of its subclasses, machine learning and deep learning. Because those two subclasses can handle large amounts of data, their algorithms can support humans in making decisions that yield the best possible results with the least amount of risk. The purpose of this research is to find out the effectiveness of the Artificial Intelligence algorithm that is used in image processing to diagnose cancer and what is the most effective image processing algorithm for cancer diagnosis. The methods that are used in this research is Systematic Literature Review (SLR). As a result, Artificial Intelligence algorithms are sufficiently effective to be used in diagnosing cancer with image processing. Meanwhile, the most effective algorithm is CatBoost with AUC of 0.939 and Support Vector Machine (SVM) with AUC of 0.934. © 2022 IEEE.

关键词： Image processing

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A large convolution model with large receptive field for human pose estimation 3

A large convolution model with large receptive field for hum...

引用

3rd international conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2022

作者： Liu, Zongyou Jin, Qinghan Liang, Hongwei Wang, Yuhan Qiu, Fengyi Liu, Shilan School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (纸本)9783800759330

We propose a large convolution model to complete the human pose estimation task, which has a large effective receptive field and reduces the destruction of channel information by the ReLU function. We adjusted the model of lightweight Openpose and used the structure of mobilenetv3 as the backbone of lightweight Openpose, hoping to use the InvertedResidual structure to reduces the destruction of channel information by the ReLU function. Insert RepLKBlock into the backbone to improve the effective receptive field of the model. Our model improves the following problems of the lightweight Openpose model. (1) Lightweight Openpose has a small effective receptive field. (2) The destruction of information by the ReLU function of lightweight Openpose. Large convolution model achieves Average Precision (AP) of 0.47 on the COCO2017 validation set. © VDE VERLAG GMBH.

关键词： Convolution

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Abstractive Text Summarization Using Hybrid Methods 3

Abstractive Text Summarization Using Hybrid Methods

引用

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

作者： Daga, Gautam Saha, Subhradeep Shah, Yash Nirmala, S Jaya National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Text summarization involves the process of constructing a concise, condensed, short, coherent, and fluent summarized version of a comparatively longer document with more text and involves the process of collection and segregation of the text's major note points. There are broadly two different kinds of approaches that can be used for the process of text summarization - a) Extractive Summarization(selects important sentences as a subset of original text) and b) Abstractive Summarization(introduces new words to create a meaning summary). Hybrid methods try to incorporate a combination of both the above models, extractive and abstractive to summarize the text where the final summary can either be abstractive or extractive based on the relative positioning of the two models. © 2022 IEEE.

关键词： Text processing

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Lung Cancer Detection Using Convolutional Neural Network 3

Lung Cancer Detection Using Convolutional Neural Network

引用

3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Jain, Dhyanendra Singh, Prashant Pandey, Amit Kumar Singh, Mayank Singh, Harikesh Singh, Amarjeet Abes Engineering College Department of CSE-AIML Ghaziabad India Sunder Deep Engineering College Department of Computer Science and Engineering Ghaziabad India Jss Academy of Technical Education Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665482684

Automatic detection of defects and abnormalities in CT scan images has gain lot of importance in the recent times as because of the deteriorating quality of air almost everyone is facing one or the other type of lung related issues thus it is of esteem importance to study the computed tomography (CT) images of the pulmonary nodules to efficiently treat the diseases like lung cancer. The use of machine learning is an efficient way to distribute the work of doctors and process the large amount of data to produce accurate results on the go. Three phases of CT image pre-processing, Deep Learning, and Convolutional Neural Network use make up the diagnosis approach. The pre-processing converts raw data into usable form and deep learning algorithm assigns weight to the data, in the last stage CNN is use to conclude the health status of the lung, i.e. normal or abnormal. © 2022 IEEE.

关键词： Convolutional neural networks

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Multi-label topic classification for COVID-19 literature annotation: A BioBERT-based feature enhancement approach 3

Multi-label topic classification for COVID-19 literature ann...

引用

2022 3rd international conference on computer Information and Big Data Applications, CIBDA 2022

作者： Wang, Xin Wang, Jian Tang, Wentai Zhang, Hongtong College of Computer Science and Technology Dalian University of Technology Dalian116024 China

ISBN: (纸本)9783800758760

With the rapid expansion and exponential growth of biomedical literatures, especially in the current environment of COVID-19 pandemic, it is urgent to explore an effective technology to automatically manage and categorize massive information for biomedical texts. The wide application and powerful performance of BERT have shown promising results in the field of natural language processing. Thus, we first choose the improved pre-trained language models CovidBERT and BioBERT as the basis, from the best performance of which further enhances semantic representation of abstract with extra title information. Finally, a novel feature enhancement method is proposed to exploit and integrate the distribution of label information effectively. The experimental results show that our model achieves an instance-based F1 score, precision and recall of 93.94%, 93.5% and 94.38% in the task of multi-label topic classification from track 5 BioCreative VII. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： COVID-19

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Research on patent classification based on hierarchical label semantics 3

Research on patent classification based on hierarchical labe...

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3rd international conference on Education, Knowledge and Information Management, ICEKIM 2022

作者： Chen, Jie Zhao, Shu Lu, Fan Liu, Feng Zhang, Yangping Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9781665416665

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model. © 2022 IEEE.

关键词： Semantics

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