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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141-150 订阅

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A Comprehensive Analysis of Deep Learning Frameworks to Mitigate the Impact of Varied Lighting and Weather Conditions 3

A Comprehensive Analysis of Deep Learning Frameworks to Miti...

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3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Mohanty, Anshuman Balasundaram, A. Shaik, Ayesha School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Center for Cyber Physical Systems Vellore Institute of Technology Chennai India

ISBN: (纸本)9781665456647

Object detection is an extensively researched field in computer vision and deep learning-related use cases. Popular frameworks such as YOLO, RCNN, SSD, FPN, RetinaNet, etc., and other cutting-edge detection systems are extensively used in various applications, such as autonomous vehicle driving scenarios and surveillance systems. However, in practical applications, the performance of the generic object detection systems suffers when subject to images with insufficient or poor lighting due to adverse weather conditions and various external factors. Several frameworks utilizing different deep learning and image processing-based approaches have been presented to enhance object detection considering recent advancements in this field. This paper provides a comprehensive overview of several contemporary techniques under various challenging lighting and weather situations. This study also talks about several public benchmark datasets and applications where varying illumination, or adverse weather conditions play a crucial role in estimating the efficacy of detection. Finally, the study summarizes the future scope and research direction. © 2022 IEEE.

关键词： Object detection

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The Epidemiology of Automatic Skin Cancer Detection by Comparative Analysis of Pre-processing and Segmentation Techniques 3

The Epidemiology of Automatic Skin Cancer Detection by Compa...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Thapar, Puneet Rakhra, Manik Singh, Aman Lovely Professional University Department of Computer Science and Engineering Punjab India Universidad Europea del Atlántico Santander39011 Spain

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Skin cancer is growing more widespread in many nations and is affecting a considerable number of individuals worldwide, including in Australia. Melanoma is a malignant skin change caused by melanocytes, which are pigment cells present in the epidermis (top layer of the skin). Early detection is strongly advised in order to effectively treat melanoma. As a result, the early excision of melanoma tissues has a significant impact on the survival rate of skin cancer patients. Traditional cancer detection methods are excruciatingly painful and time-consuming. As a result, to handle the many melanoma detection issues with high precision and accuracy, a quick and automated detection method is required. The procedure of identifying skin cancer by an automatic system is described in this work. Pre-processing is the most important of the numerous image processing phases. Its main goal is to provide a high-quality image by removing unnecessary portions and noise from the digital dermoscopy image. As a result, a detailed overview of several pre-processing methodologies is offered, as well as the work of scholars in this field who have primarily focused on pre-processing approaches. © 2022 IEEE.

关键词： Image segmentation

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Research on proofreading methods of Chinese semantic knowledge 3

Research on proofreading methods of Chinese semantic knowled...

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

作者： Bai, Xueli Li, Jianyi Wang, Hongjun Liu, He College of Computer Science North China Institute of Aerospace Engineering Hebei Langfang China TRS Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9783800759330

With the rapid increase of the trend of electronic text, it is more urgent to improve the performance of Chinese text auto-proofreading models. Due to the effective application of deep learning technology, the research on Chinese spelling proofreading and Chinese grammar proofreading has been relatively perfect, but the research on Chinese semantic knowledge proofreading methods is still less, resulting in semantic knowledge proofreading is a weak link in Chinese text proofreading. In this paper, the semantic knowledge proofreading data set is constructed by relation extraction and other techniques, aiming to make the proofreading model can further improve the knowledge reasoning ability in combination with the context. The experimental results show that the method proposed in this paper further improves the knowledge reasoning ability of the model, and can make better use of the semantic knowledge contained in the pre-trained model, which provides a new idea for the following research. © VDE VERLAG GMBH.

关键词： Deep learning

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Chinese short text classification by combining Bert and graph convolutional network 3

Chinese short text classification by combining Bert and grap...

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

作者： Chen, Mo Yao, Chunlong Li, Xu Shen, Lan School of Information Science and Engineering Dalian Polytechnic University Liaoning Dalian China Engineering Training Center Dalian Polytechnic University Liaoning Dalian China

ISBN: (纸本)9781510661240

Short Text Classification is the fundamental task in the nature language processing. There is a lack of language structure and uneven classification of data samples in short texts, which limit the development of deep learning based short text classification. To address the limitations of text sequences, we propose using a large-scale pre-trained language model Bert to obtain feature information between words and bureaus in the text, Graph Convolutional Network (GCN) with double-layer convolutional network can obtain the dependency relationships between words. We propose to combine Bert with GCN in short Chinese medical texts, where BertGCN outperforms better than other's methods in classification accuracy. © 2022 SPIE.

关键词： Convolution

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A ConvNeXt Backbone-Based Medical Image Segmentation Model for Brain Glioma 3

A ConvNeXt Backbone-Based Medical Image Segmentation Model f...

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

作者： Pan, Luhai Xiao, Zhenghong School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

ISBN: (纸本)9783800758760

Medical imaging is a very important information carrier, which can help clinicians understand the patient's condition and assist physicians in planning treatment plans [1]. Medical image segmentation technology also plays a crucial role in clinical applications, such as lesion size measurement, organ and lesion localization, lesion shape, radiotherapy planning, assisted surgery, and anatomical structure research. At present, image segmentation techniques based on traditional image processing and deep learning are widely used in the automatic segmentation of medical images [2]. The Deeplabv3+ algorithm framework is one of the algorithms with higher segmentation accuracy at present. However, the Deeplabv3+ algorithm framework is easy to lose local details when obtaining the global information of the image. Aiming at this problem of the Deeplabv3+ algorithm framework, this paper proposes a new image semantic segmentation algorithm, which integrates the ConvNeXt backbone with the Deeplabv3+ algorithm framework [3]. The ConvNeXt backbone net-work architecture has fewer activation functions and normalization layers and separates the downsampling layer [4]. The throughput advantage and robustness make ConvNeXt more valuable for industrial deployment. There is a significant improvement in the overall effect of the paper experiment. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Medical imaging

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Vehicular Ad Hoc Network performance enhancement using VOA based Road Side Unit Deployment 3

Vehicular Ad Hoc Network performance enhancement using VOA b...

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

作者： Sharma, Ayushi Pandey, Kavita Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Road side unit (RSU) is the key element in VANET architecture. It is used to improve communication range, driving awareness, traffic safety, signal acknowledgement and violation. However, VANET faces major challenges in RSU deployment. To achieve the objectives of RSU deployment problem i.e. minimizing the cost of deployment and maximizing network coverage;swarm intelligence algorithm have been used in the literature. In our work, Virus optimization algorithm (VOA) has been applied on VANET to generate optimal solutions and is compared with swarm family algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The outcome generated shows the expertise of the suggested algorithm i.e. VOA in terms of optimal RSU deployment and also on various performance metrics like overlapping rate, average transmission time and network connectivity. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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A Novel Multimodal Speech Emotion Recognition System 3

A Novel Multimodal Speech Emotion Recognition System

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

作者： Asiya, U.A. Kiran, V.K. Nss College of Engineering Department of Computer Science Palakkad India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The recognition of speech emotions is a popular study area. Speech signal is a combination of audio and text. The audio of speech is used as an input for detecting emotions in an Audio Emotion Recognition systems. The textual content of speech is used as an input for detecting emotions in Text Emotion Recognition systems. First, we focused on a unimodal technique, in which emotion recognition can be done using either audio or text. The study then shifted its focus to a multimodal approach, in which audio and text models are integrated to form a single modality. The AlexNet audio model had a 55% accuracy rate, and the text emotion Recognition model using the BERT model had 69% accuracy. The accuracy of the emotion detection system increased to 98% with the multimodal approach. The multimodal system comprises of both audio and text models, which improves the emotion recognition system. © 2022 IEEE.

关键词： Emotion Recognition

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A Dual-Size Convolutional Kernel CNN-Based Approach to EEG signal Classification 1

引用

3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Zhao, Kai Gao, Nuo Department of Information and Electrical Engineering Shandong Jianzhu University Jinan250101 China

ISBN: (数字)9789811961359

ISBN: (纸本)9789811961342

In this paper, an algorithm based on a combination of Riemannian space and convolutional neural network is proposed for the feature extraction as well as classification of motor imagery EEG signals with four classifications (left hand, right hand, foot and tongue). In terms of motor imagery EEG signal processing and feature extraction, the covariance matrix of the symmetric positive definite matrix is chosen as the descriptor to reasonably transform the EEG signal described in the spatio-temporal domain from Euclidean space to Riemannian space, followed by a low-dimensional vector estimation of the n-dimensional symmetric positive definite matrix using a specific feature mapping to obtain a low-dimensional and efficient EEG signal feature set. The convolution operation is improved in the classifier by adding a double-size convolution kernel to capture the motor imagery EEG data features more comprehensively. Experimental testing of the method using the publicly available dataset BCI2008IV-2a competition number competition dataset shows that the algorithm can effectively identify EEG signals with high classification accuracy and good robustness. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Covariance matrix

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The Evaluation of Camouflage Based on Image Edge Contour Similarity 7

The Evaluation of Camouflage Based on Image Edge Contour Sim...

引用

7th international conference on Communication, Image and signal processing, CCISP 2022

作者： Zhou, Xian Zhu, Wenwen Liu, Fuyong Yang, Wei Chu, Miao Xi'An Technological University School of Art and Media Xi'an China Xi'An Technological University School of Mechatronic Engineering Xi'an China Northwest Institute of Mechanical and Electrical Engineering 3rd Research Department Xianyang China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

As the target moves in the background, the profile dislocation will be caused and the camouflage effect will be reduced following that. Aimed at the problem, the author in the research introduces a camouflage evaluation method that based on the similarity of the edge contour of the camouflage target. The research combines Sobel and Canny algorithm to extract the edge contour of the target, uses the Euclidean distance to calculate the similarity of target's edge contour to its background after camouflage, so as to judge the quality of the camouflage effect. Finally, the rationality of the method is verified by ranking and comparing six camouflage plans with different contours, which were calculated by edge contour similarity method and image discrimination method respectively. © 2022 IEEE.

关键词： Visualization Shape Image edge detection signal processing algorithms Euclidean distance signal processing Feature extraction

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Character Recognition of Vedic Sanskrit using Deep Learning Algorithms 3

Character Recognition of Vedic Sanskrit using Deep Learning ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665462006

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. The research is performed over the Devanagari script which was developed during 1st to the 4th century CE and is common to many languages from the Indian subcontinent. © 2022 IEEE.

关键词： Character recognition

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