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

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Facial Expression Recognition System 3rd

Facial Expression Recognition System

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3rd international conference on Smart Systems - Innovations in Computing (SSIC)

作者： Pokharel, Rahisha Kaur, Mandeep Rakesh, Nitin Nand, Parma Sharda Univ Dept Comp Sci & Engn Greater Noida Uttar Pradesh India

ISBN: (纸本)9789811628771;9789811628764

A significant subject in the field of computer vision and artificial intelligence is outward appearance acknowledgment. There are two mode of correspondence one is verbal, and another is non-verbal [Xiaoxi, M., Weisi, L.: Facial emotion recognition. In: IEEE 2nd international conference on signal and Image processing (2017)]. Among verbal and non-verbal methods for correspondence, outward appearance is non-verbal methods for correspondence. Outward appearance assumes a crucial job which encourages human to communicate their feelings, express their emotions, psychological wellness, viewpoint, and so on [Kaur, M., Vashisht, R.: Comparative study of facial expression recognition techniques. Int. J. Comput. Appl. (2011)]. Understanding gets simpler when human and computer interact with each other if computer can react to non-verbal correspondence of human which is only feelings communicated. In this paper, a calculation is introduced for object discovery dependent on Viola-Jones algorithm. In this paper, there are introduced the consequences of acknowledgment of seven emotion states (neutral, happy, sad, fear, disgust, fear, surprised) in view of outward appearances. The grouping of the highlights was performed utilizing managed learning strategy, i.e., convolutional neural network (CNN).

关键词： CNN computer vision Emotion recognition Facial expression Image Viola–Jones algorithm

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Microstructure Image Classification of Metals Using Texture Features and Machine Learning 3rd

Microstructure Image Classification of Metals Using Texture ...

引用

3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

作者： Sabnis, Hrishikesh Angel Arul Jothi, J. Deva Prasad, A.M. Department of Mechanical Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Mechanical Engineering Manipal Academy of Higher Education Dubai Campus Dubai International Academic City Dubai United Arab Emirates

ISBN: (纸本)9783031057663

This research was conducted on microstructural images of four different metals such as mild steel, stainless steel, aluminium, and copper. The research aims to classify the microstructure images of the four metals obtained using light-optical microscope using texture features and machine learning techniques. The images were subjected to different steps like image pre-processing, texture feature extraction and classification. Classical machine learning algorithms like Support Vector Machines, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and XGBoost were used. Experimental results show that the XGBoost classifier achieves the highest classification accuracy of 98% using normalized texture features and cross validation using 10-folds. © 2022, Springer Nature Switzerland AG.

关键词： Decision trees

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Research on Natural Language processing Problems Based on LSTM Algorithm 22

Research on Natural Language Processing Problems Based on LS...

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3rd Asia-Pacific conference on Image processing, Electronics and computers, IPEC 2022

作者： Hu, Bochun School of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9781450395786

In the process of the development of modern society, the network text analysis system is more and more important, and the research in the field of natural language processing has been comprehensively developed. In this research process, the natural language processing system mainly uses deep learning technology to realize deep learning technology, mainly through different neural networks to complete model construction, based on language analysis, using word segmentation and sentence classification methods. The long sentences are processed to ensure that the computer can better understand the text semantics of the system. Based on the above background, this paper designs a natural language analysis system to judge the word order and semantics of language characters conduct deep mining of language features based on neural networks. And simultaneously classify data mining results. © 2022 ACM.

关键词： Natural language processing systems

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Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction 3

Attention Synchronous Based Spatial-Temporal Graph Convoluti...

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3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Cao, Zhuyou Luo, Tianwang Xiao, Jianhua Xiao, Hongbo Qing, Xinyi School of Computer Science and Engineering Huaihua University Hunan China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huai-hua Hunan418000 China

ISBN: (纸本)9798400716775

This study introduces AS-STGCN, a novel traffic flow prediction model that incorporates an attention mechanism and a synchronized spatio-temporal graph convolutional network. In the field of traffic prediction, deep learning models (e.g., STGCN) have outperformed traditional methods, especially in capturing temporal and spatial correlations. AS-STGCN introduces a spatio-temporal attention mechanism and simultaneous convolution, which improves the prediction accuracy and demonstrates a unique capability in capturing dynamic spatio-temporal patterns in urban traffic networks. This research is of great significance in advancing the field of traffic flow prediction and providing a more comprehensive and accurate method for predicting urban traffic dynamics. © 2023 ACM.

关键词： Convolution

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Maneuver Flight Partitioning Based on Important Segments in Multivariate Flight Parameters 22

Maneuver Flight Partitioning Based on Important Segments in ...

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3rd Asia-Pacific conference on Image processing, Electronics and computers, IPEC 2022

作者： Liu, Xiaodong Sun, He Lu, Jing School of Computer Science Civil Aviation Flight University of China Guanghan Sichuan China

ISBN: (纸本)9781450395786

Noise, deviation and correction will result in the degradation of the integrity and accuracy of maneuvering flight partition in training flights. A bottom-up, unitary to multivariate method is proposed for maneuver flight partitioning based on important segments (ISs). Given a threshold of the continuous change of the characteristic parameter, the IS is defined as the alternative set of maneuver flight segments. Then, the adjacent ISs with same change direction and the important correction segments are combined to form a maneuver flight segments sequence for unary flight parameter. Finally, according to the important order of characteristic parameters, the maneuvering flight sequences of multivariate flight parameters are merged and fused to realize the division of maneuver flight sequence. The experimental results show that the method can extract the maneuver flight fragments completely, and the average time errors of the manipulating start and stop points are less than 2.5 seconds. © 2022 ACM.

关键词： Piecewise linear techniques

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On Twin Bounded Support Vector Machine with Pinball Loss 1

引用

3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Anagha, P. Balasundaram, S. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110067 India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Optimized Energy Efficient Task Scheduling in Fog Computing 3rd

Optimized Energy Efficient Task Scheduling in Fog Computing

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Vispute, Shilpa Dinesh Vashisht, Priyanka Department of Computer Science and Engineering The NorthCap University Haryana Gurugram India Department of Computer Science and Engineering Amity University Haryana Gurugram India

ISBN: (纸本)9789819926015

Recently, continuous growth in use of Internet of Things (IoT) produce huge amount of data during processing, which increases load on Cloud Computing network. Cloud Computing does not support low latency for real-time applications. To overcome such drawbacks like low latency, lower response time, IoT-based application moves towards Fog Computing. The ideal solution for these IoT applications is provided by Fog and Cloud Computing. It enables user to process data near to end user. Fog environment also supports low latency and low response time than Cloud environment. Fog Computing allows to schedule application tasks on various Fog node which are available in the network. Fog nodes have limited resources than Cloud Computing hence scheduling tasks among Fog nodes becomes essential. In this paper, a scheduling algorithm, namely, Leveraging Energy for Task Scheduling in Fog Computing using optimization (LETSO) has been proposed. To check the efficiency of proposed algorithm two vital parameters, i.e. makespan and energy is considered. The simulated result of LETSO shows better result by minimizing the makespan time and reducing the energy consumption as compared to Bee Life Algorithm (BLA) and Modified Particle Swarm Optimization (MPSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Fog computing

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Health Data, Social Media, and Privacy Awareness: A Pilot Study 3rd

Health Data, Social Media, and Privacy Awareness: A Pilot St...

引用

3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Bathi, Prachotan Reddy Deepa, G. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576 104 India Thomas Lord Department of Computer Science University of Southern California Los Angeles United States

ISBN: (纸本)9789819926015

Despite several disputes and the overall perceived lack of privacy on social media platforms among the general population, they are now widely used around the globe and have become commonplace. Vast volumes of data in various formats are being posted on these platforms. Over the past decade, the widespread adoption and proliferation of online medical forums and social platforms illustrates the variety of information being shared. Usually, when dealing with sensitive medical information, to ensure that all ethical and legal criteria are met, processing and maintaining calls for a high quality of security and privacy safeguards. We were curious if medical information on such socially available platforms goes through the same quality of privacy safeguards. Our literature study identified a significant lapse in the privacy attitude of users who post medical information online. In this paper, we report on the responses from our survey about how medical information and its privacy on social media is perceived. We look at the drivers and impediments to personal health information being shared online, focusing on privacy-related issues. We then use our findings to aid in developing a workflow for a tool that provides access to a general social media user with state-of-the-art anonymizing and privacy-protecting techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Bioinformatics

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Attention Word Embedding Network-Based Lightweight Automatic Essay Scoring Model 3rd

Attention Word Embedding Network-Based Lightweight Automatic...

引用

3rd international conference on Artificial Intelligence, 2022

作者： Zhou, Xianbing Fan, Xiaochao Yang, Yong Ren, Ge School of Computer Science and Technology Xinjiang Normal University Ürümqi China

ISBN: (纸本)9789811694226

In recent years, automatic essay scoring technology has received more and more attention in the fields of education and research, and its purpose is to use natural language processing technology to automatically analyze and score the essay. In this paper, we proposes a lightweight automatic essay scoring model based on attention word embedding network. Using randomly initialized and self-trained word embedding and attention/max/mean pooling operation, we are able to score essays automatically. Our models match or outperform optimal QWK (Quadratic Weighted Kappa) performance on all eight datasets using significantly fewer parameters compared to baseline methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Engineering education

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Copy-move Forgery Detection using SIFT and DWT detection Techniques 3

Copy-move Forgery Detection using SIFT and DWT detection Tec...

引用

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

作者： Singh, Richa Verma, Sandeep Yadav, Suman Avdhesh Vikram Singh, S. Dr. B. R. Ambedkar National Institute of Technology Department of ECE Punjab Jalandhar India Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India Amity University Department of Computer Science & Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Uttar Pradesh India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Copy-Move forgery practices are defamatory tampering processes, affecting digital images. Such practices have become much more common nowadays because of the advent of significantly effective image processing tools being readily available. In copy-move forgery technique, an area from the original image is copied and pasted over another image area. This technique provides a way to conceal the important details and information in a digital image without leaving much traces behind to observe the tampering or manipulation of the image. Such practices question the authenticity of an image to forensic. In this paper, the techniques are proposed for the detection of cloning or copy-move forgery that involve both block based extraction using Discrete Wavelet Transform (DWT) and feature point extraction based detection technique i.e., Scale Invariant Feature Transform (SIFT) to extract the tampered regions more precisely. The proposed algorithm mainly involves tentacle matching of features of same features, which can be extracted from each of the blocks by calculating the dot products between their respective unit vectors. The proposed algorithm is able to achieve an efficiency of 98.12%, precision factor of 97% of and recall factor of 100%. © 2022 IEEE.

关键词： Discrete wavelet transforms

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