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COOL: Classification of Online Offensive Language Using Machine Learning and Deep Learning 7th

COOL: Classification of Online Offensive Language Using Mach...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Bhamidi, Manjari Nandyala, Manvitha Dayalan, Ragapriya Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal Puducherry609609 India Department of Civil Engineering National Institute of Technology Puducherry Karaikal Puducherry609609 India

ISBN: (纸本)9783031699818

In the dynamic realm of online communication, the surge in offensive language and hate speech has emerged as a critical concern. the proliferation of digital platforms has led to a distressing uptick in such behaviors, challenging the establishment of a secure and inclusive online environment. Identifying and categorizing such conduct is imperative from both an ethical standpoint and for the prevention of further harm. the training and validation dataset encompass a diverse array of online platforms, ensuring inclusivity across various communication styles and linguistic nuances. To capture the nuanced characteristics of abusive language, a range of feature extraction techniques were included, consisting of traditional NLP methods and state-of-the-art deep learning architectures. In tandem with these approaches, comprehensive experiments employing a variety of classifiers, such as logistic regression, SVM, stochastic gradient descent, decision trees, and ensemble models were conducted. In summary, this research contributes significantly to the ongoing battle against online toxicity and the promotion of more constructive online conversations. the RNN algorithm’s 99.47% accuracy rate in detecting hate speech has significant societal and platform-level ramifications. It represents a strong barrier against hate speech spreading online, creating a safer atmosphere. © IFIP international Federation for information processing 2024.

关键词： Decision trees

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Secured Storage of information Using Audio Steganography 7th

Secured Storage of Information Using Audio Steganography

引用

7th international conference on Emerging Research in Computing, information, Communication and Applications, ERCICA 2022

作者： Sowmya, M.R. Shreenath, K.N. Shetty, Saritha Shetty, Savitha Wajid, Salman Kantharaj, Yashas Department of Computer Science and Engineering NMIT Yelahanka Bangalore India Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Department of MCA NMAM Institute of Technology Karkala India Department of CSE NMAM Institute of Technology Karkala India

ISBN: (纸本)9789811954818

the use of smartphones and cloud storage has expanded dramatically as technology progresses. the data that are kept and used are now more vulnerable to breaches. Using the Internet services, the end users’ data files can be accessed and manipulated from any other computer. If the secrecy of the information is of very high value, it should be secured. If we want to prevent unauthorized disclosure or change of information, it must be protected. the old cryptographic methods rely on keys and have a point of failure, and they attract the third party who is on lookout for crucial information. Steganography can be used to tackle this problem. It is a secret method of encrypting data in another form so that it can be securely stored and transferred over any medium. In the following paper, the idea of steganography, implementation methods, and storing the data in an audio file is discussed. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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A study on Frequency Domain Microstate Feature Fusion for EEG Emotion Recognition 7

A study on Frequency Domain Microstate Feature Fusion for EE...

引用

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

作者： Xiao, Di Lv, Zhao Hu, Shiang Sch. of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

the microstate analysis of EEG signals makes full use of the spatial information of the brain topographic map, and reflects the active association of different brain regions. Different from the traditional EEG features that mostly focus on single-channel information, the microstate feature contains the spatio-Temporal information of EEG signals. Unlike microstate studies that mostly focus on dimensional emotions, the experiments classify positive, neutral, and negative discrete emotions using the SEED database. this work filters the data of a single subject into five frequency bands and calculates the microstate topographic maps of EEG signals in different frequency bands, respectively. the extracted features of microstate classes are coverage, duration, occurrence, and transition probability between microstates. the gender difference as to the dominant microstate pattern for emotions and the comparison between microstates, we found that the brain activity of males in three emotional states and females in negative emotions were related to the frontal-occipital pattern, the females of positive and neutral emotional states were associated with the left and right brain areas. We also investigated the traditional power spectra features, these features which be fused over frequency bands or not fused were fed into the classifiers such as the K-Nearest Neighbor (KNN) and the the Support Vector Machine(SVM) to classify discrete emotional labels in SEED. the average classification accuracy of 15 subjects was 97.67±1.4% and 92.58±3.24%, respectively. © 2022 IEEE.

关键词： Brain

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Predicting COVID-19 Outbreak in Algeria Using Long Short-Term Memory Networks 7

Predicting COVID-19 Outbreak in Algeria Using Long Short-Ter...

引用

7th international conference on Image and Signal processing and their Applications, ISPA 2022

作者： Attache, Salima Terrissa, Labib Sadek Remadna, Ikram Ayad, Soheyb Madaci, Fadila Zerhouni, Noureddine University of Biskra Dept. Civil Engineering and Hydraulics Biskra Algeria University of Biskra Dept. Computer Science Biskra Algeria Faculty of Medicine University of Algiers 1 Algiers Algeria Institut FEMTO-ST Umr Cnrs 6174 AS2M Ufc Ensmm Besancon France

ISBN: (纸本)9781665480420

From late November 2019 until today, the Coronavirus (COVID19) has been circulating worldwide on a broad scale. the World Health Organization agrees that this is the world's most serious outbreak in the last twenty years. Algeria is no exception;although the relatively early precautions taken by the Algerian government, especially the national lockdown, the spread of Coronavirus has attained all cities. this study aims at clarifying how COVID-19 has spread in Algeria so fast in a short time and perform an intelligent system to predict confirmed, deaths, recovered, and active cases. this paper presents firstly a comprehensive analytics study on the spread of the pandemic to understand the COVID19 evolution in Algeria. Secondly, a predictive model based on a Long Short Term Memory Network is proposed and studied to estimate the future number of confirmed, deaths, recovered, and Active cases. © 2022 IEEE.

关键词： COVID-19

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Pedestrian Angle Recognition Based on JDE Multi-object Tracking Algorithm 7

Pedestrian Angle Recognition Based on JDE Multi-object Track...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Yang, Chunde Xu, Tongyao Lv, Minghong Yu, Yi Jiang, Xiaoming Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications School of Bioinformatics Chongqing400065 China

ISBN: (纸本)9781665478571

We often execute multi-object tracking algorithms and pedestrian angle recognition algorithms independently when we solve complex problems that need to track multiple pedestrians and identify their angles simultaneously. Firstly, the multi-target tracking algorithm is used to locate the target and determine the target's identity. then the pedestrian angle recognition model is used to recognize the pedestrian angle. this strategy is complicated and inefficient since the two models repeatedly extract target features. this paper realizes the combination of multi-object tracking algorithm and pedestrian angle recognition algorithm in response to this problem. Specifically, we added a new classification branch based on the JDE multi-object tracking algorithm. It enables the model to perform pedestrian angle recognition while detecting and embedding feature extraction. Finally, experiments show that the improved algorithm can perform pedestrian tracking and pedestrian angle recognition simultaneously. the algorithm's ability to track large and medium targets is the same as the JDE multi-object tracking algorithm. Compared with the Hydraplus model, the improved algorithm has a higher recognition accuracy. Besides, the algorithm can achieve real-time performance. © 2022 IEEE.

关键词： Feature extraction

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Evaluation system of hub airport parking building project based on fuzzy comprehensive evaluation method 7

Evaluation system of hub airport parking building project ba...

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2022 7th international conference on information Science, computer technology and Transportation, ISCTT 2022

作者： Liu, Xiannan Zhang, Qi Lu, Yue Construction Management Department Guangzhou Baiyun International Airport Co Guangzhou China

ISBN: (纸本)9783800760565

With the rapid development of social economy and the gradual improvement of the public road network around the airport, more and more passengers who choose public transportation such as self-driving, cab and online taxi to and from the airport bring new challenges to the management way of roadside traffic. the construction of parking building rating system has always been a hot topic in the industry, but not many hub airport parking building evaluation system is clearly proposed. this study draws on the design points of traditional parking buildings, combines the unique design points of airport parking buildings, and establishes a set of quantitative evaluation system based on fuzzy comprehensive evaluation method applicable to hub airport parking buildings. the evaluation system is finally demonstrated by using the P2 and P8 parking projects of Guangzhou Baiyun Airport. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Airports

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Holographic Stereogram Portrait of a Multi-view Camera-based Digital Human 8

Holographic Stereogram Portrait of a Multi-view Camera-based...

引用

Joint 8th international conference on Digital Arts, Media and technology and 6th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Lee, Seung Hyun Lee, Ji Youn Gentet, Philippe Kwon, Soon Chul Son, Kwang Chul Kim, Cheong Ghil Kwangwoon University Dept. of Immersive Convergence Content Seoul Korea Republic of Kwangwoon University Dept. of Information Content Seoul Korea Republic of Namseoul University Dept. of Computer Science Cheonan Korea Republic of

ISBN: (纸本)9798350396546

In this paper, the production process of holographic stereograms for digital human portraits, which are increasing interest in the field of holographic art, was shown. the digital human face model was produced using real-object based 3D modeling. After taking a human portrait image with 7 multi-view cameras, it was replaced with a digital human image through post processing. In order to produce digital holographic stereograms, more viewpoint images must be extracted from the face of the digital human. 768 viewpoint images were generated using the neural network multi-view interpolation method from 7 multi-view images. Finally, the process of manufacturing digital holographic stereogram of a digital human was explained, and the results of the experiment were shown. © 2023 IEEE.

关键词： 3D modeling

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Research Progress in Multi-Sensor Data Fusion Algorithms 24

Research Progress in Multi-Sensor Data Fusion Algorithms

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2024 7th international conference on computer information Science and Artificial Intelligence, CISAI 2024

作者： Jiang, Huhai Ke, Huimeng Southwest Institute of Technical Physics Sichuan Chengdu China

ISBN: (纸本)9798400707254

With the rapid advancement of modern science and technology, the importance of multi-sensor data fusion technology in various applications has become increasingly prominent. However, traditional non-intelligent fusion methods reveal many shortcomings when dealing with complex and dynamic environments. these methods rely on preset models and algorithms, showing poor adaptability to system nonlinearities, time-varying characteristics, and uncertainties, making it challenging to achieve real-time, efficient, and accurate information fusion. Moreover, traditional methods often struggle with processing multi-source heterogeneous data, being easily affected by noise and interference, thus reducing fusion accuracy and system robustness. Intelligent multi-sensor data fusion algorithms, by incorporating advanced technologies such as machine learning, deep learning, neural networks, and reinforcement learning, can dynamically learn and adapt to environmental changes, automatically adjusting fusion strategies and overcoming the limitations of traditional methods. this paper presents a multi-sensor data fusion algorithm based on an improved BP neural network, enhanced by particle swarm optimization (PSO) to improve network performance. this method prevents premature convergence of the network, speeds up convergence, and thus enhances data fusion accuracy. the study of intelligent multi-sensor information fusion algorithms holds significant practical significance and broad application prospects. By systematically summarizing and analyzing recent research progress in intelligent fusion algorithms, this paper reveals the unique advantages of intelligent fusion technologies in improving fusion accuracy, enhancing system robustness, and adaptability. It also explores their potential applications in various fields and identifies future development directions and research priorities. © 2024 Copyright held by the owner/author(s).

关键词： information fusion

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Implementation of Smart Contracts for E-Certificate as Non-Fungible Token using Solana Network 7

Implementation of Smart Contracts for E-Certificate as Non-F...

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7th IEEE international conference on information technology and Digital Applications, ICITDA 2022

作者： Artha, Kevin Aditya Ramadhan Zain, Sarah Noor Alkautsar, Azhar Ashhafa Widianto, Mochammad Haldi Bina Nusantara University Binus Bandung School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

the use of blockchain technology and the use of non-fungible tokens has seen a significant uptick this year. As a consequence of this, the author intends to implement the usage of blockchain technology as a method that is capable of preserving the authenticity value of a certificate and preventing harmful things from being associated with certifications. the author offers solutions by utilizing the blockchain system, specifically by utilizing smart contracts and the Solana network. Specifically, the author offers these solutions. this research intends to provide a solution for the problem of certificate packaging by combining three technologies-specifically, blockchain, Smart-Contracts, and Non-Fungible Tokens-so that certificates can be converted into e-certificates, which then qualify as NFTs. Specifically, blockchain is the technology that will be combined with Smart-Contracts and Non-Fungible Tokens to achieve this goal. In particular, the technology that will be integrated with Smart-Contracts and Non-Fungible Tokens is called blockchain. To be more explicit, the goal is to work toward the realization of these certificates' potential as a method of monetary exchange. this will be accomplished by working toward the realization of this potential. © 2022 IEEE.

关键词： Smart contract

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A Novel Forgery Detection Technique for Document Images 7

A Novel Forgery Detection Technique for Document Images

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7th international conference on Computing in Engineering and technology, ICCET 2022

作者： Savakar, Dayanand Hiremath, Raju Department of Computer Science Rani Channamma University Vidyasangama Belagavi India

ISBN: (纸本)9781839537042

Digital documents play an essential role in the day-to-day life of every once life. the documents are shared and exchanged in routine work. At the same time, protecting the document from fraud is a challenging task. the proposed method addresses document forgery detection. In this technique, first, we perform a document enhancement process by removing the noise like salt and pepper and Gaussian using various filtering techniques. then the enhancement of the contrast by using Histogram Equalization and Contrast stretching. Finally, the deblurring is used to recover the document from degraded observations. In the second process, feature extraction is performed by extracting the color, grey, and entropy features, then the quality measures like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity Index Matrix (SSIM) are also used as features. In addition, we use Optical Character Recognition (OCR) for fetching textual information. the proposed method performs both textual and image authentication. the proposed method achieved good performance for document forgery detection compared to previous techniques in terms of accuracy. © 2022 the Institution of Engineering and technology.

关键词： Mean square error

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