Applied and Computational Engineering最新文献

Bridging the gap in online hate speech detection: A comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter 缩小网络仇恨言论检测的差距：BERT 与传统模式在 X/Twitter 上识别仇视同性恋内容的比较分析

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241346

Josh McGiff, Nikola S. Nikolov

{"title":"Bridging the gap in online hate speech detection: A comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter","authors":"Josh McGiff, Nikola S. Nikolov","doi":"10.54254/2755-2721/64/20241346","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241346","url":null,"abstract":"Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced sentiment analysis models, particularly BERT, and traditional machine learning methods, we developed a nuanced approach to identify homophobic content on X/Twitter. This research is pivotal due to the persistent underrepresentation of homophobia in detection models. Our findings reveal that while BERT outperforms traditional methods, the choice of validation technique can impact model performance. This underscores the importance of contextual understanding in detecting nuanced hate speech. By releasing the largest open-source labelled English dataset for homophobia detection known to us, an analysis of various models' performance and our strongest BERT-based model, we aim to enhance online safety and inclusivity. Future work will extend to broader LGBTQIA+ hate speech detection, addressing the challenges of sourcing diverse datasets. Through this endeavour, we contribute to the larger effort against online hate, advocating for a more inclusive digital landscape. Our study not only offers insights into the effective detection of homophobic content by improving on previous research results, but it also lays groundwork for future advancements in hate speech analysis.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"54 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of deep learning models based on Chest X-ray image classification 基于胸部 X 光图像分类的深度学习模型比较

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241352

Yiqing Zhang, Yukun Xu, Zhengyang Kong, Zheqi Hu

{"title":"Comparison of deep learning models based on Chest X-ray image classification","authors":"Yiqing Zhang, Yukun Xu, Zhengyang Kong, Zheqi Hu","doi":"10.54254/2755-2721/64/20241352","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241352","url":null,"abstract":"Pneumonia is a common respiratory disease characterized by inflammation in the lungs, emphasizing the importance of accurate diagnosis and timely treatment. Despite some progress in medical image segmentation, overfitting and low efficiency have been observed in practical applications. This paper aims to leverage image data augmentation methods to mitigate overfitting and achieve lightweight and highly accurate automatic detection of lung infections in X-ray images. We trained three models, namely VGG16, MobileNetV2, and InceptionV3, using both augmented and unaugmented image datasets. Comparative results demonstrate that the augmented VGG16 model (VGG16-Augmentation) achieves an average accuracy of 96.8%. While the accuracy of MobileNetV2-Augmentation is slightly lower than that of VGG16-Augmentation, it still achieves an average prediction accuracy of 94.2% and the number of model parameters is only 1/9 of VGG16-augmentation. This is particularly beneficial for rapid screening of pneumonia patients and more efficient real-time detection scenarios. Through this study, we showcase the potential application of image data augmentation methods in pneumonia detection and provide performance comparisons among different models. These findings offer valuable insights for the rapid diagnosis and screening of pneumonia patients and provide useful guidance for future research and the implementation of efficient real-time monitoring of lung conditions in practical healthcare settings.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"77 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integration of computer networks and artificial neural networks for an AI-based network operator 计算机网络与人工神经网络的整合，打造基于人工智能的网络运营商

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241370

Binbin Wu, Jingyu Xu, Yifan Zhang, Bo Liu, Yulu Gong, Jiaxin Huang

引用次数: 0

Detection and classification of wilting status in leaf images based on VGG16 with EfficientNet V3 algorithm 基于 VGG16 和 EfficientNet V3 算法的叶片图像枯萎状态检测与分类

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241347

Qixiang Li, Yiming Ma, Ziyang Luo, Ying Tian

引用次数: 0

Predictive optimization of DDoS attack mitigation in distributed systems using machine learning 利用机器学习对分布式系统中的 DDoS 攻击缓解进行预测优化

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241350

Baoming Wang, Yuhang He, Zuwei Shui, Qi Xin, Han Lei

引用次数: 0

DOA estimation technology based on array signal processing nested array 基于阵列信号处理嵌套阵列的 DOA 估算技术

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241345

Muye Sun, Tianyu Duanmu

{"title":"DOA estimation technology based on array signal processing nested array","authors":"Muye Sun, Tianyu Duanmu","doi":"10.54254/2755-2721/64/20241345","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241345","url":null,"abstract":"Research on non-uniform arrays has always been a focus of attention for scholars both domestically and internationally. Part of the research concentrates on existing non-uniform arrays, while another part focuses on optimizing the position of array elements or expanding the structure. Of course, there are also studies on one-dimensional and two-dimensional DOA estimation algorithms based on array spatial shapes, despite some issues. As long as there is a demand for spatial domain target positioning, the development and refinement of non-uniform arrays will continue to be a hot research direction. Nested arrays represent a unique type of heterogeneous array, whose special geometric shape significantly increases degrees of freedom and enhances estimation performance for directional information of undetermined signal sources. Compared to other algorithms, the one-dimensional DOA estimation algorithm based on spatial smoothing simplifies algorithm complexity, improves estimation accuracy under nested arrays, and can effectively handle the estimation of signal sources under uncertain conditions. The DFT algorithm it employs not only significantly improves angular estimation performance but also reduces operational complexity, utilizing full degrees of freedom to minimize aperture loss. Furthermore, the DFT-MUSIC method greatly reduces algorithmic computational complexity while performing very closely to the spatial smoothing MUSIC algorithm. The sparse arrays it utilizes, including minimum redundancy arrays, coprime arrays, and nested arrays, are a new type of array. Sparse arrays can increase degrees of freedom compared to traditional uniform linear arrays and solve the estimation of signal source angles under uncertain conditions, while also enhancing algorithm angular estimation performance.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning vulnerability analysis against adversarial attacks 针对对抗性攻击的深度学习漏洞分析

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241377

Chi Cheng

引用次数: 0

Intelligent medical detection and diagnosis assisted by deep learning 深度学习辅助下的智能医疗检测和诊断

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241356

Jingxiao Tian, Hanzhe Li, Yaqian Qi, Xiangxiang Wang, Yuan Feng

引用次数: 0

A road semantic segmentation system for remote sensing images based on deep learning 基于深度学习的遥感图像道路语义分割系统

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241342

Shutong Xie

{"title":"A road semantic segmentation system for remote sensing images based on deep learning","authors":"Shutong Xie","doi":"10.54254/2755-2721/64/20241342","DOIUrl":"https://doi.org/10.54254/2755-2721/64/20241342","url":null,"abstract":"With the rapid development of deep learning of computer science nowadays in China, many fields in academic research have experienced the powerful and efficient advantages of deep learning and have begun to integrate it with their own research. To be specific, in the field of remote sensing, the challenge of road extraction from the original images can be effectively solved by using deep learning technology. Getting a high precision in road extraction can not only help scientists to update their road map in time but also speed up the process of digitization of roads in big cities. However, until now, compared to manual road extraction, the accuracy is not high enough to meet the needs of high-precision road extraction for the deep learning model because the model cannot extract the roads exactly in complex situations such as villages. However, this study trained a new road extraction model based on UNet model by using only datasets from large cities and can get a pretty high precision in extraction for roads in big cities. Undoubtedly, this can lead to over-fitting, but its unique high accuracy ensures that the model's ability to extract roads can be well utilized under the situations of large cities, helping researchers to update road maps more conveniently and quickly in large cities.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"33 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Precision gene editing using deep learning: A case study of the CRISPR-Cas9 editor 利用深度学习进行精准基因编辑：CRISPR-Cas9 编辑器案例研究

Applied and Computational Engineering Pub Date : 2024-05-15 DOI: 10.54254/2755-2721/64/20241357

Zhengrong Cui, Luqi Lin, Yanqi Zong, Yizhi Chen, Sihao Wang

引用次数: 0