2023 Intelligent Methods, Systems, and Applications (IMSA)最新文献_第3页

Session (4) 会话(4)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217375

引用次数: 0

Empowering MBTI Personality Classification through Transformer-Based Summarization Model 基于变压器的摘要模型增强MBTI人格分类能力

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217442

Seif Elmoushy, M. Saeed, W. H. Gomaa

引用次数: 0

End of Session (3) 会议结束(3)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217544

引用次数: 0

Empowering Topic Classification Accuracy by Integrating Transformer-Based with Machine Learning and Deep Learning Techniques 通过整合基于变压器的机器学习和深度学习技术来增强主题分类的准确性

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217434

Rana Reda Waly, Mosatafa Mohamed Saeed, Mena Hany, Abdelrahman Ezzeldin Nagib, W. H. Gomaa

{"title":"Empowering Topic Classification Accuracy by Integrating Transformer-Based with Machine Learning and Deep Learning Techniques","authors":"Rana Reda Waly, Mosatafa Mohamed Saeed, Mena Hany, Abdelrahman Ezzeldin Nagib, W. H. Gomaa","doi":"10.1109/IMSA58542.2023.10217434","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217434","url":null,"abstract":"Identifying the topic of a piece of text is a crucial responsibility in natural language processing and the analysis of written language. This study investigates the performance of two approaches - machine learning and deep learning - for topic classification. In the machine learning approach, we tested ten different classifiers after applying various pre-processing techniques to our dataset and used T5 X-Large as the pre-trained embedding model. The best result was achieved using the Support Vector Machine (SVM) classifier with minimal pre-processing, i.e., only removing special characters. This method obtained an accuracy of 89%. While in the deep learning approach, we experimented with different pre-trained models, pre-processing techniques, and input layers for our neural network architecture. The optimal configuration was found using a Recurrent Neural Network (RNN) as the input layer, applying lowercase as the only pre-processing technique, and employing T5 X-Large as the embedding model. This configuration achieved an accuracy score of 89.91%. The results demonstrate that both approaches have their merits, but the deep learning approach offers a slight improvement in classification performance. This research provides valuable insights for researchers and practitioners working on topic classification tasks in natural language processing and text analysis.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116228218","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

Decision Level Fusion of Transfer Learning based Models for Diagnosing Lung and Colon Cancer 基于迁移学习的肺癌和结肠癌诊断模型的决策级融合

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217757

Shireen Al-Ofary, Hamza Osman Ilhan

{"title":"Decision Level Fusion of Transfer Learning based Models for Diagnosing Lung and Colon Cancer","authors":"Shireen Al-Ofary, Hamza Osman Ilhan","doi":"10.1109/IMSA58542.2023.10217757","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217757","url":null,"abstract":"Lung and colon cancers are one of the most known cancer types that cause death. However, in most cases, early detection of the disease has critical impact in limiting the spread of tumor cells. Therefore, the diagnosis of the disease is considered the first step for treatment to improve the chance of survival. Artificial intelligence provides great assistance for specialists in the field of diagnosing pathological tissue by saving effort and time with accurate decisions. In this study, the high potential of ensemble learning was adapted to diagnosing colon and lung cancers by establishing multiple deep networks based computer-aided diagnosis system for infected cancer cells. In the model ensemble, the individual classification results of DenseNet201, ResNet101 and EfficientNet-b0 models were combined in terms of hard voting idea. The individual classification accuracies of DenseNet201, ResNet101, and EfficientNet-b0 are resulted in 99.94%, 99.93%, and 98.64% for colon cancer and 99.05%, 98.93%, and 95.79% for Lung cancer, respectively. However, the ensemble approach increased the performances to 99.98% and 99.78% for colon and lung cancer datasets respectively.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115082691","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

Correlating Healthcare Staff’s Stress Level and Cybersecurity Practices in Norway 挪威医疗保健人员的压力水平与网络安全实践的相关性

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217783

M. Fauzi, P. Yeng, Bian Yang

{"title":"Correlating Healthcare Staff’s Stress Level and Cybersecurity Practices in Norway","authors":"M. Fauzi, P. Yeng, Bian Yang","doi":"10.1109/IMSA58542.2023.10217783","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217783","url":null,"abstract":"This paper examines the relationship between stress levels and cybersecurity practices among hospital employees in Norway. As the healthcare sector increasingly uses technology to improve patient care, hospital data became more vulnerable to cyber attacks and the human factor remains a significant vulnerability that can be exploited by hackers. Stress is one such factor that can affect cybersecurity practices. This study hypothesized that employees with higher stress levels are more likely to engage in risky security practices. An online survey was conducted to collect data on demographic details, stress levels, and cybersecurity practices of healthcare staff. Respondents’ stress levels were assessed using the Perceived Stress Scale (PSS), and hospital staff’s hazardous cybersecurity practices were evaluated using the Hospital Staff’s Risky Cybersecurity Practices Scale (HS-RCPS). Regarding cybersecurity practices, the results show that hospital workers in Norway tend to have good cybersecurity practices. Our findings also indicate that there is no significant correlation between stress levels and cybersecurity practices (r=0.101). In addition, demographic variables including gender, age, position, and position level did not have a significant impact on healthcare professionals’ dangerous cybersecurity activities. However, years of working experience were found to be a crucial factor in determining cybersecurity practices among hospital employees ( F(3, 38) = 3.146, p = 0.036) with staff who had more than 25 years of work experience getting the highest average HS-RCPS score.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907453","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

End of Session (10) 会议结束(10)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217387

引用次数: 0

Traffic Classification with Machine Learning for Enhancing Cloud Security 基于机器学习的流量分类增强云安全

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217598

M. Hossen, T. Ahmad, Muhammad Aidiel Rachman Putra

{"title":"Traffic Classification with Machine Learning for Enhancing Cloud Security","authors":"M. Hossen, T. Ahmad, Muhammad Aidiel Rachman Putra","doi":"10.1109/IMSA58542.2023.10217598","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217598","url":null,"abstract":"Cloud computing has revolutionized business operations by facilitating rapid access to numerous resources and services. With this convenience, however, comes the challenge of protecting the cloud environment from cyberattacks. Traditional security measures have proven insufficient to combat modern security threats as cloud traffic increases. The use of machine learning techniques to provide intelligent security solutions that detect and prevent cyber-attacks in real-time has shown great promise in addressing this issue. This paper investigates machine learning algorithms for improving cloud security via traffic classification. Traffic classification aims to determine the type of traffic traversing a cloud network, whether Bot or Not Bot. In order to analyze network traffic patterns, identify anomalies, and accurately classify traffic as standard or malicious, machine learning algorithms are trained. These techniques can assist cloud providers, and businesses detect and prevent cyberattacks in real-time, enhancing the cloud environment's security. In addition, the paper highlights some obstacles associated with deploying machine learning algorithms in the cloud. These obstacles include the need for vast quantities of labeled data, specialized hardware requirements, and the possibility of false positives and negatives. The paper discusses some strategies for overcoming these challenges and achieving adequate cloud security using machine learning techniques. The proposed method classifies a large amount of data using multiple ML algorithms and Blended Ensemble with an accuracy of . %.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"91 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120825237","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

3D Mesh Reconstruction from 2D Images: A NeRF based Approach 从2D图像重建3D网格:基于NeRF的方法

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217706

M. Abdelfattah, Rania Reda, Mohammed Abdel-Megeed Salem, Slim Abdennadher

引用次数: 0

Real and Fake Face Detection: A Comprehensive Evaluation of Machine Learning and Deep Learning Techniques for Improved Performance 真假人脸检测:提高性能的机器学习和深度学习技术的综合评估

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217736

Noha A. Saad Eldien, Raghda Essam Ali, Farid Ali Moussa

{"title":"Real and Fake Face Detection: A Comprehensive Evaluation of Machine Learning and Deep Learning Techniques for Improved Performance","authors":"Noha A. Saad Eldien, Raghda Essam Ali, Farid Ali Moussa","doi":"10.1109/IMSA58542.2023.10217736","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217736","url":null,"abstract":"The progress of digital manipulation methods has led to the creation of incredibly realistic fake faces, making it more challenging for humans to differentiate between genuine and fabricated ones while our brains are wired to interpret facial features, the use of sophisticated technology and artificial intelligence is blurring the line between real and manipulated images. As a consequence, techniques such as deep learning are becoming popular in distinguishing between real and fake faces with greater accuracy and reliability. In this study, a combination of machine learning and deep learning approaches was utilized to develop models for the detection of genuine and fabricated faces. The initial model implemented was an artificial neural network model, which utilized a Fourier-based technique for feature extraction. The model underwent training and testing using a benchmark dataset labeled as \"Real and Fake Faces\", and produced an accuracy rate of 0.57. ResNet18 approach involved the utilization of multiple models of convolutional neural networks that were trained on the same dataset and their results enhanced the overall precision of the classification process. The implementation of ResNet18 in deep learning has greatly enhanced the overall performance by achieving a substantially elevated accuracy level of 0.77.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122585623","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