2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)最新文献_第3页

AI-Based Cybersecurity Policies and Procedures 基于人工智能的网络安全政策和程序

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433845

Shadi Jawhar, Jeremy Miller, Zeina Bitar

引用次数: 0

AI-Driven Customized Cyber Security Training and Awareness 人工智能驱动的定制网络安全培训和认识

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433829

Shadi Jawhar, Jeremy Miller, Zeina Bitar

引用次数: 0

AI-Based Cybersecurity Policies and Procedures 基于人工智能的网络安全政策和程序

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433845

Shadi Jawhar, Jeremy Miller, Zeina Bitar

引用次数: 0

Robotics in Healthcare: The African Perspective 医疗保健领域的机器人技术：非洲视角

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433838

C. Mutongi, Billy Rigava

{"title":"Robotics in Healthcare: The African Perspective","authors":"C. Mutongi, Billy Rigava","doi":"10.1109/ICAIC60265.2024.10433838","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433838","url":null,"abstract":"Today one has to run very fast to stay on the same position. We are no longer competing with humans only, we are now also competing with robots as they are involved in learning, leading to Machine Learning (ML). Robots are increasingly being adopted in healthcare to carry out various tasks that enhance patient care. Robots in health care have revolutionized the health ecosystem. There are different types of healthcare robots which include nursing robots, surgical robots, clinical Training, Prescription Dispensing, care robots, Telepresence, Rehabilitation Robots, Health Call Centre Robots, Ambulance Robots and Physical Therapy Robots. Healthcare robots are mostly found in the developed countries. This paper seeks to establish robotics in healthcare considering the African perspectives and Zimbabwe in particular. A qualitative study was conducted whereby twenty students at a university were interviewed concerning their views on healthcare robots in the African context. It was found out that healthcare robots are still at their conception in Africa and Zimbabwe in particular, there is fear of the unknown, some indicated that robots will affect their indigenous way of life as they are used to interact with each other as human beings and not as robot to human as shown by the concept of Ubuntu, power challenges, connectivity, lack of awareness challenges, as well as cultural and religious challenges. However, some participants indicated that they greatly welcome the robots as they may cease the health professional shortages in Africa and also they consider them to be more precise and accurate as compared to humans. Some indicated that more privacy will be promoted due to the use of robots. It was recommended that there is need for immense healthcare robots conscientisation, awareness, training, robots to mimic the African way of living and language.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"1 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895510","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

Sentiment Analysis of Financial News Data using TF-IDF and Machine Learning Algorithms 使用 TF-IDF 和机器学习算法对金融新闻数据进行情感分析

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433843

Gideon Popoola, Khadijat-Kuburat Abdullah, Gerard Shu Fuhnwi, Janet O. Agbaje

{"title":"Sentiment Analysis of Financial News Data using TF-IDF and Machine Learning Algorithms","authors":"Gideon Popoola, Khadijat-Kuburat Abdullah, Gerard Shu Fuhnwi, Janet O. Agbaje","doi":"10.1109/ICAIC60265.2024.10433843","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433843","url":null,"abstract":"Blogs, online forums, comment sections, and social networking sites like Facebook, Twitter (now known as X), and Instagram can all be called social media. The growing use of social media has made some unstructured data available, which can benefit us if we clean, structure, and analyze the data. Twitter is a popular microblogging social media platform where people share and express their opinions about any topic. The act of analyzing these opinions of people is called sentimental analysis. Sentimental analysis can be helpful to individuals, businesses, government agencies, etc. In this study, tweets related to financial news were extracted, labeled, and analyzed to capture the opinions of people around the world. This paper proposes a novel machine learning-based approach to analyze social media data for sentiment analysis. The presented approach is divided into three steps. The first stage is preprocessing, where the tweets are refined and filtered. In the second stage, feature extraction was performed using Term Frequency and Inverse Document Frequency (TF-IDF). The third stage involves using the extracted features to make predictions using machine learning algorithms. Three machine learning models were used, namely, random forest classifier (RF), Naïve Bayes (NB), and k-nearest neighbor (KNN). The evaluation results show that both NB and RF perform better than KNN in accuracy, precision, Recall, and F1-score metrics. These results also show an overwhelmingly positive opinion regarding financial news.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"283 8","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896070","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

Leveraging Advanced Visual Recognition Classifier For Pneumonia Prediction 利用先进的视觉识别分类器进行肺炎预测

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433800

M. Raval, Jin Aobo, Yun Wan, Hardik A. Gohel

{"title":"Leveraging Advanced Visual Recognition Classifier For Pneumonia Prediction","authors":"M. Raval, Jin Aobo, Yun Wan, Hardik A. Gohel","doi":"10.1109/ICAIC60265.2024.10433800","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433800","url":null,"abstract":"Pneumonia prediction using chest X-ray images is a challenging task because of the complex image processing involved. The radiographic features of pneumonia, especially in the earlier stages, easily overlap with other lung conditions, which makes the differentiation even more challenging. Moreover, X-ray image quality varies due to equipment, patient condition, and techniques, particularly in rural areas with undertrained radiologists and medical experts. The use of Artificial Intelligence (AI) models in detecting pneumonia is a novel but crucial research field and rapid advancement in medical imaging technology and neural network models along with the availability of large de-identified public datasets has paved the way for this life-saving biomedical research. In this paper, we propose a unique comprehensive solution for predicting pneumonia using chest X-ray images. We utilize an enhanced VGGNet model tailored for the binary classification task. The modified VGG19 with a binary classifier provides a solid foundation for feature extraction and leverages the pretrained features and deep architecture to differentiate between normal and pneumonia-affected lung images. The use of transfer learning allows us to extend the pre-trained model on a diverse and large-scale dataset by further training it on limited-size medical imaging data for the crucial task of biomedical classification without the need for large, labeled training data or computational resources. The robust model displays high accuracy of 92% with a high recall of 96.4% and AUC of 97%. With high adaptability and efficient learning capacity from limited data. This implementation may serve as a powerful tool assisting medical professionals in diagnosing pneumonia by quickly analyzing X-ray images with the same consistency and accuracy. During crises such as pandemics where lung diseases might surge, such tools can aid in rapid screening and monitoring of public health.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"30 11‐12","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893349","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

Federated Learning Based Smart Horticulture and Smart Storage of Fruits Using E-Nose, and Blockchain: A Proposed Model 使用电子鼻和区块链的基于联合学习的智能园艺和水果智能存储：一个拟议模型

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433834

Shakhmaran Seilov, Bishwajeet Pandey, Akniyet Nurzhaubayev, Dias Abildinov, Assem Konyrkhanova, Bibinur Zhursinbek

引用次数: 0

Leveraging Advanced Visual Recognition Classifier For Pneumonia Prediction 利用先进的视觉识别分类器进行肺炎预测

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433800

M. Raval, Jin Aobo, Yun Wan, Hardik A. Gohel

{"title":"Leveraging Advanced Visual Recognition Classifier For Pneumonia Prediction","authors":"M. Raval, Jin Aobo, Yun Wan, Hardik A. Gohel","doi":"10.1109/ICAIC60265.2024.10433800","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433800","url":null,"abstract":"Pneumonia prediction using chest X-ray images is a challenging task because of the complex image processing involved. The radiographic features of pneumonia, especially in the earlier stages, easily overlap with other lung conditions, which makes the differentiation even more challenging. Moreover, X-ray image quality varies due to equipment, patient condition, and techniques, particularly in rural areas with undertrained radiologists and medical experts. The use of Artificial Intelligence (AI) models in detecting pneumonia is a novel but crucial research field and rapid advancement in medical imaging technology and neural network models along with the availability of large de-identified public datasets has paved the way for this life-saving biomedical research. In this paper, we propose a unique comprehensive solution for predicting pneumonia using chest X-ray images. We utilize an enhanced VGGNet model tailored for the binary classification task. The modified VGG19 with a binary classifier provides a solid foundation for feature extraction and leverages the pretrained features and deep architecture to differentiate between normal and pneumonia-affected lung images. The use of transfer learning allows us to extend the pre-trained model on a diverse and large-scale dataset by further training it on limited-size medical imaging data for the crucial task of biomedical classification without the need for large, labeled training data or computational resources. The robust model displays high accuracy of 92% with a high recall of 96.4% and AUC of 97%. With high adaptability and efficient learning capacity from limited data. This implementation may serve as a powerful tool assisting medical professionals in diagnosing pneumonia by quickly analyzing X-ray images with the same consistency and accuracy. During crises such as pandemics where lung diseases might surge, such tools can aid in rapid screening and monitoring of public health.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"10 11","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896051","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

Secure federated learning applied to medical imaging with fully homomorphic encryption 利用全同态加密技术将安全联合学习应用于医学成像

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433836

Xavier Lessage, Leandro Collier, Charles-Henry Bertrand Van Ouytsel, Axel Legay, Saïd Mahmoudi, Philippe Massonet

{"title":"Secure federated learning applied to medical imaging with fully homomorphic encryption","authors":"Xavier Lessage, Leandro Collier, Charles-Henry Bertrand Van Ouytsel, Axel Legay, Saïd Mahmoudi, Philippe Massonet","doi":"10.1109/ICAIC60265.2024.10433836","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433836","url":null,"abstract":"This study explores the convergence of Federated Learning (FL) and Fully Homomorphic Encryption (FHE) through an innovative approach applied to a confidential dataset composed of mammograms from Belgian medical records. Our goal is to clarify the feasibility and challenges associated with integrating FHE into the context of Federated Learning, with a particular focus on evaluating the memory constraints inherent in FHE when using sensitive medical data. The results highlight notable limitations in terms of memory usage, underscoring the need for ongoing research to optimize FHE in real-world applications. Despite these challenges, our research demonstrates that FHE maintains comparable performance in terms of Receiver Operating Characteristic (ROC) curves, affirming the robustness of our approach in secure machine learning applications, especially in sectors where data confidentiality, such as medical data management, is imperative. The conclusions not only shed light on the technical limitations of FHE but also emphasize its potential for practical applications. By combining Federated Learning with FHE, our model preserves data confidentiality while ensuring the security of exchanges between participants and the central server","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"259 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896084","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 Reinforcement Learning-based Malicious URL Detection with Feature Selection 基于深度强化学习的恶意 URL 检测与特征选择

2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) Pub Date : 2024-02-07 DOI: 10.1109/ICAIC60265.2024.10433827

Antonio Maci, Nicola Tamma, Anthony J. Coscia

{"title":"Deep Reinforcement Learning-based Malicious URL Detection with Feature Selection","authors":"Antonio Maci, Nicola Tamma, Anthony J. Coscia","doi":"10.1109/ICAIC60265.2024.10433827","DOIUrl":"https://doi.org/10.1109/ICAIC60265.2024.10433827","url":null,"abstract":"Data theft through web applications that emulate legitimate platforms constitutes a major network security issue. Countermeasures using artificial intelligence (AI)-based systems are often applied because they can effectively detect malicious websites, which are extremely outnumbered by legitimate ones. In this domain, deep reinforcement learning (DRL) emerges as an attractive field for the development of network intrusion detection models, even in the case of highly skewed class distributions. However, DRL requires training time that increases with data complexity. This paper combines a DRL-based classifier with state-of-the-art feature selection techniques to speed up training while retaining or even improving classification performance. Our experiments used the Mendeley dataset and five different statistical and correlation-based feature-ranking strategies. The results indicated that the selection technique based on the calculation of the Gini index reduces the number of columns in the dataset by 27%, saving more than 10% of training time and significantly improving classification scores compared with the case without selection strategies.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"6 3","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895703","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