Applied Computational Intelligence and Soft Computing最新文献_第4页

An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection 基于深度学习的短信和垃圾邮件检测智能框架

Applied Computational Intelligence and Soft Computing Pub Date : 2023-09-20 DOI: 10.1155/2023/6648970

Umair Maqsood, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Tahani Alsaedi, Mahwish Kundi

{"title":"An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection","authors":"Umair Maqsood, Saif Ur Rehman, Tariq Ali, Khalid Mahmood, Tahani Alsaedi, Mahwish Kundi","doi":"10.1155/2023/6648970","DOIUrl":"https://doi.org/10.1155/2023/6648970","url":null,"abstract":"The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend to be from a trusted company to provide “financial or personal information” are phishing e-mails. These e-mails contain some links; users might download malicious software on their computers when they click on them. Most techniques and models are developed to automatically detect these “SMS and e-mails” but none of them achieved 100% accuracy. In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. After conducting experiments on the real dataset, the researchers concluded that the proposed system performed better and more accurately than previously existing models. Specifically, the support vector machine (SVM) classifier outperformed all others. These results suggest that SVM is the optimal choice for classification purposes.","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136264272","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: 1

ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal 基于SpO2信号预测睡眠呼吸暂停的深度卷积神经网络模型

Applied Computational Intelligence and Soft Computing Pub Date : 2023-09-19 DOI: 10.1155/2023/8888004

Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit

{"title":"ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal","authors":"Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit","doi":"10.1155/2023/8888004","DOIUrl":"https://doi.org/10.1155/2023/8888004","url":null,"abstract":"Sleep apnea is one of the most common sleep disorders in the world. It is a common problem for patients to suffer from sleep disturbances. In this paper, we propose a deep convolutional neural network (CNN) model based on the oxygen saturation (SpO2) signal from a smart sensor. This is the reason why we called ZleepNet a network for sleep apnea detection. The proposed model includes three convolutional layers, which include ReLu activation function, 2 dense layers, and one dropout layer for predicting sleep apnea. In this proposed model, the use of signals for detecting the sleep apnea can be reduced from 25 sensors to 1 sensor. We conducted experiments to evaluate the performance of the proposed CNN using real patient data and compared them with traditional machine learning methods such as least discriminant analysis (LDA) and support vector machine (SVM), baggy representation tree, and artificial neural network (ANN) on publicly available sleep datasets using the same parameter setting. The results show that the proposed model outperformed the other methods with the accuracy of 91.30% with the split rate of 0.2% in which the training data are 20% and testing data are 80%. The accuracy of the proposed CNN is 90.33% when compared with the LDA which achieved 86.5% accuracy with the split rate of 0.5% in which training data are 50% and testing data are 50%. It achieved 91.56% accuracy when compared with the support vector machine (SVM) in which training data are 70% and testing data are 30%. The achieved accuracy of the proposed CNN is 91.89% when compared with bagging representation tree in which training data are 90% and testing data are 10%. The accuracy of the proposed CNN is 91.30% in which training data are 83% and testing data are 17% when compared with artificial neural networks (ANN).","PeriodicalId":44894,"journal":{"name":"Applied Computational Intelligence and Soft Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059306","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

An Accurate and Fast Method for Improving ADC Nonlinearity 一种精确、快速改善ADC非线性的方法

Applied Computational Intelligence and Soft Computing Pub Date : 2023-09-15 DOI: 10.1155/2023/8899666

Mohammed Abdulmahdi Mohammedali, Qais Al-Gayem

引用次数: 0

An Effective Hybrid Algorithm Based on Particle Swarm Optimization with Migration Method for Solving the Multiskill Resource-Constrained Project Scheduling Problem 基于粒子群优化和迁移法的混合算法求解多技能资源约束项目调度问题

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2022-02-15 DOI: 10.1155/2022/6230145

Huu Dang Quoc, Loc Nguyen The, Cuong Nguyen Doan

引用次数: 5

Boolean Algebra of Soft Q-Sets in Soft Topological Spaces 软拓扑空间中软q集的布尔代数

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2022-01-01 DOI: 10.1155/2022/5200590

S. Ghour

引用次数: 0

Research on Simulink/Fluent Collaborative Simulation Zooming of Marine Gas Turbine 船用燃气轮机Simulink/Fluent协同仿真变焦研究

IF 2.9

Applied Computational Intelligence and Soft Computing Pub Date : 2017-03-02 DOI: 10.1155/2017/8324810

Wang Zhitao, Li Jian, Li Tielei, Liu Shuying

引用次数: 3