2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)最新文献

筛选

英文中文

Potential Application of Brainwaves to Optimise Evacuation Wayfinding Performance During Fires 脑电波在火灾中优化疏散寻路性能的潜在应用

2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) Pub Date : 2023-03-03 DOI: 10.1109/CSPA57446.2023.10087615

Fathimah Zakiyah Othman, M. Taib, Mariam Felani Shaari, Yazid Zaiki

{"title":"Potential Application of Brainwaves to Optimise Evacuation Wayfinding Performance During Fires","authors":"Fathimah Zakiyah Othman, M. Taib, Mariam Felani Shaari, Yazid Zaiki","doi":"10.1109/CSPA57446.2023.10087615","DOIUrl":"https://doi.org/10.1109/CSPA57446.2023.10087615","url":null,"abstract":"optimising human evacuation wayfinding performance is crucial for effective and safe egress during building fires. Recent studies show various cognitive processes significantly influence indoor wayfinding performance. However, neuro-architecture studies that characterize cognitive brain activity during fire evacuation remain limited. Electroencephalogram (EEG) brainwaves have been adopted for accurate measurement and representation of human cognitive processes. The application of EEG brainwaves that reflect cognitive brain activity during fire evacuation could be useful to optimise evacuation wayfinding performance during fires. This conceptual paper proposes the potential application of EEG brainwaves to optimise evacuation wayfinding performance by highlighting the brainwave patterns that occur during cognitive activity under various indoor scenarios. Significant brainwave features and patterns are highlighted as potential extraction targets (through signal processing) to accurately characterize cognitive brain activity and evacuation wayfinding performance during fires. Findings are envisioned as significant foundations for future neuro-architecture studies to improve building fire safety designs by optimising occupant evacuation wayfinding performance.","PeriodicalId":268233,"journal":{"name":"2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124669168","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

A Review: Heart Disease Prediction in Machine Learning & Deep Learning 综述:机器学习和深度学习中的心脏病预测

2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) Pub Date : 2023-03-03 DOI: 10.1109/CSPA57446.2023.10087837

Wan Aezwani Wan Abu Bakar, N. L. N. Josdi, M. Man, M. Zuhairi

{"title":"A Review: Heart Disease Prediction in Machine Learning & Deep Learning","authors":"Wan Aezwani Wan Abu Bakar, N. L. N. Josdi, M. Man, M. Zuhairi","doi":"10.1109/CSPA57446.2023.10087837","DOIUrl":"https://doi.org/10.1109/CSPA57446.2023.10087837","url":null,"abstract":"The whole world has come to know the fact that heart disease is not a trivial issue. Although the years have changed, throngs of patients are diagnosed with this lethal disease and not only is it not decreasing, but increasing and this is evident from the analysis of death rates across the country. Heart disease is caused by several main factors such as negligence in taking care of diet and daily life. Causes such as age and genetics cannot be fully controlled by humans. So it is the responsibility of each individual to take care of critical risk factors that can be controlled by humans. Innovation and data mining technology should also help to reduce the death rate caused by heart disease. Existing data should be used to find hidden relationships, which are very useful for early diagnosis, which can be a new advanced technology and potentially improve the level of the national health system. This paper underlines previous studies that leverage machine learning and deep learning expertise to predict heart disease. Each algorithm produces variations in accuracy and this study will examine the variables that enable these results. At the end of the study, the results achieved by deep learning proved to be ahead of machine learning by showing consistency in a satisfactory range between 84% to 99%.","PeriodicalId":268233,"journal":{"name":"2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129593896","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

Investigation of Different Classifiers for Stress Level Classification using PCA-Based Machine Learning Method 基于pca的机器学习方法在应力水平分类中的不同分类器研究

2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) Pub Date : 2023-03-03 DOI: 10.1109/CSPA57446.2023.10087367

Muhammad Rasydan Bin Mazlan, Abdull Sukor, A. H. Adom, R. Jamaluddin, S. Awang

{"title":"Investigation of Different Classifiers for Stress Level Classification using PCA-Based Machine Learning Method","authors":"Muhammad Rasydan Bin Mazlan, Abdull Sukor, A. H. Adom, R. Jamaluddin, S. Awang","doi":"10.1109/CSPA57446.2023.10087367","DOIUrl":"https://doi.org/10.1109/CSPA57446.2023.10087367","url":null,"abstract":"Undergraduate students experience several changes and face various problems during their time transitioning from adolescence to adulthood. One of the issues during this time is a mental stress disorder. Stress burdens the students either through mental or physical capabilities. The common method of determining stress includes physical examination and clinical diagnosis. However, the method is subjective and time-consuming as doctors need to make sure that their diagnosis is accurate. Thus, the severity of the stress stages could not be easily determined. A new method using machine learning-based algorithms coupled with EEG devices promises to overcome the issues with the current approaches. This paper presents an investigation using machine learning techniques based on Principal Component Analysis (PCA) which allows for the reduction in the dimensionality of datasets to enhance their interpretability while minimizing information loss. The pre-processed EEG data and PCA-based EEG data were compared and analyzed using three machine learning classifiers such as K-Nearest Network (KNN), Naive Bayes (NB) and Multilayer Perceptron (MLP). The results indicate that KNN demonstrated the highest average classification accuracy of 99%, while the other approaches mentioned above averaged around 50% and 80% for NB and MLP respectively. This investigation shows that the KNN classifier is most suitable for the proposed approach.","PeriodicalId":268233,"journal":{"name":"2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758171","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

首页上一页