2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献_第6页

A Study of Dimensionality Reduction’s Influence on Heart Disease Prediction 降维对心脏病预测影响的研究

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555550

Gaoshuai Wang, Fabrice Lauri, A. Hassani

{"title":"A Study of Dimensionality Reduction’s Influence on Heart Disease Prediction","authors":"Gaoshuai Wang, Fabrice Lauri, A. Hassani","doi":"10.1109/IISA52424.2021.9555550","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555550","url":null,"abstract":"Heart disease is a serious threat to human life due to its suddenness and ponderance. It’s urgent and meaningful to build a diagnosis system to detect heart disease earlier and accurately. In the field of medicine, doctors have summarized lots of experience on heart disease diagnosis. Duo to a large number of samples and attributes, the work done by the human is not efficient. And, computer-aided disease diagnosis has shown its advantages. Many researchers have applied machine learning methods to heart disease detection. For pursuing better performance, dimensionality reduction methods are often used for selecting key features or accelerating the processing speed. In this research, we investigate the influence of dimensionality reduction by using PCA and LDA methods on the machine learning methods’ prediction. PCA and LDA represent two famous dimensionality reduction, unsupervised and supervised methods. The results display that the performance of PCA is better than LDA’s evaluated by several metrics. Additionally, PCA indeed promotes many different methods’ prediction effects. There is an optimal amount of features when using PCA. It seems that the dataset with more features is easy to obtain better results. Otherwise, the dataset itself also has a significant influence on prediction result even their structures are the same. The dimensionality reduction will influence the time consumption of machine learning methods. Finally, we reveal that complex models are not always better than simple ones.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"493 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115861699","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: 4

Quantum Neural Network Parameter Estimation for Photovoltaic Fault Detection 光伏故障检测中的量子神经网络参数估计

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555558

Glen S. Uehara, Sunil Rao, Mathew Dobson, C. Tepedelenlioğlu, A. Spanias

引用次数: 13

Quantum Information Processing Algorithms with Emphasis on Machine Learning 强调机器学习的量子信息处理算法

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555570

Glen S. Uehara, A. Spanias, William Clark

引用次数: 15

Evaluating the user experience of a fuzzy-based Intelligent Tutoring System 基于模糊的智能辅导系统的用户体验评价

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555516

K. Chrysafiadi, M. Virvou

{"title":"Evaluating the user experience of a fuzzy-based Intelligent Tutoring System","authors":"K. Chrysafiadi, M. Virvou","doi":"10.1109/IISA52424.2021.9555516","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555516","url":null,"abstract":"Nowadays, the scientific interest for educational software and Intelligent Tutoring Systems increases. The main goal of an Intelligent Tutoring System is to offer a personalized learning experience, adapting the learning content and process to each individual student, and maximizing the learning outcomes. However, for ensuring the system’s quality the user experience evaluation is equally important to the learning effectiveness evaluation. Concerning the above, in this paper we evaluate thoroughly the user experience of a fuzzy-based Intelligent Tutoring System, which teaches the logic of computer programming and the programming language ‘C’. For the evaluation’s needs, we used observation and questionnaires. Furthermore, for ensuring the validity of the evaluation’s results, t-tests were conducted. The evaluation results are very positive and show that the embedded fuzzy-based mechanism realizes lesson’s sequence and learning content adaptation to the learners’ needs in such a way that they consider the system as a useful pleasant and helpful tool for learning computer programming.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124040695","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: 4

Secure Decision Making and Inference in Critical Systems 关键系统中的安全决策与推理

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555559

Stella Pantopoulou, Maria Pantopoulou, L. Tsoukalas

引用次数: 0

Optimal Team Pairing of Elder Office Employees with Machine Learning on Synthetic Data 基于合成数据的机器学习的老年办公室员工最优团队配对

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555511

Elias Dritsas, Nikos Fazakis, O. Kocsis, K. Moustakas, N. Fakotakis

引用次数: 6

Reasoning over Bayesian Networks using Semantic Artificial Neural Networks 基于语义人工神经网络的贝叶斯网络推理

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555501

Sotiris Batsakis, G. Antoniou

引用次数: 1

Comparison of Image segmentation, HOG and CNN Techniques for the Animal Detection using Thermography Images in Automobile Applications 图像分割、HOG和CNN技术在汽车热成像动物检测中的应用比较

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555562

Yuvaraj Munian, Antonio Martinez-Molina, M. Alamaniotis

{"title":"Comparison of Image segmentation, HOG and CNN Techniques for the Animal Detection using Thermography Images in Automobile Applications","authors":"Yuvaraj Munian, Antonio Martinez-Molina, M. Alamaniotis","doi":"10.1109/IISA52424.2021.9555562","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555562","url":null,"abstract":"Animal Vehicle Collision is an inviolability concern that comes with the cost of both humankind and animals. It has popularly resulted in millions of deer-vehicle collisions claims and fatalities. The only way to prevent the above-saddened statics is to drive wildlife safely away from roadways due to morbidity and injuries. This paper undrapes the optimal comparative study between edge-based image segmentation and CNN-HOG for self-acting animal detection. As the fatal crashes peaks during night-time, night vision image detection is focused on this paper with the mounted camera in the vehicle. Edge-based image segmentation is applied to the intelligent animal detection system to demonstrate the prowess of animal detection. The intelligent system processes thermographic images and feature extractions used for the object existence prediction. Deer is the overly populated animal and most commonly spotted animal used as the subject of detection in this research. The animal detection is done using the Histogram of Oriented Gradient (HOG) transform, whereas optimization is demonstrated using image segmentation. Image segmentation helps in precise animal detection by extending the continuity of the images, which is crucial for image processing during detection. The results vividly conclude the contribution of image segmentation accuracy to the existing HOG-based intelligent system with 91% accuracy using the wide roadsides of San Antonio, TX, in the USA.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122183987","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

Handling Uncertainty in Predictive Business Process Monitoring with Bayesian Networks 用贝叶斯网络处理预测业务流程监控中的不确定性

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555507

Ioannis Prasidis, Nikolaos-Paraskevas Theodoropoulos, Alexandros Bousdekis, Georgia Theodoropoulou, G. Miaoulis

引用次数: 4

Prototype Selection and Generation with Minority Classes Preservation 保留少数类的原型选择与生成

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555514

Konstantinos Xouveroudis, Stefanos Ougiaroglou, Georgios Evangelidis, D. Dervos

{"title":"Prototype Selection and Generation with Minority Classes Preservation","authors":"Konstantinos Xouveroudis, Stefanos Ougiaroglou, Georgios Evangelidis, D. Dervos","doi":"10.1109/IISA52424.2021.9555514","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555514","url":null,"abstract":"Instance-based classifiers become inefficient when the size of their training dataset or model is large. Therefore, they are usually applied in conjunction with a Data Reduction Technique that collects prototypes from the available training data. The set of prototypes is called the condensing set and has the benefit of low computational cost during classification, while, at the same time, accuracy is not negatively affected. In case of imbalanced training data, the number of prototypes collected for the minority (rare) classes may be insufficient. Even worse, the rare classes may be eliminated. This paper presents three methods that preserve the rare classes when data reduction is applied. Two of the methods apply data reduction only on the instances that belong to common classes and avoid costly under-sampling or over-sampling procedures that deal with class imbalances. The third method utilizes SMOTE over-sampling before data reduction. The three methods were tested by conducting experiments on twelve imbalanced datasets. Experimental results reveal high recall and very good reduction rates.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115723835","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