2022 International Conference on Applied Artificial Intelligence (ICAPAI)最新文献

Real-Time Environmental Forecasting For Autonomous Aircraft 自主飞行器的实时环境预测

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801570

G. Carmeli, B. B. Moshe, B. Ferrier

{"title":"Real-Time Environmental Forecasting For Autonomous Aircraft","authors":"G. Carmeli, B. B. Moshe, B. Ferrier","doi":"10.1109/ICAPAI55158.2022.9801570","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801570","url":null,"abstract":"The research intends to examine the feasibility of predicting a ship’s environmental conditions in real time in order to maximize the efficiency and safety of landing autonomous aircraft on its deck. The ship state is represented by 2 main axes: Roll and Pitch. The study will deal with predicting these 2 axes a few seconds ahead, which will allow landing on the ship more safely. According to conversations with pilots, and after looking at accidents that occurred while landing helicopters on ships, there seems to be a real need to increase safety conditions when making manned or autonomous landings. The research will include the development of an artificial intelligence platform that will enable forecasting the pitch and roll conditions on deck. The forecast data of the ship’s position will be one of the main factors to be transmitted in real time to the aircraft; knowledge of the ship’s immediate and future position will facilitate and ensure a soft landing of the aircraft on its deck. The ability to predict the ship’s future conditions will equip the ship and the drone with a technological advantage, as the platform will enable the aircraft to plan its landing and perform it more safely.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"185 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124926081","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

The application of artificial intelligence on different types of literature reviews - A comparative study 人工智能在不同类型文献综述中的应用——比较研究

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801564

Henry Müller, Simran Pachnanda, F. Pahl, C. Rosenqvist

{"title":"The application of artificial intelligence on different types of literature reviews - A comparative study","authors":"Henry Müller, Simran Pachnanda, F. Pahl, C. Rosenqvist","doi":"10.1109/ICAPAI55158.2022.9801564","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801564","url":null,"abstract":"The growing number of published academic literature poses challenges to the research community which struggles to keep up with the vast amount of publications through traditional research methods that are highly manual in nature. Researchers are struggling to determine the most relevant research gaps, yielding insignificant publications that constitute a waste of resources. As a consequence, AI applications are being applied increasingly to automate and facilitate the review process of these vast amounts of papers. However, scholars have so far only addressed a limited number of scientific fields and focused their efforts on one end of the spectrum in automating systematic literature reviews (SLRs). Yet, these are not sufficient to cover the full range of research questions and available data sources. This paper offers a comparative study of systematic and semi-systematic literature reviews to determine the potential of AI applications in both types of literature review processes. The analysis addresses the status quo and discusses apparent limitations of AI to automate reviews. Results are synthesized in proposing a new tool integrating various AI applications along the research process that improve the speed, quality, and cost-efficiency of the overall research process.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522218","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

Cluster Analysis on Sustainable Transportation: The Case of New York City Open Data 可持续交通的聚类分析:以纽约市开放数据为例

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801569

Gerjane Joy Cabunagan-Cinco, Enayat Rajabi, Sławomir Nowaczyk

引用次数: 0

Cyber Intrusion Detection System based on Machine Learning Classification Approaches 基于机器学习分类方法的网络入侵检测系统

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801566

R. Ogundokun, Sanjay Misra, A. N. Babatunde, S. Chockalingam

{"title":"Cyber Intrusion Detection System based on Machine Learning Classification Approaches","authors":"R. Ogundokun, Sanjay Misra, A. N. Babatunde, S. Chockalingam","doi":"10.1109/ICAPAI55158.2022.9801566","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801566","url":null,"abstract":"As the internet has advanced over the years, so has the number of cyber-attacks. A sophisticated Intrusion Detection System (IDS) is essential to protect the cyberspace. The goal of IDS is to monitor and evaluate the operations that occur in a network for any signals of probable abnormalities. Although little research has been done in this area, more comprehensive research has yet to be completed. By examining the combinations of most prominent feature extraction (FE) techniques and classifiers, this research offers an IDS for networks based on machine learning (ML) that has a good union of FE techniques and classifiers. This paper introduced a feature extraction (FE) approach for classification issues, using independent component analysis (ICA). We can generate new features independent of each other by utilizing ICA to solve supervised classification issues, and we can also accurately express the output information. A set of significant features is selected from the original collection of features using FE algorithms. The set of significant features is then used to train various types of classifiers to produce the IDS. The proposed methods were evaluated in terms of five different performance measures using the DARPA KDD 99. Finally, it is discovered that the proposed ICA+RF classifier outperforms the others with an accuracy of 99.6%, f-score of 92.6%, and false alarm rate (FAR) value of 0.0029. The result was further compared with state-of-the-art, and it was deduced that our system performed better with higher accuracy and lower FAR.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128613408","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

Applying Transfer Learning to Traffic Surveillance Videos for Accident Detection 将迁移学习应用于交通监控视频的事故检测

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801568

Ajeet Ram Pathak, A. Elster

{"title":"Applying Transfer Learning to Traffic Surveillance Videos for Accident Detection","authors":"Ajeet Ram Pathak, A. Elster","doi":"10.1109/ICAPAI55158.2022.9801568","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801568","url":null,"abstract":"Automated traffic video surveillance is a crucial research domain in computer vision due to the need to enable highway safety. It is very important to detect road accidents from traffic surveillance videos in an automated manner to take necessary actions and save the lives of people and properties. Motivated by the same, this paper proposes a method to detect road accidents from traffic surveillance videos in an automated manner. Specifically, we use an object-centric accident detection model using the YOLOv2 architecture based on the transfer learning technique. The YOLOv2 model is a homogeneous convolutional architecture that makes it faster to predict bounding boxes. This work includes a brief description of the YOLOv2 architecture and how we fine-tune a 32-layer variant pre-trained on the VOC dataset to our custom accident dataset. Our experiments using a real-world anomaly detection dataset show significant results in terms of mean average precision. Moreover, our model works in real-time, achieving 60 FPS on an NVIDIA Tesla K80 GPU and ~16.67 FPS on a standard laptop with a 4GB GT GPU. Our implementation can thus provide a near real-time accident localization with 76% mAP on the road accident dataset.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134259328","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: 2

Customer Churn Prediction in the Iranian Banking Sector 伊朗银行业的客户流失预测

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801574

S. J. Haddadi, Mohammad Ostad Mohammadi, Mojtaba Bahrami, Elham Khoeini, M. Beygi, Mehrdad Haddad Khoshkar

{"title":"Customer Churn Prediction in the Iranian Banking Sector","authors":"S. J. Haddadi, Mohammad Ostad Mohammadi, Mojtaba Bahrami, Elham Khoeini, M. Beygi, Mehrdad Haddad Khoshkar","doi":"10.1109/ICAPAI55158.2022.9801574","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801574","url":null,"abstract":"in the financial system such as the banking sector, customers are valuable, and losing them is very expensive as customer churn is a major challenge facing banks. In this paper, we present a time series Deep Neural Networks (DNNs)-based approach for customer retention, in which a dataset has been collected from retail banking customers in the Republic Islamic of Iran. The dataset consists of real daily transactional data of about 50,000 customers in Pasargad bank in the months of November and December 2021. The goal of this study is to perform a highly churned customer predictor, attempting to observe the customer information in 30 days and predict the customer behavior in the next 30 days. Also, unlike other research in this field where the labels of customers are already determined, we present a new definition of the churned banking customer to label the data. Then, the data is cleaned, preprocessed, and prepared to import to a Bi-LSTM neural network. The proposed model has shown a significant superiority over Traditional Machine Learning Techniques. This paper can guide researchers in the field of banking and artificial intelligence, providing business knowledge to managers in the banking sector to reduce the risk of losing their customers.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121926800","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: 6

Feasibility of Machine Learning Support for Holistic Review of Undergraduate Applications 机器学习支持本科申请整体审查的可行性

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-05-05 DOI: 10.1109/ICAPAI55158.2022.9801571

Barbara Martinez Neda, S. Gago-Masague

{"title":"Feasibility of Machine Learning Support for Holistic Review of Undergraduate Applications","authors":"Barbara Martinez Neda, S. Gago-Masague","doi":"10.1109/ICAPAI55158.2022.9801571","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801571","url":null,"abstract":"College admissions processes have traditionally relied on academic characteristics like GPA and standardized testing, as well as supplementary application materials. In California, the introduction of Proposition 209 in 1996 prohibited the consideration of gender and ethnicity. In an attempt to increase diversity, many universities adopted holistic review to fairly evaluate and consider applicants’ abilities inside and outside the classroom. However, this increases subjective assessment which could have implications for human bias. As such, Machine Learning (ML) should be explored as a means of assistance while also reducing potential bias.Minimal data regarding ML in undergraduate holistic review has been evaluated. In this paper, we discuss performances of supervised classifiers that could provide verification of the scores that application reviewers assign. We utilize a dataset of applicants to the Computer Science department at the University of California, Irvine to train our models. Collected data includes demographics, academic history, high school information, and essay responses. The best-performing classifiers trained on this data were Logistic Regression and Gradient Boost. Both achieved 0.871 AUROC scores, and Logistic Regression obtained the highest accuracy of 0.783. With feature coefficient analysis, we observed the effects of academic achievement, extracurricular involvement and writing complexity on the model’s predictions.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"109 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120872593","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

Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning 利用深度学习分析南美野火的多光谱卫星图像

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2022-01-19 DOI: 10.1109/ICAPAI55158.2022.9801567

Christopher Sun

{"title":"Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning","authors":"Christopher Sun","doi":"10.1109/ICAPAI55158.2022.9801567","DOIUrl":"https://doi.org/10.1109/ICAPAI55158.2022.9801567","url":null,"abstract":"Since frequent severe droughts are lengthening the dry season in the Amazon Rainforest, it is important to detect wildfires promptly and forecast possible spread for effective suppression response. Current wildfire detection models are not versatile enough for the low-technology conditions of South American hot spots. This deep learning study first trains a Fully Convolutional Neural Network on Landsat 8 images of Ecuador and the Galapagos, using Green and Short-wave Infrared bands to predict pixel-level binary fire masks. This model achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus band using K-Means Clustering to simplify continuous pixel values into three discrete classes representing differing degrees of cirrus cloud contamination. Three additional Convolutional Neural Networks are trained to conduct a sensitivity analysis measuring the effect of simplified features on model accuracy and train time. The Experimental model trained on the segmented cirrus images provides a statistically significant decrease in train time compared to the Control model trained on raw cirrus images, without compromising binary accuracy. This proof of concept reveals that feature engineering can improve the performance of wildfire detection models by lowering computational expense.","PeriodicalId":132826,"journal":{"name":"2022 International Conference on Applied Artificial Intelligence (ICAPAI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122720443","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

Additional Reviewers 额外的评论家

2022 International Conference on Applied Artificial Intelligence (ICAPAI) Pub Date : 2018-09-01 DOI: 10.1109/QSIC.2005.11

Riaz Ahmad, M. Bouillon, S. Bres, M. Cai

引用次数: 0