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2023 IEEE Conference on Artificial Intelligence (CAI)最新文献

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Transforming Rapid Diagnostic Tests into Trusted Diagnostic Tools in LMIC using AI 使用人工智能将快速诊断测试转换为LMIC中可信的诊断工具
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00136
Krishnam Gupta, Yongshao Ruan, Ahmed Ibrahim, Rouella Mendonca, Shawna Cooper, Sarah Morris, David Hattery
{"title":"Transforming Rapid Diagnostic Tests into Trusted Diagnostic Tools in LMIC using AI","authors":"Krishnam Gupta, Yongshao Ruan, Ahmed Ibrahim, Rouella Mendonca, Shawna Cooper, Sarah Morris, David Hattery","doi":"10.1109/CAI54212.2023.00136","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00136","url":null,"abstract":"In low and middle-income countries (LMICs), Rapid Diagnostic Tests (RDTs) are often the only way to diagnose diseases such as malaria, HIV, and COVID efficiently and cost effectively, especially in rural settings. However, basic RDTs are often misinterpreted, reducing their reliability for medical treatment or official case counts. AI-based mobile solutions are difficult to implement in LMICs due to limited resources available on commonly used phones and unstable Internet connectivity. HealthPulse AI algorithms aim to address these issues by providing a lightweight, yet highly accurate library of Computer Vision (CV) models for the detection and interpretation of common RDTs for conditions such as malaria, HIV, and COVID. The complete system can function end-to-end offline on phones with as little as 1 GB of total device memory. In addition to detecting the RDT type and interpreting the results, the system can flag image quality issues such as bad lighting or blurriness. If required, it can ask the user for a photo retake in real-time, reducing the need for re-testing. The system provides accurate and consistent result interpretation for surveillance or decision support use cases, helping health systems better understand current disease prevalence which may help mitigate the next pandemic. The AI algorithm pipeline uses deep learning to analyze RDT images, with multiple computer vision models working together to confirm the presence of the expected RDT, flag adverse image conditions, and provide accurate and consistent results. HealthPulse AI prioritizes privacy, accountability, and accessibility while aiming to revolutionize care delivery in LMICs by transforming low-cost RDTs into trusted diagnostic tools using computer vision and AI.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643554","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
Post-Stroke Virtual Assessment Using Deep Learning 使用深度学习的中风后虚拟评估
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00056
N. Razfar, R. Kashef, F. Mohammadi
{"title":"Post-Stroke Virtual Assessment Using Deep Learning","authors":"N. Razfar, R. Kashef, F. Mohammadi","doi":"10.1109/cai54212.2023.00056","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00056","url":null,"abstract":"Various machine learning (ML) models, including Linear SVM, SVM with RBF, and KNN, have been adopted to classify affected vs. non-affected body parts post-stroke. However, the quality and accuracy of each model depend on the data shape, class distribution, and configurations. Deep learning (DL) models such as CNN and LSTM have shown promising classification results compared to traditional machine learning. However, obtaining a robust training process is prominent. In this paper, we propose robust post-stroke assessment models adopting the methodology of ensemble and hybrid learning. Using a dataset derived from wearable sensors called Xsens sensors collected from twenty stroke survivors, we compared the performance of DL- based and the proposed models to detect the affected hand of the stroke survivors from non-affected hands. Experimental results show the ensemble hybrid DL-based model achieved the highest accuracy compared to the individual DL models.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114438716","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
Forecasting Teeth Cavities By Convolutional Neural Network 用卷积神经网络预测牙洞
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00054
Tiantian Wang, Muhammad Aqeel, P. Norouzzadeh, Salih Tutun, Eli Snir, Golnesa Rouie Miab, Laila Al Dehailan, Bahareh Rahmani
{"title":"Forecasting Teeth Cavities By Convolutional Neural Network","authors":"Tiantian Wang, Muhammad Aqeel, P. Norouzzadeh, Salih Tutun, Eli Snir, Golnesa Rouie Miab, Laila Al Dehailan, Bahareh Rahmani","doi":"10.1109/CAI54212.2023.00054","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00054","url":null,"abstract":"Oral health is crucial to overall good health, and yet because dental services are expensive, time consuming and stressful for patients, dental problems are often left unaddressed, leading to the need for far more expensive and painful interventions down the road. The main goal of this project is using AI and deep learning to develop software to detect cavities and other dental problems and providing the corresponding app to improve dental health. We applied convolutional neural network (CNN) to find the teeth caries. Canny Edge CNN model performed the best training.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117104953","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
Autoregressive Self-Evaluation: A Case Study of Music Generation Using Large Language Models 自回归自我评价:基于大型语言模型的音乐生成案例研究
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00118
Berker Banar, S. Colton
{"title":"Autoregressive Self-Evaluation: A Case Study of Music Generation Using Large Language Models","authors":"Berker Banar, S. Colton","doi":"10.1109/CAI54212.2023.00118","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00118","url":null,"abstract":"Autoregressive models have shown significant success in many tasks such as natural language generation and music composition. However, generic training mechanisms with off-the-shelf loss functions (e.g. cross-entropy), where not much attention is paid to the specifics of the task, do not necessarily guarantee success as different data modalities (e.g. text, visuals, music) exhibit different natures. In this study, we present a novel autoregressive self-evaluation framework to assess the performance of autoregressive models with both domain-agnostic and domain-specific metrics. We demonstrate this strategy with a case study of music generation using GPT-2 within a transfer learning paradigm. We contrast and compare the effects of fundamental parameters in autoregressive generation such as the temperature in sampling and the length of the generated sequence.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129417732","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
Eliminating External Factors with Variables Standardization for Monitoring Applications 用变量消除外部因素的监测应用标准化
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00107
Maxime Leiber, Y. Marnissi, S. Razakarivony, Dong Quan Vu, Mohammed El Badaoui
{"title":"Eliminating External Factors with Variables Standardization for Monitoring Applications","authors":"Maxime Leiber, Y. Marnissi, S. Razakarivony, Dong Quan Vu, Mohammed El Badaoui","doi":"10.1109/CAI54212.2023.00107","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00107","url":null,"abstract":"This paper proposes a novel preprocessing method for normalizing the measured variables of a system, with respect to external conditions. Our approach transforms the measured quantities into corrected ones that capture the internal behavior of the system while eliminating the impact of external variables on this behavior. We demonstrate the effectiveness of our approach through an experiment focused on vibration health monitoring in aeronautics. This preprocessing technique enables the use of consistent data for analysis and prediction across different operating conditions and thus enhances the accuracy and reliability of system monitoring.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129089234","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
Explainability of AI-Driven Air Combat Agent ai驱动的空战特工的可解释性
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00044
Emre Saldiran, M. Hasanzade, G. Inalhan, A. Tsourdos
{"title":"Explainability of AI-Driven Air Combat Agent","authors":"Emre Saldiran, M. Hasanzade, G. Inalhan, A. Tsourdos","doi":"10.1109/CAI54212.2023.00044","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00044","url":null,"abstract":"In safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125656409","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
Risk assessment model based on centrifugal governors and artificial neural networks 基于离心调速器和人工神经网络的风险评估模型
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00101
F. Oliveira, F. Lezama, L. Gomes, J. Soares, Z. Vale
{"title":"Risk assessment model based on centrifugal governors and artificial neural networks","authors":"F. Oliveira, F. Lezama, L. Gomes, J. Soares, Z. Vale","doi":"10.1109/cai54212.2023.00101","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00101","url":null,"abstract":"In today’s industry, old machines, that were not manufactured according to Industry 4.0 standards, may not be equipped with sophisticated sensors for monitoring critical values and ensuring the machine's proper health and operation. As a result, third-party sensors, such as thermometers and vibration sensors, are often integrated into these machines. Unfortunately, despite being able to obtain effective measurements, such sensors lack relativization of these values to the contextual values of each machine. This paper proposes a risk assessment model that digitally mimics a real-life centrifugal governor's operation. The system combines machine learning and data analysis and uses a context-aware algorithm that can work with single or multiple sensors to output aggregated information on a machine’s health.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125676343","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
Predicting Cognitive Load with Wearable Sensor Signals 用可穿戴传感器信号预测认知负荷
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00063
Olha Shaposhnyk, S. Yanushkevich
{"title":"Predicting Cognitive Load with Wearable Sensor Signals","authors":"Olha Shaposhnyk, S. Yanushkevich","doi":"10.1109/CAI54212.2023.00063","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00063","url":null,"abstract":"This research focuses on predicting the affective state, such as a cognitive load of a person performing cognitive tasks. The predictors included the physiological data, demographics, and personality type available in the CogLoad dataset. Specifically, the chosen physiological data included heart rate, intervals between successive heartbeats, galvanic-skin response, and temperature. We experimented with several machine-learning models. Among the classifiers, the LightGBM achieved the best accuracy of 74.41% and F1-score of 77.10% in detecting the cognitive load.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131932906","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
Uncertainty-Aware Reinforcement Learning for Safe Control of Autonomous Vehicles in Signalized Intersections 信号交叉口自动驾驶车辆安全控制的不确定性感知强化学习
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00042
Mehrnoosh Emamifar, S. F. Ghoreishi
{"title":"Uncertainty-Aware Reinforcement Learning for Safe Control of Autonomous Vehicles in Signalized Intersections","authors":"Mehrnoosh Emamifar, S. F. Ghoreishi","doi":"10.1109/CAI54212.2023.00042","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00042","url":null,"abstract":"This paper proposes a reinforcement learning approach for the control of autonomous vehicles at signalized intersections. The proposed method is a modified version of the Q-learning approach that takes into account the risky scenarios that might arise in the control of an autonomous vehicle due to the inherent uncertainties in the system. The proposed algorithm enables robust and risk-aware decision-making in uncertain and sensitive environments. The proposed algorithm is evaluated in a simulated autonomous vehicle scenario, where it outperforms the standard Q-learning in terms of safety.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127887749","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 Case Study of Privacy Protection Challenges and Risks in AI-Enabled Healthcare App 人工智能医疗应用中隐私保护的挑战和风险案例研究
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00132
Ping Wang, Hosseinali Zare
{"title":"A Case Study of Privacy Protection Challenges and Risks in AI-Enabled Healthcare App","authors":"Ping Wang, Hosseinali Zare","doi":"10.1109/CAI54212.2023.00132","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00132","url":null,"abstract":"Artificial intelligence (AI) is increasingly used in healthcare systems and applications (apps) with questions and debates on ethical issues and privacy risks. This research study explores and discusses the ethical challenges, privacy risks, and possible solutions related to protecting user data privacy in AI-enabled healthcare apps. The study is based on the healthcare app named Charlie in one of the fictional case studies designed by Princeton University to elucidate critical thinking and discussions on emerging ethical issues embracing AI.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125499796","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
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