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Application of XAI-based framework for PV Energy Generation Forecasting 基于xai的框架在光伏发电预测中的应用
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00036
B. Teixeira, Leonor Carvalhais, T. Pinto, Z. Vale
{"title":"Application of XAI-based framework for PV Energy Generation Forecasting","authors":"B. Teixeira, Leonor Carvalhais, T. Pinto, Z. Vale","doi":"10.1109/CAI54212.2023.00036","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00036","url":null,"abstract":"The structural changes in the energy sector caused by renewable sources and digitization have resulted in an increased use of Artificial Intelligence (AI), including Machine Learning (ML) models. However, these models’ black-box nature and complexity can create issues with transparency and trust, thereby hindering their interpretability. The use of Explainable AI (XAI) can offer a solution to these challenges. This paper explores the application of an XAI-based framework to analyze and evaluate a photovoltaic energy generation forecasting problem and contribute to the trustworthiness of ML solutions.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"41 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":"126531607","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
Transforming AI Solutions in Healthcare—The Medical Information Tokens 医疗保健领域的人工智能解决方案转型——医疗信息令牌
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00134
W. Hafez, Sherif Elshamy, Abdelaziz Farid, R. Camara
{"title":"Transforming AI Solutions in Healthcare—The Medical Information Tokens","authors":"W. Hafez, Sherif Elshamy, Abdelaziz Farid, R. Camara","doi":"10.1109/cai54212.2023.00134","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00134","url":null,"abstract":"Comparatively to other fields, the use of artificial intelligence (AI)-based medical decisions remains limited. In general, AI-based medical decisions necessitate modeling the conditions being treated, including potential treatment alternatives and trajectories. Due to the heterogeneity of most medical conditions, developing these models requires a vast quantity of data. Although current healthcare systems generate abundant digital data, this data is frequently fragmented, stored in multiple locations, organized according to various structures, and lacks context, posing a challenge for integrating and utilizing these data to model the relevant conditions. Effective AI medical solutions, we argue, necessitate the development of an AI-specific medical vocabulary, which we call information tokens. The proposed vocabulary would enable AI methods to access diverse medical records and provide the context for developing treatment models, instead of only conditions-specific models. Then, we introduce a reinforcement learning-based agent, the human digital twin, which models medical conditions and provides patient-specific treatment recommendations based on the condition vocabulary. Finally, we define a framework for managing and coordinating the utilization and update of the vocabulary, and the treatment models throughout a healthcare system.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"16 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":"114240169","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
Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture 光伏组件制造中故障检测与诊断的深度学习模型
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00095
Khuong Nguyen-Vinh, Quang-Nguyen Vo-Huynh, Minh Hoang, Khoa Nguyen-Minh
{"title":"Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture","authors":"Khuong Nguyen-Vinh, Quang-Nguyen Vo-Huynh, Minh Hoang, Khoa Nguyen-Minh","doi":"10.1109/CAI54212.2023.00095","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00095","url":null,"abstract":"The usage of photovoltaic (PV) systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry’s manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module. Currently, fault detection and diagnosis (FDD) are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning (DL) have proven its feasibility in image classification and object detection. Thus, DL can be extended to visual fault detection using data generated by electroluminescence (EL) imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of EL data and several techniques based on supervised learning to detect and diagnose visual faults and defects presented in a module.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"38 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":"116652020","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
Faulty Neural Networks 故障神经网络
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00129
Shiuan-Wen Chen, Brendan Duke, P. Aarabi
{"title":"Faulty Neural Networks","authors":"Shiuan-Wen Chen, Brendan Duke, P. Aarabi","doi":"10.1109/CAI54212.2023.00129","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00129","url":null,"abstract":"This study aims to investigate the response of the nervous system to injury through experiments using a neural network model trained with the MNIST dataset [1]. Multiple experiments are performed to examine the relationship between neural network damage and accuracy. How the damaged network can restore its functionality or accuracy with the aid of another neural network is also investigated. By analyzing these results, a better understanding of the nervous system’s ability to respond to injury and adapt to changes in neural networks can be gained.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"12 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":"125151606","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
LSTM-based network churn classification from EDA phasic data 基于lstm的EDA相位数据网络流失分类
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00115
Ana Coelho, P. S. Moreira, P. Almeida, Nuno Dias
{"title":"LSTM-based network churn classification from EDA phasic data","authors":"Ana Coelho, P. S. Moreira, P. Almeida, Nuno Dias","doi":"10.1109/CAI54212.2023.00115","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00115","url":null,"abstract":"Understanding television watching behavior of consumers can be useful in many contexts, such as evaluating the influence of a TV network, building recommendation systems, or providing insights regarding commercials for advertisers. Electrodermal activity (EDA) is a psychophysiological indicator of emotional arousal and attention that reflects the variation of the electrical properties of the skin. Given that it is a measure that reflects the emotional status of consumers and has advantages over self-report of emotions, it has been widely used in consumer research studies. In this study, we built a classification model using long-short term memory networks and EDA phasic signals to classify network switch/churn occurrence. The developed model had an accuracy of 71%, which demonstrates that EDA phasic activity is a good candidate to predict channel churn occurrence.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"44 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":"125258179","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
Leveraging the Power of Artificial Intelligence and Blockchain in Recruitment using Beetle Platform 利用甲虫平台在招聘中发挥人工智能和区块链的力量
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00117
Kolawole Lamikanra, Tayo Obafemi-Ajayi
{"title":"Leveraging the Power of Artificial Intelligence and Blockchain in Recruitment using Beetle Platform","authors":"Kolawole Lamikanra, Tayo Obafemi-Ajayi","doi":"10.1109/CAI54212.2023.00117","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00117","url":null,"abstract":"The recruitment process can be arduous and time-consuming for both recruiters and job seekers. To address this issue, we propose the Beetle platform that leverages artificial intelligence (AI) and Blockchain technology to provide seamless and efficient solutions for the recruitment journey. Beetle platform includes a ChatGPT styled prompt interface, a Tinder-styled matching interface, an AI-powered video based digital twin bot for interview preparation, as well as a Blockchain-driven vetting system. The searching/shortlisting phase utilizes continuous machine learning algorithms to enhance the matching of job seekers with relevant recruiters and/or positions. The digital twin bot integrates Natural Language Processing to prepare candidates for interviews and provide feedback. The vetting phase uses Blockchain technology to store information to improve Beetle’s reliability and security. Information stored on it is immutable, transparent, and easily verifiable, thus making the vetting process faster and more reliable.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"31 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":"123890072","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
Machine Learning prediction of ultimate strain of CFRP/GFRP-RC column with lap spliced rebars subjected to cyclic loads 循环荷载作用下CFRP/GFRP-RC搭接柱极限应变的机器学习预测
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/cai54212.2023.00083
Joseph Aina, Nakisa Haghi, S. Efe
{"title":"Machine Learning prediction of ultimate strain of CFRP/GFRP-RC column with lap spliced rebars subjected to cyclic loads","authors":"Joseph Aina, Nakisa Haghi, S. Efe","doi":"10.1109/cai54212.2023.00083","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00083","url":null,"abstract":"Fiber Reinforced Polymers (FRPs) are widely being used to retrofit steel and concrete structures due to their high resistance to corrosion and high mechanical qualities. To extend the application of FRPs in the construction industry, there is a need to provide a powerful model to predict the load-carrying capacity of FRP concrete elements such as beams and columns. Herein, different techniques were applied to predict the ultimate strain of FRP rectangular concrete columns subjected to cyclic loads using machine-learning models. A comprehensive database of 318 specimens available in the literature was collected. Six Artificial Intelligence models including five machine learning models named as K-Nearest Neighbors (KNN), and Decision Tree (DT), CatBoost (CB), AdaBoost (AB), Random Forest (RF) and one deep learning model named Artificial Neural Network (ANN) were considered. The result showed that DT, and RF models are able to predict the ultimate strain of the column with high accuracy of 96.4% and 96.5%, respectively.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"21 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":"127616125","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
Artificial Intelligence for Medical Image Interpretation Using Expert Knowledge and Machine Learning 使用专家知识和机器学习的医学图像解释人工智能
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00059
Lars E.O. Jacobson, A. Hopgood, M. Bader-El-Den, V. Tamma, David Prendergast, P. Osborn, S. Siddiqui, A. Gegov, Farzad Arabikhan
{"title":"Artificial Intelligence for Medical Image Interpretation Using Expert Knowledge and Machine Learning","authors":"Lars E.O. Jacobson, A. Hopgood, M. Bader-El-Den, V. Tamma, David Prendergast, P. Osborn, S. Siddiqui, A. Gegov, Farzad Arabikhan","doi":"10.1109/CAI54212.2023.00059","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00059","url":null,"abstract":"In 2022 268,490 new cases and 34,500 deaths was estimated for prostate cancer in the United States. Diagnosis of prostate cancer is primarily based on prostate-specific antigen (PSA) screening and trans-rectal ultrasound (TRUS)-guided prostate biopsy. PSA has a low specificity of 36% since benign conditions can elevate the PSA levels. The data set used for prostate cancer consists of t2-weighted MR images for 1,151 patients and 61,119 images. This paper presents an approach to applying knowledge-based artificial intelligence together with image segmentation to improve the diagnosis of prostate cancer using publicly available data. Complete and reliable segmentation into the transition zone and peripheral zone is required in order to automate and enhance the process of prostate cancer diagnosis.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"25 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":"132534252","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
3D Dental Biometrics: Transformer-based Dental Arch Extraction and Matching 三维牙科生物识别:基于变压器的牙弓提取和匹配
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00067
Zhiyuan Zhang, Xin Zhong
{"title":"3D Dental Biometrics: Transformer-based Dental Arch Extraction and Matching","authors":"Zhiyuan Zhang, Xin Zhong","doi":"10.1109/CAI54212.2023.00067","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00067","url":null,"abstract":"The dental arch is a significant anatomical feature that is crucial in assessing tooth arrangement and configuration and has a potential for human identification in biometrics and digital forensic dentistry. In a previous study, we proposed an auto pose-invariant arch feature extraction Radial Ray Algorithm (RRA) and a matching framework [1] based solely on 3D dental geometry. To enhance the identification accuracy and speed of our previous work, we propose in this study a transformer architecture that can extract dental keypoints by encoding both local and global features. The dental arch is then constructed through robust interpolation of the dental keypoints using B-Spline and is compared using the same identification framework. To evaluate the effectiveness of our proposed approach, we conducted experiments by matching the same 11 post-mortems (PM) samples against 200 antemortem (AM) samples. Our results show that our approach achieves higher accuracy and faster speed compared to our previous work. Specifically, 11 samples (100%) achieved a top 6.5% (13/200) accuracy out of the 200-rank list, compared to the top 15.5% (31/200) accuracy previously [1]. Additionally, the time required to identify a single subject from 200 subjects has been reduced from 5 minutes to 3 minutes. The dental arch can be used as a powerful filter feature. Our findings make a significant contribution to the existing literature on dental identification and demonstrate the potential practical applications of our approach in diverse fields such as biometrics, forensic dentistry, orthodontics, and anthropology.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"31 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":"132724239","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
Ensemble Deep Convolutional Neural Network to Identify Fractured Limbs using CT Scans 集成深度卷积神经网络识别肢体骨折的CT扫描
2023 IEEE Conference on Artificial Intelligence (CAI) Pub Date : 2023-06-01 DOI: 10.1109/CAI54212.2023.00075
Anup Khanal, Rodrigue Rizk, K. Santosh
{"title":"Ensemble Deep Convolutional Neural Network to Identify Fractured Limbs using CT Scans","authors":"Anup Khanal, Rodrigue Rizk, K. Santosh","doi":"10.1109/CAI54212.2023.00075","DOIUrl":"https://doi.org/10.1109/CAI54212.2023.00075","url":null,"abstract":"Accurate classification between fractured and intact bones in Computed Tomography (CT) scan serves as a precursor to further treatment planning. CNN is no exception to handle this, and as an example AlexNet ranked top in the ImageNet challenge (2012). To overcome generalization errors, we propose to ensemble deep convolutional neural networks to check how well fractured limbs can be analyzed. It primarily includes voting (soft and hard), stacking, bagging, and feature soup on a backbone consisting of VGG19, ResNet152, Inception, MobileNet, and DenseNet169. On a clinically annotated dataset of size 5,567 CT scans, we achieved the highest accuracy of 0.977, precision of 0.959, recall of 0.960, F1-score of 0.960, and AUC of 0.971. To the best of our knowledge, this is the first time this dataset has been used to classify fractured and intact bones.","PeriodicalId":129324,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence (CAI)","volume":"24 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":"133840219","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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