2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献_第9页

Predicting the Trajectory of AI Utilizing the Markov Model of Machine Learning 利用机器学习的马尔可夫模型预测人工智能的发展轨迹

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201251

Matilda Isaac, Olukunle Mobolaji Akinola, Bintao Hu

{"title":"Predicting the Trajectory of AI Utilizing the Markov Model of Machine Learning","authors":"Matilda Isaac, Olukunle Mobolaji Akinola, Bintao Hu","doi":"10.1109/CCAI57533.2023.10201251","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201251","url":null,"abstract":"The next generation of Artificial Superintelligence (ASI) poses a variety of important societal problems, including the possible crises and upheavals of the AI machine, which could cause fundamental changes. As the discussion around Artificial Superintelligence underscores the importance of continual dialogue between man and its ability to control technology, it also raises the problem of designing intelligent interactive and collaborative tools and systems to allow this dialogue. Historically, the term “AI” was used from 1950 to 1975, then fell out of favor during the” AI winter” from 1975 to 1995, and was narrowed to ANI (Artificial Narrow Intelligence). As a result, terms like “Machine Learning,” “Natural language Processing,” and “Data Science” were frequently mislabelled as AI. Today, AI has allowed clinicians to rely heavily on ML which is highly integrated with coding, billing, medical records, scheduling, contracting, medication ordering, and administrative functions. AI is now a thriving industry with massive capital investments and once again is on the verge of a great revolution. There are compelling reasons to investigate artificial super intelligence. This type of AI is capable of surpassing human intellect by expressing cognitive skills and developing its own mental capabilities. ASI is a highly sophisticated, and intelligent type of AI that goes beyond normal intellectual capacity. This paper will discuss the societal impact and the current academic impact of ASI. Finally, this study would attempt to utilize the Markov Decision Model of Machine Learning to predict the trajectory of ASI in the very near future.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121895436","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

MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression MedLens:通过医学体征选择和回归改善死亡率预测

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-19 DOI: 10.1109/CCAI57533.2023.10201302

Xuesong Ye, Jun Wu, Chengjie Mou, Weina Dai

{"title":"MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression","authors":"Xuesong Ye, Jun Wu, Chengjie Mou, Weina Dai","doi":"10.1109/CCAI57533.2023.10201302","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201302","url":null,"abstract":"Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in Electronic Health Records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR, which exceeds the previous benchmark.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116557666","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: 5

Full-Spectrum Wireless Communications for 6G and Beyond: From Microwave, Millimeter-Wave, Terahertz to Lightwave 6G及以上的全频谱无线通信:从微波、毫米波、太赫兹到光波

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-11 DOI: 10.1109/CCAI57533.2023.10201316

Wei Jiang, H. Schotten

引用次数: 1

Ultra Sharp : Study of Single Image Super Resolution Using Residual Dense Network 超锐利:使用残差密集网络的单幅图像超分辨率研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-04-21 DOI: 10.13140/RG.2.2.25001.06246

K. Gunasekaran

{"title":"Ultra Sharp : Study of Single Image Super Resolution Using Residual Dense Network","authors":"K. Gunasekaran","doi":"10.13140/RG.2.2.25001.06246","DOIUrl":"https://doi.org/10.13140/RG.2.2.25001.06246","url":null,"abstract":"For years, Single Image Super Resolution (SISR) has been an interesting and ill-posed problem in computer vision. The traditional super-resolution (SR) imaging approaches involve interpolation, reconstruction, and learning-based methods. Interpolation methods are fast and uncomplicated to compute, but they are not so accurate and reliable. Reconstruction-based methods are better compared with interpolation methods, but they are time-consuming and the quality degrades as the scaling increases. Even though learning-based methods like Markov random chains are far better than all the previous ones, they are unable to match the performance of deep learning models for SISR. This study examines the Residual Dense Networks architecture proposed by Yhang et al. and analyzes the importance of its components. By leveraging hierarchical features from original low-resolution (LR) images, this architecture achieves superior performance, with a network structure comprising four main blocks, including the residual dense block (RDB) as the core. Through investigations of each block and analyses using various loss metrics, the study evaluates the effectiveness of the architecture and compares it to other state-of-the-art models that differ in both architecture and components.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129522081","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