Pub Date : 2021-01-01DOI: 10.1007/978-3-030-85713-4_17
Joaquín Bautista-Valhondo
{"title":"Solving the Permutation Heijunka Flow Shop Scheduling Problem with Non-unit Demands for Jobs","authors":"Joaquín Bautista-Valhondo","doi":"10.1007/978-3-030-85713-4_17","DOIUrl":"https://doi.org/10.1007/978-3-030-85713-4_17","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81665249","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}
Pub Date : 2017-05-01Epub Date: 2017-04-11DOI: 10.1007/978-3-319-57351-9_30
Yue Gu, Xinyu Li, Shuhong Chen, Jianyu Zhang, Ivan Marsic
We present a novel multimodal deep learning structure that automatically extracts features from textual-acoustic data for sentence-level speech classification. Textual and acoustic features were first extracted using two independent convolutional neural network structures, then combined into a joint representation, and finally fed into a decision softmax layer. We tested the proposed model in an actual medical setting, using speech recording and its transcribed log. Our model achieved 83.10% average accuracy in detecting 6 different intentions. We also found that our model using automatically extracted features for intention classification outperformed existing models that use manufactured features.
{"title":"Speech Intention Classification with Multimodal Deep Learning.","authors":"Yue Gu, Xinyu Li, Shuhong Chen, Jianyu Zhang, Ivan Marsic","doi":"10.1007/978-3-319-57351-9_30","DOIUrl":"10.1007/978-3-319-57351-9_30","url":null,"abstract":"<p><p>We present a novel multimodal deep learning structure that automatically extracts features from textual-acoustic data for sentence-level speech classification. Textual and acoustic features were first extracted using two independent convolutional neural network structures, then combined into a joint representation, and finally fed into a decision softmax layer. We tested the proposed model in an actual medical setting, using speech recording and its transcribed log. Our model achieved 83.10% average accuracy in detecting 6 different intentions. We also found that our model using automatically extracted features for intention classification outperformed existing models that use manufactured features.</p>","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261374/pdf/nihms-993283.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36729394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-05-01DOI: 10.1007/978-3-642-38457-8_7
Ramakanth Kavuluru, Sifei Han, Daniel Harris
Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient's medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult.
{"title":"Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques.","authors":"Ramakanth Kavuluru, Sifei Han, Daniel Harris","doi":"10.1007/978-3-642-38457-8_7","DOIUrl":"https://doi.org/10.1007/978-3-642-38457-8_7","url":null,"abstract":"<p><p>Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient's medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult.</p>","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-642-38457-8_7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35204921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a reinforcement learning architecture designed for problems with an infinite number of states, where each state can be seen as a vector of real numbers and with a finite number of actions, where each action requires a vector of real numbers as parameters. The main objective of this architecture is to distribute in two actors the work required to learn the final policy. One actor decideswhat actionmust be performed;meanwhile, a second actor determines the right parameters for the selected action. We tested our architecture and one algorithmbased on it solving the robot dribbling problem, a challenging robot control problem taken from the RoboCup competitions. Our experimental work with three different function approximators provides enough evidence to prove that the proposed architecture can be used to implement fast, robust, and reliable reinforcement learning algorithms.
{"title":"A novel reinforcement learning architecture for continuous state and action spaces","authors":"Victor Uc-Cetina","doi":"10.1155/2013/492852","DOIUrl":"https://doi.org/10.1155/2013/492852","url":null,"abstract":"We introduce a reinforcement learning architecture designed for problems with an infinite number of states, where each state can be seen as a vector of real numbers and with a finite number of actions, where each action requires a vector of real numbers as parameters. The main objective of this architecture is to distribute in two actors the work required to learn the final policy. One actor decideswhat actionmust be performed;meanwhile, a second actor determines the right parameters for the selected action. We tested our architecture and one algorithmbased on it solving the robot dribbling problem, a challenging robot control problem taken from the RoboCup competitions. Our experimental work with three different function approximators provides enough evidence to prove that the proposed architecture can be used to implement fast, robust, and reliable reinforcement learning algorithms.","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74919519","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}
Pub Date : 2012-10-27DOI: 10.1007/978-3-642-37807-2_39
María José Fresnadillo Martínez, E. Merino, E. G. Sánchez, José E. García Sánchez, Á. M. D. Rey, G. R. Sánchez
{"title":"A Graph Cellular Automata Model to Study the Spreading of an Infectious Disease","authors":"María José Fresnadillo Martínez, E. Merino, E. G. Sánchez, José E. García Sánchez, Á. M. D. Rey, G. R. Sánchez","doi":"10.1007/978-3-642-37807-2_39","DOIUrl":"https://doi.org/10.1007/978-3-642-37807-2_39","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87377044","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}
Pub Date : 2007-05-28DOI: 10.1007/978-3-540-72665-4_8
Kwangsoo Kim, H. Ryoo
{"title":"Selecting Genotyping Oligo Probes Via Logical Analysis of Data","authors":"Kwangsoo Kim, H. Ryoo","doi":"10.1007/978-3-540-72665-4_8","DOIUrl":"https://doi.org/10.1007/978-3-540-72665-4_8","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83658037","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}
Pub Date : 1990-08-01DOI: 10.1007/978-1-4613-9052-7_6
V. Raskin
{"title":"Ontology, sublanguage, and semantic networks in natural language processing","authors":"V. Raskin","doi":"10.1007/978-1-4613-9052-7_6","DOIUrl":"https://doi.org/10.1007/978-1-4613-9052-7_6","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85506839","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}
Pub Date : 1990-08-01DOI: 10.1007/978-1-4613-9052-7_12
S. P. Morgan, J. Gammack
{"title":"Partial orders as a basis for KBS semantics","authors":"S. P. Morgan, J. Gammack","doi":"10.1007/978-1-4613-9052-7_12","DOIUrl":"https://doi.org/10.1007/978-1-4613-9052-7_12","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89659954","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}
Pub Date : 1990-08-01DOI: 10.1007/978-1-4613-9052-7_11
S. Battle
{"title":"A partial orders semantics for constraint based systems","authors":"S. Battle","doi":"10.1007/978-1-4613-9052-7_11","DOIUrl":"https://doi.org/10.1007/978-1-4613-9052-7_11","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72903383","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}
Pub Date : 1990-08-01DOI: 10.1007/978-1-4613-9052-7_5
E. Nissan
{"title":"Theory formation for interpreting an unknown language","authors":"E. Nissan","doi":"10.1007/978-1-4613-9052-7_5","DOIUrl":"https://doi.org/10.1007/978-1-4613-9052-7_5","url":null,"abstract":"","PeriodicalId":91830,"journal":{"name":"Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77205828","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}
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