Le Cheng, Lyu Chang, Yanhong Song, Haibo Wang, Yuetang Bian
By studying the biological behavior of cockroaches, a bionic algorithm, Cooperative Learning Cockroach Colony Optimization (CLCCO), is presented in this paper. The aim of CLCCO is to provide an efficient method to solve Robot Path Planning (RPP) problems. The CLCCO algorithm is based on the idea of synergy behavior of cockroach colony and machine learning. With pheromone, the cockroach colony achieves population synergy, which includes the follow and diversion behaviors. The strategy of Fibonacci transformation is used for the cockroach individual to choose the next feasible cell. The technologies of λ-geometry and multi-objective search make the paths searched smoother and greatly improve the algorithm search efficiency. In particular, the CLCCO algorithm requires only two parameters to be set. When CLCCO is applied to real robots, a path compression technique is designed. The simulation results show that the CLCCO algorithm demonstrates high efficiency in mostly tests.
{"title":"A Robot Path Planning Method Based on Synergy Behavior of Cockroach Colony","authors":"Le Cheng, Lyu Chang, Yanhong Song, Haibo Wang, Yuetang Bian","doi":"10.34028/iajit/20/5/4","DOIUrl":"https://doi.org/10.34028/iajit/20/5/4","url":null,"abstract":"By studying the biological behavior of cockroaches, a bionic algorithm, Cooperative Learning Cockroach Colony Optimization (CLCCO), is presented in this paper. The aim of CLCCO is to provide an efficient method to solve Robot Path Planning (RPP) problems. The CLCCO algorithm is based on the idea of synergy behavior of cockroach colony and machine learning. With pheromone, the cockroach colony achieves population synergy, which includes the follow and diversion behaviors. The strategy of Fibonacci transformation is used for the cockroach individual to choose the next feasible cell. The technologies of λ-geometry and multi-objective search make the paths searched smoother and greatly improve the algorithm search efficiency. In particular, the CLCCO algorithm requires only two parameters to be set. When CLCCO is applied to real robots, a path compression technique is designed. The simulation results show that the CLCCO algorithm demonstrates high efficiency in mostly tests.","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121997850","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}
Mohammed Abebe Yimer, Özlem Aktaş, Süleyman Sevinç, A. Şişman
Various studies use numerous probabilistic methods to establish a cause-effect relationship between a drug and a disease. However, only a limited number of machine learning studies on establishing cause-effect relationships can be found on the internet. In this study, we explore machine learning approaches for interpreting large quantities of multivariate patient-based laboratory data for establishing cause-effect relationships for critically ill patients. We adopt principal component analysis as a primary method to capture daily patient changes after a medical intervention so that the causal relationship between the medical treatments and the outcomes can be established. Model validity and stability are evaluated using bootstrap testing. The model exhibits an acceptable significance level with a two-tailed test. Moreover, results show that the approach provides promising results in interpreting large quantities of patient data and establishing cause-effect relationships for making informed decisions for critically ill patients. If fused with other machine learning and probabilistic models, the proposed approach can provide the healthcare industry with an added tool for daily routine clinical practices. Furthermore, the approach will be able to support clinical decision-making and enable effective patient-tailored care for better health outcomes.
{"title":"Establishing Cause-Effect Relationships from Medical Treatment Data in Intensive Care Unit Settings","authors":"Mohammed Abebe Yimer, Özlem Aktaş, Süleyman Sevinç, A. Şişman","doi":"10.34028/iajit/20/5/1","DOIUrl":"https://doi.org/10.34028/iajit/20/5/1","url":null,"abstract":"Various studies use numerous probabilistic methods to establish a cause-effect relationship between a drug and a disease. However, only a limited number of machine learning studies on establishing cause-effect relationships can be found on the internet. In this study, we explore machine learning approaches for interpreting large quantities of multivariate patient-based laboratory data for establishing cause-effect relationships for critically ill patients. We adopt principal component analysis as a primary method to capture daily patient changes after a medical intervention so that the causal relationship between the medical treatments and the outcomes can be established. Model validity and stability are evaluated using bootstrap testing. The model exhibits an acceptable significance level with a two-tailed test. Moreover, results show that the approach provides promising results in interpreting large quantities of patient data and establishing cause-effect relationships for making informed decisions for critically ill patients. If fused with other machine learning and probabilistic models, the proposed approach can provide the healthcare industry with an added tool for daily routine clinical practices. Furthermore, the approach will be able to support clinical decision-making and enable effective patient-tailored care for better health outcomes.","PeriodicalId":161392,"journal":{"name":"The International Arab Journal of Information Technology","volume":"56 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419622","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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