Computational Intelligence and Machine Learning最新文献

{"title":"A Systematic Review of Current Advances in Ischemic Stroke Detection and Segmentation","authors":"Ruthra E, R. A","doi":"10.36647/ciml/03.01.a005","DOIUrl":"https://doi.org/10.36647/ciml/03.01.a005","url":null,"abstract":"Ischemic stroke is now one of the vital factors for disability and mortality that globally affects millions of individuals each year in accordance with the World Health Organization (WHO) contrast to hemorrhagic stroke. Treatment for an ischemic stroke as soon as possible can assist to limit prolonged damage and even decreases the risk of mortality. The diagnosis is based on a neurologist's visual observation, which may differ from one to another. On the other hand, Manual segmentation is a tedious and instinctive procedure that has a conspicuous impact on Acute ischemic stroke encountered patient’s prognosis. Numerous automated computer Aided Diagnosis (CAD) systems dependent on many statistical learning algorithms of machine learning (ML) and multi-neural network architecture of deep learning (DL) were considered to reduce the complexity of prediction and lesion segmentation in ischemic stroke and also lower the time required for the manual procedure. This paper contemplates the Imaging modalities, Pre-processing techniques, and segmentation algorithms of ischemic stroke, as well as their performance based on comparing different evaluation parameters and their disadvantages. It highlights the current needs, preferred modality, and possible research ideas in the stroke sector. Keyword : Brain MRI; Deep Learning; Ischemia; Machine Learning; Pre-Processing;","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127777045","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}

{"title":"Digital Literacy Skills And Engagement On The Advanced Classroom Tools And Soft-Wares Of Elementary Teachers In Relation To Their Coping Skills","authors":"C. Vidal","doi":"10.36647/ciml/03.01.a002","DOIUrl":"https://doi.org/10.36647/ciml/03.01.a002","url":null,"abstract":"This study aimed to measure the relationship between Digital literacy skills of Elementary Teachers and their extent of engagement on the use of advanced digital classroom tools and software, Degree of digital literacy skills of Elementary Teachers and their coping skills, and the extent of engagement of Elementary Teachers on the use of the advanced digital classroom tools and software. The researcher utilized three survey questionnaires on Digital Literacy, Teachers’ Engagement on Digital Classroom Tools and Software, and Teachers’ Coping Skills. The following survey was conducted through google forms, 60 Elementary Teachers from 10 selected private schools in Cavite, Philippines was purposefully chosen to be the respondents of this study, hence, Descriptive correlation method was employed. In the light of statistical analysis and the findings of this study, the following conclusions and recommendations were drawn: First, Elementary Teachers have a high degree of literacy in performing tasks successfully in a digital environment, with digital meaning information represented in numeric form and basically for utilization through a computer. Second, Elementary Teachers in selected private schools in Cavite, Philippines are moderately engaged on the Use of Advanced Digital Classroom Tools and Software. Third, Elementary Teachers have a moderate level of coping skills which involves steady value or religious belief system, problem solving, social skills, health-energy, and commitment to a social organization. Fourth, there is a significant relationship between the digital literacy skills of Elementary Teachers in terms of operational skills and creative use and their extent of engagement on the use of advanced digital classroom tools and software. Fifth, there is a significant relationship between the digital literacy skills of Elementary Teachers in terms of information navigation and creative use and their coping skills. Sixth, there is a significant relationship between the extent of engagement of Elementary Teachers on the use of advanced digital classroom tools and software and their coping skills. The overall findings influenced the researcher to develop a Technology Enrichment Program to further enhance the Degree of Digital Literacy Skills, Extent of engagement on the use of Advanced Digital classroom tools and software, and the coping skills of Elementary Teachers who participated in this study. Keyword : Digital Literacy, Coping Skills, extent of engagement on digital tools and software.","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124597860","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}

{"title":"Predictive Analytics of Selected Datasets using DTREG Data Mining Tool","authors":"Megha N","doi":"10.36647/ciml/03.01.a003","DOIUrl":"https://doi.org/10.36647/ciml/03.01.a003","url":null,"abstract":"Predictive analytics is making a significant wave in healthcare Industry. Predictive analytics is an analytics offshoot which helps to make future predictions, resulting in more informed decisions. Data is central to accurate predictions. Several concepts like Data Mining, AI (Artificial Intelligence), Machine Learning and statistics need to work in tandem to ensure precise predictions. The main aim of the research work was to analyse the datasets of selected diseases using the DTREG data mining tool. Two datasets namely Alzheimer’s and Breast Cancer were taken from a public repository and analysed. Various algorithms namely single tree, decision tree, tree boost, support vector machine and neural network were studied. The results obtained were interpreted to understand which algorithm works best in each case. Also, the important predictors in each study were recorded. Interpretation of Alzheimer’s and breast cancer data using DTREG revealed neural network as the best algorithm. The significant predictors for Alzheimer’s were estimated as total intracranial blood volume, clinical dementia rating and age, and for breast cancer were uniformity of cell size, cell shape, benign and malignant and clump thickness. Data mining, artificial Intelligence and machine learning can thus be of very good help in determining the line of treatment to be followed by extracting knowledge from such suitable databases. Keyword : Terms—Algorithms, Alzheimer’s, Breast Cancer, DTREG","PeriodicalId":203221,"journal":{"name":"Computational Intelligence and Machine Learning","volume":"413 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124410504","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}