检索结果-内蒙古大学图书馆

Emerging Technologies for Real-Time Intraoperative Margin Assessment in Future Breast-Conserving Surgery

ADVANCED SCIENCE 2020年第9期7卷 1901519页

作者： Pradipta, Ambara R. Tanei, Tomonori Morimoto, Koji Shimazu, Kenzo Noguchi, Shinzaburo Tanaka, Katsunori RIKEN Cluster Pioneering Res Biofunct Synthet Chem Lab 2-1 Hirosawa Wako Saitama 3510198 Japan Tokyo Inst Technol Sch Mat & Chem Technol Dept Chem Sci & Engn Meguro Ku 2-12-1 Ookayama Tokyo 1528552 Japan Osaka Univ Dept Breast & Endocrine Surg Grad Sch Med 2-2-E10 Yamadaoka Suita Osaka 5650871 Japan Kazan Fed Univ Biofunct Chem Lab A Butlerov Inst Chem 18 Kremlyovskaya St Kazan 420008 Russia RIKEN Baton Zone Program GlycoTargeting Res Lab 2-1 Hirosawa Wako Saitama 3510198 Japan

Clean surgical margins in breast-conserving surgery (BCS) are essential for preventing recurrence. Intraoperative pathologic diagnostic methods, such as frozen section analysis and imprint cytology, have been recognized as crucial tools in BCS. However, the complexity and time-consuming nature of these pathologic procedures still inhibit their broader applicability worldwide. To address this situation, two issues should be considered: 1) the development of nonpathologic intraoperative diagnosis methods that have better sensitivity, specificity, speed, and cost;and 2) the promotion of new imaging algorithms to standardize data for analyzing positive margins, as represented by artificial intelligence (AI), without the need for judgment by well-trained pathologists. Researchers have attempted to develop new methods or techniques;several have recently emerged for real-time intraoperative management of breast margins in live tissues. These methods include conventional imaging, spectroscopy, tomography, magnetic resonance imaging, microscopy, fluorescent probes, and multimodal imaging techniques. This work summarizes the traditional pathologic and newly developed techniques and discusses the advantages and disadvantages of each method. Taking into consideration the recent advances in analyzing pathologic data from breast cancer tissue with AI, the combined use of new technologies with AI algorithms is proposed, and future directions for real-time intraoperative margin assessment in BCS are discussed.

关键词： artificial intelligence algorithms breast cancer breast-conserving surgery deep learning imaging intraoperative diagnosis

来源：评论

学校读者我要写书评

暂无评论

Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps

引用

JOURNAL OF BIOMEDICAL OPTICS 2021年第1期26卷 015001-015001页

作者： Lai, Lily L. Blakely, Andrew Invernizzi, Marta Lin, James Kidambi, Trilokesh Melstrom, Kurt A. Yu, Kevin Lu, Thomas City Hope Natl Med Ctr Dept Surg Duarte CA 91010 USA NCI Dept Surg Natl Inst Hlth Campus Bethesda MD 20892 USA City Hope Natl Med Ctr Div Gastroenterol Duarte CA USA Jet Prop Lab Pasadena CA USA

Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination. Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE). Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps. Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images. Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： artificial intelligence algorithms deep learning polyp discrimination colorectal cancer narrow-band imaging color channel separation

来源：评论

学校读者我要写书评

暂无评论

Color classification using machine learning

Color classification using machine learning

引用

作者： Mahajan, Mihir Rajendra California State University – Sacramento

学位级别：硕士

Machine learning and artificial intelligence continue to be active research fields focused on real-world problems. Machine learning uses computers to make predictions based on the provided data set or previous experience. Using machine learning methods such as supervised and unsupervised learning, we can process large data and solve classification problems. In this project, we have applied supervised learning, which is the most often used task-driven classification type of machine learning. The main goal of our project is classification of different color shades using machine learning under ideal and different non-ideal conditions. In this project, we used a binary classification technique of supervised learning to classify different colors.

关键词： CNN artificial intelligence algorithms Color classification Convolution neural network Machine learning Machine vision

来源：评论

学校读者我要写书评

暂无评论

Application of fuzzy decision tree in EOR screening assessment

引用

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 177卷 167-180页

作者： Khazali, Nastaran Sharifi, Mohammad Ahmadi, Mohammad Ali Amirkabir Univ Technol Polytech Tehran Dept Petr Engn Tehran Iran Univ Calgary Dept Chem & Petr Engn Calgary AB Canada

Ranking of best possible Enhanced Oil Recovery (EOR) technics for implementing on a target field is one of the most important questions that should be answered by reservoir engineers. EOR screening can be considered as a tool for recommending the most appropriate EOR methods. Although for each candidate reservoir, the applicability of EOR processes must be investigated specifically, EOR screening can be used as an indicator before economic evaluations or reservoir descriptions are done and executive decisions are made. Implementing an EOR project for predictions that pass this screening is the next step. In this study, the fuzzy decision tree method (with the ability to rank and classify EOR methods simultaneously) is introduced for EOR screening. Basic features for this study are permeability, viscosity, depth, temperature, saturation, and API. Using a fuzzy decision tree enables us to design an expert system which generates EOR rules automatically. This is one of the noticeable features of this study which reduces the importance of a human expert role while designing the system and making it as expert as possible. Here, the fuzzy decision tree method is implemented on a dataset consisting of 548 observations related to 10 different EOR techniques. Predictions made by this method which are ranked from the most applicable EOR method to the least one include the EOR method mentioned in the dataset for every observation in both training and test set. Moreover, using the procedure introduced here for training the trees enables the expert system to be adaptive whenever the dataset is updated.

关键词： Data mining Expert system artificial intelligence algorithms EOR screening Fuzzy logic Fuzzy decision tree Automatic rule generation

来源：评论

学校读者我要写书评

暂无评论

Leveraging artificial intelligence to Improve Voice Disorder Identification Through the Use of a Reliable Mobile App

引用

IEEE ACCESS 2019年 7卷 124048-124054页

作者： Verde, Laura De Pietro, Giuseppe Alrashoud, Mubarak Ghoneim, Ahmed Al-Mutib, Khaled N. Sannino, Giovanna CNR Inst High Performance Comp & Networking ICAR I-80131 Naples Italy King Saud Univ CCIS Dept Software Engn SWE Riyadh 11543 Saudi Arabia

The evolution of the Internet of Things, cloud computing and wireless communication has contributed to an advance in the interconnectivity, efficiency and data accessibility in smart cities, improving environmental sustainability, quality of life and well-being, knowledge and intellectual capital. In this scenario, the satisfaction of security and privacy requirements to preserve data integrity, confidentiality and authentication is of fundamental importance. In particular, this is essential in the healthcare sector, where health-related data are considered sensitive information able to reveal confidential details about the subject. In this regard, to limit the possibility of security attacks or privacy violations, we present a reliable mobile voice disorder detection system capable of distinguishing between healthy and pathological voices by using a machine learning algorithm. This latter is totally embedded in the mobile application, so it is able to classify the voice without the necessity of transmitting user data to or storing user data on any server. A Boosted Trees algorithm was used as the classifier, opportunely trained and validated on a dataset composed of 2003 voices. The most frequently considered acoustic parameters constituted the inputs of the classifier, estimated and analyzed in real time by the mobile application.

关键词： Voice disorders smart healthcare monitoring artificial intelligence algorithms smart cities security privacy

来源：评论

学校读者我要写书评

暂无评论

Hybrid artificial intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

引用

IEEE ACCESS 2019年 7卷 162530-162548页

作者： Topal, Ibrahim Ucar, Muhammed Kursad Natl Def Univ Dept Comp Technol TR-77720 Yalova Turkey Natl Def Univ Vocat Sch TR-77720 Yalova Turkey Sakarya Univ Fac Engn Dept Elect & Elect Engn TR-54050 Sakarya Turkey

Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as "Turkey Tourism Year" in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.7

关键词： Marketing tourism travel user profile turkey tourism year Chinese tourism tripadvisor artificial intelligence algorithms ensemble classification

来源：评论

学校读者我要写书评

暂无评论

Dynamic routing of traffic based on greenhouse gases measurements 23

Dynamic routing of traffic based on greenhouse gases measure...

引用

23rd International Conference on System Theory, Control and Computing (ICSTCC)

作者： Puscasiu, Adela Fanca, Alexandra Gota, Dan-Ioan Valean, Honoriu Tech Univ Cluj Napoca Automat Dept Cluj Napoca Romania

ISBN: (纸本)9781728106991

Road traffic is one of the main sources of greenhouse gas emissions, keeping in mind the fact that most of the cars have internal combustion engine. Due to the large number of cars, the pollution problem is a significant issue for any major city in the world. The importance of this work is to find ways to reduce pollution in a locality without affecting human lifestyle. Certainly, the number of cars involved in road traffic will be approximatively constant but controlling the flow of traffic to minimize the pollution in some specific areas by offering alternative routes is a way to reduce emissions. This is necessary because of the fact that the industry of electric cars building is not yet well developed to provide an alternative to fossil fuel-burning machines. The idea of controlling road traffic according to the number of vehicles is already present in the literature but this paper offers the possibility of testing the possible algorithms underlying road traffic control, namely algorithms based on the level of pollution at intersections in a locality. This paper also provides a solution to the fluidization of traffic, taking into account that an intersection or crowded streets in a locality involves a higher level of pollution than normal. This paper presents a simulation of road traffic management based on the measurement of greenhouse gases through a software application, a database and algorithms. An application has been obtained that illustrates part of a road infrastructure of a locality and how to manage road traffic according to greenhouse gas emissions.

关键词： artificial intelligence algorithms pollution reduce greenhouse gases traffic

来源：评论

学校读者我要写书评

暂无评论

Feature Selection algorithms in Intrusion Detection System: A Survey

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第10期12卷 5079-5099页

作者： Maza, Sofiane Touahria, Mohamed Univ Ferhat Abbas Setif 1 Dept Comp Sci Setif 19000 Algeria

Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

关键词： Intrusion Detection System Feauture Selection artificial intelligence algorithms Deterministic algorithms Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Constructing a Sorting Machine for Degenerative Cerebropathia 19

Constructing a Sorting Machine for Degenerative Cerebropathi...

引用

19th International Conference on Advanced Communication Technology (ICACT)

作者： Moon, Seohyun Choi, Bogeum An, Jiwoo Yoon, Taeseon Hankuk Acad Foreign Studies Nat Sci Yongin Gyeongii Do South Korea Hankuk Acad Foreign Studies Yongin Gyeongii Do South Korea

ISBN: (纸本)9788996865094

Dementia with Lewy bodies and Parkinson's Disease has the common cause: mutation of gene CYP2D6. Thus, it is hard to distinguish those two diseases. In order to classify them clearly, construction of sorting machine is required. Various algorithms including Apriori, Decision tree, Support Vector Machine, K-means and Neural Network was used in the construction of sorting machine. Two kinds of sorting machines were constructed: one classifying whether the patient is under Dementia with lewy bodies or Parkinson's Disease, the other classifying whether the gene is normal or mutation has undergone.

关键词： Parkinson's Disease Dementia with Lewy bodies Classification Machine Function Construction artificial intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

Constructing a Sorting Machine for Degenerative Cerebropathia

Constructing a Sorting Machine for Degenerative Cerebropathi...

引用

International Conference on Advanced Communication Technology

作者： Seohyun Moon Bogeum Choi Jiwoo An Taeseon Yoon Natural Science Hankuk Academy of Foreign Studies Faculty Hankuk Academy of Foreign Studies

ISBN: (纸本)9781509048922

关键词： Parkinson's Disease Dementia with Lewy bodies Classification Machine Function Construction artificial intelligence algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：