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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza makhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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苏里南Edaphus属二新种（鞘翅目：隐翅虫科）

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Entomotaxonomia 1995年第1期17卷 35-37页

作者： Dewanand makhan(Giessenplein, 81, 3522 KG, Utrecht, The Netherlands) 荷兰

苏里南Ｅｄａｐｈｕｓ属二新种（鞘翅目：隐翅虫科）ＤｅｗａｎａｎｄＭａｋｈａｎ荷兰，乌特勒克市，Ｇｉｅｓｓｅｎｐｌｅｉｎ８１号，３５２２ＫＧ关键词鞘翅目，隐翅虫科，Ｅｄａｐｈｕｓ属，新种，苏里南本文记述采自南美洲苏里南... 详细信息

苏里南Ｅｄａｐｈｕｓ属二新种（鞘翅目：隐翅虫科）ＤｅｗａｎａｎｄＭａｋｈａｎ荷兰，乌特勒克市，Ｇｉｅｓｓｅｎｐｌｅｉｎ８１号，３５２２ＫＧ关键词鞘翅目，隐翅虫科，Ｅｄａｐｈｕｓ属，新种，苏里南本文记述采自南美洲苏里南北部丛林落叶下的Ｅｄａ户ｂｕｓ属隐...

关键词：鞘翅目隐翅虫科 Edaphus属新种

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Mass Distribution Campaign of Long-Lasting Insecticidal Nets in the Democratic Republic of Congo from 2018 to 2021 in the Context of the Emergence of COVID-19: Results and Lessons Learned

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Journal of Biosciences and Medicines 2022年第2期10卷 136-159页

作者： Joris Losimba Likwela Adrien N’siala Kumbi Michèle Luntadila Kiamenga Mireille Lusiense Zena Daniela Mundele Iris Simon-Pierre Kazadi Mutuba Eric Mukomena Sompwe Patrick Kanku-Ka-Lukusa Albert Kalonji Ntumba Ghislain makhan Yav Didier Gasigwa Baneti Eric Tsasa Mbuku Charlotte Ndolerire Isingoma Muriel Nzazi Nsambu Philippe Lukanu Ngwala SANRU Asbl Kinshasa Democratic Republic of the Congo Public Health Department Faculty of Medicine and Pharmacy University of Kisangani Kisangani Democratic Republic of the Congo National Malaria Control Program Kinshasa Democratic Republic of the Congo School of Public Health University of Lubumbashi Lubumbashi Democratic Republic of the Congo Financial Support and Management Unit Ministry of Public Health Prevention and Hygiene Kinshasa Democratic Republic of the Congo Interchuch Medical Assistance World Heath Kinshasa Democratic Republic of the Congo World Health Organisation Kinshasa Sub-Office Democratic Republic of the Congo Department of Family Medicine et Primary Health Care Protestant University of Congo Kinshasa Democratic Republic of the Congo

Introduction: The DRC had planned 23 mass distribution campaigns distribution of long-lasting insecticidal nets (LLIN) for the years 2018-2020, the implementation of which spanned from 2018 to July 2021. This article reviews the campaign’s planning process, the results, challenges, and lessons learned. Methods: A descriptive method was used to postpone the planning and implementation process according to the National Malaria Control Program (NMCP) standards adapted to the COVID-19 context. The changes and adaptations implemented as well as the challenges encountered are described. Results: Between January 2018 to June 2021, 23 LLIN mass distribution campaigns were organized in the DRC with the financial support of The Global Fund to Fight against Tuberculosis, AIDS and Malaria (GFTAM) and Against Malaria Foundation (AMF) allowing the distribution of 55,273,473 LLINs to 19,048,372 households at risk of malaria transmission with an average of 2.9 LLINs per household. The enumerated population (111,081,191) exceeded 7% of the micro plans projected population (102,790,391) while the number of households enumerated (19,311,629) was 3% lower compared to the micro planning projection households (19,862,417). Compared to a reported household coverage of 96% of households achieved over the expected households, the independent monitoring carried out revealed 91% of households served in the intervention areas. The main reasons for not reaching households mentioned by the respondents were absence at the time of distribution (26%) followed by the loss of vouchers (16%). Several communication channels were used among which, community workers were the most frequently mentioned (63.1%), followed by radios (18.5%) and churches (12.4%). Conclusion: Good planning, effective coordination of stakeholders, and revision of the implementing campaigns methods following the COVID-19 were factors in the success of this campaign. An effort to respect the schedules for renewing LLINs in househo

关键词： Vector Control LLIN Campaigns COVID-19 Lessons Democratic Republic of Congo

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