DSpace 7

DSpace is the world leading open source repository platform that enables organisations to:

  • easily ingest documents, audio, video, datasets and their corresponding Dublin Core metadata
  • open up this content to local and global audiences, thanks to the OAI-PMH interface and Google Scholar optimizations
  • issue permanent urls and trustworthy identifiers, including optional integrations with handle.net and DataCite DOI

Join an international community of leading institutions using DSpace.

The test user accounts below have their password set to the name of this software in lowercase.

  • Demo Site Administrator = dspacedemo+admin@gmail.com
  • Demo Community Administrator = dspacedemo+commadmin@gmail.com
  • Demo Collection Administrator = dspacedemo+colladmin@gmail.com
  • Demo Submitter = dspacedemo+submit@gmail.com
Photo by @inspiredimages
 

Communities in DSpace

Select a community to browse its collections.

Recent Submissions

Item
Predictive Analytics: Anemia Disease Forecasting with Machine Learning and Deep Learning Models
(2024-02) Shamim, Muhammad
Remarkable breakthroughs in medical research are creating important information that we utilize every day. To gain appropriate details for analysis, prediction, creating suggestions, and establishing choices, this data has to be analyzed. Turn present data into information by applying data mining or machine learning approaches. Accurate illness forecasting is vital in medicine for both preventative and efficient treatment planning. On event, a lack of precision might be deadly. In order to anticipate anemia, this work investigates several machine learning (ML) classification strategies in a large dataset to diagnose anemia , and the performance of these algorithms is confirmed using measurements such as error rate, accuracy, precision, recall, and F-Measure. Strategies were tried in the experiment, and it was determined that Random forest functioned better than any ML methodology, with the maximum accuracy of 100 percent when compared to other algorithms.
Item
Course Code: Phy-1101, Course Title: Physics-1
(Department of Computer and Communication Engineering, 2024-04) IIUC
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Course Code: Phy-1101, Course Title: Physics-1
(Department of Computer and Communication Engineering, 2024-04) IIUC
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Course Code: Math-1101, Course Title: Differential and Interegal Calculus
(Department of Electronic and Telecommunication Engineering, 2024-04) IIUC
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Course Code: Math-1101, Course Title: Differential and Interegal Calculus
(Department of Electronic and Telecommunication Engineering, 2024) IIUC