Multi-Faced Automatic Classification of IR Objects | Oral Defense | Robert M'sendo | March 17 2023
Channel: Lighton Phiri
Date: 20/03/2023

Robert M'sendo did his MSc in Computer Science oral examination defense, titled "Multi-Faced Automatic Classification Of Institutional Digital Objects: A Case Study Of The University Of Zambia", on Friday, March 17, 2023, at 10H30 GMT+2. 

This oral examination defense is a partial fulfillment of the MSc in Computer Science programme [1] offered in the Department of Computer Science [2] at The University of Zambia [3]

## Video Timeline
00:00:00 Speaker Introduction
00:00:16 Introduction
00:02:28 Statement of The Problem
00:03:27 Intermittent Connection
00:03:56 Aims of The Study
00:04:10 Research Objectives
00:04:47 Research Questions
00:05:07 Significance of The Study
00:05:34 Literature Review
00:07:30 Methodology
00:07:54 Results and Discussion
00:14:07 Conclusions
00:14:33 Future Works and Recommendations


## Title of Talk
Multi-Faced Automatic Classification Of Institutional Digital Objects: A Case Study Of The University Of Zambia

## Abstract
Institutional Repositories (IRs) provide the ability to store, manage and disseminate intellectual products created by an institution. They provide a complementary method of the traditional system of scholarly communication making it easier to demonstrate the scientific, social and financial value of an institution. The potential benefit of an IR goes beyond the desire to increase an institution’s profile. They also increase authors’ visibility and provide users with easy access to information. Despite the hasty pace at which organization are creating IRs, and with all the potential benefits they offer, recent studies have established that two biggest existing problems are that of digital objects having missing important metadata elements and wrong classification of digital objects into communities. The aim of this study was to design, develop and implement three classification models and a prototype tool that uses the classification models for effective ingestion of digital objects into an IR. To achieve this, firstly, a situational analysis was conducted to appreciate the challenges of the current system being experienced in the tagging and ingestion process of digital objects into the IR. Furthermore, an exploratory study was conducted in order to assess the full extent of the problem. Finally, three classification models were implemented. Experiments on classification using the developed models were conducted and the results demonstrated the possibility of automatically classifying digital objects into an IR with an accuracy of 77 % for collection classification model, 75 % for Document type model and 0.005 % Hamming loss for the subject classification model. These results can thus help in addressing of the two biggest existing problems related to IRs

## Biography
Robert M’sendo holds a Master’s Degree in Computer Science (DMI St Eugene University), Bachelor of Science in Computer Science (University of Hertfordshire), Routing and Switching(Huawei), Diploma in Information System and Programming(NIPA), Certificate in Information System and Programming(NIPA).

He is currently working as Senior Lecturer -  Information & Communication Technology at NIPA in the Business Studies Division. Also served as Senior Database Administrator at the same Institute for 3 Years. He also worked as Consultant – Information Technology for 8 Years at the same Institute.

[1] http://www.cs.unza.zm
[2] http://www.cs.unza.zm 
[3] https://www.unza.zm