Draft:Neema Mathias Mduma

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Neema Mathias Mduma ( (1989-10-21) 21 October 1989 (age 34) , in Morogoro, Tanzania) is a Tanzanian computer scientist, entrepreneur and educator. She earned her Doctor of Philosophy (PHD) at a young age, distinguishing herself as one of the few Tanzanians to achieve this academic milestone early in life^[1]. She is recognized for her effort to inspire and support girls in pursuing education, particularly in the fields of science, technology, engineering, and mathematics (STEM)^[2].

Neema Mathias Mduma is the daughter of Mathias Mduma, a church pastor, and Judith Tesha, a teacher.

She attended Bungo Primary School in Morogoro from 1996 to 2002. She then Joined Kilakala Secondary School , a special talent school for girls from 2003 - 2006 and continued with form five and six at the same school, and completed her studies in 2009.^[3]

In 2009, Neema began higher education at the University of Iringa (formerly Tumaini University), pursuing an undergraduate degree in information and Communication Science. After earning her degree, she joined NM-AIST, where she completed a master's degree (MSc) and later a doctorate degree (PhD) in Science and Communication Technology, specializing in Artificial Intelligence (IA)^[4].

Her Masters Theses and Dissertations [CoCSE] Titled "An integrated mobile application for enhancing management of nutrition information in Tanzania" ^{[5] [6][7]}

In 2013, Dr. Neema Mduma began her Career as a Tutorial Assistant at Tumaini University. She transitioned to become an Assistant lecturer at the Nelson Mandela African Institution of Science and Technology (NM-AIST) from 2012 to 2013^[8]. Her academic journey culminated in early 2024 when she was appointed as a senior Lecturer at NM-AIST^[9].

In addition to her academic roles, Dr. Mduma was selected in early 2024 by the Minister of State, President's Office Planning and investment, to serve as a member of the technical team responsible for drafting Tanzania Development Vision 2050^[10]. This appointment highlights her experience and leadership in shaping long term national strategies, particularly in integrating technology and education for sustainable development in Tanzania.

Neema worked as a consultant with UNESCO's project on strengthening disaster preventation approaches in East Africa (STEDPEA): artificial intelligence for disaster reduction and this was august 2021, in Zanzibar Tanzania and on august 2022 she also consulted with UNESCO development framework of guidelines to support the execution of the functions of Zanzibar research agenda.

While Neema was finishing her doctoral studies (PhD), she focused her research on addressing the issue of school absenteeism, which often leads to students failing or dropping out^[11]. She developed a system called "Baki Shule", which utilizes artificial intelligence to detect signs of excessive absenteeism or potential absenteeism among students. This system aids parents and teachers in identifying issues early, preventing the problem from severely impacting a student's academic performance^[12].

To support the Tanzanian community, Neema dedicates her spare time to visiting schools and encouraging girls to pursue science and mathematics.^[13]. She provides training in coding, helping them develop a passion for science and mathematics, empowering them to achieve their dreams of becoming scientists like Neema or pursuing other advanced careers^[14].

1. Machines Learning tools for early detection of maize and common bean disease for climate change adaption in Tanzania funded by Growfuther^[15].
2. Deep learning tools for early detection of diseases affecting common bean and irish potato in the southern highlands regions of tanzania funded by IDRC/SIDA and TWAS - UNESCO through organization for women in science for the developing world (OWSD)^[16].
3. Machine learning datasets for crop diseases, imagery and sprectrometry data funded by international resarch centre (IDRC) and Swedish International Development cooperational agency (SIDA) through Lacuna fund in agriculture ^[17].
4. Data science africa university program funded by Data schience Africa (DSA).
5. Deep learning techniques for early detection of crops diseases funded by IDRC/SIDA through Africa Centrer for Technology studies (ACTS)^[18].

1. 100 Tanzanian changemakers awarded by Serengeti Bytes, 2023^[19]
2. Appointed member of the committee for preparing Tanzania Development vision 2050, 2023^[20]
3. Appointed member of the committee for preparing the concept on supporting the establishment of the Africa center of excellent (ACEs) in Tanzania, 2023
4. Distinguished staff awarded by the school of CoCSE at the Nelson Mandela African Institute of Science and Technology, 2023
5. Best worker for Academic staff at the Nelson Mandela Africa Institute of Science and Technology, 2023
6. WIMA STEM awarded by Women in Management Africa (WIMA), 2021
7. Excellence in Science and Technology leadership awarded by Coca-Cola Kwanza Ltd, 2021^[21]
8. International Women's Day recognition awarded by Puma energy Tanzania, 2021
9. L'Oréal - UNESCO for women in Science, 20 young talents in sub Sahara Africa, 2020^[22]
10. Women in science awarded by Next Einstein forum, 2019
11. Tanzania sheroes awarded by the Launchpad and embassy of Sweden, 2019
12. Queen Elizabeth scholar awarded by the Carleton University in Ottawa, Canada, 2019
13. Deep learning Indaba conference, MIT press book award in Nairobi, Kenya, 2019
14. The 4th business plan competition. First winning team in Nelson Mandela African Institution of science nd technology, 2016
15. Nelson Mandela week exhibtions, third winner in Nelson Mandela Africa Institute of Science and Technology, 2015

1. Mobile-Based convolutional neural network model for the early identification of banana diseases. Smart Agricultural Technology, Vol 7 (2024). https://doi.org/10.1016/j.atech.2024.100423 (Co-authored with Christian Elinisa)
2. Dataset of Banana Leaves and Stem Images for Object Detection, Classification and Segmentation: A Case of Tanzania. Data in Brief, Vol 49 (2023). https://doi.org/10.1016/j.dib.2023.109322 (Co-authored with Judith Leo)
3. A Machine Learning Model for Detecting Covid-19 Misinformation in Swahili Language. Engineering, Technology & Applied Science Research, Vol 13 (2023). https://doi.org/10.48084/etasr.5636 (Co-authored with Filbert Mlawa and Elizabeth Mkoba)
4. A Deep Learning Model for Predicting Stock Prices in Tanzania. Engineering, Technology & Applied Science Research, Vol 13 (2023). https://doi.org/10.48084/etasr.5710 (Co-authored with Samuel Joseph and Devotha Nyambo)
5. Machine Learning Imagery Dataset for Maize Crop: A Case of Tanzania. Data in Brief, Vol 48 (2023). https://doi.org/10.1016/j.dib.2023.109108 (Co-authored with Hudson Laizer)
6. Data Balancing Techniques for Predicting Student Dropout Using Machine Learning. Data, Vol 8 No 49 (2023). https://doi.org/10.3390/data8030049
7. Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania. East African Health Research Journal, Vol 6 No 2 (2022). https://doi.org/10.24248/eahrj.v6i2.696 (Co-authored with Martina Mariki and Elizabeth Mkoba)
8. Poultry diseases diagnostics models using deep learning. Frontiers in Artificial Intelligence. 5:733345. https://doi.org/10.3389/frai.2022.733345 (Co-authored with Dina Machuve, Ezinne Nwankwo and Jimmy Mbelwa)
9. Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach. Applied Artificial Intelligence, 2022, https://doi.org/10.1080/08839514.2022.2031826 (Co-authored with Martina Mariki and Elizabeth Mkoba)
10. Machine Learning Model for Predicting Student Dropout: A Case of Tanzania, Kenya and Uganda. 2021 IEEE AFRICON, 2021, pp. 1-6; https://doi.10.1109/AFRICON51333.2021.9570956 (Co-authored with Dina Machuve)
11. Document Digitization Technology and Its Application in Tanzania. In: Arai K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_53. (Co-authored with Mbonimpaye John, Beatus Mbunda, Victor Willa, Dina Machuve and Shubi Kaijage)
12. An Ensemble Predictive Model Based Prototype for Student Drop-out in Secondary Schools – Journal of Information Systems Engineering & Management, 4(3). DOI: https://doi.org/10.29333/jisem/5893 (Co-authored with Khamisi Kalegele and Dina Machuve)
13. A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction - Data Science Journal, 8:14, pp. 1-10. DOI: https://doi.org/10.5334/dsj-2019-014 (Co- authored with Khamisi Kalegele and Dina Machuve)
14. Machine Learning Approach for Reducing Students Dropout Rates – International Journal of Advanced Computer Research, Vol 9(42), 2019, https://www.accentsjournals.org/PaperDirectory/Journal/IJACR/2019/5/3.pdf, ISBN: 2277- 7970 (Co-authored with Khamisi Kalegele and Dina Machuve)
15. Enhancing Management of Nutrition Information Using Mobile Application: Prenatal and Postnatal Requirements - IST Africa Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation, 2017, https://ieeexplore.ieee.org/abstract/document/8102282, ISBN: 978-1-905824-56-4. (Co- authored with Khamisi Kalegele)
16. An Integrated Mobile Application for Enhancing Management of Nutrition Information in Arusha Tanzania - The International Journal of Computer Science and Information Security, Vol. 13 No. 7, July 2015, https://sites.google.com/site/ijcsis/vol-13-no-7-jul-2015, ISSN 1947- 5500. (Co-authored with Khamisi Kalegele)
17. Evaluating the Challenges of Technology Enhanced Learning in Universities in Tanzania - IST Africa Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation, 2013, https://ieeexplore.ieee.org/document/6701796, ISBN: 978-1-905824-38-0. (Co-authored with Josephat Oroma)