Adnan Amin | Customer Analytics, Machine Learning, Churn Prediction | Member

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Adnan Amin | Customer Analytics, Machine Learning, Churn Prediction | Member

Dr Adnan Amin Institute of Management Sciences Peshawar, Pakistan

Adnan Amin earned his M.Sc. in Computer Science from the University of Peshawar in 2008. He received his MS-CS degree (highest 1st class honors with distinction) and a Ph.D. degree (with Scholastic Honours) in data mining and machine learning from the Institute of Management Sciences (IMSciences) Peshawar, Pakistan, in 2015 and 2023, respectively. In November 2015, he joined IMSciences as a lecturer at the Center for Excellence in Information Technology. He is the lead of IM|DigiSol and the faculty adviser for the Computing and Innovation Society (IMCIS) at IMSciences. Before, he worked as an international consultant for curriculum and academic development at the School of ICT, NIMA Kabul, which was connected to the University of Jyvaskyla in Finland. This was part of a World Bank-funded project for Maxwell Stamp PLC, London (Project Code: P102573). Dr. Adnan’s research and innovation interests are industry-driven and cross-disciplinary. He focuses on developing and commercializing digital solutions and data mining/machine learning research innovations for a variety of industries, such as education, telecommunications, and healthcare. He has completed nearly 8 research projects in collaboration with international universities. He has served as the track chair of the WorldCist’21 workshop (LCBUDAMLT), the session chair of the ICITA’22, and a member of the program committees of numerous conferences including WorldCist, SDIWC, and DATA’23. He has (co) authored 29+ publications, including 16 journal articles, 11 conferences, and 2 book chapters in Springer, and obtained nearly 1000 citations with an h-index score of 14 and an i-index score of 16.

Education:

He completed his Ph.D. in Computer Science with a specialization in Data Mining & Machine Learning from the Center for Excellence in IT at the Institute of Management Sciences, Peshawar, Pakistan, in March 2023. His research focused on developing an adaptive learning approach for customer churn prediction using naïve Bayes. Prior to his Ph.D., he earned his MS in Computer Science with the highest 1st class honors and distinction from the same institution in May 2015, where he conducted research on a similar topic. He also holds an M.Sc. in Computer Science from the University of Peshawar, which he completed with a Grade of A+ in March 2008.

🌐 Profile Links:

Experience:

He has been serving as a lecturer at the Institute of Management Sciences in Peshawar, Pakistan, since November 2015. In this role, he teaches both graduate and undergraduate students and leads IM DigiSol, the institute’s in-house software house. Additionally, he holds the position of Web & Database Administrator (BPS-17) at the Directorate of Information Technology, Government of KP, where he spearheads the computerization efforts of the Special Branch Police Department and Counter Terrorism Department. Prior to this, he worked as an I.T/ERP Trainer at Rehman Medical Institute & Hospital in Peshawar, Pakistan, from 2012. He also served as an international consultant for curriculum and academic development at the School of ICT, NIMA Kabul, Afghanistan, from 2009 to 2011, as part of a World Bank-funded project implemented through Maxwell Stamp PLC, London. In this capacity, he provided teaching support to undergraduate students.

Publications:

  1. Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study Cited By : 294, Published By : 2016
  2. Customer churn prediction in the telecommunication sector using a rough set approach Cited By : 258, Published By : 2017
  3. Customer churn prediction in telecommunication industry using data certainty Cited By : 232, Published By : 2018
  4. Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods Cited By : 88, Published By : 2019
  5. Churn prediction in telecommunication industry using rough set approach Cited By : 51, Published By : 2015
  6. Customer churn prediction in telecommunication industry: with and without counter-example Cited By : 40, Published By : 2014
  7. Just-in-time customer churn prediction in the telecommunication sector Cited By : 39, Published By : 2020
  8. Just-in-time customer churn prediction: With and without data transformation Cited By : 30, Published By : 2018
  9. A comparison of two oversampling techniques (smote vs mtdf) for handling class imbalance problem: A case study of customer churn prediction Cited By : 29, Published By : 2015
  10. Compromised user credentials detection in a digital enterprise using behavioral analytics Cited By : 27, Published By : 2019