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Dr. Adnan Amin
Institute of Management Sciences Peshawar Pakistan, Peshawar, Khyber Pakhtunkhwa, Pakistan
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(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications26 October 2016 in IEEE Access
Journal Article
AAAdnan Amin
Adnan AminSASajid Anwar
Sajid AnwarAAAwais Adnan
Awais Adnan
MNMuhammad Nawaz
Muhammad Nawaz
NHNewton Howard
Newton Howard
AHAmir Hussain
Amir Hussain +2
Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring the future behavior of the customers. However, the performances of predictive models are greatly affected when the real-world data set is highly imbalanced. A data set is called imbalanced if the samples size from one class is very much smaller or larger than the other classes. The most commonly used technique is over/under sampling for handling the class-imbalance problem (CIP) in various domains. In this paper, we survey six well-known sampling techniques and compare the performances of these key techniques, i.e., mega-trend diffusion function (MTDF), synthetic minority oversampling technique, adaptive synthetic sampling approach, couples top-N reverse k-nearest neighbor, majority weighted minority oversampling technique, and immune centroids oversampling technique. Moreover, this paper also reveals the evaluation of four rules-generation algorithms (the learning from example module, version 2 (LEM2), covering, exhaustive, and genetic algorithms) using publicly available data sets. The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.
1 January 2019 in Journal of Business Research
Journal Article
AAAdnan Amin
Adnan AminFAFeras Al-Obeidat
Feras Al-Obeidat
BSBabar Shah
Babar ShahAAAwais Adnan
Awais Adnan
Jonathan LooSASajid Anwar
Sajid Anwar21 February 2023 in Applied Soft Computing
Journal Article
13 October 2017 in The Journal of Supercomputing
Journal Article
AAAdnan Amin
Adnan AminFAFeras Al-Obeidat
Feras Al-Obeidat
BSBabar Shah
Babar ShahMTMay Al Tae
May Al Tae
CKChangez Khan
Changez Khan
SASajid Anwar
Sajid Anwar +1
1 January 2015
Book Chapter
AAAdnan Amin
Adnan AminSSSaeed Shehzad
Saeed ShehzadCKChangez Khan
Changez Khan
IAImtiaz Ali
Imtiaz AliSASajid Anwar
Sajid Anwar25 January 2021 in IEEE Access
Journal Article
Link mining is an important task in the field of data mining and has numerous applications in informal community. Suppose a real-world complex network, the responsibility of this function is to anticipate those links which are not occurred yet in the given real-world network. Holding the significance of LP, the link mining or expectation job has gotten generous consideration from scientists in differing exercise. In this manner, countless strategies for taking care of this issue have been proposed in the late decades. Various articles of link prediction are accessible, however, these are antiquated as multiples new methodologies introduced. In this paper, give a precise assessment of prevail link mining approaches. The investigation is through, it consists the soonest scoring-based approaches and reaches out to the latest strategies which confide on different link prediction strategies. We additionally order link prediction strategies because of their specialized methodology and discussion about the quality and weaknesses of various techniques. Additionally, we compared and expounded various top link prediction techniques. The experimental results of these techniques, over twelve data-sets are ordered here based on performance, RA, 0.7411 > AA, 0.7285 > PA, 0.7202 > Katz, 0.7141 > CN, 0.6951 > HP, 0.6924 > LHN, 0.6017 > PD, 0.3978.
About Me
Adnan Amin, is a Lecturer at the Centre of Excellence in Information Technology, IMSciences, Peshawar, Pakistan. He received his MSc CS and MS CS (research degree) in 2008 and 2015, respectively, with distinction (Gold Medal). Currently, he is pursuing his Ph.D. research in data mining/machinel earning in IMSciences. Previously, he worked for Maxwell Stamp PLC (London) as an international consultant for curricular and academic development activities for the School of ICT, National Institute of Management and Administration affiliated with the University of Jyvaskyla, Finland, under the World Bank-funded project (P102573). He is a leading expert in data mining, machine learning, and data science. His research interests are cross-disciplinary and mostly applied industry-oriented. They include: churn prediction, simulated expert, customer analytics, fake news, real-time classification and segmentation, cyber security, rough set theory, oversampling, and big data analytics. He has been a track chair of the WorldCist’21 workshop (LCBUDAMLT) and a program committee member in numerous conferences such as WorldCist-15, WorldCist-16, and SDIWC, and also an active reviewer for 30+ reputable journals. He has conducted and led software development projects and collaborative scientific research projects with academia and industry. He supervises many MS research students in the area of data mining and machine learning. He has authored 28+ research papers, including 15+ journal articles.