Dr. Nailah Al-Madi

Nailah Al-Madi received her PhD degree in Computer Science from North Dakota State University, USA, in 2014 with GPA 4.00/4.00. During her PhD study, she worked as a research assistant, and got two awards (Doctoral Dissertation award, and being selected as one of the top 10% students to join Phi Kappa Phi Society). She earned her M.Sc. degree in Computer Science from Jordan University of Science and Technology, Jordan, in 2009. Her MSc thesis was titled "Chain Based Distributed Routing Algorithms For Wireless Sensor Networks". She received her B.Sc. degree in Computer Information Systems from Al al-Bayt University, Jordan, in 2005, and was ranked first. Before getting her PhD, she worked as a lecturer in University of Jordan and Middle East University (Amman) for about 2 years. She is currently working as an Assistant Professor in Princess Sumaya University for Technology, Jordan. Her research interests include: Optimization and Evolutionary Computation, Data Mining, Big Data, MapReduce and Hadoop Framework, Robotics, and Wireless Sensor Networks.



  • Suleiman, D., Awajan, A., & Al-Madi, N. (2017, October). Deep Learning Based Technique for Plagiarism Detection in Arabic Texts. In 2017 International Conference on New Trends in Computing Sciences (ICTCS) (pp. 216-222). IEEE. 

  • Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 1-21.

  • Aljarah, I., Faris, H., Mirjalili, S., Al-Madi, N., Sheta, A., & Mafarja, M.(2019). Evolving neural networks using bird swarm algorithm for data classification and regression applications. Cluster Computing, Springer.

  • Al-Madi, N., Faris, H., & Abukhurma R. (2018). Cost-Sensitive Genetic Programming for Churn Prediction and Identification of the Influencing Factors in Telecommunication Market, International journal of advanced science and technology.

  • Aljarah, I., Faris, H., Mirjalili, S., & Al-Madi, N. (2018). Training radial basis function networks using biogeography-based optimizer. Neural Computing and Applications, 29(7), 529-553.

  • Faris, H., Aljarah, I., Al-Madi, N., & Mirjalili, S. (2016). Optimizing the learning process of feedforward neural networks using lightning search algorithm. International Journal on Artificial Intelligence Tools, 25(06), 1650033.

  • Al-Madi, N. (2016). Mike Preuss: Multimodal optimization by means of evolutionary algorithms. Genetic Programming and Evolvable Machines, 17(3), 315-316.

  • Al Shorman A., Faris H., Castillo P., J.J. Merelo, & Al-Madi, N. (2018). The Influence Of Input Data Standardization Methods On The Prediction Accuracy Of Genetic Programming Generated Classifiers. The 10th International Joint Conference On Computational Intelligence.

  • Yasen, M., Al-Madi, N., & Obeid, N. (2018, July). Optimizing Neural Networks using Dragonfly Algorithm for Medical Prediction. In 2018 8th International Conference on Computer Science and Information Technology (CSIT) (pp. 71-76). IEEE.

  • Yasen, M. Z. Y., Al-Jundi, R. A. S., & Al-Madi, N. (2017, October). Optimized ANN-ABC for Thunderstorms Prediction. In 2017 International Conference on New Trends in Computing Sciences (ICTCS) (pp. 98-103). IEEE.

  • Alafeef, I., Awad, F., & Al-Madi, N. (2017, October). Energy-aware geographic routing protocol with sleep scheduling for wireless multimedia sensor networks. In 2017 14th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT) (pp. 93-97). IEEE.

  • Al-Jundi, R., Yasin, M., & Al-Madi N. (2017). Thunderstorms Prediction using Genetic Programming, The 2ⁿᵈ International Computer Sciences and Informatics Conference (ICSIC).

  • Bilbeisi, G., Al-Madi, N., & Awad, F. (2015, November). PSO-AG: A Multi-Robot Path Planning and obstacle avoidance algorithm. In 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)(pp. 1-6). IEEE.

  • Al-Madi, N., Aljarah, I., & Ludwig, S. A. (2014, December). Parallel glowworm swarm optimization clustering algorithm based on MapReduce. In 2014 IEEE Symposium on Swarm Intelligence (pp. 1-8). IEEE.

  • Al-Madi, N., & Ludwig, S. A. (2013, August). Scaling genetic programming for data classification using mapreduce methodology. In 2013 World Congress on Nature and Biologically Inspired Computing (pp. 132-139). IEEE.

  • Al-Madi, N., & Ludwig, S. A. (2013, July). Segment-based genetic programming. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation (pp. 133-134). ACM.

  • Al-Madi, N., & Ludwig, S. A. (2013, April). Improving genetic programming classification for binary and multiclass datasets. In 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (pp. 166-173). IEEE.

  • Al-Madi, N., & Ludwig, S. A. (2012, November). Adaptive genetic programming applied to classification in data mining. In 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) (pp. 79-85). IEEE.