Nailah Al-Madi received her PhD degree in Computer Science from North Dakota State University, USA. She earned her M.Sc. degree in Computer Science from Jordan University of Science and Technology, Jordan. She received her B.Sc. degree in Computer Information Systems from Al al-Bayt University, Jordan. She is currently working as an Assistant Professor in Princess Sumaya University for Technology, Jordan. Her research interests include: Data Mining, Optimization and Evolutionary Computation, Artificial Intelligence, Big Data, MapReduce and Hadoop Framework, Robotics, and Wireless Sensor Networks.
King Hussein School of Computing Sciences
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- Nailah Al-Madi, Hossam Faris, Ruba Abukhurma, “Cost-Sensitive Genetic Programming for Churn Prediction and Identification of the Influencing Factors in Telecommunication Market”, International journal of advanced science and technology, 2018 .
- Amaal R. Al Shorman, Hossam Faris, Pedro A. Castillo, J.J. Merelo, and Nailah Al-Madi, “The Influence Of Input Data Standardization Methods On The Prediction Accuracy Of Genetic Programming Generated Classifiers”, The 10th International Joint Conference On Computational Intelligence, 2018
- Mais Yasin, Nailah Al-Madi, Nadim, Obeid, “Optimizing Neural Networks using Dragonfly Algorithm for Medical Prediction”, The 8th international conference on Computer Science and Information Technology (CSIT), 2018.
- Ruba Al-Jundi, Mais Yasin, Nailah Al-Madi, “Thunderstorms Prediction using Genetic Programming”, To appear in The 2ⁿᵈ International Computer Sciences and Informatics Conference (ICSIC), 2017.
- Ibrahim Alafeef, Fahed Awad, Nailah Al-Madi, “Energy-Aware Geographic Routing Protocol with Sleep Scheduling for Wireless Multimedia Sensor Networks”, 14th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), 2017.
- Mais Yasin, Ruba Al-Jundi, Nailah Al-Madi, “Optimized ANN-ABC for Thunderstorms Prediction”, to appear in The International Conference on new Trends in Computing Sciences (ICTCS), Octobar,2017.
- Dima Suleiman, Arafat Awajan, Nailah Al-Madi, “Deep Learning Based Technique for plagiarism detection in Arabic texts”, to appear in The International Conference on new Trends in Computing Sciences (ICTCS), Octobar,2017.
- Al-Madi Nailah, “Mike Preuss: Multimodal optimization by means of evolutionary algorithms”. Genetic Programming and Evolvable Machines (Springer), Volume 17, Issue 3, pp 315-316. 2016
- Aljarah I., Faris H., Mirjalili S. and Al-Madi Nailah. “Training radial basis function networks using biogeography-based optimizer”. Neural Computing and Applications (Springer). pp 1-25. 2016.
- Faris H., Aljarah I., Mirjalili S. and Al-Madi Nailah “Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm”. International Journal on Artificial Intelligence Tools. Volume 25, Issue 3=6, pp 1650033 (32 pages).2016
- Ghaith Bilbeisi, Nailah Al-Madi, Fahed Awad, "A Multi-Robot Path Planning and Obstacle Avoidance Algorithm", In Proceedings of IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (IEEE AEECT '15), 2015
- Nailah Al-Madi, Ibrahim Aljarah, Simone Ludwig, "Parallel Glowworm Swarm Optimization Clustering Algorithm based on MapReduce", In Proceeding of IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014)
- Nailah Al-Madi, Simone Ludwig, "Scaling Genetic Programming for Data Classification using MapReduce Methodology" , In Proceedings of Fifth World Congress on Nature and Biologically Inspired Computing IEEE NaBIC) 2013
- Nailah Al-Madi, Simone Ludwig, "Segment-based Genetic Programming", In Proceedings of the Genetic and Evolutionary Computation Conference (ACM GECCO'13), 2013
- Nailah Al-Madi, Simone Ludwig, "Improving Genetic Programming Classification For Binary And Multiclass Datasets", In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), 2013
- Nailah Al-Madi, Simone Ludwig, "Adaptive Genetic Programming applied to Classification in Data Mining", In Proceedings of Fourth World Congress on Nature and Biologically Inspired Computing (IEEE NaBIC), 2012
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