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Ahmad B. A. Hassanat
Researcher at Mutah University
Publications - 85
Citations - 1814
Ahmad B. A. Hassanat is an academic researcher from Mutah University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 15, co-authored 67 publications receiving 944 citations. Previous affiliations of Ahmad B. A. Hassanat include University of Tabuk & University of Buckingham.
Papers
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Journal ArticleDOI
Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach
Ahmad B. A. Hassanat,Khalid Almohammadi,Esra'a Alkafaween,Eman Abunawas,Awni Hammouri,V. B. Surya Prasath +5 more
TL;DR: New deterministic control approaches for crossover and mutation rates are defined, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic decreasing of High Crossover (ILM/DHC).
Journal ArticleDOI
Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review.
Haneen Arafat Abu Alfeilat,Ahmad B. A. Hassanat,Omar Lasassmeh,Ahmad S. Tarawneh,Mahmoud Bashir Alhasanat,Mahmoud Bashir Alhasanat,Hamzeh Eyal Salman,V. B. Surya Prasath +7 more
TL;DR: In this paper, the performance of the KNN classifier is evaluated using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise.
Journal ArticleDOI
Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review
TL;DR: Evaluating the performance of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances.
Posted Content
Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach
TL;DR: The experimental results show that the proposed classifier outperforms the traditional KNN classifier that uses a different number of neighbors, is competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications.
Journal ArticleDOI
Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
TL;DR: A new dataset is collected because there were no common data sets that contain modern DDoS attacks in different network layers, such as (SIDDoS, HTTP Flood), and this work incorporates three well-known classification techniques: Multilayer Perceptron (MLP), Naive Bayes and Random Forest.