Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach حسب S. Punitha (17945654)

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Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect حسب Habeeb, Abdallah

"…The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). …"
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Using artificial bee colony to optimize software quality estimation models. (c2015) حسب Abou Assi, Tatiana Antoine

"…In this thesis, we propose a novel heuristic based on Artificial Bee Colony (ABC) to optimize rule-based software quality prediction models. …"
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Correlation Clustering with Overlaps حسب Fakhereldine, Amin

"…We present a heuristic algorithm and a semi-exact algorithm for the Multi-Parameterized Cluster Editing with Vertex Splitting problem. …"
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV حسب Zulkifli, Muhammad

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Recent advances on artificial intelligence and learning techniques in cognitive radio networks حسب Abbas, Nadine

"…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …"
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Multidimensional Gains for Stochastic Approximation حسب Saab, Samer S.

"…The proposed algorithms here aim for per-iteration minimization of the mean square estimate error. …"
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On the complexity of multi-parameterized cluster editing حسب Abu-Khzam, Faisal

"…In other words, Cluster Editing can be solved efficiently when the number of false positives/negatives per single data element is expected to be small compared to the minimum cluster size. …"
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