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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
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Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring
Published 2016“…The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. …”
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Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients
Published 2017“…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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Enhanced recognition methods for text and slider CAPTCHA vulnerability assessment
Published 2025“…By comparing them with the benchmark models in terms of recognition accuracy, precision, computational complexity, storage space, and other metrics, the superiority of our proposed algorithm is proved. …”
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Analysis and decentralised optimal flow control of heterogeneous computer communication network models
Published 1993“…A new method of general model reduction using the Norton' s theorem for general queueing networks in conjunction with the universal maximum entropy algorithm is proposed for the analysis of xix large general closed queueing networks. …”
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Development of compound clustering techniques using hybrid soft-computing algorithms
Published 2006“…To overcome this problem, fuzzy memberships have been employed to find optimal parameters. Since fuzzy clustering methods such as the fuzzy c-means and fuzzy G – K are computationally exhaustive in terms of time and memory requirements, a hierarchical approach have also been used in this work for their implementation. …”
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Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning
Published 2023“…This research contributes novel insights into the application of clustering algorithms in banking, proposing pragmatic solutions for efficient data analysis and campaign optimization. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…Most of the existing algorithms consider the noise filtering step precedes trajectory segmentation step which mean that there is no preprocessing method to partition trajectory into set of segments and remove noise points in real time with low computational cost. …”
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Study of hand gesture recognition using impulse radio ultra wideband (IRUWB) radar sensor
Published 2023“…Hand gesture recognition technology has gained significant attention in recent years due to its potential to revolutionize human-computer interaction by offering a natural and intuitive means of communication. …”
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Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin
Published 2021“…Instead of randomly selecting the inputs, the test generator learns how to act in an optimal way that explores new states by using new actions to gain more rewards. …”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model
Published 2023“…The Transformer model with transferred weights outperformed models trained from scratch using supervised learning. To select the optimal hyperparameters for the Transformer model, the Tree Parzen Estimator(TPE) optimization in combination with the Hyperband pruning algorithm is employed to search for the best combination that yields the lowest Root Mean Squared Error(RMSE)and Mean Absolute Error (MAE) error metrics. …”
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A near-optimal centroids initialization in K-means algorithm using bees algorithm
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A web-based implementation of k-means algorithms
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Movie recommendation system / Najwa Syamimie Hasnu
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