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Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…Firstly, an architecture for the clustering ensemble based on incremental genetic-based algorithms is proposed consisting of two phases: (i) to produce cluster partitions as initial populations, (ii) to combine cluster partitions and to generate final clustering solution by incremental genetic based clustering ensemble learning algorithm. …”
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Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

“…Fed-batch fermentation process has always been a challenge for optimisation because it is highly non-linear and complex. Deep reinforcement learning is a self-learning algorithm through trial and error and experience, without any prior knowledge. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification by Wong S.Y., Yap K.S., Yap H.J., Tan S.C.

Autonomous path planning robot using geographical information by Ismail Ishaq Ibrahim

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A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification by Wong, S.Y., Yap, K.S., Yap, H.J., Tan, S.C.

“…The idea of developing FAM-OELM is motivated by the ELM concept proposed by Huang et al., for being an efficient learning algorithm that provides better generalization performance at a much faster learning speed. …”
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A harmony search-based learning algorithm for epileptic seizure prediction by Kee, Huong Lai, Zainuddin, Zarita, Ong, Pauline

“…The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. …”
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Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling by Ullah, Wasif, Mohd Fadzil Faisae, Ab Rashid, Muhammad Ammar, Nik Mu’tasim

“…However, limited attention has been given to CHFS when considering holistic cost models using efficient algorithms. This paper presents a novel Greedy-Assisted Teaching-Learning-Based Optimization (GTLBO) algorithm for CHFS. …”
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Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm by Zainuddin, Zarita

“…The backpropagation algorithm has proven to be one of the most successful neural network learning algorithms. …”
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Solving the optimal path planning of a mobile robot using improved Q-learning by Low, Ee Soong, Ong, Pauline, Cheah, Kah Chun

“…In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. …”
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Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences by Adnan Ashraf, Sobia Pervaiz, Waqas Haider Bangyal, Kashif Nisar, Ag. Asri Ag. Ibrahim, Joel J. P. C. Rodrigues, Danda B. Rawat

“…To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. …”
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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

“…Although the swmcan algorithm solves the noise problem in multi-view data, its initial and final graphs are independent and cannot learn from each other. …”
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Machine Learning Algorithms for Diabetes Prediction: A Review Paper by Al-Sideiri A., Cob Z.B.C., Drus S.B.M.

Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi

“…As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. …”
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Image classification of Aedes mosquitoes using transfer learning / Zetty Ilham Abdullah by Abdullah, Zetty Ilham

“…The advancement and rapid growth of machine learning field should not overlook this issue. Transfer learning concept in machine learning has been shown to improve learning of the targeted task by extending the original algorithm with knowledge gathered from the initial training to improve the performance of new model. …”
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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

“…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar

“…As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. …”
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Detection on ambiguous software requirements specification written in malay using machine learning by Zahrin, Mohd Firdaus

“…Four (4) algorithms have been evaluated to find the suitable classification algorithm for this purpose. …”
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Wavelet network based online sequential extreme learning machine for dynamic system modeling by Mohammed Salih, Dhiadeen, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Raja Ahmad, Raja Mohd Kamil

“…This attains good performance at extremely fast learning. The initial kernel parameters of WN played a big role to ensure fast and better learning performance. …”
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Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation by Alia, Osama Moh’d Radi

“…Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. …”
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