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Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems by Abdul Hamid, Norhamreeza, Mohd Nawi, Nazri, Ghazali, Rozaida, Mohd Salleh, Mohd Najib

“…The back propagation (BP) algorithm is a very popular learning approach in feedforward multilayer perceptron networks. …”
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Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

“…Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

“…These problems will occur because these fields are mainly used machine learning classifiers. …”
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The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement by Hosseini E., Al-Ghaili A.M., Kadir D.H., Daneshfar F., Gunasekaran S.S., Deveci M.

“…In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. …”

An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective by Mousavi, Seyed Mohsen, Sadeghi R., Kiarash, Lee, Lai Soon

“…However, there needs to be more research examining the role of interactive methods in multiobjective optimisation problems. To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. …”
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Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

“…Similarly, the algorithm proposed in MALESAbrain can, be used to deal, the problem of conducting a meeting among learners to solve problems. …”
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Opposition-based learning simulated kalman filter for Numerical optimization problems by Mohd Falfazli, Mat Jusof

“…Using CEC2014 benchmark suite, it is found that the SKF with opposition-based learning outperforms the original SKF algorithm in most cases. …”
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic by Yap, Soon Teck

“…The CDRQ Routing Algorithm provides a solution to the problem addressed above by integrating the advantages of CQ Routing Algorithm and Dual Reinforcement Learning. …”
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Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm by Ullah, Wasif, Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

“…This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. …”
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Automated bilateral negotiation with incomplete information in the e-marketplace. by Jazayeriy, Hamid

“…This algorithm comes with some corollaries that form a learning approach in one-side incomplete problem. …”
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Algorithm as a problem solving technique for teaching and learning of the Malay language by Nazir, Faridah, Jano, Zanariah, Omar, Norliza

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Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

“…Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. …”
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Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

“…Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. …”
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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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Algorithm As A Problem Solving Technique For Teaching And Learning Of The Malay Language by Jano, Zanariah, Omar, Norliza, Nazir, Faridah

“…This conceptual teaching and learning algorithm was conducted in five steps namely the induction set; step 1; step 2; step 3; and enrichment and recovery. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…There are two general paradigms for pattern recognition classification which are supervised and unsupervised learning. The problems in applying unsupervised learning/clustering is that this method requires teacher during the classification process and it has to learn independently which may lead to poor classification. …”
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A machine learning approach to tourism recommendations system by Chia, An

“…This project aims to develop a tourism attractions recommendation system by integrating machine learning recommendation algorithms. The main problem encountered when developing a powerful recommendation system is cold start problem, data sparsity and scalability problems. …”
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Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying by Liu , Zongying

“…Recent years, Machine correlation and potential non stationary of the data can be automatically analyzed. However, the problems with traditional offline and online learning algorithms in machine learning algorithms are usually faced with parameter dependency, concept drift handling problem, connectionless of neural net and unfixed reservoir. …”
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Algorithm as a problem solving technique for teaching and learning of the Malay language by Faridah Nazir, Zanariah Jano, Norliza Omar

“…This conceptual teaching and learning algorithm was conducted in five steps namely the induction set; step 1; step 2; step 3; and enrichment and recovery. …”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

“…Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. …”
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