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Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

“…Moreover, the extreme learning machine does not have any design parameters such as learning rate, covariance matrix, etc. …”
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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites by Din, Fakhrud

“…Unlike most existing meta-heuristic algorithms, and by virtue of being parameter-free, TLBO does not have any specific parameter controls. …”
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Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

“…This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. …”
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

“…In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.…”
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Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat by Chia , Yu Huat

“…This is due to the data generated from the numerical model possess the pattern for the ML algorithm ease of prediction. …”
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Water Quality Parameter Prediction Using Machine Learning Method by Muhammad Shaiful Bin Abdul Halim

Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS by Jaafar, Jafreezal

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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

“…The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. …”
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Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying by Liu , Zongying

“…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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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning by Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping

“…In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. …”
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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Saifulnizam, Abd.Khalid, Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Shareef, Hussain

“…The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ 2 , and adapt a supervised learning approach to train the LS-SVM model. …”
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Effect of chaos noise on the learning ability of back propagation algorithm in feed forward neural network by Azian Azamimi, Uwate, Yoko, Nishio, Yoshifumi

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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Dr., Saifulnizam, Abd. Khalid, Dr., Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Hussain, Shareef, Dr.

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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Hence, the algorithm must overcome the problem of dynamic updates in the internal parameters or counter the concept drift. …”
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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting by Lee Chin, Kho, Sze Song, Ngu, Annie, Joseph, Siti Kudnie, Sahari, Kuryati, Kipli, R., Rulaningtyas

“…The results show that the deep learning algorithm provides reliable forecasting results.…”
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Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA by Zahari, Taha, Farzad, Tahriri, Siti Zawiah, Md Dawal

“…The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. …”
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Development of an interactive learning tool for analysis of fast fourier transform from butterfly diagram by Mohamad Syeihri Bin Mohamad Ismail

“…Thus, the development of the software will be useful to study the effect of varying parameter and to identify the difference between the algorithm of Decimation-in-Time (DIT) and Decimation-in-Frequency (DIF).…”

Design and optimization of a test case generation algorithm for real-time embedded systems based on adaptive Q-Learning by Yingbei, Niu, Chai, Soo See

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Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool by Samuel Simbine, Augusto

“…This final year project identified relevant parameters through literature research, analysis and expert interview, and evaluated different machine learning algorithms and identified linear regression as the most applicable and efficient with its R-square of 0.8015, qualifying it to be used for the development of a hybrid model for the AI-based tool for predictive process optimization for chemical plants.…”
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