Search Results - (( using solution based algorithm ) OR ( using combination learning algorithm ))

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

“…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

“…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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WCBP: A new water cycle based back propagation algorithm for data classification by Mohd. Nawi, Nazri, Khan, Abdullah, Firdaus, Naim, M. Z., Rehman, Siming, Insaf Ali

“…Therefore, this paper proposed the use of WC algorithm in combination with Back-Propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory. …”
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BAT-BP: A new BAT based back-propagation algorithm for efficient data classification by Mohd. Nawi, Nazri, M. Z., Rehman, Hafifi, Nurfarian, Khan, Abdullah, Siming, Insaf Ali

“…The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. …”
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Advances of metaheuristic algorithms in training neural networks for industrial applications by Chong H.Y., Yap H.J., Tan S.C., Yap K.S., Wong S.Y.

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.

“…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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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.

“…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

“…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

“…Meta-heuristic algorithms are search techniques used to solve complexoptimization problems, and these algorithms can help provide reasonable solutions in a shorter time thanexact methods. …”
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An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection by Do N.Q., Selamat A., Lim K.C., Krejcar O.

Cooperative multi agents for intelligent intrusion detection and prevention systems / Shahaboddin Shamshirband by Shamshirband, Shahaboddin

“…Later, we investigate the game based-FQL algorithm by combining the game theoretic approach and the fuzzy Q-learning algorithm. …”
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A new experiential learning electromagnetism-like mechanism for numerical optimization by Tan J.D., Dahari M., Koh S.P., Koay Y.Y., Abed I.A.

An improved bees algorithm local search mechanism for numerical dataset by Al-Dawoodi, Aras Ghazi Mohammed

“…Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). …”
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A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network by Mustapha, Ibrahim

“…Simulation results show convergence, learning and adaptability of the RL based algorithms to dynamic environment toward achieving the optimal solutions. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. …”

Interpretation of machine learning model using medical record visual analytics by Mohd Khalid, Nur Hidayah, Ismail, Amelia Ritahani, Abdul Aziz, Normaziah

“…There is work that uses visual analytics techniques to in- terpret the machine learning output to ease the understanding of medical ex- perts. …”
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A new experiential learning electromagnetism-like mechanism for numerical optimization by Tan, J.D., Dahari, M., Koh, S.P., Koay, Y.Y., Abed, I.A.

“…The Electromagnetism-like Mechanism algorithm (EM) is a population-based search algorithm which has shown good achievements in solving various types of complex numerical optimization problems so far. …”
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Malicious URL Detection with Distributed Representation and Deep Learning by Do N.Q., Selamat A., Lim K.C., Krejcar O.