Search Results - (( parameter optimisation based algorithm ) OR ( variable machine learning algorithm ))

Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm by Nik Mohamed Hazli, Nik Muhammad Aiman

“…Optimisation is difficult to optimise as there are three parameters that need to be tuned, Kp, Integral parameter, Ki, and derivative parameter, Kd. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. …”
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Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine.A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables.The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM.Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. …”
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A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application by Zainal Abidin, Zulkifli

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Prediction of Machine Failure by Using Machine Learning Algorithm by Fakhrurazi, Nur Amalina

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Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems by Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Mohd Saberi, Mohamad, Watada, Junzo

“…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

“…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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Single-solution Simulated Kalman Filter algorithm for global optimisation problems by Abdul Aziz, N.H., Ibrahim, Z., Ab Aziz, N.A., Mohamad, M.S., Watada, J.

“…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms by Chia, See Leng

“…Least Square Support Vector Machine (LSSVM) base models with linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel and its hybrid models with integration of Hybrid of Particle Swarm Optimisation and Genetic Algorithm (HPSOGA), Whale Optimisation Algorithm based on Self-adapting Parameter Adjustment and Mix Mutation Strategy (SMWOA) and Ameliorative Moth Flame Optimisation (AMFO) were developed and used to predict the WQI at stations 1K06, 1K07 and 1K08 of the Klang River in Selangor, Malaysia. …”
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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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Depression prediction using machine learning: a review by Abdul Rahimapandi, Hanis Diyana, Maskat, Ruhaila, Musa, Ramli, Ardi, Norizah

“…The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. …”
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Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

“…The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. …”
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A comparative evaluation of PID-based optimisation controller algorithms for DC motor by Ahamed S.R., Parumasivam P., Hossain Lipu M.S., Hannan M.A., Ker P.J.

Augmented model based double iterative loop techniques for integrated system optimisation and parameter estimation of large scale industrial processes by Normah Abdullah, Brdys, M.A., Roberts, P.D.

“…The double iterative loop structures of the proposed algorithms use the real process measurement within the outer loops while the inner loops involve model based computation only. …”
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Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China by Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.

“…In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). …”

Machine learning classifications of multiple organ failures in a malaysian intensive care unit by Norliyana, Nor Hisham Shah, Normy Norfiza, Abdul Razak, Athirah, Abdul Razak, Asma’, Abu-Samah, Fatanah, M. Suhaimi, Ummu Kulthum, Jamaludin

“…This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. …”
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Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction by Masrom, S., Baharun, N., Razi, N.F.M., Rahman, R.A., Abd Rahman, A.S.

“…Particle Swarm Optimization is useful to optimize the best variables combination for automating the features selection in machine learning models. …”
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Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data by Ong, Song Quan, Pradeep Isawasan, Ahmad Mohiddin Mohd Ngesom, Hanipah Shahar, As’malia Md Lasim, Gomesh Nair

“…Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. …”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

“…Numerical weather prediction is the process of using existing numerical data on weather conditions to forecast the weather using machine learning algorithms. This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms by Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.

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