Search Results - (( based optimization model algorithm ) OR ( variable machine learning algorithm ))

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. …”
Get full text
Get full text

Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…The models are based on the four machine learning algorithms: logistic regression, support vector machine, decision tree, and neural network; two ensemble techniques: adaptive boost and bootstrap aggregation; three deep learning algorithms: recurrent neural network, long short-term memory(LSTM), and gated recurrent unit (GRU). …”
Get full text
Get full text

Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

“…In this work, the optimal base pressure is determined using the PCA-BAS-ENN-based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for the smooth flow of aerodynamic vehicles. …”
Get full text
Get full text
Get full text
Get full text
Get full text

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.

Subjects:

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. …”
Get full text
Get full text
Get full text
Get full text

Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…Based on the results obtained, a better prediction result can be produced by the proposed GA-BPNN learning algorithm.…”
Get full text
Get full text

A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…The dissertation aims to develop an effectively decomposed time-series nongradient- based artificial intelligence model for forecasting a time-series regression machine learning task. …”
Get full text
Get full text
Get full text

Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
Get full text
Get full text

Prediction of lattice constant of pyrochlore compounds using optimized machine learning model by Mohamad Zamri, Isma Uzayr, Abd Rahman, Mohd Amiruddin, Bundak, Caceja Elyca

“…In this study, we used an optimized machine learning technique based on Particle Swarm Optimization-Support Vector Regression (PSO-SVR) to learn the relationship between its structural properties with its lattice constants. …”
Get full text
Get full text

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. …”
Get full text
Get full text

Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.

“…The presence of MWCNTs in the Sn-5Sb solder alloy significantly prevented IMC formation at the interface and enhanced the shear strength, according to empirical observations, which were supported by the excellent properties of MWCNTs. An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”

Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. …”
Get full text
Get full text

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region by Anees, Shoaib Ahmad, Mehmood, Kaleem, Khan, Waseem Razzaq, Sajjad, Muhammad, Alahmadi, Tahani Awad, Alharbi, Sulaiman Ali, Luo, Mi

“…Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. …”
Get full text
Get full text
Get full text

Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj

“…The presence of MWCNTs in the Sn-5Sb solder alloy significantly prevented IMC formation at the interface and enhanced the shear strength, according to empirical observations, which were supported by the excellent properties of MWCNTs. An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
Get full text
Get full text
Get full text
Get full text

A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

“…Moreover, K-Nearest Neighbor (KNN) classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm. The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
Get full text
Get full text

Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

Get full text
Get full text
Get full text

Genetic-based approach for cue phrase selection in dialogue act recognition by Yahya, Anwar Ali, Ramli, Abdul Rahman

“…Furthermore, we validate the use of the genetic-based approach for machine learning applications. …”
Get full text
Get full text
Get full text

Hospital site suitability assessment using three machine learning approaches: evidence from the Gaza strip in Palestine by Almansi, Khaled Yousef, Mohamed Shariff, Abdul Rashid, Abdullah, Ahmad Fikri, Syed Ismail, Sharifah Norkhadijah

“…To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). …”
Get full text
Get full text
Get full text

Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

“…Similarly, the model performance was also influenced by the nature of the optimization algorithms. …”
Get full text
Get full text

Air quality forecasting and mapping in Malaysian urban areas: A hybrid deep learning approach by Nur'atiah Zaini