Search Results - predictive a difference ((optimisation algorithm) OR (optimization algorithm))

Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction by Ehteram M., Ahmed A.N., Latif S.D., Huang Y.F., Alizamir M., Kisi O., Mert C., El-Shafie A.

Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components by Liang G., Panahi F., Ahmed A.N., Ehteram M., Band S.S., Elshafie A.

Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty by Reza M.S., Hannan M.A., Mansor M.B., Ker P.J., Tiong S.K., Hossain M.J.

“…This paper presents an improved approach for predicting the RUL and capacity of LIB using a long short-term memory (LSTM) deep neural network-integrated with a gravitational search algorithm (GSA) to address the challenges associated with predicting battery life. …”

Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level by Sheikh Khozani Z., Barzegari Banadkooki F., Ehteram M., Najah Ahmed A., El-Shafie A.

Novel Adaptive Bacterial Foraging Algorithms for Global Optimisation with Application to Modelling of a TRS by Ahmad Nor Kasruddin, Nasir, Nor Maniha, Abd Ghani, Tokhi, M. O.

“…Moreover, based on the statistical result, non-parametric Friedman and Wilcoxon signed rank tests and parametric t-test are performed to check the significant difference in the performance of the algorithms. The algorithms are further employed to predict a neural network dynamic model of a laboratory-scale helicopter in the hovering mode. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

“…As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. …”

Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm by Kamaruddin, Shafie, Ridzuan, Arman Hilmi, Sukindar, Nor Aiman

“…Comparative analysis between predicted and actual surface roughness measurements showed good agreement with differences of less than 2%, indicating a significant prediction method. …”
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Design of experiments meets immersive environment: optimising eating atmosphere using artificial neural network by Kantono, Kevin, How, Muhammad Syahmeer, Wang, Qian Janice

“…In this study, an artificial neural network (ANN) with particle swarm optimisation algorithm (PSO; hereafter ANN-PSO) was selected and compared with classical Response Surface Method (RSM) as ANN-PSO has been reported to yield better reliability and predictability compared to RSM. …”
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Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction o... by Chong, D.J.S., Chan, Y.J., Arumugasamy, S.K., Yazdi, S.K., Lim, J.W.

“…Confirmatory experiments were carried out in the biogas plant under this set of optimised variables for a period of two months. The predicted biogas production and methane yield are highly correlated to the actual data with small percentage difference of 1.25 and 5.09 respectively, indicating that ANFIS model was accurate and reliable. …”
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Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data by Fanos, Ali Mutar, Pradhan, Biswajeet, Mansor, Shattri, Md Yusoff, Zainuddin, Abdullah, Ahmad Fikri, Jung, Hyung Sup

“…Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.…”
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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia by Lay, Usman Salihu

“…The general objective of the study was the development of optimized hybrid debris flow models using airborne laser scanning data and Machine learning algorithms in Malaysia. …”
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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

“…We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. …”
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Rainfall modeling using two different neural networks improved by metaheuristic algorithms by Sammen S.S., Kisi O., Ehteram M., El-Shafie A., Al-Ansari N., Ghorbani M.A., Bhat S.A., Ahmed A.N., Shahid S.

“…Different statistical measures (mean absolute error (MAE) and Nash�Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models� performance. …”

Modelling and optimisation of blood glucose control for type 1 diabetes using multi-parametric programming and model-based predictive control (mp-MPC) / Associate Professor Dr Ayub... by Md. Som, Ayub

“…This research attempts to develop a new control algorithm to regulate the blood glucose level (BGL) for Type 1 Diabetes. …”
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Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction by Ghazvinian H., Mousavi S.-F., Karami H., Farzin S., Ehteram M., Hossain M.S., Fai C.M., Hashim H.B., Singh V.P., Ros F.C., Ahmed A.N., Afan H.A., Lai S.H., El-Shafie A.

An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM) by Shamim, Akhtar

“…Finally, the proposed algorithms were also validated on another dataset of a university campus in a different region. …”
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Reliability assessment of power system generation adequacy with wind power using population-based intelligent search methods by Kadhem, Athraa Ali

“…This study sought to examine the performance of three newly proposed techniques, for reliability assessment of the power systems, namely Disparity Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO), and Differential Evolution Optimization Algorithm (DEOA). …”
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Metaheuristic algorithms applied in ANN salinity modelling by Khudhair, Zahraa S., Zubaidi, Salah L., Dulaimi, Anmar, Al-Bugharbee, Hussein, Muhsen, Yousif Raad, Putra Jaya, Ramadhansyah, Mohammed Ridha, Hussein, Raza, Syed Fawad, Ethaib, Saleem

“…The CPSOCGSA performance was evaluated by various single-based ones, including multi-verse optimiser (MVO), marine predator's optimisation algorithm (MPA), particle swarm optimiser (PSO), and the slim mould algorithm (SMA). …”
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Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems by Tan, Joe Yee

“…Predictive-reactive strategy was investigated, in which Genetic Algorithm (GA) and dispatching rules were used for predictive scheduling and reactivity controls. …”
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Evaluation of data mining models for predicting concrete strength by Wong, Chuan Ming

“…The best performing model is selected and used to generate different sets objective-function that will be selected and used in a Particle Swarm Optimization algorithm to solve a single objective optimization problem that finds the optimal values of each concrete feature to maximize the strength of concrete. …”
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