Search Results - machine ((loading problem) OR (learning process))

Sediment load prediction in Johor river: deep learning versus machine learning models by Latif S.D., Chong K.L., Ahmed A.N., Huang Y.F., Sherif M., El-Shafie A.

“…The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. …”

Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying by Liu , Zongying

“…Besides, concept drift problem in on-line learning model is solved by Drift Detection Machine (DDM). …”
Get full text
Get full text
Get full text

A comparison of machine learning models for suspended sediment load classification by AlDahoul N., Ahmed A.N., Allawi M.F., Sherif M., Sefelnasr A., Chau K.-W., El-Shafie A.

“…To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. …”

Green machine learning approach for QoS improvement in cellular communications by Saeed, Mamoon M., Saeed, Rashid A., Azim, Mohammad Abdul, Ali, Elmustafa Sayed, Mokhtar, Rania A., Khalifa, Othman Omran

“…Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. …”
Get full text
Get full text
Get full text

Mixed integer goal programming model for flexible job shop scheduling problem (FJSSP) with load balancing / Shirley Sinatra Gran by Gran, Shirley Sinatra

“…The MIGP model formulated is to solve FJSSP with three objective functions, which are to minimize the makespan, the total machining time and the mean absolute deviation of the total machining time to achieve machine’s load balancing. …”
Get full text
Get full text

Development of hybrid machine learning model for simulation of chemical reactors in water treatment applications: Absorption in amino acid by Yanjie Zhang, Lakshmi Thangavelu, Talib Zeedan Taban, Walid Kamal Abdelbasset, Wanich Suksatan, Mohd Sani Sarjadi, Md Lutfor Rahman, Shaheen M. Sarkar, May Alashwal, Mohammed Zwawi, Mohammed Algarni

“…A number of CO2 solubility data are collected from resources and used for training and validation of machine learning computations. Several inputs were considered for the developed machine learning models. …”
Get full text
Get full text
Get full text
Get full text
Get full text

Mixed-load machine utilization improvement and transfer batch size optimization using hybrid simulation approach by Asih, Hayati Mukti

“…Firstly, the problem of mixed-load tester was formulated through a mathematical model. …”
Get full text
Get full text
Get full text
Get full text

Mixed pixel classification on hyperspectral image using imbalanced learning and hyperparameter tuning methods by Purwadi, Abu, Nur Azman, Mohd, Othman, Kusuma, Bagus Adhi

“…After obtaining the characteristics, they are entered into a guided learning model using a support vector machine (SVM) for the five-class or multiclass classification. …”
Get full text
Get full text
Get full text
Get full text

Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…The Machine Learning Method is utilized for this task, which gives the computer the ability to learn. …”
Get full text
Get full text

Fuzzy multi-criteria analysis for machine tool selection and optimal machine loading in flexible manufacturing cell / Nguyen Huu Tho by Nguyen , Huu Tho

“…In particular, production planning related to machine loading problem (MLP) should be firstly considered when starting production process. …”
Get full text
Get full text
Get full text

Improved feature selection and stream traffic classification based on machine learning in software-defined networks by Arwa M. Eldhai, Mosab Hamdan, Ahmed Abdelaziz, Ibrahim Abaker Targio Hashem, Sharief F. Babiker, M. N. Marsono, Muzaffar Hamzah, Noor Zaman Jhanjhi

“…Traffic classification (TC) in software-defined networks (SDN) using machine learning (ML) appears to be a viable option for improving network management. …”
Get full text
Get full text
Get full text

Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy by Yap K.S., Yap H.J.

Subjects:

NanoPC ARM-Based Panel Saw Machine with Industrial Internet of Things by Farag, Mohannad, Aboosaeedan, Ehsan, Eugene Ng, Keng Leong

“…Floor-mounted machines comprise of a loading station and cutting surface where panels are transferred from the loading to the cutting station. …”
Get full text
Get full text
Get full text

E4ML: Educational Tool for Machine Learning by Sainin, Mohd Shamrie, Siraj, Fadzilah

“…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
Get full text
Get full text

Applications of machine learning to friction stir welding process optimization by Nasir, Tauqir, Asmaela, Mohammed, Zeeshan, Qasim, Solyali, Davut

“…It observed that the number of applications of machine learning increased in FSW, FSSW process which sheared the Machine-learning approaches like, artificial Neural Network (ANN), Regression model (RSM), Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). …”
Get full text
Get full text
Get full text

Optimization and design of machine learning computational technique for prediction of physical separation process by Haiqing Li, Chairun Nasirin, Azher M. Abed, Dmitry Olegovich Bokov, Lakshmi Thangavelu, Haydar Abdulameer Marhoon, Md Lutfor Rahman

“…Machine learning (ML) methods were developed and optimized for description and understanding a physical separation process. …”
Get full text
Get full text
Get full text
Get full text
Get full text

Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer by Salleh N.S.M., Suliman A., J�rgensen B.N.

“…Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression problem; Times series; Long short-term memory…”

Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations by Nouri, Hossein, Tang, Sai Hong

“…In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. …”
Get full text
Get full text

A hybrid bio-inspired and musical-harmony approach for machine loading optimization in flexible manufacturing system by Yusof, Umi Kalsom, Budiarto, Rahmat, Deris, Safaai

“…Manufacturing industries are facing fierce challenges in handling product competitiveness, shorter product cycle time and product varieties.The situation demands a need to improve the effectiveness and efficiency of capacity planning and resource optimization while still maintaining their flexibilities.Machine loading - one of the important components of capacity planning is known for its complexity that encompasses various types of flexibilities pertaining to part selection, machine and operation assignment along with constraints.Various studies are done to balance the productivity and flexibility in Flexible Manufacturing System (FMS).From the literature, researchers have developed many approaches to reach a suitable balance of exploration (global improvement) and exploitation (local improvement).We adopt a hybrid of population approaches; hybrid constraint-chromosome genetic algorithm and harmony search algorithm (H-CCGaHs), to solve this problem that aims at mapping a feasible solution to the domain problem.The objectives are to minimize the system unbalance as well as to increase the through-put while satisfying the constrains such as machine time availability and tool slots.The proposed algorithm is tested for it performance on 10 sample problems available in FMS literature and compared with existing solution approaches.…”
Get full text
Get full text
Get full text

Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

“…Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperature…”