Search Results - (( quantity solution machine algorithm ) OR ( java implication based algorithm ))

Parametric Optimization of End Milling Process Under Minimum Quantity Lubrication With Nanofluid as Cutting Medium Using Pareto Optimality Approach by Najiha, M. S., M. M., Rahman, K., Kadirgama

“…In this paper a genetic algorithm based multi-objective optimization approach is applied in order to predict the optimal machining parameters for the end milling process of aluminium alloy 6061 T6 combined with minimum quantity lubrication (MQL) conditions using waterbased TiO2 nanofluid as cutting fluid. …”
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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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Bundle Sorting Mobile Application by Zulkifley, Muhammad Dzofir Hafiz

“…It also offers insight into the method and process of improved surveillance of clothing in the fabric production line via an image processing algorithm that reduces the uncertainty workflow throughout actual clothing storage and conversion of clothing details into quantity. …”
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Blockchain for Healthcare Medical Records Management System with Sharing Control by Haddad, Alaa, Habaebi, Mohamed Hadi, Islam, Md. Rafiqul, Zabidi, Suriza Ahmad

“…Nowadays, with large quantities of data in every industry and the advancement of technology, solutions to a wide range of problems can be resolved. …”
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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.

“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”

Efficient Model for Waste Load and Route Optimization by Achmad, Nopransyah, Tri Basuki, Kurniawan, Misinem, ., Muhammad Izman, Herdiansyah, Edi Surya, Negara

“…This research presents a holistic approach toenhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Investigating The Performance Of Metaheuristics To Optimize The Revenue Of Semiconductor Supply Chain by Roslee, Muhammad Sharifuddin

“…On a typical wafer, there are between 300 and 500 manufacturing steps, and the product cycle time is frequently more than a month. In addition, machines are divided into serial and batch types based on the quantity of items being processed at the same time. …”
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Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain by Mohammad Asif , Hossain

“…The selection would be made based on the hybrid machine learning (ML) algorithm. A fuzzy-based Naive Bayes algorithm has been used in this case. …”
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Comprehensive review on the state-of- the-arts and solutions to the test redundancy reduction problem with taxonomy by Rahman, Mizanur, Kamal Zuhairi, Zamli, Kader, Md Abdul, Roslina, Mohd Sidek, Din, Fakhrud

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Performance investigation and multi-objective optimization of end milling of aluminium alloy 6061 T6 with coated and uncoated carbide tools under various cooling conditions by Syeda Najiha, Masood

“…Comprehensive multi-objective optimization model using genetic algorithm is developed to optimize machining performance measures under different MQL conditions, based on Pareto optimal design approach. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Malicious URL classification using artificial fish swarm optimization and deep learning by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, K. Nour, Mohamed, M. Asiri, Mashael, M. Al-Sharafi, Ali, Othman, Mahmoud, Motwakel, Abdelwahed

“…These attacks hijack huge quantities of the available data, incurring heavy financial loss. …”
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A comparative study between rough and decision tree classifiers by Mohamad Mohsin, Mohamad Farhan

“…Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice”…”
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