Search Results - (( pattern ((machine algorithm) OR (means algorithm)) ) OR ( e learning algorithm ))

Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia by Latif S.D., Ahmed A.N., Sathiamurthy E., Huang Y.F., El-Shafie A.

Image segmentation using an adaptive clustering technique for the detection of acute leukemia blood cells images by Jabar F.H.A., Ismail W., Salam R.A., Hassan R.

Subjects:

An efficient fuzzy C-least median clustering algorithm by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Nizamuddin, Mohammed Khaja, Aboosalih, K C

“…In this paper we are discussing our new procedure for clustering called Fuzzy C-least median of squares algorithm which is an improvement to Fuzzy C-means (FCM) algorithm. …”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network by Zanial W.N.C.W., Malek M.B.A., Reba M.N.M., Zaini N., Ahmed A.N., Sherif M., Elshafie A.

“…Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. …”

Social media mining: a genetic based multiobjective clustering approach to topic modelling by Alfred, Rayner, Loo, Yew Jie, Obit, Joe Henry, Lim, Yuto, Haviluddin, Haviluddin, Azman, Azreen

“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
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Tracking student performance in introductory programming by means of machine learning by Khan I., Al Sadiri A., Ahmad A.R., Jabeur N.

“…Big data; Decision trees; Education computing; Learning algorithms; Learning systems; Machine learning; Smart city; Students; Trees (mathematics); Educational data mining; Educational institutions; Hidden patterns; Introductory programming; Introductory programming course; Student performance; Student's performance; Weka; Data mining…”

Clustering Student Performance Data Using k-Means Algorithms by Sultan Alalawi, Sultan Juma, Mohd Shaharanee, Izwan Nizal, Mohd Jamil, Jastini

“…This work aims to provide insights into the data obtained from Oman Education Portal (OEP) related to the student’s performance by manipulating the k-means algorithm.…”
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Development of prediction model for phosphate in reservoir water system based machine learning algorithms by Latif S.D., Birima A.H., Ahmed A.N., Hatem D.M., Al-Ansari N., Fai C.M., El-Shafie A.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. …”

Social media mining: a genetic based multiobjective clustering approach to topic modelling by Rayner Alfred, Loo Yew Jie, Joe Henry Obit, Yuto Lim, Haviluddin Haviluddin, Azreen Azman

“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
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A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification by Wong S.Y., Yap K.S., Yap H.J., Tan S.C.

“…Algorithms; Benchmarking; E-learning; Knowledge acquisition; Learning systems; Pattern recognition; Bench-mark problems; Efficient learning; Extreme learning machine; Fuzzy ARTMAP; Generalization performance; Online learning; Online learning algorithms; Online sequential extreme learning machine; Learning algorithms…”

Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

Subjects: “…Knuth-Morris-Pratt (KMP) pattern matching algorithm…”

A machine learning approach to predict the activity of smart home inhabitant by Marufuzzaman M., Tumbraegel T., Rahman L.F., Sidek L.M.

Identifying diseases and diagnosis using machine learning by Iswanto I., Laxmi Lydia E., Shankar K., Nguyen P.T., Hashim W., Maseleno A.

“…Multi-dimensional and high dimensional are used in machine learning. By using machine learning automatic and classy algorithms can build. � BEIESP.…”

Market prices trend forecasting supported by Elliott Wave's theory by Vantuch, T., Zelinka, I., Vasant, P.

“…The combination of ML algorithms and EW pattern detector achieved significantly higher performance compare to the ML algorithms only.…”
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A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications by Usmani, U.A., Happonen, A., Watada, J.

“…Without human input, these algorithms discover patterns or groupings in the data. …”
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Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms by Anuar N.K., Bakar A.A., Ahmad A.R., Yussof S., Rahim F.A., Ramli R., Ismail R.

Analysis of banana plant health using machine learning techniques by Thiagarajan, Joshva Devadas, Kulkarni, Siddharaj Vitthal, Jadhav, Shreyas Anil, Waghe, Ayush Ashish, Raja, S.P., Rajagopal, Sivakumar, Poddar, Harshit, Subramaniam, Shamala

“…Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…There are two general paradigms for pattern recognition classification which are supervised and unsupervised learning. …”
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A New Probabilistic Output Constrained Optimization Extreme Learning Machine by Wong S.Y., Yap K.S., Li X.C.

“…Benchmarking; Classification (of information); Constrained optimization; Decision making; Electric power systems; Iterative methods; Knowledge acquisition; Learning algorithms; Pattern recognition; Probability; Confidence threshold; Decision making process; Extreme learning machine; Machine learning approaches; Pattern classification problems; Post-processing procedure; Power system applications; Probabilistic output; Machine learning…”