Search Results - (( machine ((loading algorithm) OR (learning algorithm)) ) OR ( machines training algorithm ))

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

“…The scope of this study is tri folded, First, an exhaustive and parametric comparative study on a wide variety of machine learning algorithms is presented to evaluate the performance of machine learning algorithms in energy load prediction. …”
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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…”

Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…Bag of Words model was used to convert text into numerical inputs. The machine learning algorithm, Support Vector Machine was chosen after comparing it with other algorithms such as Multinomial Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. …”
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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Dr., Saifulnizam, Abd. Khalid, Dr., Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Hussain, Shareef, Dr.

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Tracing the real power transfer of individual generators to loads using least squares support vector machine with continuous genetic algorithm by Mohd Wazir, Mustafa, Saifulnizam, Abd.Khalid, Mohd Herwan, Sulaiman, Siti Rafidah, Abd Rahim, Omar, Aliman, Shareef, Hussain

“…This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. …”
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An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system by M. W., Mustafa, H., Shareef, M. H., Sulaiman, S. N., Abd. Khalid, S. R., Abd. Rahim, Omar, Aliman

“…This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). …”
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Implementation of Health Monitoring System for Patients using Machine Learning Algorithms by Hariprasad, U.S., UshaSree, R.

“…We employed the Decision Tree Algorithm to train and assess a model that produced a perfection of 66.66%.…”
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Transmission loss allocation in deregulated power system using the hybrid genetic algorithm-support vector machine technique by M. W, Mustafa, Mohd Herwan, Sulaiman, H., Shareef, S. N., Abd. Khalid

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An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system by Mohd. Herwan, Sulaiman, Siti Rafidah, Abdul Rahim, Mohd Wazir, Mustafa, Hussain, Shareef, Dr., Saifulnizam, Abd. Khalid, Dr., Omar, Aliman

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Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”

Prediction of COVID-19 outbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman by Mohd Azman, Muhammad Qayyum

“…Evaluation metrics, including a comprehensive analysis of the confusion matrix, highlight the consistent superiority of the SVM model with the sigmoid kernel across various training/testing split ratios. This research significantly contributes to the understanding of machine learning applications in the context of COVID-19 outbreak prediction, emphasizing the importance of algorithm and configuration selections for robust forecasting. …”
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Carbon dioxide reforming of methane over Ni-based catalysts: Modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms by Ayodele B.V., Alsaffar M.A., Mustapa S.I., Kanthasamy R., Wongsakulphasatch S., Cheng C.K.

“…Catalysts; Conjugate gradient method; Learning algorithms; Methane; Multilayer neural networks; Multilayers; Sensitivity analysis; Supervised learning; Auto-regressive; Bayesian regularization; CH$-4$; Greenhouse gasse; Multilayers perceptrons; Neural-networks; Nonlinear autoregressive exogenous; Performance; Process parameters; Supervised machine learning; Carbon dioxide…”

Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine by Mohd Wazir, Mustafa, Prof. Dr., Mohd Herwan, Sulaiman, Hussain, Shareef, Dr., Siti Nur Hidayah, Abd Khalid

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Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting by Hassan, S., Khosravi, A., Jaafar, J.

“…Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). …”
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Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin by Mohd Shahirudin, Syamir

“…The Windows malware dataset has been trained and tested by these three machine learning algorithms to get the percentage detection accuracy. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…The design network is trained by presenting several target machining data that the network must learn according to a learning rule (algorithm). …”
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CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS by TEH, CHOON KEONG

“…Therefore, this project introduces three learning algorithms which are Extreme Learning Machine (ELM), Finite Impulse Response Extreme Learning Machine (FIR-ELM) and Discrete Fourier Transform Extreme Learning Machine (DFT-ELM) to improve the bearing fault diagnosis accuracy and shorten the time used to train and test the neural network.…”
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Applying learning to filter text by Sainin, Mohd Shamrie

“…The use of probabilistic approaches such as naïve Bayes algorithm is the effective algorithms currently known for learning to filter or classify text document.Naïve Bayes algorithm is one of the algorithms in Machine Learning that manipulates probability estimation or reasoning about the observed data.The growing of bulk e-mail or known as spam e-mail becomes a threat to users’ privacy and network load and in the case of e -mail filtering,naïve Bayes classifier can be trained to automatically detect spam messages.Similar to the e-mail, forum application may be misused by the user to send bad messages and in some extent may offence other readers.Forum filtering may be less important compared to e-mail spam filtering; however there is a possibility of using naïve Bayes to learn the messages and automatically detect bad messages.Most of the forum application found in the web is applying keyword based text filtering which scan the words and change the detected words into certain representation.Instead of defining a set of keywords to filter the forum messages, this paper will explains the experiment in applying a learning to filter text especially in the educational and anonymous forum message, where there is no user registration required to submit messages.…”
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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…”

Octane number prediction for gasoline blends using convolution neural network / Zhu Yue by Zhu , Yue

“…At present, there are many prediction algorithms based on machine learning. According to the "80/20 rule" for building machine learning model, 80% of the time is spent of finding, cleaning, and organizing data, while the remaining 20% for training of the machine learning model. …”
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