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Teaching and learning via chatbots with immersive and machine learning capabilities by Nantha Kumar Subramaniam

“…These chatbots support learning of Java via problem-solving steps through “learning by doing”. …”
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Variable order variable stepsize algorithm for solving nonlinear Duffing oscillator by Nurullah Rasedee A.F., Ishak N., Hamzah S.R., Ijam H.M., Suleiman M., Ibrahim Z.B., Abdul Sathar M.H., Ramli N.A., Kamaruddin N.S.

Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

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Optimization of machining parameters in turning for different hardness using multi-objective genetic algorithm / Mimi Muzlina Mukri, Nor Atiqah Zolpaka and Sunil Pathak by Mukri, Mimi Muzlina, Zolpaka, Nor Atiqah Zolpaka, Pathak, Sunil

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3D surface roughness measurement for scaliness scoring of psoriasis lesions by Ahmad Fadzil, M.H., Prakasa, E., Asirvadam, V.S., Nugroho, H., Affandi, A.M., Hussein, S.H.

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A review of crossover methods and problem representation of genetic algorithm in recent engineering applications by Zainuddin, Farah Ayiesya, Abd Samad, Md Fahmi

“…GA has many variations such as real coded and binary coded depending on the problem representation and so affects the forms of genetic operators. When optimizing process variables, the efficiency of crossover method is crucial. …”
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A proactive fuzzy-guided link labeling algorithm based on MIH framework in heterogeneous wireless networks by Salih Y.K., See O.H., Yussof S., Iqbal A., Mohammad Salih S.Q.

A machine learning approach of predicting high potential archers by means of physical fitness indicators by Muazu Musa, Rabiu, Abdul Majeed, Anwar P.P., Taha, Zahari, Chang, Siow Wee, Ab. Nasir, Ahmad Fakhri, Abdullah, Mohamad Razali

“…Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. …”
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Detection of mental disorders based on the analysis of emotion, facial expressions and facial movements in a video stream by Nurzhanova, Aizhan, Mussabek, Miras, Ince, Gokhan, Mustaffa, Mas Rina, Zhumadillayeva, Ainur

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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.

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study by Islam, Saima Sharleen, Haque, Md Samiul, Miah, M. Saef Ullah, Sarwar, Talha, Nugraha, Ramdhan

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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…ANN based STLF models commonly use back-propagation algorithm, which generally exhibits a slow and improper convergence that affects the forecast accuracy. …”
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Comparison of meta-heuristic algorithms for fuzzy modelling of covid-19 illness’ severity classification by Nur Azieta, Mohamad Aseri, Mohd Arfian, Ismail, Abdul Sahli, Fakharudin, Ashraf Osman, Ibrahim, Shahreen, Kasim, Noor Hidayah, Zakaria, Sutikno, Tole

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Image Based Congestion Detection Algorithms And Its Real Time Implementation by Khdiar, Ahmed Nidhal

“…The methodology includes vehicle detection by using backlight pairing feature algorithm and modified Watershed algorithm. The results returned by the algorithms are transmitted and received wirelessly using the SFFSDR platform, including the use of RF, FPGA, and DSP modules for variable distances. …”
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A machine learning approach of predicting high potential archers by means of physical fitness indicators by Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha

“…Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. …”
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An improvised SIMPLS estimator based on MRCD-PCA weighting function and its application to real data by Siti Zahariah, Midi, Habshah, Mustafa, Mohd Shafie

“…The execution of SIMPLS is based on the empirical covariance matrix of explanatory variables and response variables. Nevertheless, SIMPLS is very easily affected by outliers. …”
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Development and implementation of a technique for norms-adaptable agents in open multi-agent communities by Mahmoud M., Ahmad M.S., Mohd Yusoff M.Z.

Seismic wave modeling and high-resolution imaging by Bashir, Y., Ghosh, D.P., Alashloo, S.Y.M.

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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

“…Modeling of hydrological process has been increasingly complicated since we need to take into consideration an increasing number of descriptive variables. In recent years soft computing methods like fuzzy logic and genetic algorithm are being used in modeling complex processes of hydrologic events. …”
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A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science by Balogun, A.-L., Tella, A., Baloo, L., Adebisi, N.

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