Search Results - (( java application tree algorithm ) OR ( parameter optimization isotherm algorithm ))

Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

Get full text
Get full text

Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
Get full text
Get full text
Get full text

Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj

“…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
Get full text
Get full text
Get full text
Get full text

Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

Get full text
Get full text

Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
Get full text
Get full text
Get full text

Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.

“…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”

Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

“…The optimization by the RSM was significant with minimum number of experimental runs, lesser error and showed a simultaneous interaction of the adsorption parameters in predicting MET adsorption capacity. …”
Get full text
Get full text

Effective adsorption of metolachlor herbicide by MIL-53(Al) metal-organic framework: Optimization, validation and molecular docking simulation studies by Ahmad Isiyaka, H., Jumbri, K., Soraya Sambudi, N., Uba Zango, Z., Ain Fathihah Binti Abdullah, N., Saad, B.

“…The optimization by the RSM was significant with minimum number of experimental runs, lesser error and showed a simultaneous interaction of the adsorption parameters in predicting MET adsorption capacity. …”
Get full text
Get full text

Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
Get full text
Get full text
Get full text

Performance of amidoxime-modified poly(acrylonitrile- Co-acrylic acid) for removal of boron in aqueous solution by Lau, Kia Li

“…The best fit model for adsorption isotherm was Sips model with heterogeneity factor (n) = 0.7611. …”
Get full text
Get full text

Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…The adsorption kinetics and isotherm models show the presence of multilayer adsorption and chemisorption controlled adsorption process. …”
Get full text
Get full text

Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…The adsorption kinetics and isotherm models show the presence of multilayer adsorption and chemisorption controlled adsorption process. …”
Get full text
Get full text

Optimizations and docking simulation study for metolachlor adsorption from water onto MIL-101(Cr) metal�organic framework by Isiyaka, H.A., Jumbri, K., Sambudi, N.S., Zango, Z.U., Abdullah, N.A.F.B., Saad, B.

“…The adsorption kinetics and isotherm models show the presence of multilayer adsorption and chemisorption controlled adsorption process. …”
Get full text
Get full text

AI powered asthma prediction towards treatment formulation: an android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Md Muzahid, Abu Jafar, Sarker, Md Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md

“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
Get full text
Get full text
Get full text

Modelling and simulation of hollow profile aluminium extruded product by Sulaiman, Shamsuddin, Baharudin, B. T. Hang Tuah, Mohd Ariffin, Mohd Khairol Anuar, Magid, Hani Mizhir

“…This process is an isothermal process with an extrusion ratio of 3.3. Subsequently, the optimized algorithm for these extrusion parameters was suggested based on the simulation results. …”
Get full text
Get full text
Get full text

Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan T., Manan T.S.B., Isa M.H., Ghanim A.A.J., Beddu S., Jusoh H., Iqbal M.S., Ayele G.T., Jami M.S.

Modeling and simulation of forward Al extrusion process using FEM by Magid, Hani Mizhir, Sulaiman, Shamsuddin, Mohd Ariffin, Mohd Khairol Anuar, Baharudin, B. T. Hang Tuah

“…Optimized algorithms for extrusion parameters were proposed regarding the concluded simulating results. …”
Get full text
Get full text
Get full text

AI powered asthma prediction towards treatment formulation : An android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Muzahid, Abu Jafar Md, Sarker, Md. Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md.

“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
Get full text
Get full text
Get full text

Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan, T., Manan, T.S.B., Isa, M.H., Ghanim, A.A.J., Beddu, S., Jusoh, H., Iqbal, M.S., Ayele, G.T., Jami, M.S.

“…The Fletcher-Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). …”
Get full text
Get full text

Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann) by Khan, Taimur, Abd Manan, Teh Sabariah, Hasnain Isa, Mohamed, A. J. Ghanim, Abdulnoor, Beddu, Salmia, Jusoh, Hisyam, Iqbal, Muhammad Shahid, Ayele, Gebiaw T, Jami, Mohammed Saedi

“…The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). …”
Get full text
Get full text
Get full text
Get full text