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Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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

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

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation by Kumar, A., Ridha, S., Ganet, T., Vasant, P., Ilyas, S.U.

“…The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm. © 2020 by the authors.…”
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Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants by M, Irfan, N, Lukman, A. A, Alfauzi, J, Jumadi

“…In this study comparing the performance classification techniques of Support Vector Machine (SVM) and C4.5 algorithms. The attributes used consist of Leaves, Stems, and Fruits. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Investigation of throughput and packet drop for Hata model on VANET using NCTUns simulation software for open area and suburban area / Rosmawani Samsudin by Samsudin, Rosmawani

“…The investigation was done from 5 to up 20 V ANET nodes. The algorithms considered are Ad-hoc On-demand Distance Vector (AODV) protocol. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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Artificial intelligent power prediction for efficient resource management of WCDMA mobile network by Tee Y.K., Tinng S.K., Koh J., David Y.

“…This artificial intelligent call admission control (CAC) was validated using a dynamic WCDMA mobile network simulator. A few comparative results in downlink have shown that our integrated support vector regression assists genetic algorithm (SVRaGA) is capable of predicting next interval power consumption at Node B with low prediction error and improving the quality of service (QoS) by reducing dropped calls. � 2008 IEICE.…”

Hybrid artificial intelligent algorithm for call admission control in WCDMA mobile network by Tee Y.K., Tiong S.K., Johnny K.S.P., Yeoh E.C.

“…The proposed algorithm, support vector regression assists genetic algorithm (SVRaGA) was tested in a dynamic WCDMA mobile network simulator. …”

A new countermeasure to combat the embedding-based attacks on the goldreich-goldwasser-halevi lattice-based cryptosystem by Arif Mandangan, Nazreen Syazwina Nazaruddin, Muhammad Asyraf Asbullah, Hailiza Kamarulhaili, Che Haziqah Che Hussin, Babarinsa Olayiwola

“…Consequently, the simplified CVP can be reduced to a Shortest-Vector Problem (SVP) variant which can be solved by using lattice-reduction algorithms such as the LLL algorithm in a shorter amount of time. …”
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A new countermeasure to combat the embedding-based attacks on the Goldreich-Goldwasser-Halevi lattice-based cryptosystem by Mandangan, Arif, Nazaruddin, Nazreen Syazwina, Asbullah, Muhammad Asyraf, Kamarulhaili, Hailiza, Che Hussin, Che Haziqah, Olayiwola, Babarinsa

“…Consequently, the simplified CVP can be reduced to a Shortest-Vector Problem (SVP) variant which can be solved by using lattice-reduction algorithms such as the LLL algorithm in a shorter amount of time. …”
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AntNet: a robust routing algorithm for data networks by Haseeb, Shariq, Sidek, Khairul Azami, Ismail, Ahmad Faris, Weng Kin, Lai, Yit Mei, Aw

“…The performance matrix used to compare the algorithms is based on average throughput, packet loss, packet drop and end-to-end delay. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability by Bouke, Mohamed Aly, Abdullah, Azizol

“…Additionally, we find that some algorithms are more sensitive to data leakage than others, as seen by the drop in model accuracy when built without leakage. …”
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Algorithm of the effect of g-jittren on free convection adjacent to a stretching sheet by Shafie, Sharidan

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Machine learning algorithms for early predicting dropout student online learning by Dewi, Meta Amalya, Kurniadi, Felix Indra, Murad, Dina Fitria, Rabiha, Sucianna Ghadati, Awanis, Romli

“…Of the 4 algorithms used, the highest recall value is in Naive Bayes (1), the highest precision is in Logistic Regression with Lasso (1), while the highest accuracy value (0.99) and F1score (0.97) are obtained from the Support Vector Machine which has value equal to Logistic Regression with Lasso. …”
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A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation by Goh, Hui Hwang, An, Zhen, Zhang, Dongdong, Dai, Wei, Kurniawan, Tonni Agustiono, Goh, Kai Chen

“…This study employs two optimization algorithms, namely, particle swarm optimization (PSO) and sparrow optimization algorithm (SSA), in conjunction with least squares support vector machine (LSSVM) to compare the model against three conventional models, namely, Gaussian process regression (GPR), convolutional neural networks (CNN), and long short-term memory (LSTM). …”
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A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation by Hui Hwang Goh, Hui Hwang Goh, Zhen An, Zhen An, Dongdong Zhang, Dongdong Zhang, Wei Dai, Wei Dai, Tonni Agustiono Kurniawan, Tonni Agustiono Kurniawan, Kai Chen Goh, Kai Chen Goh

“…This study employs two optimization algorithms, namely, particle swarm optimization (PSO) and sparrow optimization algorithm (SSA), in conjunction with least squares support vector machine (LSSVM) to compare the model against three conventional models, namely, Gaussian process regression (GPR), convolutional neural networks (CNN), and long short-term memory (LSTM). …”
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