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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis by Tubishat, Mohammad, Abushariah, Mohammad A.M., Idris, Norisma, Aljarah, Ibrahim

“…In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. …”
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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

“…The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. …”
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Web Algorithm search engine based network modelling of Malaria Transmission by Eze, Monday Okpoto

“…There are observed cases of attempting vector control on a trial and errors basis, with no scientific way of determining the locations of critical vector densities. …”
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Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms by Afzal, Asif, Alshahrani, Saad, Alrobaian, Abdulrahman, Buradi, Abdulrajak, Khan, Sher Afghan

“…This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and support vector regressor (SVR). …”
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Distance vector-hop range-free location algorithm for wireless sensor network by Zazali, Azyyati Adiah

“…Distance Vector-Hop (DV-Hop) algorithm has become the focus of studies for range-free localization algorithms. …”
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Time series predictive analysis based on hybridization of meta-heuristic algorithms by Mustaffa, Zuriani, Sulaiman, Mohd Herwan, Rohidin, Dede, Ernawan, Ferda, Kasim, Shahreen

“…The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). …”
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Short term forecasting based on hybrid least squares support vector machines by Zuriani, Mustaffa, M. H., Sulaiman, Ernawan, Ferda, Noorhuzaimi, Mohd Noor

“…In this study, hybrid Least Squares Support Vector Machines (LSSVM) with four meta-heuristic algorithms viz. …”
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Flood Control Distance Vector Hop (FCDV-Hop) localization in wireless sensor networks by Zazali, Azyyati Adiah, Subramaniam, Shamala, Ahmad Zukarnain, Zuriati

“…Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. …”
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Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors by Hamed, Y., Ibrahim Alzahrani, A., Shafie, A., Mustaffa, Z., Che Ismail, M., Kok Eng, K.

“…The hybrid algorithm was evaluated using a dataset of pipeline corrosion measurements collected by a Magnetic Flux Leakage (MFL) sensor (with an error margin of ±20 of the true values), and an Ultrasonic (UT) device (with an error margin of ±4). …”
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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

“…In this study, we showed that the error vector �⃗! is not unique. We proposed another error vector �⃗∗ to combat the embedding-based attacks. …”
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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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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

“…Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. …”
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An enhanced distance vector-hop algorithm using new weighted location method for wireless sensor networks by Han, Fengrong, Izzeldin Ibrahim, Mohamed Abdelaziz, Liu, Xinni, Kamarul Hawari, Ghazali

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Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

“…Alan Fan et aI., [2] use Support Vector Machine (SVM) to stock market prediction. The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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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.…”

Error Detection of Personalized English Isolated-Word Using Support Vector Machine by Yap, David F. W.

“…Subsequently, a well-known classification type of artificial intelligent algorithm namely Support Vector Machine (SVM) is used to evaluate those features under two class types of words with proper segregation of correct and erroneous words in two data sets. …”
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Optimizing support vector machine parameters using continuous ant colony optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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Anomaly behavior detection using flexible packet filtering and support vector machine algorithms by Abdul Wahid, Mohammed N.

“…A unique method that combines the Flexible Packet Filtering (FPF) with Support Vector Machine (SVM) algorithm is proposed to classify the behavior of the network traffics. …”
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