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Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm by Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai

“…The main intention of this study is to find the significant peak features in time domain approach and this can be done using feature selection methods such as gravitational search algorithm (GSA) and particle swarm optimization (PSO). …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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Genetic algorithm based ensemble framework for sentiment analysis by Lai, Po Hung

“…Since there are many methods involved in each task of the multilayered ensemble, genetic algorithm is added to optimize the overall framework in order to select the optimal combinations of methods in each layer that can produce satisfactory results. …”
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Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…Hence, this thesis presents the optimization of parameters values for the tree-based method by genetic algorithm. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa'

“…This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa’

“…This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Also, additional experiments to compare the relative performance of the IFS with five related feature selection algorithms were carried out using natural domain datasets. …”
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LEMABE: a novel framework to Improve analogy-based software cost estimation using learnable evolution model by Dashti, Maedeh, Gandoman, Taghi Javdani, Adeh, Dariush Hasanpoor, Zulzalil, Hazura, Md Sultan, Abu Bakar

“…To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. …”
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Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction by Nasser, Abdullah, H.M. Ghanem, Waheed Ali, H.Y. Saad, Abdul-Malik, Hamed Abdul-Qawy, Antar Shaddad, A. Ghaleb, Sanaa A, Mohammed Alduais, Nayef Abdulwahab, Din, Fakhrud, Ghetas, Mohamed

“…To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). …”
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Algorithm optimization and low cost bit-serial architecture design for integer-pixel and sub-pixel motion estimation in H.264/AVC / Mohammad Reza Hosseiny Fatemi by Hosseiny Fatemi, Mohammad Reza

“…This thesis is concerned with algorithm optimization and efficient low cost architecture design for integer motion estimation (IME) and sub-pixel motion estimation (SME) of H.264/AVC. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…To summarise, metaheuristic algorithms can give a superior optimization approach than the traditional artificial neural network method, providing the computing time is within an acceptable range. …”
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Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning by Yan, Suqing, Luo, Baihui, Sun, Xiyan, Xiao, Jianming, Ji, Yuanfa, Kamarul Hawari, Ghazali

“…However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. …”
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Identification of continuous-time model of hammerstein system using modified multi-verse optimizer by Most. Julakha, Jahan Jui

“…The mMVO based method is then used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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Image watermarking optimization algorithms in transform domains and feature regions by Tao, Hai

“…As a result,it will first introduce the theories about the feature extraction and the basic principles on how feature points can act as locating resynchronization between watermark insertion and extraction discussed in detail.Subsequently,it will present several content-based watermark embedding and extraction methods which can be directly implemented based on the synchronization scheme.Further detailed watermarking schemes which combine feature regions extraction with counter propagation neural network-based watermarks synapses memorization are then presented.The performance of watermarking schemes based on framework of feature point shows the following advantages:a)Good imperceptibility. …”
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Robust correlation feature selection based support vector machine approach for high dimensional datasets by Baba, Ishaq Abdullahi, Mohammed, Mohammed Bappah, Jillahi, Kamal Bakari, Umar, Aliyu, Hendi, Hasan Talib

“…Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. …”
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system by Nurnajmin Qasrina Ann, Ayop Azmi

“…In this study, Simulated Kalman Filter (SKF) is applied to image template matching application as the optimization algorithm. SKF is compared with conventional algorithms for image template matching which are performance index value (PIM) and correlation by using DC components of image (TMC) and by using power of images (TMP) methods. …”
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