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Global-Local Partial Least Squares Discriminant Analysis And Its Extension In Reproducing Kernel Hilbert Space by Muhammad, Aminu

“…Thus, subspace learning techniques are employed to reduce the dimensionality of the data prior to employing other learning algorithms. …”
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Machine-Learning Based QoE Prediction For Dash Video Streaming by Tan, Jun Yuan

“…The framework is further improved by integrating ensemble learning in the prediction phase. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

“…Lastly, (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy are proposed in the VDEO framework. The overall performances of the three proposed frameworks have been compared with several current state-of-the-art clustering algorithms on 15 benchmark datasets from the UCI repository. …”
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Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

“…The thesis proposed a solution that employs unique characteristics of machine learning and game-theoretic framework to enable a model-free and energy-efficient small cell network. …”
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Overhead view based person counting using deep learning by Kaw, Chee Zhao

“…Third, the accurate tracking of each detected person is performed using the deep learning based tracking framework, known as DeepSORT. …”
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Adapting robot kinematics for human-arm motion recognition by Chan, C.S., Liu, H., Brown, D.

“…This paper presents a novel method to the analysis of human-arm motion, in particular improving the efficiency of conventional motion recognition algorithms. Contrary to the prior art methods, this research develops a framework for human-arm motion recognition where qualitative normalised templates (QNTs) is proposed to replace the conventional approaches. …”
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An improved directed random walk framework for cancer classification using gene expression data by Seah, Choon Sen

“…SDW achieves an accuracy of 95.03% in average which is higher by 8.97% compare to conventional DRW for all cancer datasets. This study provides a foundation for further studies and research on early diagnosis of cancer with machine learning technique. …”
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Predictive maintenance framework for hard disk media production by Mohamed Noor, Megat Norulazmi Megat, Jusoh, Shaidah

“…This paper proposes a novel framework for the implementation of predictive maintenance in hard disk media production.A novel technique to visualize the temporal data into pattern that can be trained with machine learning algorithm is introduced. …”
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The conceptual framework of knowledge of large scale and incomplete graphs of skyline queries optimization using machine learning by Noor, Ubair, Hassan, Raini, Dwi Handayani, Dini Oktarina

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Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification by Wong S.Y., Yap K.S.

“…Using random computational hidden neurons, ELM shows faster learning speed over the traditional learning algorithms. …”

Hyperparameter tuning in deep learning using NSGA-III: a Multi-Objective perspective by Mohamad Rom, Abdul Rahman

“…This thesis proposes and implements a novel framework: the Multi-Objective NSGA-III-DL model, wherein the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) is directly infused into the deep learning optimization process. …”
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Multi-agent reinforcement learning for Bai ethnic traditional dwelling protection in Dali : cultural identity-oriented community relationship optimization and urbanization adaptati... by Xiaohua, Qian, Neilson Ilan, Mersat, Haslina, Hashim, Bemen Wong, Win Keong

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Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and... by Noviandy, Teuku Rizky, Idroes, Ghalieb Mutig, Hardi, Irsan

“…We incorporated the Shapley Additive exPlanations (SHAP) framework to address the challenge of interpretability in machine learning. …”
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Dynamic force-directed graph with weighted nodes for scholar network visualization by Mohd. Aris, Khalid Al-Walid, Ramasamy, Chitra, Mohd Aris, Teh Noranis, Zolkepli, Maslina

“…The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. …”
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Advances in materials informatics: A review by Sivan, Dawn, Kumar, K. Satheesh, Aziman, Abdullah, Raj, Veena, Izan Izwan, Misnon, Ramakrishna, Seeram, Jose, Rajan

“…Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. …”
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Optimization-driven extreme learning machine for floating photovoltaic power prediction: A teaching learning-based approach by Mohd Redzuan, Ahmad, Nor Farizan, Zakaria, Mohd Shawal, Jadin, Mohd Herwan, Sulaiman

“…This study presents a novel Teaching–Learning-Based Optimization enhanced Extreme Learning Machine (TLBO-ELM) framework that achieves optimal parameter configuration without algorithmic tuning while maintaining computational efficiency for real-time deployment. …”
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Beyond the classroom: digital language learning in the Malaysian context by Mohd Nadzrin, Nur Amelia, Md Yunus, Melor, Mohd Said, Nur Ehsan

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New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

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A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

“…The study then proposes a stacked ensemble deep learning framework for a faster and more efficient water quality analysis. …”
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A conceptual framework for a lightweight AI system for skin disease risk prediction using epidemiological data in rural Bangladesh by Islam, Mohammad Raihanul, Abdul Kadir, Andi Fitriah, Ismail, Syazwan Aizat

“…Preliminary experiments conducted on an existing dataset demonstrate that conventional machine learning algorithms, particularly K-Nearest Neighbors (KNN) and Random Forest, achieve strong predictive performance, with accuracy reaching up to 88% in train–test evaluations and 80% in 10-fold cross-validation. …”
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