Search Results - (( developing driven prediction algorithm ) OR ( java implication based algorithm ))

Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions by Hossain Lipu M.S., Miah M.S., Ansari S., Hannan M.A., Hasan K., Sarker M.R., Mahmud M.S., Hussain A., Mansor M.

A Fusion-Based Framework For Explainable Suicide Attempt Prediction by Nordin, Noratikah

“…Existing studies on the framework for predictive models using data-driven and knowledge-driven approaches are insufficiently explained and unable to provide an understandable prediction of suicide attempts for suicide prevention in a systematic way. …”
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A smart food security monitoring and predicting algorithm for rice productivity in Southeast Asia by Chuan, Zun Liang, Tham, Ren Sheng, Abraham Lim, Bing Sern, David Lau, King Luen

“…Specifically, the methodology integrates Simple Linear Regression–Multivariable Linear Regression (SLR-MLR) into a unified predictive algorithm. By incorporating all four dimensions of food security: availability, accessibility, stability, and utilization, the proposed algorithms outperform conventional Artificial Intelligence (AI) predictive algorithms, offering superior accuracy and actionable insights. …”
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An overview to chip-based power transistor and prognostics techniques / Muhamad Hazwan Abdul Halim … [et al.] by Abdul Halim, Muhamad Hazwan, Buniyamin, Norlida, N., Naoe, A., Imazawa, Rosman, M. Syafiq

“…The algorithm will be termed as RULPOV (Remaining Useful Life Prediction based on Voids). …”
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Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow by Osman A., Afan H.A., Allawi M.F., Jaafar O., Noureldin A., Hamzah F.M., Ahmed A.N., El-shafie A.

A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems by Aziz, N., Akhir, E.A.P., Aziz, I.A., Jaafar, J., Hasan, M.H., Abas, A.N.C.

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Industry 5.0 and Education 5.0: Transforming Vocational Education through Intelligent Technology by Zhang, Hongli, Leong, Wai Yie

“…By analyzing the research gaps in personalized learning paths, emotion-driven learning, crossdisciplinary integration, and long-term learning behavior analysis, the paper proposes four improved algorithms: the adaptive learning path generation algorithm, the emotion-driven personalized learning algorithm, the cross-disciplinary knowledge graph algorithm, and the long-term learning behavior prediction algorithm. …”
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Homeostatic-inspired controller algorithm for a hybrid-driven autonomous underwater glider by Isa, Khalid

“…Thus, the main objective of this research is to design and develop a controller algorithm that is able to make the glider adaptive despite facing these constraints. …”
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Predicting sales trends for school cooperative using Market Basket Analysis by Azeman, Nur Itqan Mardhiah

“…A Power BI dashboard was consequently developed to visualize these patterns and help strategic decisionmaking. …”
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Evaluating critical success factors in AI-driven drug discovery using AHP: a strategic framework for optimization by Mohamed Talib, Amir, Al-Hgaish, Areen Metib, Binti Atan, Rodziah, Alshammari, Abdulaziz, Omar Alomary, Fahad, Yaakob, Razali, Alsahli, Abdulaziz, Osman, Mohd Hafeez

“…Expert-driven pairwise comparisons identified Accuracy (ACC), Generalizability (GEN), and Experimental Validation (EV) as top priorities, highlighting the importance of reliable data, robust algorithms, and rigorous validation processes to ensure trustworthy AI outputs. …”
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Sales prediction for Adha Station by using predictive analytics by Mohd Mokhid, Muhammad Amier Latieff

“…In today's increasingly data-driven corporate landscape, predictive analytics is pertinent for improving operational efficiency and decision-making in small enterprises. …”
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Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables by Che Dom, Nazri, Mohd Hardy Abdullah, Nur Athen, Dapari, Rahmat, Salleh, Siti Aekbal

“…However, current models often rely on coarse regional data and fail to account for microclimatic variations, limiting their predictive accuracy in dengue hotspots. This study developed fine-scale predictive models using machine learning algorithms; Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM) to estimate mosquito abundance and dengue risk at the species level based on daily microclimatic data (temperature, relative humidity, and rainfall) collected over 26 weeks in Kuala Selangor, Malaysia. …”
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Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao by He , Biao

“…Traditional methods have been developed to predict overbreak. These predictions use either empirical-, statistical-, or numerical-based models. …”
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A Comprehensive Study On Developing Neural Network Models For Predicting The Coagulant Dosage And Treated Water Qualities For A Water Treatment Plant by Jayaweera, Chamanthi Denisha

“…Determination of the optimum coagulant dosage for water treatment is traditionally carried out using the jar test, which is a time consuming procedure incapable of responding to sudden changes in water qualities. Therefore, data driven modeling techniques such as neural networks are used for developing predictive models for the coagulation process. …”
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Analyzing UiTMCTKKT vehicle utilization and travel pattern using predictive analytics by Syed Mohamad, Sharifah Masyitah

“…The findings confirm that predictive analytics can significantly enhance vehicle management efficiency by supporting data-driven decision-making and resource planning. …”
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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. …”

Predicting sea levels using ML algorithms in selected locations along coastal Malaysia by Hazrin N.A, Chong K.L, Huang Y.F, Ahmed A.N, Ng J.L, Koo C.H, Tan K.W, Sherif M, El-shafie A

“…Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. …”

Predicting YSPSAH's product preferences across department of private hospitals by Abd Malik, Nur Aisyah Syahirah

“…An interactive Power BI dashboard was developed to visualise product predictions and MBA results, providing stakeholders with clear and actionable insights to support data-driven procurement decisions. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia by Hazrin N.A., Chong K.L., Huang Y.F., Ahmed A.N., Ng J.L., Koo C.H., Tan K.W., Sherif M., El-shafie A.

“…Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. …”

Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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