Search Results - (( risk prediction learning algorithm ) OR ( based optimization approach algorithm ))*

An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach by Ahmed, Marzia, Sulaiman, M. H., Mohamad, A. J., Rahman, Md. Mostafijur

“…This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters.…”
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Forecasting of fine particulate matter based on LSTM and optimization algorithm by Zaini N., Ahmed A.N., Ean L.W., Chow M.F., Malek M.A.

“…Long short-term memory based on metaheuristic algorithms, namely particle swarm optimization and sparrow search algorithm (PSO-LSTM and SSA-LSTM), are first developed and applied to determine the significance input combination to the changes of PM2.5 concentration at respective target stations. …”

Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…These techniques have demonstrated their superiority over traditional approaches in many CCRA studies. Machine learning model selection is an iterative process of exploring, evaluating, and improving algorithms. …”
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SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration by Arshad, U., Taqvi, S.A.A., Buang, A., Awad, A.

“…Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. …”
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Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao by He , Biao

“…This thesis addresses the limitations of existing overbreak prediction methods by introducing a novel data-driven approach based on deep continual learning. …”
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Development of a motion planning and obstacle avoidance algorithm using adaptive neuro fuzzy inference system for mobile robot navigation by Muslim, Farah Kamil Abid

“…Finally, the last objective is to improve the optimality of the new approach using a robust Machine Learning strategy. …”
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A Hybrid Machine Learning and Optimisation-Based Model for Predicting the Success of Business-To-Consumer Software Development Projects in Indonesia by Setiawan, Rudi

“…Employing a mixed-methods design, the study first identifies 28 critical success factors through empirical investigation, highlighting elements such as evaluation progress, realistic scheduling, human resource competency, project planning, and risk management. Building on these findings, a predictive framework is constructed by integrating machine learning algorithms with advanced optimization and data handling strategies. …”
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EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT by Ren Jie, Chin, Sai Hin, Lai

“…Advancements in computational power and data availability enable machine learning to complement traditional models. Algorithms such as ANNs, SVMs, and RF enhance hydrological forecasting, while deep learning methods (LSTMs, CNNs) improve spatio-temporal predictions. …”
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Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region by Bhattacharya S., Ali T., Chakravortti S., Pal T., Majee B.K., Mondal A., Pande C.B., Bilal M., Rahman M.T., Chakrabortty R.

“…In recent years, statistical methods and, increasingly, machine learning-based approaches have gained popularity for landslide susceptibility modeling. …”

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study by Islam, Saima Sharleen, Haque, Md Samiul, Miah, M. Saef Ullah, Sarwar, Talha, Nugraha, Ramdhan

“…For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. …”
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Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023 by Soomro, Anam, Zakariyah, Habeebullah, Aftab, S.M.A., Muflehi, Mohamad, Shah, Asadullah, Meraj, Syeda

“…This study conducts a systematic literature review (SLR) on the prediction of loan defaults using machine learning algorithms (MLAs) from 2020 to 2023. …”
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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

“…Integrating time-lagged microclimatic variables into machine learning frameworks enhances the predictive accuracy of dengue risk indicators at a fine spatial scale. …”
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Integrated deep learning for cardiovascular risk assessment and diagnosis: An evolutionary mating algorithm-enhanced CNN-LSTM by Ahmed Alsarori, Ahmed Mohammed, Mohd Herwan, Sulaiman

“…The model predicts both a continuous risk score and a binary diagnostic outcome, supporting both quantitative assessment and early clinical decision-making. …”
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Predicting Customer Behaviour on Buying Life Insurance using Machine Learning by Fabu Ayub A Ghafar, Norhana Abd Rahim, Yumn Suhaylah Yusoff

New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Dasril, Yosza, swanto, Iswanto

“…However, the big problem with prediction is data imbalance and low performance classification algorithms. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi b, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…However, the big problem with prediction is data imbalance and low performance classification algorithms. …”
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New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

“…However, the big problem with prediction is data imbalance and low performance classification algorithms. …”
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Poverty risk prediction based on socioeconomic factors using machine learning approach by Mohd Zawari, Nur Farhana Adibah

“…This study seeks to develop a predictive model of measuring poverty risk using socioeconomic factors based on a machine learning framework. …”
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Predictive risk assessment credit scoring using supervised learning by Khor, Wei Heng

“…The core focus is on assessing the predictive capabilities of supervised learning algorithms—specifically Logistic Regression, Random Forest, K-Nearest Neighbours, Support Vector Machines and Gradient Boosting—against established credit scoring methods. …”
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Predictive Modelling of Stroke Occurrence among Patients using Machine Learning by Sures, Narayasamy, Thilagamalar, Maniam

“…The results demonstrate that the machine learning-based approach achieved high predictive accuracy in identifying individuals at risk of stroke. …”
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