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A modified reweighted fast consistent and high-breakdown estimator for high-dimensional datasets
Baba, Ishaq A.; Midi, Habshah; June, Leong W.; Gafurjan … - In: Decision analytics journal 10 (2024), pp. 1-11
Outlier detection and classification algorithms play a critical role in statistical analysis. The reweighted fast consistent and high breakdown point (RFCH) estimator is an outlier-resistant estimator of multivariate location and dispersion. Still, some difficulties hamper the application of the...
Persistent link: https://www.econbiz.de/10014543490
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A new optimisation framework based on Monte Carlo embedded hybrid variant mean-variance mapping considering uncertainties
Norhafidzah Mohd Saad; Sujod, Muhamad Zahim; Mohd … - In: Decision analytics journal 10 (2024), pp. 1-19
This study proposes a new optimisation framework based on Monte Carlo embedded hybrid variant mean-variance mapping … (MVMO-SH) optimisation​ for planning Photovoltaic Distributed Generation (PVDG) in the urban Radial Distribution Network … index compared to genetic algorithm and particle swarm optimisation when the probabilistic power flow with PVDG was …
Persistent link: https://www.econbiz.de/10014533468
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A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises
Mishra, Bhupesh Kumar; Dahal, Keshav; Pervez, Zeeshan; … - In: Decision analytics journal 5 (2022), pp. 1-10
look after where the objectives are mostly contradictorily creating a barrier to simultaneous optimisation of each … optimisation with the greedy heuristic search has been applied for the generation of relief items distribution schedules under …. In addition, this optimisation process also incorporated the minimisation of unmet relief items demand at the disaster …
Persistent link: https://www.econbiz.de/10014336753
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The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction
Chhajer, Parshv; Shah, Manan; Kshirsagar, Ameya - In: Decision analytics journal 2 (2022)
The future is unknown and uncertain, but there are ways to predict future events and reap the rewards safely. One such opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is turbulent, yet using artificial intelligence to...
Persistent link: https://www.econbiz.de/10013163098
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A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization
Lahmiri, Salim; Bekiros, Stelios; Avdoulas, Christos - In: Decision analytics journal 6 (2023), pp. 1-8
The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested...
Persistent link: https://www.econbiz.de/10014505865
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A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning
Bansal, Malti; Goyal, Apoorva; Choudhary, Apoorva - In: Decision analytics journal 3 (2022), pp. 1-21
Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data...
Persistent link: https://www.econbiz.de/10013339256
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A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction
Hassan, Md. Mehedi; Hassan, Md. Mahedi; Yasmin, Farhana; … - In: Decision analytics journal 7 (2023), pp. 1-17
Breast cancer is the most common life-threatening cancer in women and one of the leading causes of death. Early diagnosis is one of the best defenses against the spread of breast cancer. Machine learning (ML) tools are now available for cancer detection and prediction. This study presents a...
Persistent link: https://www.econbiz.de/10014497358
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A spatiotemporal analysis of traffic congestion patterns using clustering algorithms : a case study of Casablanca
Rouky, Naoufal; Bousouf, Abdellah; Benmoussa, Othmane; … - In: Decision analytics journal 10 (2024), pp. 1-14
(TTI) to study traffic congestion on a macro level (zones) without limiting our research to clustering the zones by …
Persistent link: https://www.econbiz.de/10014541885
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A new intuitionistic fuzzy correlation coefficient approach with applications in multi-criteria decision-making
Bajaj, Jyoti; Kumar, Satish - In: Decision analytics journal 9 (2023), pp. 1-15
, medical illness diagnosis, clustering, and Multi-Criteria Decision-Making (MCDM) to further exhibit its practicability and …
Persistent link: https://www.econbiz.de/10014532457
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An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity
Alam, Shafiq; Ayub, Muhammad Sohaib; Arora, Sakshi; … - In: Decision analytics journal 9 (2023), pp. 1-14
, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification … comparing the performance of imputation techniques using clustering metrics and algorithms (e.g., k-means, Partitioning Around …
Persistent link: https://www.econbiz.de/10014532502