Spatial analysis model for traffic accident-prone roads classification: a proposed framework

Vitianingsih, Anik Vega; Suryana, Nanna; Othman, Zahriah

doi:10.11591/ijai.v10.i2.pp365-373

Cited by 8 publications

(11 citation statements)

References 41 publications

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“…Fuzzy modelling is possible to be automated using metaheuristic algorithms. Metaheuristic algorithms are a type of computational intelligence paradigm that is particularly well-suited for solving complex optimization problems [29], [67]- [75]. Metaheuristic algorithms is potential to construct an automated method for determining the severity of a COVID-19 infection infected person based their symptoms [76].…”

Section: Int J Artif Intell Issn: 2252-8938mentioning

confidence: 99%

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Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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Section: Int J Artif Intell Issn: 2252-8938mentioning

confidence: 99%

“…This section discusses how meta-heuristic algorithms [29], [67]- [70], [73]- [75], [82], [90], [95], [109], [119], [160], [163], [176], [179]- [188] are included in the fuzzy modelling process. The implementation of fuzzy meta-heuristic algorithms is depicted in Figure 6.…”

Section: Fuzzy Meta-heuristic Algorithmsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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“…The study provides a scientific basis for the application of artificial intelligence technology to determine the professional adaptive capabilities of construction management staff. If we base the development of an artificial intelligence information system for multi-value classification on the results of youth vocational guidance tests, it will improve the diagnosis of professional selection and will be able to provide recommendations for improving the productivity of professional implementation [2]. The level of trust of young people in the artificial intelligence when it comes to the questions of career guidance, choosing  ISSN: 2252-8938 a professional direction or profession according to their wishes, aptitudes, capabilities, and so on, is then determined.…”

Section: Introductionmentioning

confidence: 99%

Information system based on multi-value classification of fully connected neural network for construction management

Honcharenko

Akselrod²,

Shpakov³

et al. 2023

IJ-AI

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This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems. It is proposed fully connected feed-forward neural network (FCF-FNN) architecture and performed empirical modeling to create a data set. Model of artificial intelligence system allows evaluating the processes in an FCF-FNN during the execution of multi-value classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to “learn” from training data. To train the neural network, a data set of 35 input parameters and 29 output parameters was used; the amount of data in the set is 936 data lines. Neural network training occurred in the proportion of 10% and 90%, respectively. Results of this study research can be used to further improve the knowledge and skills necessary for successful professional realization.

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“…Spatial data modeling in geographic information systems (GIS) is related to behavior and the behavior of heterogeneous spatial-temporal datasets (HSTA) [7,8] because 0.96 of the data uses private spatial datasets [9,10]. The HSTA data types occur because of GIS characteristics used to solve specific region problems [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Application of the Synthetic Over-Sampling Method to Increase the Sensitivity of Algorithm Classification for Class Imbalance in Small Spatial Datasets

2022

IJIES

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The difficulty of acquiring data from numerous intergovernmental agencies/institutions for prone road traffic accidents (PRTA) spatial datasets produces a small-scale dataset that causes dataset imbalance. Class imbalance in small-scale datasets causes uncertainty in the results of the modeling PRTA classification. The proposed research is a scenario-based case representation model on the pre-processing data stage to increase the sensitivity of algorithm classification in a small-scale dataset that causes dataset imbalance using machine learning (ML), the synthetic oversampling method. The retrieval of attributes from the spatial dataset is transformed into the raw dataset, the normalized dataset, the synthetic minority over-sampling technique (SMOTE) raw dataset, and SMOTE normalized dataset scenarios. Balancing datasets using four variants of SMOTE, namely ADASYN, Borderline-SMOTE, K-Means SMOTE, and SVM-SMOTE resampled. To evaluate how well the PRTA classification model performed, we utilized the hyper-parameters optimization technique and the genetic algorithm (GA) search cross-validation. The experiment was run with the ML classifier method, including the k-nearest neighbor (KNN), support vector machines (SVM), multilayer perceptron (MLP), naive bayes (NB), logistic regression (LR), and random forest (RF). The Area Under Curve (AUC) was used to evaluate the results of the experiments. The results of the dataset test in a predetermined scenario conclude that a single algorithm that is computationally light to produce an optimal classifier tends to use a raw dataset that is balanced using SMOTE. The KNN method as a single algorithm for classification based on the distance between samples is superior, with an AUC value of 0.89, which is included in the good classification category of all ML classifiers proposed to handle small data sets imbalanced classes using SMOTE raw datasets for K-Means variants SMOTE.

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Spatial analysis model for traffic accident-prone roads classification: a proposed framework

Cited by 8 publications

References 41 publications

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Information system based on multi-value classification of fully connected neural network for construction management

Application of the Synthetic Over-Sampling Method to Increase the Sensitivity of Algorithm Classification for Class Imbalance in Small Spatial Datasets

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