Artificial Neural Networks in Decision Support Systems

Delen, Dursun; Sharda, Ramesh

doi:10.1007/978-3-540-48713-5_26

Cited by 24 publications

(7 citation statements)

References 75 publications

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“…Furthermore, despite the above limitations, the strength of the ANNs used in this study was the absence of normality assumptions and their ability to find and describe, with acceptable precision, the dynamics of the mosquito population patterns despite limited data. Therefore, although many models have been developed to examine vector population dynamics, the proposed ANNs modeling approach has many potentials to be further improved and used to predict mosquito vector dynamics for decision support [89]. Furthermore, more exploration is required into the prediction of vector-borne disease dynamics incorporating more variables to improve the accuracy in real practice.…”

Section: Discussionmentioning

confidence: 99%

Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks

Damos

Tuells

Caballero

2021

Insects

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A central issue of public health strategies is the availability of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases. In this work, we present, for the first time, a soft system computing modeling approach using two dynamic artificial neural network (ANNs) models to describe and predict the non-linear incidence and time evolution of a medically important mosquito species, Culex sp., in Northern Greece. The first model is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the temperature as an exogenous variable and mosquito abundance as endogenous variable. The second model is a focused time-delay neural network (FTD), which takes into account only the temperature variable as input to provide forecasts of the mosquito abundance as the target variable. Both models behaved well considering the non-linear nature of the adult mosquito abundance data. Although, the NARX model predicted slightly better (R = 0.623) compared to the FTD model (R = 0.534), the advantage of the FTD over the NARX neural network model is that it can be applied in the case where past values of the population system, here mosquito abundance, are not available for their forecasting.

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Section: Discussionmentioning

confidence: 99%

Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks

Damos

Tuells

Caballero

2021

Insects

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“…Several neural network systems have succeeded in recognizing intricate patterns, learning from experience, drawing conclusions, and generating predictions. A review by Delen and Sharda (2008) discusses the application of ANNs in decision support systems in a wide range of areas, with the most attention given to finance and medicine [19]. In one application, Walczak and Velanovich (2018) A study by Bussmann et al (2021) provides an explanatory AI model for credit risk management, specifically assessing the risks associated with peer-to-peer lending [23].…”

Section: 5applications Of Artificial Neural Network In Decision-makingmentioning

confidence: 99%

Improved Decision Support System for Personal Loan Eligibility Using Artificial Neural Networks

Alfawaz

2023

Preprint

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Making a loan decision is a task that entails a certain level of risk. When an individual who wants to get credit, such as a loan, credit card, or mortgage, decides to apply, the creditor conducts a comprehensive evaluation to assess the borrower's creditworthiness. This study aims to create a system that can assist financial institutions in the loan approval process to reduce the frequency of non-performing loans (NPLs), which can result in financial losses. In addition, the approach facilitates borrowers' understanding of the potential risks associated with loan acquisition. A classification model was built using a neural network to classify borrowers' eligibility based on features that substantially impact a loan decision. An interpretation model was also built to explain the reason behind the outputs using the local interpretable model-agnostic explanations (LIME) method. The classification model achieved an accuracy of roughly 65%, accompanied by a precision of around 0.65, a recall of 0.62, and an F1-score of 0.61. Ultimately, a user interface with interactive features was developed to effectively display the findings of the model.

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“…However, some unsupervised learning applications can also be found [ 21 ]. In this research, the neural network models (deep and convolutional) were utilized both because their usage in the task of haulage cycle identification has not yet been researched and because they tend to perform better than standard approaches [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks

Skoczylas

Rot

Stefaniak

et al. 2023

Sensors

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The task of ore transportation is performed in all mines, regardless of their type (open pit/underground) or mining process. A substantial number of enterprises utilize wheeled machines to perform ore haulage, especially haul trucks and loaders. These machines’ work consists of repeating cycles, and each cycle can be divided into 4 operations: loading, driving with full box/bucket, unloading and driving with empty box/bucket. Monitoring this process is essential to create analytical tools that support foremen and other management crew in achieving effective and optimal production and planning activities. Unfortunately, information gathered regarding the process is frequently based on operators’ oral testimony. This process not only allows for abuse but is also a repetitive and tedious task that must be performed by foremen. The time and attention of foremen is valuable as they are responsible for managing practically everything in their current mine section (machines, operators, works, repairs, emergencies, safety, etc.). Therefore, the automatization of the described process of information gathering should be performed. In this article, we present two neural network models (one for haul trucks and one for loaders) build for detecting work cycles of the ore haulage process. Both models were built utilizing a 2-stage approach. In the first stage, the models’ structures were optimized, while the second was focused on optimizing hyperparameters for the structure with best performance. Both of the proposed models were trained using data collected from on-board monitoring systems over hundreds of the machines’ work hours and utilized the same input features: vehicle speed, fuel consumption, selected gear and engine rotational speed. Models have been subjected to comprehensive testing during which the efficiency and stability of the model responsible for haul trucks was proven. Results for loaders were not as high quality for haul trucks; however, some interesting facts were discovered that indicate possible directions for future development.

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Artificial Neural Networks in Decision Support Systems

Cited by 24 publications

References 75 publications

Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks

Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks

Improved Decision Support System for Personal Loan Eligibility Using Artificial Neural Networks

Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks

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