Dengue Outbreak Prediction Based on Artificial Neural Networking Model Using Climatic Parameters

And Control ◽

The Relationship

Abstract Background: Dengue fever is a vector-borne tropical disease radically amplified by 30 times in occurrence between 1960 and 2010. The upsurge is considered to be because of urbanization, population growth and climate change. Therefore, Meteorological parameters (temperature, precipitation and relative humidity) have impact on the occurrence and outbreaks of dengue fever. There are not many studies that enumerate the relationship between the dengue cases in a particular locality and the meteorological parameters. This study explores the relationship between the dengue cases and the meteorological parameters. In prevalent localities, it is essential to alleviate the outbreaks using modelling techniques for better disease control.Methods: An artificial neural network (ANN) model was developed for predicting the number of dengue cases by knowing the meteorological parameters. The model was trained with 7 years of dengue fever data of Kamrup and Lakhimpur district of Assam, India. The practicality of the model was corroborated using independent data set with satisfactory outcomes. Findings: It was apparent from the sensitivity analysis that precipitation is more sensitive to the number of dengue cases than other meteorological parameters. Conclusion: This model would assist dengue fever alleviation and control in the long run.

Applying artificial neural networks for modelling ship speed and fuel consumption

Neural Computing and Applications ◽

10.1007/s00521-020-05111-2 ◽

2020 ◽

Vol 32 (23) ◽

pp. 17379-17395 ◽

Cited By ~ 1

Author(s):

Wieslaw Tarelko ◽

Krzysztof Rudzki

Keyword(s):

Decision Support ◽

Decision Support System ◽

Fuel Consumption ◽

Support System ◽

Ann Model ◽

Data Set ◽

Ann Models ◽

Artificial Neural ◽

Ship Speed

AbstractThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality.

Predicting the Workability of Self-Compacting Concrete Using Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.168-170.1730 ◽

2010 ◽

Vol 168-170 ◽

pp. 1730-1734

Author(s):

Fang Xian Li ◽

Qi Jun Yu ◽

Jiang Xiong Wei ◽

Jian Xin Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ann Model ◽

Self Compacting Concrete ◽

Data Set ◽

Slump Flow ◽

Super Plasticizer ◽

Input Parameters ◽

Artificial Neural ◽

An artificial neural network (ANN) is presented to predict the workability of self compacting concrete (SCC) containing slump, slump flow and V-test. A data set of a laboratory work, in which a total of 23 concretes were produced, was utilized in the ANNs study. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, fly ash, blast furnace slag, super plasticizer, sand ratio and water/binder, three output parameters which are slump, slump flow and V-test of SCC. ANN-1, ANN-2 and ANN-3 models which containing 15 ,11 and 5 neurons in the hidden layers, respectively are found to predict workability of concrete well within the ranges of the input parameters considered. The three models are tested by comparing to the results to actual measured data. The results showed that ANN-2 is the best suitable for predicting the workability of SCC using concrete ingredients as input parameters.

Thrips (Thysanoptera) identification using artificial neural networks

Bulletin of Entomological Research ◽

10.1017/s0007485308005750 ◽

2008 ◽

Vol 98 (5) ◽

pp. 437-447 ◽

Cited By ~ 21

Author(s):

P. Fedor ◽

I. Malenovský ◽

J. Vaňhara ◽

W. Sierka ◽

J. Havel

Keyword(s):

High Reliability ◽

Multilayer Perceptrons ◽

Ann Model ◽

Automated Identification ◽

Data Set ◽

Males And Females ◽

Artificial Neural ◽

Hidden Layer ◽

Simultaneous Identification

AbstractWe studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.

Spatial Wind Speed Forecasting Using Artificial Neural Networks

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol11.iss4.2020.514 ◽

2021 ◽

Vol 11 (4) ◽

pp. 37-42

Author(s):

Muhammad Shoaib ◽

Saif Ur Rehman ◽

Imran Siddiqui ◽

Shamim Khan ◽

Syed Zeeshan Abbas

Keyword(s):

Wind Speed ◽

Spatial Interpolation ◽

Ann Model ◽

Assistance Program ◽

Data Set ◽

Wind Speeds ◽

Measuring Devices ◽

Neighboring Site ◽

Artificial Neural ◽

Spatial interpolation is a commonly used technique to simulate wind speeds in areas which are devoid of such measuring devices. In this paper authors examine the applicability and efficiency of Artificial-Neural- Network (ANN) formalism aimed at interpolating wind speeds in space domain. Additionally, the effect of the correlation between the wind speed at target site and its correlated neighboring site is also examined in the present paper. Hourly wind speed data set comprising of wind speeds recorded from April 2016 to August 2018 provided by Energy Sector Management Assistance Program of World Bank is used for the study. The study is supported by including four different wind speed measuring stations in Pakistan, namely, Tando Ghulam Ali, Umer Kot, Sujawal and Sanghar. Best estimates from ANN model are obtained for Tando Ghulam Ali (MAPE= 7.37%) and worst estimates are observed forSanghar site (MAPE= 10.61%).

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105110 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5110

Author(s):

Shu-Farn Tey ◽

Chung-Feng Liu ◽

Tsair-Wei Chien ◽

Chin-Wei Hsu ◽

Kun-Chen Chan ◽

...

Keyword(s):

Neural Network ◽

Early Stage ◽

Characteristic Curve ◽

Patient Discharge ◽

Ann Model ◽

Healthcare Settings ◽

High Area ◽

Icd 10 ◽

Artificial Neural ◽

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

High Energy ◽

Ann Model ◽

Artificial Neural ◽

Rotary Kilns ◽

Input Variables ◽

Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.