scholarly journals Prediction of indoor Total Volatile Organic Compound in a university hostel using a Neural Network Model

2021 ◽  
Vol 40 (2) ◽  
pp. 186-190
Author(s):  
F.C. Nkeshita ◽  
A.A. Adekunle ◽  
A. Abegunrin
Keyword(s):  
Neural Network ◽  
Health Workers ◽  
Back Propagation ◽  
Coefficient Of Determination ◽  
Total Volatile ◽  
Data Set ◽  
Volatile Organic ◽  
Public Health Workers ◽  
Using Data ◽  
Total Volatile Organic Compounds

The study predicted the concentration of indoor total volatile organic compounds (TVOC) concentration in a randomly selected room at the Umar Kabir male Hostel located at the Federal University of Agriculture, Abeokuta, Nigeria. Readings were taken using an active sampler to measure particulate matter, PM (1.0, 2.5 and 10), TVOC, Relative Humidity (RH), Temperature and Formaldehyde. Two network types namely; feedforward back propagation and the cascaded forward back propagation were adopted randomly to predict TVOC as an output variable using data set generated from six different parameters mentioned earlier. The best performing neural network was the cascaded feed forward with a coefficient of determination of 0.98 which exhibited the lowest mean square error of 0.000124 with a network structure of 6-15-1-1. The results show the ability of Artificial Neural Networks to map inputs and outputs in complex non-linear situations such as the existence of volatile compounds in the atmosphere. It can be adopted for monitoring environmental systems by engineers and public health workers, stakeholders can use such models for initiating environmental related policies aimed at safeguarding human health.

scholarly journals Investigations of Museum Indoor Microclimate and Air Quality. Case Study from Romania

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 286
Author(s):  
Dorina Camelia Ilieș ◽  
Florin Marcu ◽  
Tudor Caciora ◽  
Liliana Indrie ◽  
Alexandru Ilieș ◽  
...  
Keyword(s):  
Air Quality ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Construction Materials ◽  
International Standards ◽  
Total Volatile ◽  
Negative Effects ◽  
Museum Exhibits ◽  
Volatile Organic ◽  
Total Volatile Organic Compounds

Poor air quality inside museums is one of the main causes influencing the state of conservation of exhibits. Even if they are mostly placed in a controlled environment because of their construction materials, the exhibits can be very vulnerable to the influence of the internal microclimate. As a consequence, museum exhibits must be protected from potential negative effects. In order to prevent and stop the process of damage of the exhibits, monitoring the main parameters of the microclimate (especially temperature, humidity, and brightness) and keeping them in strict values is extremely important. The present study refers to the investigations and analysis of air quality inside a museum, located in a heritage building, from Romania. The paper focuses on monitoring and analysing temperature of air and walls, relative humidity (RH), CO2, brightness and particulate matters (PM), formaldehyde (HCHO), and total volatile organic compounds (TVOC). The monitoring was carried out in the Summer–Autumn 2020 Campaign, in two different exhibition areas (first floor and basement) and the main warehouse where the exhibits are kept and restored. The analyses aimed both at highlighting the hazard induced by the poor air quality inside the museum that the exhibits face. The results show that this environment is potentially harmful to both exposed items and people. Therefore, the number of days in which the ideal conditions in terms of temperature and RH are met are quite few, the concentration of suspended particles, formaldehyde, and total volatile organic compounds often exceed the limit allowed by the international standards in force. The results represent the basis for the development and implementation of strategies for long-term conservation of exhibits and to ensure a clean environment for employees, restorers, and visitors.

scholarly journals Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5773-5779
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Food Waste ◽  
Back Propagation ◽  
Ann Model ◽  
Neuron Network ◽  
Volatile Organic ◽  
Artificial Neural

This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

scholarly journals Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yadollah Abdollahi ◽  
Azmi Zakaria ◽  
Nor Asrina Sairi ◽  
Khamirul Amin Matori ◽  
Hamid Reza Fard Masoumi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visible Light ◽  
Visible Light Irradiation ◽  
Back Propagation ◽  
Light Irradiation ◽  
Maximum Efficiency ◽  
Data Set ◽  
Artificial Neural

The artificial neural network (ANN) modeling ofm-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration ofm-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

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