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2023 Vol.14, Issue 4 Preview Page

General Article

Utilizing machine learning for the assessment of mosquito repellent effectiveness and decision support in product selection
Dharmendra Kumar1
,
Ankit Verma2
,
Mukesh Kumar3
,
Vipin Maurya4
,
Ashutosh Mishra56*
1Assistant Professor, Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology (TIET), Patiala, Punjab, India
2Researcher, Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
3Assistant Professor, School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India
4Researcher, Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
5Research Professor, School of Integrated Technology, Yonsei University, South Korea
6Adjunct Professor, Department of Electronics & Communication Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India
*Corresponding Author
30 December 2023. pp. 519-533
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Abstract

The significance of effective insect repellents is underscored by the substantial threat posed by mosquito-borne diseases to global health. Machine learning (ML) algorithms have garnered attention as a potential approach for classifying insect repellents and evaluating their effectiveness. In this study, we explore the application of decision tree (DT), random forest (RF), logistic regression (LR), K-Nearest Neighbor (KNN), Naive Bayes (NB), AdaBoost (AB), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms for categorizing insect repellents. ML techniques are employed to classify mosquito repellents by training various ML classification models using a labeled dataset in which each repellent is assigned a specific effectiveness level. These algorithms can identify patterns and determine the efficacy of a particular repellent by analyzing a range of features such as chemical composition, concentration, duration of protection, and environmental conditions. E-Nose technology offers several advantages, including high sensitivity, selectivity, rapid analysis, non-invasiveness, portability, extensive data analysis capabilities, cost-effectiveness, and versatility. In this research, we propose the use of a non-selective gas sensor array comprising eight MQ series elements, combined with ML analytics, to distinguish the type, concentration, and application of four different types of mosquito repellents commonly used in real-world scenarios. The sensor node prototype was exposed to each of the potential mosquito repellents for 15 to 20 minutes individually to create the experimental dataset. Subsequently, various ML models were trained using this dataset to accurately classify unknown data samples. SVM and MLP models achieved model accuracy scores of 98.01% and 98.10%, respectively, while DT, RF, and KNN models achieved training accuracies of 98.99%, 98.98%, and 98.61%, respectively. Classification performance error was also assessed using Mean Absolute Error (MAE), R2 Score, and Mean Squared Error (MSE).

Keywords
artificial intelligence
decision support systems
E-Nose
intelligent systems
sensors
sustainable engineering
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 14
  • No :4
  • Pages :519-533
  • Received Date : 2023-11-01
  • Accepted Date : 2023-11-28
  • DOI :https://doi.org/10.22712/susb.20230040
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