Negative selection algorithm for dengue outbreak detection

Mousavi, Maryam; Bakar, Azuraliza Abu; Zainudin, Suhaila; Long, Zalizah Awang; Sahani, Maneesh; Vakilian, Mohammadmahdi

doi:10.3906/elk-1206-91

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…In the literature, AIS is applied in many areas such as pattern recognition [17,18], computer security [19,20], network security [21], dynamic business programming [22], disease diagnosis [23,24], dynamic control of group elevator systems [25], and optimization of job shop scheduling problems [26], and successful results are obtained.…”

Section: The Proposed Ais Structurementioning

confidence: 99%

Artificial immune system based wastewater parameter estimation

Sertkaya¹,

Yurtay²

2018

Turk J Elec Eng & Comp Sci

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The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewater treatment process. An estimation of the obtained wastewater treatment characteristics provides the information needed to set up the current process steps, and it is important to have an optimum treatment. In this study, an artificial immune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO, DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset is provided from the University of California Irvine (UCI) Machine Learning Library. The current dataset is analyzed by principal component analysis (PCA) to obtain maximum system performance. As a result of the simulation, the output parameters are successfully predicted using the AIS model with real data. The classifier's performance ratios are studied separately using the coefficient of determination (R 2) and the mean squared error of prediction (MSEP), and their rates are given in this study.

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Section: The Proposed Ais Structurementioning

confidence: 99%

Artificial immune system based wastewater parameter estimation

Sertkaya¹,

Yurtay²

2018

Turk J Elec Eng & Comp Sci

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“…AIS-based algorithm approaches, which are characterized by natural problem learning and solving, are widely used in a variety of applications such as learning, anomaly detection and optimization. Some specific examples include: Robot control [13], Diagnosis of diabetes [14], Outbreak detection [15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Secondary Structures of Hemoglobin Using Clonal Selection Algorithm

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

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Protein structure prediction is one of the most important research areas in bioinformatics. Knowing the structure of the protein provides significant information about the function of protein. However, it is a challenging process to identify the 3-dimensional structure of a protein. Methods such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy require long duration and high costs, furthermore, these methods are not appropriate for every protein. Artificial intelligence and heuristic methods are preferred in the prediction of 3-dimensional structures of proteins recently because of their significant contribution. In this paper, the use of the Clonal Selection Algorithm (CSA) method for protein secondary structure prediction is studied and the results are compared to other artificial intelligence and heuristic methods. Artificial immune system (AIS) based CSA predicted hemoglobin secondary structure with a high prediction accuracy of 88.38 %.

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“…Nevertheless, feature selection has the potential to improve classification result as proven by Mousavi et al (2013). Mousavi et al (2013) proposed negative selection algorithm, a variant of artificial immune system (AIS) for detecting dengue outbreak and resulted in 79.85% accuracy for 8505 dengue patient records with 12 attributes.…”

Section: Related Work Dengue Outbreak Detectionmentioning

confidence: 99%

“…Mousavi et al (2013) proposed negative selection algorithm, a variant of artificial immune system (AIS) for detecting dengue outbreak and resulted in 79.85% accuracy for 8505 dengue patient records with 12 attributes. This work aimed to find the most related attributes and features which lead to better dengue outbreak detection by applying feature selection process.…”

Section: Related Work Dengue Outbreak Detectionmentioning

confidence: 99%