Sensor data classification using machine learning algorithm

Rose, Lina; Mary, Xavier

doi:10.1080/09720510.2020.1736319

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The proposed method outperformed compared to these approaches. On comparing our proposed approach with the techniques presented in [23,42], which aims to predict the salinity and estimates TDS in seawater using various machine learning and deep learning techniques, it was observed that our implemented technique was proved to be powerful and accurate in terms of accuracy. Furthermore, the performance and robustness of the proposed technique was also tested with [43], who targeted the salinity of seawater using mixtures of machine learning models on a real world dataset.…”

Section: 13 1251mentioning

confidence: 99%

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Deep Learning Based Approach to Classify Saline Particles in Sea Water

Alshehri

Kumar

Bhardwaj

et al. 2021

Water

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Water is an essential resource that facilitates the existence of human life forms. In recent years, the demand for the consumption of freshwater has substantially increased. Seawater contains a high concentration of salt particles and salinity, making it unfit for consumption and domestic use. Water treatment plants used to treat seawater are less efficient and reliable. Deep learning systems can prove to be efficient and highly accurate in analyzing salt particles in seawater with higher efficiency that can improve the performance of water treatment plants. Therefore, this work classified different concentrations of salt particles in water using convolutional neural networks with the implementation of transfer learning. Salt salinity concentration images were captured using a designed Raspberry Pi based model and these images were further used for training purposes. Moreover, a data augmentation technique was also employed for the state-of-the-art results. Finally, a deep learning neural network was used to classify saline particles of varied concentration range images. The experimental results show that the proposed approach exhibited superior outcomes by achieving an overall accuracy of 90% and f-score of 87% in classifying salt particles. The proposed model was also evaluated using other evaluation metrics such as precision, recall, and specificity, and showed robust results.

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Section: 13 1251mentioning

confidence: 99%

“…All analyzed water samples presented low sodality and high alkalinity. Rose and Marry [23] showed an alternative by identifying the solvents present in the water body ensuring desalination of the particular ionic compound or metal. The primary objective was to classify the sensor data, viz, the salts in TDS.…”

Section: Literature Workmentioning

confidence: 99%

Deep Learning Based Approach to Classify Saline Particles in Sea Water

Alshehri

Kumar

Bhardwaj

et al. 2021

Water

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“…Appropriate data processing and analysis algorithmic tools such as statistical analysis, regressions, Fourier transform, − and machine learning − are required to calibrate sensor readings and attain trustworthy data. On-site testing data are usually discrete within a short period of time (e.g., STDM and STCM), resulting in a limiting temporal resolution.…”

Section: Current State and Challenge Of Data Processing For Ltcmmentioning

confidence: 99%

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

Huang

Wang

Xiang

et al. 2022

Environ. Sci. Technol.

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Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

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