Development of artificial neural networks software for arsenic adsorption from an aqueous environment

Maurya, A.K.; Nagamani, M.; Kang, Seung Won; Yeom, Jong‐Taek; Hong, Jae‐Keun; Sung, Hyokyung; Park, C.H.; Paturi, Uma Maheshwera Reddy; Reddy, N.S.

doi:10.1016/j.envres.2021.111846

Cited by 13 publications

(2 citation statements)

References 41 publications

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“…The application of ANNs started in the 1980s, along with development of computational techniques based on self-organizing properties and parallel information systems [55]. Due to their capability to solve real problems, ANNs have been extensively used in areas such as engineering [56,57], biology [57], and agriculture [58]. ANNs mimic the data processing of the human brain.…”

Section: Near-infrared Spectroscopymentioning

confidence: 99%

Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes

Sokač Cvetnić,

Krog,

Benković

et al. 2023

Applied Sciences

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The implementation of a suitable and ecologically friendly solid waste management plan is accepted as an essential need. Given that organic matter constitutes the majority of solid waste, composting has gained popularity as an alternative way of organic refuse recycling. Compost quality is defined by its stability and maturity, both of which must be assessed by measuring a large number of physical–chemical parameters, microbiological variables, and enzymatic activities. These procedures are complex and time-consuming, making it difficult to assess compost quality correctly. Spectroscopy methods could be used as an efficient alternative. In this work, general information about composting processes and near-infrared spectroscopy (NIRS) is given. A discussion and comparison of the different approaches of coupling NIRS and chemometric tools for the monitoring and/or control of composting processes are presented in this work.

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Section: Near-infrared Spectroscopymentioning

confidence: 99%

Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes

Sokač Cvetnić,

Krog,

Benković

et al. 2023

Applied Sciences

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“…Applications of artificial intelligence in disease detection include pneumonia fast testing [36], breast cancer detection [37]- [41], gastric cancer identification [42], tumor detection [43]- [45], epilepsy detection [20], [46]- [48], malaria detection [49], and blood cancer detection [50]. The artificial intelligence approach is carried out using either a windowbased application [51]- [53], a mobile phone [54]- [56], or a centralized computer application [57], [58].…”

Section: Introductionmentioning

confidence: 99%

Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application

Fadlil,

Fathurrahman,

Lin

et al. 2023

Journal of Robotics and Control

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Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window.

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A Comprehensive Review on Green and Eco-Friendly Nano-Adsorbents for the Removal of Heavy Metal Ions: Synthesis, Adsorption Mechanisms, and Applications

Bakhtiari,

Salari,

Shahrashoub

et al. 2024

Curr Pollution Rep

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Development of artificial neural networks software for arsenic adsorption from an aqueous environment

Cited by 13 publications

References 41 publications

Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes

Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes

Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application

A Comprehensive Review on Green and Eco-Friendly Nano-Adsorbents for the Removal of Heavy Metal Ions: Synthesis, Adsorption Mechanisms, and Applications

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