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12,170 results

1. Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm.

Author

Wang F, Xiao M, Qi J, and Zhu L

Subjects
Fluorescence, Spectrometry, Fluorescence methods, Biosensing Techniques methods, Biosensing Techniques instrumentation, Anti-Bacterial Agents analysis, Anti-Bacterial Agents pharmacology, Algorithms, Quantum Dots chemistry, Machine Learning, Carbon chemistry, Paper, Bacteria isolation & purification
Abstract

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 10 3  CFU/mL to 1.0 × 10 7  CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria., (© 2024. The Author(s).)

Published
2024
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2. Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease.

Author

Pereira I, Sboto JNS, Robinson JL, and Gill CG

Subjects
Humans, Paper, Albuminuria diagnosis, Albuminuria urine, Rapid Diagnostic Tests, Renal Insufficiency, Chronic diagnosis, Renal Insufficiency, Chronic urine, Machine Learning, Mass Spectrometry methods
Abstract

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.

Published
2024
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6. Development of Paper Temperature Prediction Method in Electrophotographic Processes by Using Machine Learning and Thermal Network Model.

Author

Takamasa Hase, Takumi Ishikura, Shinichi Kuramoto, Koichi Kato, and Kazuyoshi Fushinobu

Subjects
MACHINE learning, THERMOPHYSICAL properties, THERMAL conductivity, THERMAL properties, TEMPERATURE, SPECIFIC heat
Abstract

Since the fusing process in electrophotography has a significant impact not only on printing quality but also on machine internal temperature and toner blocking on outlet tray, accurate paper temperature prediction for various types of papers is essential, especially in the production printing. To develop the thermophysical model of fusing process to predict the paper temperature after the fusing process, thermal properties such as thermal conductivity, specific heat, and thermal contact resistance of several types of papers are necessary. However, paper is composed of complex fiber, surface coating, filler, and moisture, making it difficult to measure thermophysical properties of paper accurately. This work developed a machine learning (ML) model that can predict the thermophysical properties of paper based on a conventionally used 1D thermal network model of the fusing process and experiment results. The thermophysical properties of each paper obtained by ML and the thermophysical properties obtained by the conventional method were input to the thermal network model to predict the paper temperature after the fusing process and compared with the measured paper temperatures of the experiment. The results showed that the paper temperature was predicted with higher accuracy by using thermophysical properties obtained by ML than that by the conventional method. Although the method for predicting paper temperature by using only ML had been proposed, it had the disadvantage of requiring a large number of training experiments. In contrast, this method trained under the conditions of one fusing temperature and two printing speeds, and was able to predict under five fusing temperatures and four printing speeds. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Revealing Insights into Criminal Behaviour: Exploring Patterns and Trends Through Machine Learning Predictive Models

Author

Patil, Manisha M., Harshwal, Jatinkumar R., Patil, Shivani, Pawar, Janardan A., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Rajagopal, Sridaran, editor, Popat, Kalpesh, editor, Meva, Divyakant, editor, and Bajeja, Sunil, editor

Published
2024
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9. AI Insights: Unleashing Financial Distress Signals

Author

Deshmukh, Devraj, Mishra, Nishant, Tripathi, Kshitij, Menon, Rohan, Aylani, Amit, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jabbar, M. A., editor, Tiwari, Sanju, editor, Ortiz-Rodríguez, Fernando, editor, Groppe, Sven, editor, and Bano Rehman, Tasneem, editor

Published
2024
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14. Evaluation of Selected Autoencoders in the Context of End-User Experience Management

Author

Beckmann, Sven, Bauer, Bernhard, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos M., editor, and Umeton, Renato, editor

Published
2024
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15. Machine Learning Economy for Next Generation Industrial IoT: A Vision Under Web 3.0

Author

Bharti, Sourabh, Ranathunga, Tharindu, Dhanapala, Indika, Rea, Susan, McGibney, Alan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chbeir, Richard, editor, Benslimane, Djamal, editor, Zervakis, Michalis, editor, Manolopoulos, Yannis, editor, Ngyuen, Ngoc Thanh, editor, and Tekli, Joe, editor

Published
2024
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16. Comparison of Classical and AI-Based Decision-Making Methodologies

Author

Dolle, Nicolas, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2024
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17. Adoption of Machine Learning by Rural Farms: A Systematic Review

Author

Gilani, Sayed Abdul Majid, Tantry, Ansarullah, Askri, Soumaya, Gernal, Liza, Sergio, Rommel, Mataruna-Dos-Santos, Leonardo Jose, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zakaria, Nur Haryani, editor, Mansor, Nur Suhaili, editor, Husni, Husniza, editor, and Mohammed, Fathey, editor

Published
2024
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18. Non-Cryptographic Privacy Preserving Machine Learning Methods: A Review

Author

Şahinbaş, Kevser, Catak, Ferhat Ozgur, Kuzlu, Murat, Tabassum, Maliha, Sarp, Salih, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ortis, Alessandro, editor, Hameed, Alaa Ali, editor, and Jamil, Akhtar, editor

Published
2024
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19. Anomaly Detection Algorithm with Blockchain to Detect Potential Security Attacks in the IIoT Model of Industry 5.0

Author

Pant, Piyush, Goyal, S. B., Rajawat, Anand Singh, Potgantwar, Amol, Bedi, Pradeep, Djeddi, Chawki, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ortis, Alessandro, editor, Hameed, Alaa Ali, editor, and Jamil, Akhtar, editor

Published
2024
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20. Securing IoT Using Supervised Machine Learning

Author

Iqbal, Sania, Qureshi, Shaima, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Challa, Rama Krishna, editor, Aujla, Gagangeet Singh, editor, Mathew, Lini, editor, Kumar, Amod, editor, Kalra, Mala, editor, Shimi, S. L., editor, Saini, Garima, editor, and Sharma, Kanika, editor

Published
2024
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