Search

Showing total 116,518 results
Start Over Topic machine learning Publication Year Range Last 3 years

Refine your results

more »

more »

more »

more »

more »

more »

more »

more »
116,518 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
Full Text
View/download PDF

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
Full Text
View/download PDF

7. A regression-based machine learning approach for pH and glucose detection with redox-sensitive colorimetric paper sensors.

Author

Lee T, Lee HT, Hong J, Roh S, Cheong DY, Lee K, Choi Y, Hong Y, Hwang HJ, and Lee G

Subjects
Oxidation-Reduction, Glucose, Hydrogen-Ion Concentration, Colorimetry methods, Machine Learning
Abstract

Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with the naked eye. Herein, we demonstrate that a machine learning (ML)-based paper sensor platform accurately determines the color changes. We fabricated a colorimetric paper sensor by adsorbing polyaniline nanoparticles (PAni-NPs), whose color changes from blue to green when the ambient pH decreases. Adding glucose oxidase (GOx) to the paper sensor enables colorimetric glucose detection. Target analytes (10 μL) were aliquoted onto the paper sensors, and their images were taken with a smartphone under the same conditions in a darkroom. The red-green-blue (RGB) data from the images were extracted and used to train and test three regression models: support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR). Of the three regression models, RFR performed the best at estimating pH levels ( R 2 = 0.957) ranging from pH 2 to 10 and glucose concentrations ( R 2 = 0.922) ranging from 0 to 10 mg mL -1 .

Published
2022
Full Text
View/download PDF

9. A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field.

Author

Alohali YA, Fayed MS, Mesallam T, Abdelsamad Y, Almuhawas F, and Hagr A

Subjects
Algorithms, Linear Models, Machine Learning, Otolaryngology
Abstract

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2022 Yousef A. Alohali et al.)

Published
2022
Full Text
View/download PDF

10. Five points to consider when reading a translational machine-learning paper.

Author

Dwyer D and Krishnadas R

Subjects
Humans, Precision Medicine, Machine Learning, Models, Statistical
Abstract

Machine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.

Published
2022
Full Text
View/download PDF

11. 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
Full Text
View/download PDF

12. 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
Full Text
View/download PDF

14. 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
Full Text
View/download PDF

19. 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
Full Text
View/download PDF

20. 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
Full Text
View/download PDF

21. 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
Full Text
View/download PDF

22. 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
Full Text
View/download PDF

23. 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
Full Text
View/download PDF

24. 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
Full Text
View/download PDF

25. 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
Full Text
View/download PDF

26. Big Data Techniques Utilization in Intelligent Transportation System Environment

Author

Sharma, Sumit, Aujla, Gagangeet Singh, Bali, Rasmeet Singh, 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
Full Text
View/download PDF

27. Analysis of Employee Attrition using Statistical and Machine Learning Approaches

Author

Singh, Akash Kumar, Thakral, Prateek, 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
Full Text
View/download PDF

28. An Assessment of ChatGPT on Log Data

Author

Mudgal, Priyanka, Wouhaybi, Rita, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhao, Feng, editor, and Miao, Duoqian, editor

Published
2024
Full Text
View/download PDF

33. Empirical Analysis of Machine Learning Algorithms for Predicting Thyroidism

Author

Dholakia, Neel H., Kshatriya, Teesha, Ladva, Vipul, Shukla, Madhu, 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
Full Text
View/download PDF

35. State of the Art in Zero-Knowledge Machine Learning: A Comprehensive Survey

Author

Sathe, Aneesh, Saxena, Varun, Akshay Bharadwaj, P., Sandosh, S., 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
Full Text
View/download PDF

37. Explainable AI for Predicting User Behavior in Digital Advertising

Author

Al-Khafaji, Ashraf, Karan, Oguz, 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, García Márquez, Fausto Pedro, editor, Jamil, Akhtar, editor, Hameed, Alaa Ali, editor, and Segovia Ramírez, Isaac, editor

Published
2024
Full Text
View/download PDF

38. Software Defects Detection in Explainable Machine Learning Approach

Author

Al-Isawi, Muayad Khaleel, Abdulkader, Hasan, 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, García Márquez, Fausto Pedro, editor, Jamil, Akhtar, editor, Hameed, Alaa Ali, editor, and Segovia Ramírez, Isaac, editor

Published
2024
Full Text
View/download PDF

39. IntelliTweet: A Multifaceted Feature Approach to Detect Malicious Tweets

Author

Dzeha, Eric Edem, Jourdan, Guy-Vincent, 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, Mosbah, Mohamed, editor, Sèdes, Florence, editor, Tawbi, Nadia, editor, Ahmed, Toufik, editor, Boulahia-Cuppens, Nora, editor, and Garcia-Alfaro, Joaquin, editor

Published
2024
Full Text
View/download PDF

40. MADONNA: Browser-Based MAlicious Domain Detection Through Optimized Neural Network with Feature Analysis

Author

Senanayake, Janaka, Rajapaksha, Sampath, Yanai, Naoto, Komiya, Chika, Kalutarage, Harsha, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Meyer, Norbert, editor, and Grocholewska-Czuryło, Anna, editor

Published
2024
Full Text
View/download PDF

41. A Guava Leaf Disease Identification Application

Author

James, Nikhil, Shriwastav, Kunal Kumar, Medhi, Shilpita, Medhi, Smriti Priya, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Das, Prodipto, editor, Begum, Shahin Ara, editor, and Buyya, Rajkumar, editor

Published
2024
Full Text
View/download PDF

46. Data Assimilation with Ocean Models: A Case Study of Reduced Precision and Machine Learning in the Gulf of Mexico

Author

Voss, Daniel, Tyson, Gary, Zavala-Romero, Olmo, Bozec, Alexandra, Srinivasan, Ashwanth, 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, Zeinalipour, Demetris, editor, Blanco Heras, Dora, editor, Pallis, George, editor, Herodotou, Herodotos, editor, Trihinas, Demetris, editor, Balouek, Daniel, editor, Diehl, Patrick, editor, Cojean, Terry, editor, Fürlinger, Karl, editor, Kirkeby, Maja Hanne, editor, Nardelli, Matteo, editor, and Di Sanzo, Pierangelo, editor

Published
2024
Full Text
View/download PDF

47. Refining Detection Mechanism of Mobile Money Fraud Using MoMTSim Platform

Author

Azamuke, Denish, Katarahweire, Marriette, Muleesi Businge, Joshua, Kizza, Samuel, Opio, Chrisostom, Bainomugisha, Engineer, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Debelee, Taye Girma, editor, Ibenthal, Achim, editor, Schwenker, Friedhelm, editor, and Megersa Ayano, Yehualashet, editor

Published
2024
Full Text
View/download PDF

48. Cervical Cancer Prediction Using Machine Learning Techniques

Author

Kumawat, Gaurav, Vishwakarma, Santosh Kumar, Chakrabarti, Prasun, 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, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Mishra, Durgesh Kumar, editor, and Joshi, Amit, editor

Published
2024
Full Text
View/download PDF

49. Machine Learning Based Stroke Segmentation and Classification from CT-Scan: A Survey

Author

Taye Zewde, Elbetel, Melese Motuma, Mersibon, Megersa Ayano, Yehualashet, Girma Debelee, Taye, Wolde Feyisa, Degaga, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Debelee, Taye Girma, editor, Ibenthal, Achim, editor, Schwenker, Friedhelm, editor, and Megersa Ayano, Yehualashet, editor

Published
2024
Full Text
View/download PDF

50. An Overview of Graph Data Missing Value Imputation

Author

Wu, Jiahua, Tang, Xiangyan, Liu, Guangxing, Wu, Bofan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jin, Hai, editor, Pan, Yi, editor, and Lu, Jianfeng, editor

Published
2024
Full Text
View/download PDF