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346,836 results

1. 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. 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
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9. 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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10. Point-of-Care Serodiagnostic Test for Early-Stage Lyme Disease Using a Multiplexed Paper-Based Immunoassay and Machine Learning.

Author

Joung HA, Ballard ZS, Wu J, Tseng DK, Teshome H, Zhang L, Horn EJ, Arnaboldi PM, Dattwyler RJ, Garner OB, Di Carlo D, and Ozcan A

Subjects
Humans, Lyme Disease blood, Lyme Disease immunology, Particle Size, Surface Properties, Telemedicine, Immunoassay, Lyme Disease diagnosis, Machine Learning, Paper, Point-of-Care Testing
Abstract

Caused by the tick-borne spirochete Borrelia burgdorferi , Lyme disease (LD) is the most common vector-borne infectious disease in North America and Europe. Though timely diagnosis and treatment are effective in preventing disease progression, current tests are insensitive in early stage LD, with a sensitivity of <50%. Additionally, the serological testing currently recommended by the U.S. Center for Disease Control has high costs (>$400/test) and extended sample-to-answer timelines (>24 h). To address these challenges, we created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep-learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets and then blindly tested our xVFA using human samples ( N (+) = 42, N (-) = 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0%, respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively.

Published
2020
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11. Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning.

Author

Huang YC, Chung HH, Dutkiewicz EP, Chen CL, Hsieh HY, Chen BR, Wang MY, and Hsu CC

Subjects
Algorithms, Female, Humans, Ion Mobility Spectrometry, Spectrometry, Mass, Electrospray Ionization, Breast Neoplasms diagnosis, Machine Learning, Paper
Abstract

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

Published
2020
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12. 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
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13. 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
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15. Research Paper Screening Tool: Automating Conference Paper Evaluation and Enhancement.

Author

Rathnasiri, Hansani Upeksha, Ishara Lakshani, L. A., Amarasinghe, Nipuni Nilakna, Dissanayake, Oshan Asinda, Nawinna, Dasuni, and Attanayaka, Buddima

Subjects
TECHNOLOGICAL innovations, ARTIFICIAL neural networks, MACHINE learning, ARTIFICIAL intelligence, NATURAL language processing
Abstract

In this era of knowledge, academic researchers are growing every day, this also spikes a growth in published literature on the new innovations and findings. This leads to a problem where the reviewers at the conferences must go through many research papers to determine whether they are suitable for the conference or not. This problem has caused the necessity of an effective paper screening tool for optimizing the literature review process. This research presents a development of a new Paper Screening Tool (PST) aimed at increasing the efficiency and accuracy of the literature screening phase. Leveraging the NPL processing techniques this PST and reduces a lot of manual efforts. Through comprehensive evaluation using a diverse dataset, the tools provide high precision. The PST also has user friendly interfaces and customizable report generation which empowers the researchers screening process to their specific needs. This paper contributes to literature by solving the challenge of information overloading during the literature review. [ABSTRACT FROM AUTHOR]

Published
2023
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16. 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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18. 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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23. 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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24. 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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25. 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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26. 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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27. 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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28. 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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29. 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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30. 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
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31. 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
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32. 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
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35. 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
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37. 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
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39. 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
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40. 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
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41. 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
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42. 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
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43. 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
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48. 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, Nardellli, Matteo, editor, and Di Sanzo, Pierangelo, editor

Published
2024
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49. 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
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50. 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
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