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22,961 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. A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning.

Author

Eryilmaz M, Goncharov A, Han GR, Joung HA, Ballard ZS, Ghosh R, Zhang Y, Di Carlo D, and Ozcan A

Subjects
Humans, Paper, COVID-19 Serological Testing methods, Serologic Tests methods, COVID-19 immunology, COVID-19 diagnosis, COVID-19 virology, SARS-CoV-2 immunology, Machine Learning, Antibodies, Viral blood, Antibodies, Viral immunology, Immunoglobulin G blood, Immunoglobulin G immunology, Immunoglobulin M blood, Immunoglobulin M immunology
Abstract

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 μL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The serodiagnostic algorithm was trained with 120 measurements/tests and 30 serum samples from 7 randomly selected individuals and was blindly tested with 31 serum samples from 8 different individuals, collected before vaccination as well as after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations.

Published
2024
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3. 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. Content-based quality evaluation of scientific papers using coarse feature and knowledge entity network

Author

Zhongyi Wang, Haoxuan Zhang, Haihua Chen, Yunhe Feng, and Junhua Ding

Subjects
Paper quality evaluation, Knowledge entity, Network analysis, Machine learning, Novelty, Structural entropy, Electronic computers. Computer science, QA75.5-76.95
Abstract

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity, clarity, novelty, and significance, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions.

Published
2024
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7. 'Paper, Meet Code': A Deep Learning Approach to Linking Scholarly Articles With GitHub Repositories

Author

Prahyat Puangjaktha, Morakot Choetkiertikul, and Suppawong Tuarob

Subjects
Academic code repository mining, paper-repository relationship, text representation, machine learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Computer scientists often publish their source code accompanying their publications, prominently using code repositories across various domains. Despite the concurrent existence of scholarly articles and their associated official code repositories, explicit references linking the two are often missing. Traditionally, identifying whether scholarly content and code repositories pertain to the same research project requires manual inspection, a time-consuming task. This paper proposes a deep learning-based algorithm for automatically matching scholarly articles with their corresponding official code repositories. Our findings indicate that the most common linking information includes the paper title and BibTeX entries, typically found in the repository’s readme document. In this study, we employed SPECTER for vector embedding of paper and repository metadata. Utilizing these embedding representations with the Light Gradient Boosting Machine (LGBM), our method achieved an F1 score of 0.94. Moreover, combining our best model with a rule-based approach improved performance by 5.31%. This study successfully delineates a connection between academic papers and associated official code repositories, minimizing reliance on explicit bibliographic information in repositories.

Published
2024
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10. Prediction of Values of Borsa Istanbul Forest, Paper, and Printing Index Using Machine Learning Methods

Author

İlker Akyüz, Kinyas Polat, Selahattin Bardak, and Nadir Ersen

Subjects
machine learning, forest industry, index prediction, xkagt, Biotechnology, TP248.13-248.65
Abstract

It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results.

Published
2024

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
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12. Machine Learning and Thermal Imaging in Precision Agriculture

Author

Mirzaev, Kostas-Gkouram, Kiourt, Chairi, 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, Bourbakis, Nikolaos, editor, Tsihrintzis, George A., editor, Virvou, Maria, editor, and Jain, Lakhmi C., editor

Published
2024
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13. The Impact of Data Scaling Approaches on Deep Learning, Random Forest and Nearest Neighbour-Based Network Intrusion Detection Systems for DoS Detection in IoT Networks

Author

Pawlicki, Marek, Kozik, Rafał, Choraś, Michał, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, You, Ilsun, editor, Choraś, Michał, editor, Shin, Seonghan, editor, Kim, Hwankuk, editor, and Astillo, Philip Virgil, editor

Published
2024
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17. 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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19. 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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24. 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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25. 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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26. 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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27. 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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28. 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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29. 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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30. 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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31. 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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32. 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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33. 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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34. Research Progress and Prospect of Condition Assessment Techniques for Oil–Paper Insulation Used in Power Systems: A Review.

Author

Jiang, Zaijun, Li, Xin, Zhang, Heng, Zhang, Enze, Liu, Chuying, Fan, Xianhao, and Liu, Jiefeng

Subjects
ELECTRICAL injuries, ADSORPTION isotherms, MACHINE learning, GAS analysis, RATIO analysis
Abstract

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation's degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots' oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues. [ABSTRACT FROM AUTHOR]

Published
2024
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37. A Robust and Explainable Deep Learning Method for Cervical Cancer Screening

Author

Di Giammarco, Marcello, Mercaldo, Francesco, Zhou, Xiaoli, Huang, Pan, Santone, Antonella, Cesarelli, Mario, Martinelli, Fabio, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mahmud, Mufti, editor, Ben-Abdallah, Hanene, editor, Kaiser, M. Shamim, editor, Ahmed, Muhammad Raisuddin, editor, and Zhong, Ning, editor

Published
2024
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42. A Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth

Author

Karanam, Santoshachandra Rao, Pradeep Kumar, A. B., Yandrapati, Prakash babu, Tangudu, Naresh, Peddada, Nagamani, Bollipelly, PruthviRaj Goud, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, K, Hemachandran, editor, Rodriguez, Raul Villamarin, editor, Rege, Manjeet, editor, Piuri, Vincenzo, editor, Xu, Guandong, editor, and Ong, Kok-Leong, editor

Published
2024
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43. Software Defect Detection Using Machine Learning on Data from Open-Source Programs

Author

Vasileiou, Maria, Papageorgiou, George, Tjortjis, Christos, 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, Bourbakis, Nikolaos, editor, Tsihrintzis, George A., editor, Virvou, Maria, editor, and Jain, Lakhmi C., editor

Published
2024
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44. Introduction to Advances in Information, Intelligence, Systems and Applications

Author

Tsihrintzis, George A., Virvou, Maria, Bourbakis, Nikolaos G., Jain, Lakhmi C., 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, Bourbakis, Nikolaos, editor, Tsihrintzis, George A., editor, Virvou, Maria, editor, and Jain, Lakhmi C., editor

Published
2024
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45. Not What I was Trained for – Out-of-Distribution-Tests for Interactive AIs

Author

Severin, Benedikt, Werger, Ole, Hesenius, Marc, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Deshpande, R.D., Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Harrison, Michael, editor, Martinie, Célia, editor, Micallef, Nicholas, editor, Palanque, Philippe, editor, Schmidt, Albrecht, editor, Winckler, Marco, editor, Yigitbas, Enes, editor, and Zaina, Luciana, editor

Published
2024
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46. Hidden Figures: Architectural Challenges to Expose Parameters Lost in Code

Author

Dix, Alan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Deshpande, R.D., Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Harrison, Michael, editor, Martinie, Célia, editor, Micallef, Nicholas, editor, Palanque, Philippe, editor, Schmidt, Albrecht, editor, Winckler, Marco, editor, Yigitbas, Enes, editor, and Zaina, Luciana, editor

Published
2024
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47. An Approach to Leverage Artificial Intelligence for Car-Parking Related Mobile Applications

Author

Bisante, Alba, Datla, Venkata Srikanth Varma, Trasciatti, Gabriella, Zeppieri, Stefano, Panizzi, Emanuele, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Deshpande, R.D., Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Harrison, Michael, editor, Martinie, Célia, editor, Micallef, Nicholas, editor, Palanque, Philippe, editor, Schmidt, Albrecht, editor, Winckler, Marco, editor, Yigitbas, Enes, editor, and Zaina, Luciana, editor

Published
2024
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49. Towards Behavior-Based Analysis of Android Obfuscated Malware

Author

Sawadogo, Zakaria, Taimoor Khan, Muhammad, Loukas, George, Dembele, Jean-Marie, Sakellari, Georgia, Mendy, Gervais, Goos, Gerhard, Series 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, Tekinerdoğan, Bedir, editor, Spalazzese, Romina, editor, Sözer, Hasan, editor, Bonfanti, Silvia, editor, and Weyns, Danny, editor

Published
2024
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50. Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development

Author

Marzi, Francesca, d’Aloisio, Giordano, Di Marco, Antinisca, Stilo, Giovanni, Goos, Gerhard, Series 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, Tekinerdoğan, Bedir, editor, Spalazzese, Romina, editor, Sözer, Hasan, editor, Bonfanti, Silvia, editor, and Weyns, Danny, editor

Published
2024
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