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29th Telecommunications Forum (TELFOR)

作者： Khan, Wiqar Raza, Asif Kuusniemi, Heidi Elmusrati, Mohammed Nokia CNS Espoo Finland Arcada Univ Appl Sci Dept Business Management & Analyt Helsinki Finland Univ Vaasa Digital Econ Vaasa Finland Univ Vaasa Sch Technol & Innovat Vaasa Finland

ISBN: (纸本)9781665425858

User presence determination for being inside a venue, such that the user is provided with possible value-added services, is of high significance. It will get more prominent as we move to 5G and 6G networks' rollout as we'll get further means to have better aids. In this paper, machine learning (ML) algorithms computation results are obtained and analysed. Such algorithms would be candidate to be deployed for finding the confidence in decision making for a user's location with respect to a venue. Number of UEs (User Equipment) are simultaneously placed inside and outside a venue and kept over a longer duration. Data such as received reference signal received power for serving cells and neighbour candidate cells etc. data is collected by each UE. The different available neighbours' level in each data set is analysed. k-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) algorithms are used to find the accuracy based on neighbours' depth among the available info. Very convincing results are observed over different level of neighbours being included in each machine learning (ML) algorithms.

关键词： Android LTE machine learning algorithms Positioning Python RSRP

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Malware Detection and Classification with machine learning algorithms 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Kumar, R. Vinoth Islam, Md Mojahidul Apon, Abir Hossain Prantha, C. S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

The Windows platform can be easily affected by abnormal growth of malicious software, so we need to find abnormal activities in the system and vary them effectively. Manually finding an abnormal growth of activities in the system is not possible at all times and takes time. We need to assign humans to monitor activities. To solve this problem, we have to develop an automated system for finding abnormal activities at all times without human interaction. We applied several kinds of techniques involving machine learning to quickly determine whether the algorithm models were effective and detect any abnormal system growth. machine learning methods such as XGBoost, Naive Bayes, K-NN, Random Forest, decision tree, logistic regression, and Support Vector machine have been utilized to detect abnormal growth in the system. We found that the XGBoost classification algorithm performs the most accurate classification among the algorithms tested. At the end of the testing, a 99.28% accurate prediction was obtained.

关键词： Malicious software machine learning algorithms XGBoost

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An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity

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BENI-SUEF UNIVERSITY JOURNAL OF BASIC AND APPLIED SCIENCES 2022年第1期11卷 1-18页

作者： Akano, Theddeus T. James, Chinemerem C. Univ Lagos Dept Syst Engn Lagos Nigeria Univ Botswana Dept Mech Engn Gaborone Botswana

Background: Prediction of accurate crude oil viscosity when pressure volume temperature (PVT) experimental results are not readily available has been a major challenge to the petroleum industry. This is due to the substantial impact an inaccurate prediction will have on production planning, reservoir management, enhanced oil recovery processes and choice of design facilities such as tubing, pipeline and pump sizes. In a bid to attain improved accuracy in predictions, recent research has focused on applying various machine learning algorithms and intelligent mechanisms. In this work, an extensive comparative analysis between single-based machine learning techniques such as artificial neural network, support vector machine, decision tree and linear regression, and ensemble learning techniques such as bagging, boosting and voting was performed. The prediction performance of the models was assessed by using five evaluation measures, namely mean absolute error, relative squared error, mean squared error, root mean squared error and root mean squared log error. Results: The ensemble methods offered generally higher prediction accuracies than single-based machine learning techniques. In addition, weak single-based learners of the dataset used in this study (for example, SVM) were transformed into strong ensemble learners with better prediction performance when used as based learners in the ensemble method, while other strong single-based learners were discovered to have had significantly improved prediction performance. Conclusion: The ensemble methods have great prospects of enhancing the overall predictive accuracy of single-based learners in the domain of reservoir fluid PVT properties (such as undersaturated oil viscosity) prediction.

关键词： Pressure volume temperature Undersaturated oil viscosity machine learning algorithms Evaluation measures Ensemble method

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Short-term Wind Speed Forecasting using machine learning algorithms

Short-term Wind Speed Forecasting using Machine Learning Alg...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Fonseca, Sebastiao B. de Oliveira, Roberto Celio L. Affonso, Carolina M. Fed Univ Para Fac Elect & Biomed Engn Belem Para Brazil

ISBN: (纸本)9781665435970

This paper evaluates the performance of several machine learning algorithms for short-term wind speed forecasting. The algorithms evaluated include: Long Short-Term Memory, Extra-Tree, Gradient Boosting Tree, Extreme Gradient Boosting Tree, Voting Averaged, Multi-layer Perceptron, K-Nearest Neighbors, and Support Vector machine. The performance of the algorithms was evaluated with different error metrics using real wind speed and meteorological data collected from the city of Maceio, Brazil. First, pre-processing methods are applied in the large database to deal with outliers, noisy and missing values. Then, variable selection technique is employed to select the most significant set of variables and their lag-values as input to the forecast algorithm. Results show Voting Averaged algorithm performs better for all forecast time horizons considered, which are 1 hour, 2 hours and 3 hours ahead.

关键词： machine learning algorithms short-term wind speed forecasting variable selection

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Detection and Classification of Myocardial Infarction Transmurality Using Cardiac MR Image Analysis and machine learning algorithms 44

Detection and Classification of Myocardial Infarction Transm...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Hernandez-Casillas, Andrea Del-Canto, Irene Ruiz-Espana, Silvia Lopez-Lereu, Maria Pilar Monmeneu, Jose Vicente Moratal, David Univ Politecn Valencia Ctr Biomat & Tissue Engn Valencia Spain ASCIRES Biomed Grp Valencia Spain

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

The presence of abnormalities when the left ventricle is deformed is related to the patients' prognosis after a first myocardial infarction. These deformations can be detected by performing a cardiac magnetic resonance (CMR) study. Currently, late gadolinium enhancement (LGE) is considered to be the gold standard when performing CMR imaging. However, CMR with LGE overestimates infarct size and underestimates recovery of dysfunctional segments after myocardial infarction. Based on this statement, the objective is to detect, characterize, and quantify the extent of myocardial infarction in patients with cardiac pathologies, using parameters derived from CMR, in order to obtain greater precision in patients' recovery predictions than when only studying LGE images. For this purpose, we studied the infarct presence and extension from a total of 105 images from 35 patients, and calculated myocardium strain and torsion to characterize and quantify the affected tissue. A total of twenty-one parameters were selected to create predictive models. Moreover, we compared two feature extraction methods, and the performance of five machine learning algorithms. Results show that both temporal and strain parameters are the most relevant to detect and characterize the extent of myocardial infarction. The use of imaging techniques and machine learning algorithms have great potential and show promising results when it comes to detecting the presence and extent of myocardial infarction.

关键词： Pathology Image segmentation machine learning algorithms Image analysis Imaging Magnetic resonance Myocardium

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A comparative analysis of prognostic regression models and machine learning algorithms in surgical decision-making of cardial submucosal tumors

Gastroenterology and Endoscopy

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Gastroenterology and Endoscopy 2024年第1期2卷 19-24页

作者： Geng, Zi-Han Zhu, Yan Fu, Pei-Yao Qu, Yi-Fan Li, Quan-Lin Zhou, Ping-Hong Endoscopy Center and Endoscopy Research Institute Zhongshan Hospital Fudan University Shanghai China

Background and aims: Non-tunneling and submucosal tunneling endoscopic resection (STER) techniques are the most frequent treatments for cardial submucosal tumor (SMT). Here, we analyzed common machine learning (ML) algorithms and compared them with traditional regression models in surgical decision-making for cardial SMTs. Methods: Using key baseline predictive factors, ML algorithms and logistic regression (LR) were conducted in 246 patients. For the ML algorithms, gradient boosting machines (GBM), artificial neural networks (ANN), random forests (RF), and support vector machines (SVM), were included. For small sample-sized data, a technique for k-fold cross-validation was exploited to avoid over-fitting. Meanwhile, we tuned the parameters through several replications. Then, we quantified the discrimination (area under the curve, AUC) and predictive ability (Brier score, F1 score, specificity, sensitivity, and accuracy) of models. We divided patients (n = 246) into STER-treated (n = 97) and non-tunneling endoscopic resection (NTER)-treated (n = 149) groups. Results: LR outperformed among all groups (Brier score = 0.1398, F1 score = 0.7391, AUC = 0.8729, and predictive accuracy = 80.65 %). In comparison to ML algorithms, an outperformance of the traditional regression approach was also found in a low-dimensional setting for surgical decision prediction of cardial SMTs. Conclusions: The traditional regression approach outperformed ML algorithms for the prediction of the best surgical method in patients with SMTs. © 2024 The Authors

关键词： Cardial submucosal tumors Logistic regression machine learning algorithms Surgical decision-making

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Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva

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TALANTA OPEN 2024年 10卷

作者： Honorio-Silva, Ghabriel Guevara-Vega, Marco Silva, Nagela Bernadelli Sousa Garcia-Junior, Marcelo Augusto Alves, Deborah Cristina Teixeira Goulart, Luiz Ricardo Martins, Mario Machado Oliveira, Andre Luiz Vitorino, Rui Miguel Pinheiro Cunha, Thulio Marquez Martins, Carlos Henrique Gomes Carneiro, Murillo Guimaraes Sabino-Silva, Robinson Univ Fed Uberlandia Inst Biomed Sci Innovat Ctr Salivary Diagnost & Nanobiotechnol Dept Physiol Uberlandia MG Brazil Univ Fed Uberlandia Inst Biomed Sci Antimicrobial Testing Lab Uberlandia MG Brazil Univ Fed Uberlandia Inst Biotechnol Uberlandia MG Brazil Univ Fed Uberlandia Sch Med Uberlandia MG Brazil Univ Aveiro Dept Med Sci Aveiro Portugal Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil

Helicobacter pylori (H. pylori) infection can increase the risk of peptic ulcers and gastric neoplasms. H. pylori detection in gastric epithelial tissue collected by esophagogastroduodenoscopy (EGD) is an invasive, costly, and stands as an invasive and examiner-dependent procedure necessitating suitable sedation. complex execution procedure, reducing access for isolated populations. H. pylori detection by Urea Breath Test (UBT) presents high outlay cost with limited access in low- and middle-income countries. In this context, it is critical to develop novel alternative non-invasive platforms for the portable, fast, accessible through self-collection and reagent-free detection of H. pylori. Here, we used attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) supported by machine learning algorithms to identify infrared vibrational modes of H. pylori diluted in human saliva. To perform it, saliva was diluted in 4 different concentrations (108 CFU/mL, 107CFU/mL, 106 CFU/mL, and 105 CFU/mL) of H. pylori. Then, diluted saliva with or without H. pylori were applied to ATR-FTIR spectroscopy to perform a reagent-free, fast, and sustainable analysis of spectral signatures to identify unique vibrational modes to identify this pathogen. The obtained spectra were applied to Linear Discriminant Analysis (LDA) and Support Vector machine (SVM) algorithms to perform the H. pylori detection. The results indicate that the method was highly accurate between 108- 105 CFU/mL, achieving an accuracy of 89 % for 108 CFU/mL, 93 % for 107CFU/mL, 94 % for 106 CFU/mL, and 85 % for 105 CFU/mL with SVM algorithm. This proof-of-concept study demonstrates the significant potential of a biophotonic platform supported by artificial intelligence for the non-invasive detection of H. pylori in human saliva samples obtained by self-collection, without the use of reagents. The data reveal that this proof-of-concept study has significant potential for the non-invasive detection of

关键词： ATR-FTIR Helicobacter pylori Gastrointestinal infection Screening test Salivary detection Saliva non-invasive test machine learning algorithms

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Impairment Screening Utilizing Biophysical Measurements and machine learning algorithms 43

Impairment Screening Utilizing Biophysical Measurements and ...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Roshan, Saboora M. Park, Edward J. Simon Fraser Univ Sch Mechatron Syst Engn Surrey BC V3T 0A3 Canada

ISBN: (纸本)9781728111797

Drug recognition expert (DRE) officers employ a set of tests to investigate drivers who are under impairment and to determine the type of drug that they have used. Horizontal Gaze Nystagmus (HGN), Walk and Turn (WAT), and One Leg Stand (OLS) are the main three tests included in the Standardized Field Sobriety Tests (SFSTs), which lead the officers to evaluate the sobriety of drivers. Performing these tests requires trained officers, but the final decision may still be subjective. These tests along with a suite of comprehensive (yet manual) at-station testing are the basis of police decision making and are subjected to scrutiny by courts. Therefore, designing an automated system to detect impairment not only will help officers in making accurate decisions, but also will remove the subjectivity and can potentially serve as a court-admissible evidence. In this paper, a new method for automated impairment detection is introduced and implemented using data analysis and machine learning algorithms based on a comprehensive suite of tests performed on 34 participants.

关键词： Drugs Legged locomotion machine learning algorithms Law enforcement Decision making Manuals Particle measurements

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Setup Time Prediction Using machine learning algorithms: A Real-World Case Study

Setup Time Prediction Using Machine Learning Algorithms: A R...

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IFIP WG 5.7 International Conference of the Advances in Production Management Systems (APMS)

作者： Locatelli, Alberto Iori, Manuel Lippi, Marco Locatelli, Marco Univ Modena & Reggio Emilia Dept Sci & Methods Engn Reggio Emilia Italy Univ Parma Dept Engn & Architecture Parma Italy

ISBN: (纸本)9783031436727;9783031436703;9783031436697

In this paper, we explore the use of machine learning regression algorithms for setup time prediction and we apply them to a real-world scheduling application arising in the color printing industry. As the complexities associated with setup time predictions have received limited attention from the literature, we aim at exploiting a data-driven approach based on machine learning algorithms to enhance the quality of setup time evaluations and narrow the gap between scheduling theory and practice. Using a real-world industrial dataset, we train three different machine learning models: linear regression, random forests, and gradient boosting machines. The experimental results demonstrate that the gradient boosting machine approach obtains the best performance overall, immediately followed by random forests. The accuracy of the obtained predictions shows the effectiveness of the proposed approach in setup time evaluation. The obtained results are particularly significant and valuable due to the versatile nature of the proposed machine learning approaches. These methods can be applied to various scheduling scenarios, making them suitable for integration into scheduling algorithms to potentially improve their accuracy.

关键词： Setup time prediction machine learning algorithms Sequence-dependent setup costs Color printing industry Food industry

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Novel Trends in Scaling Up machine learning algorithms 16

Novel Trends in Scaling Up Machine Learning Algorithms

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16th IEEE International Conference on machine learning and Applications (ICMLA)

作者： Lopes, Noel Ribeiro, Bernardete Polytech Guarda UDI Guarda Portugal Univ Coimbra CISUC Coimbra Portugal Univ Coimbra CISUC Dept Informat Engn Coimbra Portugal

ISBN: (纸本)9781538614174

Big Data has been a catalyst force for the machine learning (ML) area, forcing us to rethink existing strategies in order to create innovative solutions that will push forward the field. This paper presents an overview of the strategies for using machine learning in Big Data with emphasis on the high-performance parallel implementations on many-core hardware. The rationale is to increase the practical applicability of ML implementations to large-scale data problems. The common underlying thread has been the recent progress in usability, cost effectiveness and diversity of parallel computing platforms, specifically, the Graphics Processing Units (GPUs), tailored for a broad set of data analysis and machine learning tasks. In this context, we provide the main outcomes of a GPU machine learning Library (GPUMLib) framework, which empowers researchers with the capacity to tackle larger and more complex problems, by using high-performance implementations of well-known ML algorithms. Moreover, we attempt to give insights on the future trends of Big Data Analytics and the challenges lying ahead.

关键词： Graphics processing units machine learning algorithms Big Data Feature extraction Libraries Hardware Central Processing Unit

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