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Finger Vein Biometric Identification Using Discretization Method

Journal of Physics: Conference Series 2021年第1期1878卷

作者： Y H Yahaya W Y Leng S M Shamsuddin Computer Science Department. Faculty of Science and Defence Technology Universiti Pertahanan Nasional Malaysia 53100 Kuala Lumpur Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia 83100 Skudai JB Malaysia UTM Big Data Centre Universiti Teknologi Malaysia 83100 Skudai JB Malaysia

Over the past years, finger vein identification has gaining increasing attention in biometrics. It has many advantages as compared to other biometrics such as living-body identification, difficult to counterfeit because it resides underneath the finger skin and noninvasiveness. Finger vein feature extraction plays an important role in finger vein identification. The performance of finger vein identification is highly depending on the meaningful extracted features from feature extraction process. However, most of the works focus on how to extract the individual features and not presenting the individual characteristic of finger vein patterns with systematic representation. This paper proposed an improved scheme of finger vein feature extraction method by adopting discretization method. The extracted features will be represented systematically way in order to make classification task easier and increase the identification accuracy rate. The experimental result shows that the accuracy rate of identification of the proposed framework using Discretization is above 98.0%.

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MRI detects blood-brain barrier alterations in a rat model of Alzheimer's disease and lung infection

Npj imaging

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Npj imaging 2025年第1期3卷 8页

作者： Yolanda Ohene William J Morrey Elizabeth Powell Katherine F Smethers Nadim Luka Kieron South Michael Berks Catherine B Lawrence Geoff J M Parker Laura M Parkes Hervé Boutin Ben R Dickie Division of Psychology Communication and Human Neuroscience School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Geoffrey Jefferson Brain Research Centre Manchester Academic Health Science Centre University of Manchester Manchester UK. Division of Neuroscience School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Department of Medical Physics and Biomedical Engineering and Department of Neuroinflammation Centre for Medical Image Computing UCL London UK. Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Bioxydyn Limited Manchester UK. Imaging Brain & Neuropsychiatry iBraiN Université de Tours INSERM Tours France.

Pneumonia is a common infection in people suffering with Alzheimer's disease, leading to delirium, critical illness or severe neurological decline, which may be due to an amplified response of the blood-brain barrier (BBB) to peripheral insult. We assess the response of the BBB to repeated lung infection in rat model of Alzheimer's disease (TgF344-AD), at 13- and 18-months old, using dynamic contrast-enhanced (DCE) MRI and filter exchange imaging. Higher BBB water exchange rate is initially detected in infected TgF344-AD rats. BBB water exchange rates correlated with hippocampus aquaporin-4 water channel expression in infected animals. We detected no differences in BBB permeability to gadolinium contrast agent measured by DCE-MRI, confirmed by staining for tight junction proteins, occludin and claudin-5. These findings provide insight into the mechanisms of how peripheral inflammation impacts the BBB.

关键词： Diseases Imaging the immune system

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Rational evaluation of various epidemic models based on the COVID-19 data of China

arXiv

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arXiv 2020年

作者： Yang, Wuyue Zhang, Dongyan Peng, Liangrong Zhuge, Changjing Hong, Liu School of Mathematics Sun Yat-Sen University Guangzhou510275 China Yau Mathematical Sciences Center Tsinghua University Beijing100084 China Beijing Institute for Scientific and Engineering Computing Faculty of Sciences Beijing University of Technology Beijing100124 China College of Mathematics and Data Science Minjiang University Fuzhou350108 China

In this paper, based on the Akaike information criterion, root mean square error and robustness coefficient, a rational evaluation of various epidemic models/methods, including seven empirical functions, four statistical inference methods and five dynamical models, on their forecasting abilities is carried out. With respect to the outbreak data of COVID-19 epidemics in China, we find that before the inflection point, all models fail to make a reliable prediction. The Logistic function consistently underestimates the final epidemic size, while the Gompertz’s function makes an overestimation in all cases. Towards statistical inference methods, the methods of sequential Bayesian and time-dependent reproduction number are more accurate at the late stage of an epidemic. And the transition-like behavior of exponential growth method from underestimation to overestimation with respect to the inflection point might be useful for constructing a more reliable forecast. Compared to ODE-based SIR, SEIR and SEIR-AHQ models, the SEIR-QD and SEIR-PO models generally show a better performance on studying the COVID-19 epidemics, whose success we believe could be attributed to a proper trade-off between model complexity and fitting accuracy. Our findings not only are crucial for the forecast of COVID-19 epidemics, but also may apply to other infectious diseases. Copyright © 2020, The Authors. All rights reserved.

关键词： Mean square error

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On the usage of average hausdorff distance for segmentation performance assessment: hidden bias when used for ranking

arXiv

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arXiv 2020年

作者： Aydin, Orhun Utku Taha, Abdel Aziz Hilbert, Adam Khalil, Ahmed A. Galinovic, Ivana Fiebach, Jochen B. Frey, Dietmar Madai, Vince Istvan Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Research Studio Data Science Research Studios Austria Salzburg Austria Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University United Kingdom

Average Hausdorff Distance (AVD) is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, AVD is used to compare ground truth images with segmentation results allowing their ranking. We identified, however, a ranking bias of AVD making it less suitable for segmentation ranking. To mitigate this bias, we present a modified calculation of AVD that we have coined balanced AVD (bAVD). To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using AVD and bAVD. We calculated the Kendall-rank-correlation-coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by bAVD had a significantly higher average correlation (0.969) than those of AVD (0.847). In 200 total rankings, bAVD misranked 52 and AVD misranked 179 segmentations. Our proposed evaluation measure, bAVD, alleviates AVDs ranking bias making it more suitable for rankings and quality assessment of segmentations. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

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Practical Implications and Challenges of Multispectral Image Analysis

Practical Implications and Challenges of Multispectral Image...

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International Conference on computing, Mathematics and Engineering Technologies (iCoMET)

作者： Syed Muslim Jameel Abdul Rehman Gilal Syed Sajjad Hussain Rizvi Mobashar Rehman Manzoor Ahmed Hashmani Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia High-Performance Cloud Computing Center (HPC3) Center for Research in Data Science (CERDAS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Information and Communication Technology (FICT) Universiti Tunku Abdul Rahman Kampar Malaysia Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Department of computer science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Multispectral Image Analysis (MIA) is an exciting area of research and famous due to its utilization in geo monitoring, such as environmental change detection and region classification. Currently, many other areas are also emerging and are utilizing the multispectral imagery for improving the existing business processes and ensuring the reliability in the existing business methods. However, the process of generating and analyzing multispectral data needs more sophisticated techniques and methods. Also, the multispectral data possesses some temporal and structural inconsistencies, which make the existing multispectral analysis a more challenging task. Therefore, this study surveys the practical implication and possible issues in multispectral image analysis. For that reason, this study aims to investigate the two simple research questions and follow a structured analysis approach to achieve the research objectives. The rest of the paper is structured as; Section II briefly explains the systematic methodology adopted for this survey. Section III is relevant to research finding in response to the RQ1 and RQ2. Finally, Section IV presents the conclusion of this study. In future work, we will sharpen this study towards Skin Disease Analysis using Multispectral Imagery.

关键词： Image analysis Agriculture Diseases Machine learning Quality assessment Remote sensing

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Disturbance compensating model predictive control for warship heading control in missile firing mission 2

Disturbance compensating model predictive control for warshi...

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2nd Mathematics, Informatics, science and Education International Conference, MISEIC 2018

作者： Purnawan, Heri Asfihani, Tahiyatul Adzkiya, Dieky Subchan Modeling and Simulation Systems Laboratory Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

One of warship missions is shooting and avoiding enemy attacks, which can be done by missile firing strategy. Due to missile firing, the movement of ship becomes unstable. To overcome this problem, we need a control system that can overcome the disturbance caused by the impact of the missile firing. So, in this paper, we propose a method called disturbance compensating MPC (DC-MPC) control system. This control is chosen, because DC-MPC is a robust method for measurable disturbance. Based on the result of simulation, DC-MPC can overcome disturbance on the system and make ship's actual heading angle to follow the desired reference quickly which is about 10 s. After the system gets disturbance, controller is also able to adjust the value of the ship heading angle to original position. © Published under licence by IOP Publishing Ltd.

关键词： Warships

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The performance evaluation of the bivariate EWMAcontrol chart using CARL distribution and EPC

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Journal of Physics: Conference Series 2020年第1期1538卷

作者： Selly Acita Muhammad Mashuri Dedy Dwi Prastyo Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember (ITS) Surabaya 60111 Indonesia

In general, control charts are developed with the assumption that the critical quality of a production process is normally distributed with known parameters. However, in practice, critical quality is not always normally distributedand process parameters are typically unknown. In such a case, it is necessary to estimate the parameters. One of the measuresto evaluate the performance control charts is Average Run Length (ARL). When the process parameters are estimated, the run-length follows its conditional distribution so-called Conditional Average Run Length (CARL). In this study, the performance of the Bivariate Exponentially Weighted Moving Average(BEWMA)control chart will be evaluated by considering the practitioner to practitioner variability using the CARL distribution and the Exceedance Probability Criterion (EPC). The value of CARL is calculated using the Markov Chain method. The EPC is used to evaluate practitioner to practitioner variability that is closely related to parameter *** results show that to guarantee the in-control performance Phase II chart based on EPC, the large size of observations in Phase I data is needed. However, in practice, it is difficult to collect such a huge size ofdata in Phase I. Therefore, to produce the best in-control performance with available Phase I, the control limits are adjusted.

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Modeling of toddler stunting in the province of east nusa tenggara using multivariate adaptive regression splines (mars) method

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： D M Azizah E O Permatasari Statistics Department Faculty of Mathematics Computing and Data Science InstitutTeknologiSepuluhNopember GebangKidul Road number 66 Surabaya 60111 Indonesia

Stunting is a condition of failure to thrive in children under five (babies under five years old) as a result of chronic malnutrition so that the child is too short for his age. Malnutrition occurs since the baby is in the womb and in the early period after the baby is born. However, the condition of stunting only appears after the baby is 2 years old. Based on Basic Health Research in 2017 the province with the highest prevalence of short and very short toddlers in Indonesia is East Nusa Tenggara which is equal to 40.3 percent. This shows that the prevalence of stunting toddlers in NTT Province is still far from the overall prevalence of stunting toddlers in Indonesia. Cases of toddler stunting can be analyzed using the Multivariate Adaptive Regression Splines (MARS) method because the data used are high-dimensional data and do not show clear patterns of relationships between response variables and predictor variables. The data used is the percentage of stunting toddlers in NTT in 2017 with the district / city research unit. Based on the analysis, it was found that the district in NTT with the highest percentage of stunting children was South East Timor and the district with the lowest percentage was East Manggarai. The best MARS model is formed from 3 basis functions with 2 predictor variables included in the model, namely the percentage of pregnant women at risk of chronic lack of energy (X1) and the percentage of infants with low birth weight (X6). And after the classification results obtained the accuracy of the classification of 77.27% with an error of 22.72%.

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A game-theoretic model of marketing strategy using consumer segmentation

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Journal of Physics: Conference Series 2020年第1期1490卷

作者： V Lukitosari T F Simanjuntak D B Utomo Mathematics Department Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia

Game theory evolves like a biological system with species that evolved under natural selection. Game theory as a means of predicting the distribution of individual behaviour in various fields of business. Companies must learn the best marketing strategies and estimate the steps of their competitors. Marketing strategy analysis is related to decision making when there are two or more parties in a condition of competition or conflict. In this research, determining the marketing strategy is done by paying attention to consumer segmentation. With consumer segmentation generated marketing strategies for certain consumers at a predetermined size. Consumer segmentation is important for producers, among others: the company can manage its products well, know the needs and standards of consumers, and get the optimal marketing strategy.

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Joint segmentation and deformable registration with the presence of the noise using active contour and linear curvature

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AIP Conference Proceedings 2024年第1期3150卷

作者： Nurul Asyiqin Mohd Fauzi Mazlinda Ibrahim Hoo Yann Seong Abdul Kadir Jumaat Lavdie Rada Faculty of Defence Science and Technology National Defence University of Malaysia 57000 Kuala Lumpur Malaysia Centre for Defence Foundation Studies National Defence University of Malaysia 57000 Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA (UiTM) 40450 Shah Alam Selangor Malaysia Biomedical Engineering Department Bahcesehir University Besiktas Istanbul Turkey

Medical image processing requires image segmentation and image registration as important diagnostic tools. Image segmentation aims to split structures of interest into distinct components. Meanwhile, image registration finds an ideal transformation between given images. Despite their interrelationship, both tasks are often performed separately. As a result, it faces several problems when undertaken independently. Therefore, the joint models between segmentation and registration have advantages over the separate tasks, particularly when dealing with noisy images. This paper reviewed and compared three existing variational joint segmentation and registration (JSR) models based on active contour without edges and linear curvature. The models are tested on 2D mono-modal and multi-modal images with the presence of white Gaussian noise. The Dice coefficient metric and the registration performance measure are used to evaluate the performance of the models. Numerical results showed that the JSR model using the modified sum of the squared difference outperformed the other two JSR models for noisy mono-modal images. However, the JSR model using the modified normalized gradient fields performed better than the JSR model based on the modified mutual information for noisy multi-modal images.

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