A5058190008- Handwritten Text Recognition Techniques
- Natural Language Processing Techniques
- Text and Document Classification Technologies
- Algorithms and Data Compression
- Topic Modeling
- Web Data Mining and Analysis
- Advanced Text Analysis Techniques
- Hemophilia Treatment and Research
- Blood Coagulation and Thrombosis Mechanisms
- Chemokine receptors and signaling
- User Authentication and Security Systems
- Cancer Risks and Factors
- Digital and Cyber Forensics
- Cytokine Signaling Pathways and Interactions
- Mathematics, Computing, and Information Processing
- Advanced Chemical Sensor Technologies
- Geographic Information Systems Studies
- Human Mobility and Location-Based Analysis
- Hemoglobinopathies and Related Disorders
- Advanced Image and Video Retrieval Techniques
- Edcuational Technology Systems
- Air Quality Monitoring and Forecasting
- Image Retrieval and Classification Techniques
- Optical Imaging and Spectroscopy Techniques
- Data Quality and Management
Budapest University of Technology and Economics
2019-2024
Vanderbilt University Medical Center
2024
King Saud Medical City
2019-2020
This article presents SVC-onGoing1, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of art in open common platform using large-scale public databases, such as DeepSignDB2 and SVC2021_EvalDB3, standard experimental protocols. SVC-onGoing is based on ICDAR 2021 Competition On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal evaluate limits popular scenarios...
Abstract Online signature verification considers signatures as time sequences of different measurements the signing instrument. These signals are captured on digital devices and therefore consist a discrete number samples. To enrich or simplify this information, several verifiers employ resampling interpolation preprocessing step to improve their results; however, design decisions may be difficult generalize. This study investigates direct effect sampling rate input accuracy online systems...
Abstract Background Breast cancer is the most common malignant tumor in women worldwide, and disproportionately affects Sub-Saharan Africa compared to high income countries. The global disease burden growing, with reporting majority of cases. In Kenya, breast commonly diagnosed cancer, an annual incidence 7,243 new cases 2022, representing 25.5% all reported cancers women. Evidence suggests that receiving treatment are at a greater risk developing hypertension than without cancer....
Signature verification is an important emerging field.New results get published both in the online and offline areas.It's often hard to compare because researchers use different databases.Although there are some publicly available databases their quality size may vary.This paper aims deliver a comprehensive list of most significant field.Using our around world can chose best common database that fits needs.
Previous studies have shown that choosing a different sample rate or signature point count provides better accuracy in online verification systems. However, the sampling minimizes error may vary on database and signer levels. In this work, we studied effect of individual frequencies for each proposed system based signer-dependent frequency. The was tested five databases, using several features preprocessing methods. Results showed improvement 70% overall 500 tests 92% chosen where...
Abstract Amongst different approaches, dynamic time warping has shown promising results during the online signature verification competitions of previous years. To improve warping, preprocessing steps may be applied and dimensions samples compared. The choice comparing significantly influence results. Thus, to aid researchers with these decisions, a comparison made between algorithms as horizontal scaling, vertical scaling alignment using in their combinations on two datasets (SVC2004...
Online signature verification is a currently evolving field. Numerous approaches are proposed each year that claimed to be capable of delivering improved results in some aspects over others. These systems usually based on the experiences author(s), and often limited single databases. factors make it difficult compare or even reproduce results. The lack negative evaluation design choices creation new, online difficult. In this work, we addressed problem by conducting systematic most common...
There are several classification algorithms used for signature verification purposes. The k-nearest neighbor (KNN) algorithm was previously in online verification, but this paper, we present an evaluation of the using JKNN classifier, which is a generalized case KNN classifier. An optimal classifier to evaluate algorithm's main parameters that provide most accurate results. each parameter presented and tested SVC2004 database. Our results show can by optimizing algorithm. Both false...
In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, use four separate databases to present and evaluate each algorithm parameter. The results show that proposed method can increase verification accuracy by 0.73-10% compared a traditional one class k-NN classifier. has achieved reasonable different databases, 3.93% error rate when using SVC2004 database, 2.6% MCYT-100 1.75% SigComp'11...
Signatures are widely used and accepted biometrics for individual identification. categorized as offline online based on the input method. Online signatures contain more features than regular signature, making them harder to forge. Several algorithms can be signature verification, such k-nearest neighbor. It is mainly one-class classification purposes. In this paper, both neighbor jk-nearest presented, along with a comparison of verification accuracy. The results conducted using different...
Online signatures are one of the most commonly used biometrics. Several verification systems and public databases were presented in this field. This paper presents a combination knearest neighbor dynamic time warping algorithms as system using recently published DeepSignDB database. Our algorithm was applied on both finger stylus input which represent office mobile scenarios. The first tested development set It achieved an error rate 6.04% for signatures, 5.20% 6.00% types. also to...
Online signatures are one of the widely accepted biometrics used for purpose authentication and identification. Although there several approaches algorithms in online signature verification systems, nowadays machine learning leading field. In this paper, an system based on logistic regression is presented. The SVC2004 database was to test accuracy by applying different combinations features. results showed that we could achieve accuracies between 91.7% 98.08%.