U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review

Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.

Automated Contactless Attendance System

Attendance marking in a classroom is a tedious and time-consuming task. Due to a large number of students present, there is always a possibility of proxy. In recent times, the task of automatic attendance marking has been extensively addressed via the use of fingerprint-based biometric systems, radio frequency identification tags, etc. However, these RFID systems lack the factor of dependability and due to COVID-19 use of fingerprint-based systems is not advisable. Instead of using these conventional methods, this paper presents an automated contactless attendance system that employs facial recognition to record student attendance and a gesture sensor to activate the camera when needed, thereby consuming minimal power. The resultant data is subsequently stored in Google Spreadsheets, and the reports can be viewed on the webpage. Thus, this work intends to make the attendance marking process contactless, efficient and simple. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Characterizing the nature of trust & misinformation on Twitter

We analyze a dataset from Twitter of misinformation related to the COVID-19 pandemic. We consider this dataset from the intersection of two important but, heretofore, largely separate perspectives: misinformation and trust. We apply existing direct trust measures to the dataset to understand their topology, and to better understand if and how trust relates to spread of misinformation online. We find evidence for small worldness in the misinformation trust network;outsized influence from broker nodes;a digital fingerprint that may indicate when a misinformation trust network is forming;and, a positive relationship between greater trust and spread of misinformation. © 2022 IEEE.

Technology-Driven Implementation of Smart Entrances in Public Places during the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.

Evaluating the quality consistency of antiviral oral liquid by high-performance liquid chromatography five-wavelength matched average fusion fingerprint combined with electrochemical fingerprint and ultraviolet spectral quantum fingerprint.

The antiviral oral liquid (AOL) was an antiviral drug currently in clinical trials against coronavirus disease 2019. This study aimed to improve its quality consistency evaluation method using fingerprint techniques from several aspects. First, the five-wavelength matched average fusion fingerprint (FMAFFP) for HPLC, electrochemical fingerprint (ECFP), and ultraviolet spectral quantum fingerprint (UVFP) was established for 22 samples, respectively. Their quality was then assessed using the average linear quantitative fingerprint method, and 22 samples were classified into eight quality grades. OPLS and PCA were then used further to explore the characteristic parameters of these three fingerprints. Five compounds were quantified simultaneously for the first time, and then the relationship between the average linear quantitative similarity (PL) and the sum of the five quantitative components (P5c) was investigated. A linear correlation (r ≥ 0.9735) between PL and P5c suggested that PL may be used to predict chemical content. Finally, to investigate the antioxidant potential of the AOL, correlation analyses were performed for FMAFFP peaks-PEC and UVFP peaks-PEC, respectively, where the PEC value was defined as the quantitative similarity of ECFP. The Pearson correlation coefficient and gray correlation analysis were consistent, allowing us to initially explore the antioxidant capacity of the unidentified components of the samples. This study researched AOL using multidimensional fingerprints to provide a comprehensive and reliable method for quality consistency control of herbal compound preparations.

Determination of Anti-SARS-CoV-2 Activity of Compounds Based on Machine Learning

In 2020, the outbreak of pneumonia caused by novel coronavirus spread rapidly all over the world. In the absence of a specific drug, novel coronavirus is still pandemic all over the world. In this paper, we proposed an improved molecular activity prediction model by adding feature selection method on the basis of comparing different methods to extract molecular features and machine learning models. We first used the anti-SARS-CoV-2 compounds reported in recent literatures to construct the data set, and then constructed three machine learning models. In addition, we tried to use three methods to extract molecular features in each model. In order to further improve the performance of the model, we add three feature selection methods. Through the comparison of different models, finally, we used FCFP to extract molecular features and added lasso feature selection method to establish the SVM model. Its test set accuracy is 90.0%, and the AUC value is 0.961, which could well predict the anti-SARS-CoV-2 activity of the compound. Our model can be used to speed up the research and discovery of anti-SARS-CoV-2 drugs. © 2022 IEEE.

Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software. © 2022 IEEE.

Immunological fingerprint in coronavirus disease-19 convalescents with and without post-COVID syndrome.

Background: Symptoms lasting longer than 12 weeks after severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection are called post-coronavirus disease (COVID) syndrome (PCS). The identification of new biomarkers that predict the occurrence or course of PCS in terms of a post-viral syndrome is vital. T-cell dysfunction, cytokine imbalance, and impaired autoimmunity have been reported in PCS. Nevertheless, there is still a lack of conclusive information on the underlying mechanisms due to, among other things, a lack of controlled study designs. Methods: Here, we conducted a prospective, controlled study to characterize the humoral and cellular immune response in unvaccinated patients with and without PCS following SARS-CoV-2 infection over 7 months and unexposed donors. Results: Patients with PCS showed as early as 6 weeks and 7 months after symptom onset significantly increased frequencies of SARS-CoV-2-specific CD4+ and CD8+ T-cells secreting IFNγ, TNF, and expressing CD40L, as well as plasmacytoid dendritic cells (pDC) with an activated phenotype. Remarkably, the immunosuppressive counterparts type 1 regulatory T-cells (TR1: CD49b/LAG-3+) and IL-4 were more abundant in PCS+. Conclusion: This work describes immunological alterations between inflammation and immunosuppression in COVID-19 convalescents with and without PCS, which may provide potential directions for future epidemiological investigations and targeted treatments.

Targeting the I7L Protease: A Rational Design for Anti-Monkeypox Drugs?

The latest monkeypox virus outbreak in 2022 showcased the potential threat of this viral zoonosis to public health. The lack of specific treatments against this infection and the success of viral protease inhibitors-based treatments against HIV, Hepatitis C, and SARS-CoV-2, brought the monkeypox virus I7L protease under the spotlight as a potential target for the development of specific and compelling drugs against this emerging disease. In the present work, the structure of the monkeypox virus I7L protease was modeled and thoroughly characterized through a dedicated computational study. Furthermore, structural information gathered in the first part of the study was exploited to virtually screen the DrugBank database, consisting of drugs approved by the Food and Drug Administration (FDA) and clinical-stage drug candidates, in search for readily repurposable compounds with similar binding features as TTP-6171, the only non-covalent I7L protease inhibitor reported in the literature. The virtual screening resulted in the identification of 14 potential inhibitors of the monkeypox I7L protease. Finally, based on data collected within the present work, some considerations on developing allosteric modulators of the I7L protease are reported.

Rain Drop Service and Biometric Verification Based Blockchain Technology for Securing the Bank Transactions from Cyber Crimes Using Weighted Fair Blockchain (WFB) Algorithm

Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage. [ FROM AUTHOR] Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Offshore Construction Progress Management by Indoor GIS Positioning: Post-COVID-19 New Normal

Travel restrictions have been imposed among countries since the outbreak of the COVID-19 pandemic. Time delays, budget issues, and poor-quality control in construction projects due to the pandemic have severely affected the construction industry. To reduce the influence of the pandemic, the paper introduces an offshore construction site progress management system with a real case study. With the integration of indoor location-based service technology and image processing method, site superintendents (architect, project director, site manager, engineer) can monitor the site progress easily and pinpoint defects for further investigation and measurement. The visualization of site images together with BIM provides a digital twin platform that can help senior management quickly review the site progress, perform quality checks, and resolve discrepancies in early phases. Positioning of workers and equipment with the adoption of digital maps is a further step in sustainable management. The proposed integration provides a new concept for construction site management during a pandemic and supports the post-COVID-19 new normal in the construction industry. © 2022 International Conference on Construction in the 21st Century. All rights reserved.

Covid the Board System

The dark cloud of the vigorously spreading pandemic is still seen hovering over our heads. Though the situation presently seems under control, a continuation of the same is not guaranteed. The massive disturbance to the environmental assets leading to the melting of polar ice caps, ozone depletion, global warming, etc., poses a towering threat to every surviving species. The most recently faced devastation by the people is the 'Corona' virus;a ginormous pandemic, which has affected people living in almost every nook and corner of the world. For which the primary steps were to wear a mask, maintain social distancing and sanitize almost every single thing that comes in physical contact with more than one human. This brings in a crucial technical catastrophe, the physical barrier to be maintained, and the face masks covering the face, making fingerprint and face recognition systems totally impractical. This demands a grave need for a situation-friendly technical solution. © 2022 IEEE.

Multiple Face Recognition System Using OpenFace

The digitalization of human work has been an ever-evolving process. Student's and employee's attendance systems are automated by using fingerprint biometrics. Specifically covid situation has created the need for touchless attendance system. Many institutions have already implemented a face detection-based attendance system. However, the major problem in designing face-recognising biometric applications is the scalability and accuracy in time to differentiate between multiple faces from a single clip/image. This paper used the OpenFace model for face recognition and developed a multi-face recognition model. The Torch and Python deployment module of deep neural network-based face recognition was used, and it was predicated accurately in time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Screening and identification of potential MERS-CoV papain-like protease (PLpro) inhibitors; Steady-state kinetic and Molecular dynamic studies.

MERS-CoV belongs to the coronavirus group. Recent years have seen a rash of coronavirus epidemics. In June 2012, MERS-CoV was discovered in the Kingdom of Saudi Arabia, with 2,591 MERSA cases confirmed by lab tests by the end of August 2022 and 894 deaths at a case-fatality ratio (CFR) of 34.5% documented worldwide. Saudi Arabia reported the majority of these cases, with 2,184 cases and 813 deaths (CFR: 37.2%), necessitating a thorough understanding of the molecular machinery of MERS-CoV. To develop antiviral medicines, illustrative investigation of the protein in coronavirus subunits are required to increase our understanding of the subject. In this study, recombinant expression and purification of MERS-CoV (PLpro), a primary goal for the development of 22 new inhibitors, were completed using a high throughput screening methodology that employed fragment-based libraries in conjunction with structure-based virtual screening. Compounds 2, 7, and 20, showed significant biological activity. Moreover, a docking analysis revealed that the three compounds had favorable binding mood and binding free energy. Molecular dynamic simulation demonstrated the stability of compound 2 (2-((Benzimidazol-2-yl) thio)-1-arylethan-1-ones) the strongest inhibitory activity against the PLpro enzyme. In addition, disubstitutions at the meta and para locations are the only substitutions that may boost the inhibitory action against PLpro. Compound 2 was chosen as a MERS-CoV PLpro inhibitor after passing absorption, distribution, metabolism, and excretion studies; however, further investigations are required.

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach. Artificial intelligence methods, such as Convolutional Neural Network (CNN), are widely used to facilitate new drug discovery. Owing to the structural limitations of CNN, features extracted from this method are local patterns that lack global information. However, global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drugtarget affinity. A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes. This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction (FingerDTA), which uses CNN to extract local patterns and utilize fingerprints to characterize global information. These fingerprints are generated on the basis of the whole sequence of drugs or targets. Furthermore, FingerDTA achieves comparable performance on Davis and KIBA data sets. In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019 (COVID-19), 7 of the top 10 drugs have been confirmed potential by literature. Ultimately, the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets. All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git. © 2018 Tsinghua University Press.

Multiple Face Recognition System Using OpenFace

The digitalization of human work has been an ever-evolving process. Student’s and employee’s attendance systems are automated by using fingerprint biometrics. Specifically covid situation has created the need for touchless attendance system. Many institutions have already implemented a face detection-based attendance system. However, the major problem in designing face-recognising biometric applications is the scalability and accuracy in time to differentiate between multiple faces from a single clip/image. This paper used the OpenFace model for face recognition and developed a multi-face recognition model. The Torch and Python deployment module of deep neural network-based face recognition was used, and it was predicated accurately in time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Mathematical modelling for prediction of spread of corona virus and artificial intelligence/machine learning-based technique to detect COVID-19 via smartphone sensors

COVID-19 is a novel corona virus which is infectious and communicable disease and it is originated from Wuhan, China. As the virus is mutating, the world is suffering from its spread again and again. However, the spread of communicable diseases can be predicted in advance so the proper preventive measures can be taken before it become life-taking. In this paper, mathematical model (SEIR) for the prediction of infectious diseases, which is modification of conventional SIR model is described and modelled which can be used to predict the cases in advance. A novel framework to detect COVID-19 from home is also proposed using artificial intelligence, machine learning and smartphone embedded sensors. The various smartphone embedded sensors such as proximity sensor, light sensor, accelerometer, gyroscope and fingerprint sensors are used to read the symptoms or activity and scan the CT images, and can be used to detect COVID-19.

Investigating the impact of demographic factors on contactless fingerprints interoperability

Contactless fingerprints have continued to grow interoperability as a faster and more convenient replacement for contact fingerprints, and with covid-19 now starting to be a past event the need for hygienic alternatives has only grown after the sudden focus during the pandemic. Though, past works have shown issues with the interoperability of contactless prints from both kiosk devices and phone fingerprint collection apps. The focus of the paper is the evaluation of match performance between contact and contactless fingerprints, and the evaluation of match score bias based on skin demographics. AUC results indicate contactless match performance is as good as contact fingerprints, while phone contactless fingerprints fall short. Additionally, bias found for melanin showed specific ranges effected in both low melanin values and high melanin values.

Implementation of Security System Using Computer Vision and Temperature Detection

For corporate and private groups, providing security and secure access to workplaces has long been a top priority. From keypads to fingerprint sensors, there have been advancements in the way security is delivered over the years. Even these, though, have their flaws and weaknesses. Computer Vision is a more powerful and modern technique which can be integrated into a security system for the purpose of increasing the overall level of security. This project aims to create a security system that utilizes this software as well as a temperature sensing module to enable secure, monitored and contact-less, access. The facial authentication is achieved with a help of a webcam connected to the system and a python program on which this is executed, after which the main control is transferred to the Arduino UNO Microcontroller board which tests the two incoming inputs and provides access based on its decision. A training model is employed which studies the given images of the users and detects them when entry is requested. © 2022 IEEE.

Local Pre-Conditioning and Quality Enhancement to Handle Different Data Complexities in Contactless Fingerprint Classification

Biometric authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention;yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative;yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatio-temporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827, specificity of 95.38% and a reduced EER of about 0.084. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.