Implicit authentication method for smartphone users based on rank aggregation and random forest

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone’ users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone’s user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger’s pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process’s performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources

A grey wolf-based method for mammographic mass classification

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871

A second update on mapping the human genetic architecture of COVID-19

Matters Arising From: COVID-19 Host Genetics Initiative. Nature https://doi.org/10.1038/s41586-021-03767-x (2021)Data availability: Summary statistics generated by the COVID-19 HGI are available online, including per-ancestry summary statistics for African, admixed American, East Asian, European and South Asian ancestries (https://www.covid19hg.org/results/r7/). The analyses described here used the data release 7. If available, individual-level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale laboratory (http://www.nealelab.is/uk-biobank/), the Finucane laboratory (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results) and the eQTL catalogue release 3 (http://www.ebi.ac.uk/eqtl/).Code availability: The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses (https://github.com/covid19-hg/); for heritability estimation (https://github.com/AndrewsLabUCSF/COVID19_heritability); for Mendelian randomization and genetic correlation (https://github.com/marcoralab/MRcovid); and subtype analyses (https://github.com/mjpirinen/covid19-hgi_subtypes) are available at GitHub.Reporting summary: Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article online at: https://www.nature.com/articles/s41586-023-06355-3#MOESM2 .Supplementary information is available online at: https://www.nature.com/articles/s41586-023-06355-3#Sec4 .Copyright © The Author(s) 2023. Investigating the role of host genetic factors in COVID-19 severity and susceptibility can inform our understanding of the underlying biological mechanisms that influence adverse outcomes and drug development1,2. Here we present a second updated genome-wide association study (GWAS) on COVID-19 severity and infection susceptibility to SARS-CoV-2 from the COVID-19 Host Genetic Initiative (data release 7). We performed a meta-analysis of up to 219,692 cases and over 3 million controls, identifying 51 distinct genome-wide significant loci—adding 28 loci from the previous data release2. The increased number of candidate genes at the identified loci helped to map three major biological pathways that are involved in susceptibility and severity: viral entry, airway defence in mucus and type I interferon

Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia

202111 bcvcNot applicableSelf-fundedEarly release12 month

A Model Based on Genetic Algorithm for Colorectal Cancer Diagnosis

In this paper we present a method based on genetic algorithm capable of analyzing a significant number of features obtained from fractal techniques, Haralick texture features and curvelet coefficients, as well as several selection methods and classifiers for the study and pattern recognition of colorectal cancer. The chromosomal structure was represented by four genes in order to define an individual. The steps for evaluation and selection of individuals as well as crossover and mutation were directed to provide distinctions of colorectal cancer groups with the highest accuracy rate and the smallest number of features. The tests were performed with features from histological images H&E, different values of population and iterations numbers and with the k-fold cross-validation method. The best result was provided by a population of 500 individuals and 50 iterations applying relief, random forest and 29 features (obtained mainly from the combination of percolation measures and curvelet subimages). This solution was capable of distinguishing the groups with an accuracy rate of 90.82% and an AUC equal to 0.967.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Department of Computer Science and Statistics São Paulo State University (UNESP), R. Cristovão Colombo, 2265Faculty of Computation (FACOM) Federal University of Uberlândia (UFU), Av. João Naves de Ávila, 2121Center of Mathematics Computing and Cognition Federal University of ABC (UFABC), Av. dos Estados, 5001Federal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira S/NDepartment of Computer Science and Statistics São Paulo State University (UNESP), R. Cristovão Colombo, 2265CNPq: #304848/2018-2CNPq: #313365/2018-0CNPq: #427114/2016-0CNPq: #430965/2018-4FAPEMIG: #APQ-00578-1