Outlier detection for keystroke biometric user authentication.

User authentication is a fundamental aspect of information security, requiring robust measures against identity fraud and data breaches. In the domain of keystroke dynamics research, a significant challenge lies in the reliance on imposter datasets, particularly evident in real-world scenarios where obtaining authentic imposter data is exceedingly difficult. This article presents a novel approach to keystroke dynamics-based authentication, utilizing unsupervised outlier detection techniques, notably exemplified by the histogram-based outlier score (HBOS), eliminating the necessity for imposter samples. A comprehensive evaluation, comparing HBOS with 15 alternative outlier detection methods, highlights its superior performance. This departure from traditional dependence on imposter datasets signifies a substantial advancement in keystroke dynamics research. Key innovations include the introduction of an alternative outlier detection paradigm with HBOS, increased practical applicability by reducing reliance on extensive imposter data, resolution of real-world challenges in simulating fraudulent keystrokes, and addressing critical gaps in existing authentication methodologies. Rigorous testing on Carnegie Mellon University's (CMU) keystroke biometrics dataset validates the effectiveness of the proposed approach, yielding an impressive equal error rate (EER) of 5.97%, a notable area under the ROC curve of 97.79%, and a robust accuracy (ACC) of 89.23%. This article represents a significant advancement in keystroke dynamics-based authentication, offering a reliable and efficient solution characterized by substantial improvements in accuracy and practical applicability.

The Effect of Physical Activity on Sleep Quality and Sleep Disorder: A Systematic Review.

Regular physical activity has several health benefits, including improved sleep quality and symptoms of sleep disorders. With the known benefits of moderate-intensity activities to sleep quality and a growing interest in using physical activity as a treatment approach for different sleep disorders, we conducted a systematic review to provide evidence-based data on the association between physical activity and sleep. A systematic search was carried out in PubMed, Embase, MEDLINE (Medical Literature Analysis and Retrieval System Online), Google Scholar, and Scopus, using predetermined search terms (Medical Subject Headings (MeSH) terms) and keywords. The included studies focused on exploring the effect of physical activity on sleep quality and sleep disorders or the association between physical activity and sleep outcomes. Relevant data were extracted, and the quality of the studies was evaluated using suitable methods. The collected findings were synthesized and discussed. The findings of this systematic review have potential implications for healthcare, public health policies, and health promotion.

An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification.

The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time.

Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients.

Purpose: The outbreak of a new coronavirus is still spreading worldwide, affecting children and adults. However, COVID-19 in children shows distinctive characteristics in clinical and radiological presentation. We aimed to assess the diagnostic performance of chest CT and clarify the clinicoradiological CT features of COVID-19 among children with COVID-19. Material and methods: Adhering to PRISMA-DTA guidelines, we searched databases (PubMed, Google Scholar, and Web of Science) to identify relevant articles. The search keywords were: "Chest CT" AND "COVID-19" OR "coronavirus" OR "SARS-COV-2" AND "Children" OR "Pediatric". Published reports providing clinical and imaging findings of paediatric COVID-19 were included. Results: Twenty-eight studies were included, with 987 patients. Most of the patients were symptomatic (76.9%; 95% CI: 69.2-84.7%), with fever being the most frequent manifestation (64%; 95% CI: 58.0-71.2%). Only 2.3% of the cases were critical, and mortality was reported in one case. The proportion of COVID-19 detected by chest CT among children is relatively high (658/987), with ground-glass opacity (GGO) being the most prevalent feature (52.5%; 95% CI: 40.5-64.7%). The pooled sensitivity of chest CT in all patients was 67%; however, it was different between symptomatic and asymptomatic patients (71% and 33%, respectively). The pooled specificity was (67%), which was calculated after considering the symptomatic PCR-positive patients as the gold standard. Conclusions: Chest CT showed moderate pooled sensitivity and specificity among symptomatic children with COVID-19 and low sensitivity among asymptomatic children. This means that CT is not to be used as a screening tool or for confirmation of the diagnosis in children and should be reserved for specific clinical situations.

Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends.

The automation strategy of today's smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed.

Liver Tumor Segmentation in CT Scans Using Modified SegNet.

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients' death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder-decoder is named SegNet, and consists of a hierarchical correspondence of encode-decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

Basic evidence for epidermal H2O2/ONOO(-)-mediated oxidation/nitration in segmental vitiligo is supported by repigmentation of skin and eyelashes after reduction of epidermal H2O2 with topical NB-UVB-activated pseudocatalase PC-KUS.

Nonsegmental vitiligo (NSV) is characterized by loss of inherited skin color. The cause of the disease is still unknown despite accumulating in vivo and in vitro evidence of massive epidermal oxidative stress via H2O2 and peroxynitrite (ONOO(-)) in affected individuals. The most favored hypothesis is based on autoimmune mechanisms. Strictly segmental vitiligo (SSV) with dermatomal distribution is a rare entity, often associated with stable outcome. Recently, it was documented that this form can be associated with NSV (mixed vitiligo). We here asked the question whether ROS and possibly ONOO(-) could be players in the pathogenesis of SSV. Our in situ results demonstrate for the first time epidermal biopterin accumulation together with significantly decreased epidermal catalase, thioredoxin/thioreoxin reductase, and MSRA/MSRB expression. Moreover, we show epidermal ONOO(-) accumulation. In vivo FT-Raman spectroscopy reveals the presence of H2O2, methionine sulfoxide, and tryptophan metabolites; i.e., N-formylkynurenine and kynurenine, implying Fenton chemistry in the cascade (n=10). Validation of the basic data stems from successful repigmentation of skin and eyelashes in affected individuals, regardless of SSV or segmental vitiligo in association with NSV after reduction of epidermal H2O2 (n=5). Taken together, our contribution strongly supports H2O2/ONOO-mediated stress in the pathogenesis of SSV. Our findings offer new treatment intervention for lost skin and hair color.