FRACTAL ANALYSIS AS A TOOL FOR AUTOMATIC DETECTION OF ARTIFACTS IN ELECTROENCEPHALOGRAM RECORDS

В статье обсуждается применение методов фрактального анализа для решения задачи автоматической фильтрации сигнала ЭЭГ от артефактов различной природы. Изучается возможность использования показателя Херста в качестве информативного признака для алгоритмов интеллектуальной обработки данных. The article discusses the possibility of using fractal analysis to solve the problem of automatic filtering of the EEG signal from artifacts of various nature. The possibility of using the Hurst exponent as an informative feature for intelligent data processing algorithms is investigated

Replenishment of Documents of Mathematical Digital Retro-collections by Searching in Semantic Web

A method of forming a mandatory set of metadata for retro collections of a digital mathematical library is presented. The open resources of the Semantic Network were used as a source for completing metadata. With the help of the software tools of the metadata factory of the digital mathematical library Lobachevskii-DML, the main processes of text analysis of documents of digital retro collections are performed, in particular, the selection of named entities. Further, through the system of queries in the semantic network, the search and selection of information objects is carried out. After performing automatic filtering and normalization, the obtained information is included in the metadata set. As one of the results, the process of forming a mandatory set of metadata for one of the collections of the digital library Lobachevskii-DML – a retro-collection of articles of the journal "Izvestia of the Physics and Mathematics Society at Kazan University" is presented.

Single Ping Filtering of Multi-Beam Sounding Data Based on Alpha Shapes

From the standpoint of complex marine environments relative to underwater acoustic signal propagation, the application of multi-beam systems for the filtering and processing of multi-beam sounding data is critical. However, currently existing automatic filtering methods estimate data, which involve several calculations and require the respective computer to possess significant processing capabilities. In this study, raw data are measured using an interferometer multi-beam echo sounder, and the characteristics of the noise data are analyzed. The noise data are discontinuous; however, accurate data can be approximated as a continuous distribution. Under the assumption of continuous underwater terrain, in consideration of a circle of the appropriate radius rolling on the terrain profile, the continuous underwater terrain data can be extracted from the raw data by means of the alpha-shapes algorithm. Finally, on the basis of the measured data in a sail trial, the effectiveness of the proposed algorithm is verified.

Smart filtering of phase residues in noisy wrapped holograms

Phase unwrapping is one of the major challenges in multiple branches of science that extract three-dimensional information of objects from wrapped signals. In several applications, it is important to extract the unwrapped information with minimal signal resolution degradation. However, most of the denoising techniques for unwrapping are designed to operate on the entire phase map to remove a limited number of phase residues, and therefore they significantly degrade critical information contained in the image. In this paper, we present a novel, smart, and automatic filtering technique for locally minimizing the number of phase residues in noisy wrapped holograms, based on the phasor average filtering (PAF) of patches around each residue point. Both patch sizes and PAF filters are increased in an iterative algorithm to minimize the number of residues and locally restrict the artifacts caused by filtering to the pixels around the residue pixels. Then, the improved wrapped phase can be unwrapped using a simple phase unwrapping technique. The feasibility of our method is confirmed by filtering, unwrapping, and enhancing the quality of a noisy hologram of neurons; the intensity distribution of the spatial frequencies demonstrates a 40-fold improvement, with respect to previous techniques, in preserving the higher frequencies.

Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource

ObjectiveTo mine Twitter to quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions against clinical studies, and create a symptom lexicon for the research community.Materials and methodsWe retrieved tweets using COVID-19-related keywords, and performed several layers of semi-automatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard IDs, and compared the distributions with multiple studies conducted in clinical settings.ResultsWe identified 203 positive-tested users who reported 932 symptoms using 598 unique expressions. The most frequently-reported symptoms were fever/pyrexia (65%), cough (56%), body aches/pain (40%), headache (35%), fatigue (35%), and dyspnea (34%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (26%) and ageusia (24%) were frequently reported on Twitter, but not in clinical studies.ConclusionThe spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.

Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment

Distributed Denial of Service (DDoS) attacks has become the most powerful cyber weapon to target the businesses that operate on the cloud computing environment. The sophisticated DDoS attack affects the functionalities of the cloud services and affects its core capabilities of cloud such as availability and reliability. The current intrusion detection system (IDS) must cope with the dynamicity and intensity of immense traffic at the cloud hosted applications and the security attack must be inspected based on the attack flow characteristics. Hence, the proposed Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment is designed to adapt with varying kind of protocol attacks using misuse detection. The system is equipped with custom and threshold techniques that satisfies security requirements and can identify the different DDoS security attacks. The proposed system provides promising results in detecting the DDoS attacks in cloud environment with high detection accuracy and good alert reduction. Threshold method provides 98% detection accuracy with 99.91%, 99.92% and 99.94% alert reduction for ICMP, UDP and TCP SYN flood attack. The defense system filters the attack sources at the target virtual instance and protects the cloud applications from DDoS attacks.

Framework for Hybrid Book Recommender System based on Opinion Mining

Recommender system plays an important role in automatic filtering out the important and personalized information for the intended user from a large amount of available information on internet. Recommender systems for books provide personalized recommendations to the readers for reading and to the librarians for book acquisition process. The objective of this research paper is four folds. Firstly, it conducts an extensive literature review pertaining to book recommender systems, secondly it specifies the popular recommendation techniques being used in specific application area of books, thirdly the paper reflects on the methodology followed and evaluation techniques being used based on the techniques discussed. Lastly, the paper proposes a framework for a book recommender system using best-suited recommendation techniques.