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2021 International Conference on Information science and Communications Technologies, ICISCT 2021

作者： Raximov, Nodir Quvondikov, Jurabek Dusanov, Xurshid Daminova, Barno Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Dept. of Software of Information Technologies Tashkent Uzbekistan Applied Mathematics and Informatics Jizzakh Branch of National University of Uzbekistan Named after Mirzo Ulugbek Jizzakh Uzbekistan Karshi State University Methods of Teaching Computer Science Karshi Uzbekistan

ISBN: (纸本)9781665432580

Working with different data depending on the appearance and content of the object. Formation of types of objects and content of processes within the framework of human activity. Based on this, the control mechanism is also different. Management effects in the learning system are understood as the transfer of knowledge in the subject area to the student using the methods and tools formed by the teacher in the educational environment, taking into account all the factors generated in this process. The control system in this view is a simple system with a nonlinear feedback section. It combines complexes consisting of technical, software, telecommunications and methodological tools used in the educational process. They can be used to form alternative solutions not only by delivering parts of the training materials, but also by collecting and processing the information needed to manage the learning process. The training method is chosen according to the requirements of the learning environment and depends on the topic. If the chosen method is accepted, the training part is provided, which is fixed (question-answer or in another form), which makes it possible to work independently. © 2021 IEEE.

关键词： Learning systems

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Embeddings of planar quasimetrics into directed 1and polylogarithmic approximation for directed sparsest-cut

arXiv

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arXiv 2021年

作者： Kawarabayashi, Ken-Ichi Sidiropoulos, Anastasios National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo Japan Dept. of Computer Science University of Illinois at Chicago United States

The multi-commodity flow-cut gap is a fundamental parameter that affects the performance of several divide & conquer algorithms, and has been extensively studied for various classes of undirected graphs. It has been shown by Linial, London and Rabinovich [20] and by Aumann and Rabani [5] that for general n-vertex graphs it is bounded by O(log n) and the Gupta-Newman-Rabinovich-Sinclair conjecture [13] asserts that it is O(1) for any family of graphs that excludes some fixed minor. We show that the multicommodity flow-cut gap on directed planar graphs is O(log3n). This is the first sub-polynomial bound for any family of directed graphs of super-constant treewidth. We remark that for general directed graphs, it has been shown by Chuzhoy and Khanna [11] that the gap is Ω(n1/7), even for directed acyclic graphs. As a direct consequence of our result, we also obtain the first polynomial-time polylogarithmicapproximation algorithms for the Directed Non-Bipartite Sparsest-Cut, and the Directed Multicut problems for directed planar graphs, which extends the long-standing result for undirectd planar graphs by Rao [22] (with a slightly weaker bound). At the heart of our result we investigate low-distortion quasimetric embeddings into directed 1. More precisely, we construct O(log2n)-Lipschitz quasipartitions for the shortest-path quasimetric spaces of planar digraphs, which generalize the notion of Lipschitz partitions from the theory of metric embeddings. This construction combines ideas from the theory of bi-Lipschitz embeddings, with tools form data structures on directed planar graphs. © 2021, CC BY.

关键词： Polynomial approximation

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Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

TechRxiv

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TechRxiv 2021年

作者： Zhang, Xiang Gu, Yu Yan, Huan Wang, Yantong Dong, Mianxiong Ota, Kaoru Ren, Fuji Ji, Yusheng The School of Computer and Information Hefei University of Technology China I+ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China China The Dept. of Sciences and Informatics Muroran Institute of Technology Japan The National Institute of Informatics Tokyo101-8430 Japan

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras;however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs. © 2021, CC BY.

关键词： Wireless local area networks (WLAN)

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Network security analysis for signal strength based packet filitering 3

Network security analysis for signal strength based packet f...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Sundaram, B. Barani Rajkumar, P. Ananthi, M. Sravan Kumar, D. Vijaykumar, Janga Karthika, P. Bule Hora University College of Informatics Department of Computer Science Bule hora Ethiopia KGiSL Institute of Technology Dept of Computer Science and Engineering Coimbatore Tamilnadu India Institute of Technology Dept of IT KGiSL Coimbatore Tamilnadu India Balaji Institute of Technology Science Dept of CSE Warangal India Bule Hora University College of Informatics Dept. of Information Technology Bule Hora144 Ethiopia Kalasalingam Academy of Research and Education Department of Computer Applications TamilNadu India

ISBN: (纸本)9781728170893

Signal Strength based position check is all the more encouraging when contrasted with other Sybil assault discovery methods since it is lightweight and doesn't need centralization. Hubs can locally decide their areas through got signal quality varieties and illuminate neighbors about area refreshes likewise. In position confirmation, network hubs check the situation of every hub and further guarantee that each actual area is limited by just a single character at a specific time. This paper will overview the Sybil assault recognition methods with their connected problem. At last will introduce in thoughts regarding the Sybil assault discovery through one-time restriction (just the passage into organization) with fundamentally decreases correspondence in the clouds dissimilar to different plans where per parcel area following or intermittent spread of area data is utilized to distinguish Sybil assaults. © 2020 IEEE.

关键词： Network security

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Determination of molecular signatures and pathways common to brain tissues of autism spectrum disorder: Insights from comprehensive bioinformatics approach

引用

informatics in Medicine Unlocked 2022年 29卷

作者： Bristy, Sadia Afrin Islam, A.M. Humyra Andalib, K.M. Salim Khan, Umama Awal, Md Abdul Rahman, Md Habibur Bioinformatics and Biomedical Research Network of Bangladesh Dhaka Bangladesh Electronics and Communication Engineering Discipline Khulna University Khulna 9208 Bangladesh Dept. of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh

Autism spectrum disorder (ASD) is a collection of neurological disabilities marked by difficulties with behavior, speech, language, and interaction. It is a complicated and behaviorally defined static disorder of the developing brain. Recently it has become a serious concern across the world. The goal of this project was to use bioinformatics tools and network biology to uncover the molecular signatures and pathways of ASD. We investigated brain transcriptomics gene expression datasets and determined 47 dysregulated differentially expressed common genes. Several kinds of crucial neurodegeneration-related molecular mechanisms in the signaling structures were determined as a result of these investigations. We implemented gene set enrichment analysis (GSEA) using bimolecular pathways and gene ontology (GO) terms to determine the role of these differentially expressed genes (DEGs), as well as protein-protein interactions (PPI), transcriptional factor interactions, and post-transcriptional factor interactions. PPI network collected the top ten hub genes including KIT, PIN1, GATA1, GRIN2A, PBX2, BLK, ATP6V1B1, TCF7L1, TRAF1, and HSPG2. The PPI network also revealed the existence of two sub-networks. Moreover, several transcription factors (NFIC, USF2, TFAP2A, RELA, FOXL1, GATA2, YY1, FOXC1, NFKB1, and E2F1) and post-transcription factors (mir-335-5p, mir-26b-5p, mir-124-3p, mir-192-5p, mir-1-3p, mir-215-5p, mir-6825-5p, mir-146a-5p, mir-8485, and mir-93-5p) were found throughout this study. Some drug-like molecules were also predicted that might have a beneficial effect against ASD. We detected potentially novel links between pathogenic conditions in ASD patient's brain tissues. This work offers molecular biomarkers at the gene expression level and protein bases that could aid in a better understanding of molecular pathways, as well as potential pharmacological approaches and therapies for developing effective ASD treatments. © 2022 The Authors

关键词： Autism spectrum disorder Bioinformatics Gene ontology Hub gene Post-transcriptional factors Protein-protein interaction Transcriptional factors

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Smart Irrigation and Intrusions Detection in Agricultural Fields Using I.o.T.

引用

Procedia computer science 2020年 167卷 154-162页

作者： Divyansh Thakur Yugal Kumar Singh Vijendra Dept. of Computer Science and Engineering Indian Institute of Information Technology Una 177209 India Dept. of Computer Science and Engineering Jaypee University of Information Technology Solan 173234 India Dept. of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

Agriculture plays an important role in the life span of human being not only for their survival but for the better economic growth of the country too. Precision agriculture is the new trending term in the field of technology whose main motive is to reduce the workload of the farmers and increase the productivity of the farms by using technologies like I.O.T, WSNs, Remote Sensing, Drone surveillance and many more. In this paper, we show the work done by our cost effective and reliable device whose aim is to irrigate fields only when there is a need of water and to provide information about detection of any intrusion in agricultural fields. The information is sent to the farmers by using cloud application. The performance of our system is measured in terms of intrusion detection and moisture of soil for irrigation.

关键词： IoT wireless sensor networks soil moisture sensor PIR sensor smart irrigation cloud application

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Skimming of Electronic Health Records Highlighted by an Interface Terminology Curated with Machine Learning Mining

Skimming of Electronic Health Records Highlighted by an Inte...

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17th International Joint Conference on biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Dehkordi, Mahshad Koohi H. Martin Kollapally, Navya Perl, Yehoshua Geller, James Deek, Fadi P. Liu, Hao Keloth, Vipina K. Elhanan, Gai Einstein, Andrew J. Ying Wu College of Computing NJIT Dr. Martin Luther King Jr. Boulevard NewarkNJ United States Department of Computer Science Montclair State University 1 Normal Avenue MontclairNJ United States Department of Medical Informatics Yale University 51 Prospect Street New HavenCT United States Center for Genomic Medicine School of Medicine University of Nevada 1664 N. Virginia Street RenoNV United States Dept. of Medicine Cardiology Division Columbia University Irving Medical Center New York 168th Street United States

Clinical notes in Electronic Health Records (EHRs) contain large amounts of nuanced information. Health care professionals, e.g., clinicians, routinely review numerous EHR notes, further burdening their busy schedules. To capture the essential content of a note, they often quickly review its content, which can contribute to missing critical clinical information. High lighting important content of EHRs enable clinicians to fast skim by reading only the highlighted words. Furthermore, effective highlighting of EHRs will support new research and interoperability. In this paper, we design a Cardiology Interface Terminology (CIT) dedicated for the application of highlighting cardiology EHRs to support their fast skimming. Once successful, Transfer Learning can be used to design an interface terminology for other specialties. In EHRs, we observe phrases of fine granularity containing SNOMED CT concepts. In our previous work, we extract such phrases from EHR notes to be considered as CIT concepts. This early CIT serves as training data for Machine Learning (ML) techniques, further enriching CIT and improving EHR highlighting. We describe the methodology and results of curating CIT with ML techniques. Furthermore, we introduce the coverage and breadth metrics for measuring the efficacy of highlighting EHRs, and discuss future improvements, enhancing the coverage of highlighted important content. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electronic health record

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"Chatty Devices" and edge-based activity classification

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Discover Internet of Things 2021年第1期1卷 1-15页

作者： Lakoju, Mike Javed, Amir Rana, Omer Burnap, Pete Atiba, Samuelson T. Cherkaoui, Soumaya Cardiff School of Technologies Cardiff Metropolitan University Western Avenue CardiffCF5 2YB United Kingdom School of Computer Science & amp Informatics Cardiff University 5 The Parade CardiffCF24 3AA United Kingdom Interlab Lab. Dept. of Electical & ampComp. Engineering Université de Sherbrooke Sherbrooke QuebecJ1K 2R1 Canada

With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a "chatty" factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use;(ii) associate a label with product use using unsupervised learning—making use of edge-based analytics;(iii) transmission of this data to a cloud environment where labels can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy). © The Author(s) 2021.

关键词： Edge computing

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Unconstrained and constrained estimation of a linear EMG-to-force mapping during isometric force generation

Unconstrained and constrained estimation of a linear EMG-to-...

引用

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Daniele Borzelli Andrea d' Avella Sergio Gurgone Laura Gastaldi Dept. of Biomedical Dental Sciences and Morphofunctional Imaging University of Messina Messina Italy Laboratory of Neuromotor Physiology IRCCS Fondazione Santa Lucia Rome Italy Dept. of Department of Mathematical and Computer Science Physical Sciences and Earth Sciences University of Messina Messina Italy Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9781665482998

ISBN: (纸本)9781665483001

EMG-driven robotic devices require the estimation of the forces exerted by the human operator from muscle activity. Approximating the relation between EMG and force with a linear mapping may be accurate enough for numerous real-time applications, such as controlling exoskeletons or prostheses. However, while a linear mapping from the EMG activity to endpoint force may be identified by minimizing the error without any constraint, introducing some constraints may be helpful to determine a mapping which is more anatomically accurate. The presence of noise and the muscle redundancy may introduce errors in the estimation achieved by the unconstrained optimization. Contrarily, anatomical constraints, estimated from an accurate musculoskeletal model, would limit the effect of noise, but they would increase the algorithm complexity and its computational costs. This study compares the two algorithms (unconstrained and constrained) for the estimation of the forces exerted by a human participant from the EMG activity of several upper limb muscles. The two algorithms were tested on data collected during an isometric force generation task performed during multiple sessions spanning two days. Accuracy and consistency across sessions of the reconstructed forces were assessed. Data showed that the unconstrained algorithm allowed for a better reconstruction of the exerted forces, but the constrained mapping is more robust across sessions. Further studies will investigate which of the two algorithms reconstruct a mapping perceived by the participants as more natural during EMG-driven control.

关键词： Force Redundancy Estimation Muscles Particle measurements Electromyography Real-time systems

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