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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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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Suhail Razeeth Fathima Shihara Kasuni Pavithra Maran Kaliyamoorthy Samsudeen Sabraz Nawaz Rkar Kariyapper Dept. of Information and Communication Technology South Eastern University of Sri Lanka Sri Lanka Dept. of Computer Science and Informatics Uva Wellassa University of Sri Lanka Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India Department of Management and Information Technology South Eastern University of Sri Lanka Sri Lanka

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers.

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Damola Gideon Akinola Emmanuel Adetiba Abdultaofeek Abayomi Uche Nnaji Surendra Thakur Sibusiso Moyo Department of Electrical and Information Engineering Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Durban South Africa Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria KZN e-Skills CoLab Durban University of Technology Durban South Africa School for Data Science and Computational Thinking and Department of Mathematical Sciences Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.

关键词： computers Cloud computing Organizations Load management Resource management Time factors Task analysis

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

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Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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Fitness dependent optimizer with neural networks for COVID-19 patients

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computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer Machine learning Swarm intelligence

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High Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks

arXiv

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arXiv 2022年

作者： Sozos, K. Bogris, A. Bienstman, P. Sarantoglou, G. Deligiannidis, S. Mesaritakis, Charis Dept. of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo 12243 Greece Dept. of Information Technology Ghent University-Imec Technologiepark Zwijnaarde 126 Gent9052 Belgium Dept. Information and Communication Systems Engineering Engineering School University of the Aegean Palama 2 Samos83200 Greece

Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum power consumption. In this work, we present a new concept for realizing photonic recurrent neural networks and reservoir computing architectures with the use of recurrent optical spectrum slicing. This is accomplished through simple optical filters placed in an loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to filters' central frequencies and bandwidths. This new method for implementing recurrent neural processing in the photonic domain, which we call Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), is numerically evaluated on a demanding, industry-relevant task such as high baud rate optical signal equalization (>100 Gbaud), exhibiting ground-breaking performance. The performance enhancement surpasses state-of-the-art digital processing techniques by doubling the reach while minimizing complexity and power consumption by a factor of 10 compared to state-of-the-art solutions. In this respect, ROSS-NNs can pave the way for the implementation of ultra-efficient photonic hardware accelerators tailored for processing high-bandwidth (> 100 GHz) optical signals in optical communication and high-speed imaging applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical communication

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Challenges in Implementing ICT Rehabilitation Environments in Real Clinical Practice

Challenges in Implementing ICT Rehabilitation Environments i...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Valentina Tibollo Lucia Sacchi Alberto Malovini Anna Giardini Giovanni D'Addio Christian Lunetta Laboratory of Medical Informatics and Artificial Intelligence Istituti Clinici Scientifici Maugeri IRCCS Pavia Italy Dept of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Information Technology Department Istituti Clinici Scientifici Maugeri IRCCS Pavia Italy Bioengineering Unit Istituti Clinici Scientifici Maugeri IRCCS Pavia Italy Dept of Rehabilitative Neurology Department Istituti Clinici Scientifici Maugeri IRCCS Pavia Italy

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

Fit for Medical Robotics funded under the National Recovery and Resilience Plan, aims to transform current rehabilitation and assistive models for individuals of all ages with impaired motor, sensory, or cognitive functions. The project seeks to achieve this through the implementation of innovative biorobotic technologies and related digital solutions, alongside new continuum of care paradigms. A key element is the full exploitation of information and communication technology (ICT) across all stages of rehabilitation, ranging from prevention to at-home care during the chronic phase. Within this framework, it is crucial to assess the potentialities, challenges, and potential barriers associated with integrating interventions based on advanced technologies into real-world rehabilitation practices.

关键词： Technological innovation Collaboration Medical services Transforms Strategic planning Robot sensing systems Information and communication technology Stakeholders Standards Resilience

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Securing Vehicle-to-Everything (V2X) Communication in AI-Empowered Electric Vehicles

Securing Vehicle-to-Everything (V2X) Communication in AI-Emp...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Edward Naveen. V Dhivya. S M.Joe Marshell K Arul Jeyaraj Ebenezer. V Jenefa. A Dept. of CSE Sri Shakthi Inst. of Engg. and Tech. Coimbatore India Dept of Elect. and Instrumentation Engg. SRM Valliammai Engineering College Chengalpattu India Department of ECE PSNA College of Engineering and Technology Dindigul India School of computer science and tech. Karunya Institute of Tech. and Sci. Coimbatore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Data in AI-Empowered Electric Vehicles is protected by using blockchain technology for immutable and verifiable transactions, in addition to high-strength encryption methods and digital signatures. This research paper compares and evaluates the security mechanisms for V2X communication in AI-enabled EVs. The purpose of the study is to ensure the reliability of security measures by evaluating performance metrics including false positive rate, false negative rate, detection accuracy, processing time, communication latency, computational resources, key generation time, and throughput. A comprehensive experimental approach is implemented using a diverse dataset gathered from actual V2X communication condition. The evaluation reveals that the security mechanisms perform inconsistently. Message integrity verification obtains the highest detection accuracy with a low false positive rate of 2% and a 0% false negative rate. Traffic encryption has a low processing time, requiring only 10 milliseconds for encryption and decryption, and adds only 5 bytes of communication latency to V2X messages. The detection accuracy of intrusion detection systems is adequate at 95%, but they require more computational resources, consuming 80% of the CPU and 150 MB of memory. In particular attack scenarios, certificate-based authentication and secure key exchange show promise. Certificate-based authentication mitigates MitM attacks with a false positive rate of 3% and a false negative rate of 1%. Secure key exchange thwarts replication attacks with a false positive rate of 0 and a false negative rate of 2. Nevertheless, their efficacy varies based on the attack scenario, highlighting the need for adaptive security mechanisms. The evaluated security mechanisms exhibit varying rates of throughput. Message integrity verification and traffic encryption accomplish high throughput, enabling 1 Mbps and 800 Kbps, respectively, of secure data transfer rates. Overall, the results contribute to the compr

关键词： Accuracy Authentication Intrusion detection Electric vehicles Throughput Time measurement Encryption Safety Security Vehicle-to-everything

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IoT-Based Contactless Body Temperature Scanning System for a Smart and Safe Campus 4

IoT-Based Contactless Body Temperature Scanning System for a...

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4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Onn, Thoong Chun Alduais, Nayef.A.M. Abdullah, Jiwa Saad, Abdul-Malik H.Y. Ramli, Azizul Azhar Mahdin, Hairulnizam B. Kasim, Shahreen Mustapha, Aida Mostafa, Salama A Jawad, M.Saeed. Ghanem, Waheed Ali H. M. Abdul-Qawy, Antar S.H. Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu21030 Malaysia Faculty of Science SUMAIT University Science and Information Technology Dept Zanzibar Tanzania United Republic of

ISBN: (纸本)9781728166544

Nowadays, everyone is required to check the body temperature and record personal details whenever he arrives at a different place. However, there are some problems when the actions are performed. Therefore, this paper proposed an Internet of Things (IoT)-based contactless body temperature scanning system. The software and hardware used in this study were Microsoft Visual Studio, ThingSpeak, Arduino IDE, Radio Frequency Identification (RFID), and contactless body temperature sensor. A parallel development model was used as the project management methodology. The proposed system was tested through system testing, user acceptance testing, and accuracy testing. Based on the testing results, the proposed system was successfully implemented and ready to be deployed. The average accuracy of the proposed system was 99.86% compared to a standard device. © 2021 IEEE.

关键词： Physiology

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Acoustic measurement framework for audio systems based on structured periodic test signals

Acoustic measurement framework for audio systems based on st...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hideki Kawahara Kohei Yatabe Ken-Ichi Sakakibara Mitsunori Mizumachi Tatsuya Kitamura Cntr. Innovative Research and Liaison Wakayama University Wakayama Japan Dept. Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan Dept. Speech-Language-Hearing Therapy Health Sciences University of Hokkaido Hokkaido Japan Dept. Electrical and Electronic Eng. Kyushu Institute of Technology Kita-Kyushu Japan Dept. Intelligence and Informatics Konan University Kita-Kyushu Japan

We introduce a framework for acoustic measurement that allows everyday sounds into acoustic measurement test signals and standard test signals such as swept-sine and MLS (Maximum Length Sequence). The framework extends our extended Time-Stretched-Pulse generation method and safeguarding method for any signals to make them appropriate for acoustic measurement. The extension uses the periodic nature of discrete Fourier transform combined with orthogonalization based on the Walsh-Hadamerd matrix. This framework enables simultaneous measurement of acoustic attributes such as linear time-invariant response, signal-dependent non-linear and time-invariant components, and random time-varying components. We also discuss and demonstrate practical scenarios in classes and laboratory lessons using tools developed based on the proposed framework.

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