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arXiv 2021年

作者： Gkoumas, Dimitris Wang, Bo Tsakalidis, Adam Wolters, Maria Purver, Matthew Zubiaga, Arkaitz Liakata, Maria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Center for Precision Psychiatry Massachusetts General Hospital Boston United States The Alan Turing Institute London United Kingdom School of informatics University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Slovenia

Dementia affects cognitive functions of adults, including memory, language, and behaviour. Standard diagnostic biomarkers such as MRI are costly, whilst neuropsychological tests suffer from sensitivity issues in detecting dementia onset. The analysis of speech and language has emerged as a promising and non-intrusive technology to diagnose and monitor dementia. Currently, most work in this direction ignores the multi-modal nature of human communication and interactive aspects of everyday conversational interaction. Moreover, most studies ignore changes in cognitive status over time due to the lack of consistent longitudinal data. Here we introduce a novel fine-grained longitudinal multi-modal corpus collected in a natural setting from healthy controls and people with dementia over two phases, each spanning 28 sessions. The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extralinguistic information such as pen strokes and keystrokes. We present the data collection process and describe the corpus in detail. Furthermore, we establish baselines for capturing longitudinal changes in language across different modalities for two cohorts, healthy controls and people with dementia, outlining future research directions enabled by the corpus. © 2021, CC BY.

关键词： Neurodegenerative diseases

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Flexible Co-Planar Waveguide (CPW)-Fed Y-Shaped Patch UWB Antenna for Off-Body Communication 1

Flexible Co-Planar Waveguide (CPW)-Fed Y-Shaped Patch UWB An...

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1st International Conference on Education and Technology, ICETECH 2019

作者： Kassim, S. Rahim, H.A. Soh, P.J. Abdulmalek, M. Jusoh, M. Jamaluddin, M.H. Sabli, N.S. Yassin, M.N. Sabapathy, T. Wee, F.H. Osman, M.N. Ismail, N. Kampus Pauh Putra Perlis Perlis-Arau02600 Malaysia Advanced Communication Engineering Centre COE School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis02600 Malaysia Department of Engineering and Information Science University of Wollongong in Dubai Block 15 Dubai Knowledge Village Dubai United Arab Emirates Johor UTM Skudai MY81310 Malaysia Pekan Pahang Malaysia Kampus Pauh Putra Perlis Perlis-Arau02600 Malaysia

This paper intends to design an Ultra-Wideband (UWB) antenna for future Internet of Things (IoT) applications for off-body Wireless Body Area Networks (WBAN) communication. An antenna based on the Y-shaped patch fed using co-planar waveguide (CPW) line, with a full ground plane is designed. It is implemented on two different substrates, namely a 5mm thick Rogers RO4350B and a 5-mm-thick felt textile. Parametric analysis of antenna is performed by changing its critical dimensions and monitoring parameters such as gain, bandwidth, efficiency, radiation pattern when using both substrates. Besides that, the bending effects towards reflection coefficient and radiation patterns are also studied. The final patch size with the Y-shaped slot is 36 × 40 mm2 for both substrates. The antenna is capable of providing coverage for the bands from 8 to 10 GHz. Finally, the antenna designed on RO4350B substrate outperforms the antenna designed on felt by about four times in terms of bandwidth, with 3.3 GHz (7.7-11 GHz). © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Slot antennas

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Correction to: Network Functions Virtualization for Mobile Core and Heterogeneous Cellular Networks

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Wireless Personal Communications 2021年第3期122卷 2561-2561页

作者： Qureshi, Kashif Naseer Ahmad , Ejaz Anwar, Muhammad Ghafoor, Kayhan Zrar Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer Research Faisalabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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Efficient Contact Tracing for pandemics using blockchain

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Informatics in Medicine Unlocked 2021年 26卷

作者： Bari, Nida Qamar, Usman Khalid, Ayesha Knowledge & Data Science Research Centre Computer & Software Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan The Centre for Secure Information Technologies (CSIT) ECIT Institute Queen's University of Belfast Belfast BT7 1NN United Kingdom

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic. © 2021 The Authors

关键词： And pandemic Blockchain Contact tracing COVID-19 Distributed system Interplanetary file system

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Thermal radiation effect on SiC-Co 3 O 4 +Diamond/H 2 O trihybrid nanofluid with Stefan blowing and LTNE effects using classical and modified Hamilton-crosser models

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Journal of Radiation Research and Applied sciences 2025年第3期18卷

作者： Ahmed M. Galal Mostafa Mohamed Okasha Dana Mohammad Khidhi Kholoud Saad Albalawi Nouf Abdulrahman Alqahtani Mustafa Bayram Munawar Abbas Ilyas Khan Department of Mechanical Engineering College of Engineering in Wadi Alddawasir Prince Sattam bin Abdulaziz University Saudi Arabia Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University P.O 35516 Mansoura Egypt Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Department of Mathematics and Statistics College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Department of Computer Engineering Biruni University 34010 Istanbul Turkey Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Department of Mathematics The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan Department of Mathematical Sciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia

This study proposes to examine the effects of Stefan blowing and thermal radiation on the Marangoni convective velocity of a trihybrid nanofluid across a disk by using LTNE (local thermal non-equilibrium) effect and heat generation. Thermal and mass transfer properties are studied utilizing the Cattaneo-Christov mass and heat flux model. Nanoparticles of silicon carbide ( S i C ) , cobalt oxide ( C o 3 O 4 ) , and diamond ( N D ) dissolved in water ( H 2 O ) make up the trihybrid nanofluid flow model. In this work, the trihybrid nanofluid's rate of heat transfer based on diamond − S i C − C o 3 O 4 / water is investigated by c contrasting the improved model with the Hamilton-Crosser model in its classical form. Thermal transmission efficiency in nuclear reactors, energy storage devices, and microelectronic cooling is improved by the investigation of Cattaneo-Christov flux in trihybrid nanofluids with LTNE effects. The design of effective cooling mechanisms in the automotive and aerospace industries is also aided by the optimization of thermal conductivity predictions through the use of classical and improved Hamilton-Crosser models. The system of PDEs is converted into a non-linear ODE system by applying the required transformations. The Bvp4c method is applied to solve this problem mathematically. When the values of the thermal and solutal relaxation parameters increase, the thermal and solutal distributions decrease.

关键词： Cattaneo-christove heat and mass flux model Trihybrid nanofluid Classical and modified Hamilton-crosser models Stefan blowing impacts

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Detection of Pulsar Candidates using Bagging Method

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Procedia computer science 2020年 170卷 1096-1101页

作者： Mourad Azhari Abdallah Abarda Altaf Alaoui Badia Ettaki Jamal Zerouaoui Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco

The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.

关键词： Bagging CVM ANN KNN CDT AUC Error Rate Computation Time(CT)

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[Retracted] Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy

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Journal of Healthcare engineering 2023年第1期2023卷

作者： Jaskirat Kaur Deepti Mittal Sharaf Malebary Soumya Ranjan Nayak Devendra Kumar Manoj Kumar Gagandeep Simrandeep Singh Department of Electronics and Communication Engineering Punjab Engineering College (Deemed to be University) Sector 12 Chandigarh 160012 India Electrical and Instrumentation Engineering Department Thapar Institute of Engineering and Technology Patiala 147004 Indiathapar.edu Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah 21911 Saudi Arabiakau.edu.sa School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 Odisha India Department of Computer Science Wachemo University Hosaena Ethiopiawcu.edu.et Faculty of Engineering and Information Sciences University of Wollongong in Dubai Dubai Knowledge Park UAEuowdubai.ac.ae MEU Research Unit Middle East University Amman 11831 Jordanmeu.edu.jo Computer Science Engineering Department Chandigarh Engineering College Mohali *** Electronics and Communication Engineering Department UCRD Chandigarh University Mohali Indiachandigarhuniversity.ac.in

Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.

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WSL-DS: Weakly supervised learning with distant supervision for query focused multi-document abstractive summarization

arXiv

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arXiv 2020年

作者： Laskar, Md Tahmid Rahman Hoque, Enamul Huang, Jimmy Xiangji Department of Electrical Engineering and Computer Science York University Canada School of Information Technology York University Canada Information Retrieval and Knowledge Management Research Lab York University TorontoON Canada

In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multi-document gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results in Document Understanding Conferences1 (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： Supervised learning

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Drug-Drug Interactions Prediction Based on Drug Embedding and Graph Auto-Encoder

Drug-Drug Interactions Prediction Based on Drug Embedding an...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Sukannya Purkayastha Ishani Mondal Sudeshna Sarkar Pawan Goyal Jitesh K Pillai Department of Computer Science and Engineering IIT Kharagpur Excelra Knowledge Solutions Hyderabad

Identification of potential Drug-Drug Interactions (DDI) for newly developed drugs is essential in public healthcare. Computational methods of DDI prediction rely on known interactions to learn possible interaction between drug pairs whose interactions are unknown. Past work has used various similarity measures of drugs to predict DDIs. In this paper, we propose an effective approach to DDI Prediction using rich drug representations utilizing multiple knowledge sources. We have used the Drug-Target Interaction (DTI) Network to learn an embedding of drugs by using the metapath2vec algorithm. We have also used drug representation gained from the rich chemical structure representation of drugs using Variational Auto-Encoder. The DDI prediction problem is modeled as a link prediction problem in the DDI network containing known interactions. We represent the nodes in the DDI network as their embeddings. We apply a link prediction algorithm based on Graph Auto-Encoders to predict additional edges in this network, which are potential interactions. We have evaluated our approach on three benchmark DDI datasets, namely DrugBank, SemMedDB, and BioSNAP. Experimental results demonstrate that the proposed method outperforms the prior methods in terms of several performance metrics (AUC, AUPR, and F1-score) on all the datasets. Furthermore, we have also evaluated the role of the individual type of drug representation embeddings in boosting up the performance of DDI Prediction.

关键词： Drugs Chemicals knowledge engineering Prediction algorithms Diffusion tensor imaging Task analysis Semantics

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