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Building Expertise on FAlR Through Evolving Bring Your Own Data(BYOD)Workshops:Describing the Data,Software,and Management-focused Approaches and Their Evolution

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Data Intelligence 2024年第2期6卷 429-456页

作者： César H.Bernabé Lieze Thielemans Rajaram Kaliyaperumal Claudio Carta Shuxin Zhang Celia W.G.van Gelder Nirupama Benis Luiz Olavo Bonino da Silva Santos Ronald Cornet Bruna dos Santos Vieira Nawel Lalout Ines Henriques Alberto Camara Ballesteros Kees Burger Martijn G.Kersloot Friederike Ehrhart Esther van Enckevort Chris T.Evelo Alasdair J.G.Gray Marc Hanauer Kristina Hettne Joep de Ligt Arnaldo Pereira Nuria Queralt-Rosinach Erik Schultes Domenica Taruscio Andra Waagmeester Mark D.Wilkinson Egon L.Willighagen Mascha Jansen Barend Mons Marco Roos Annika Jacobsen Biosemantics Group Department of Human GeneticsLeiden University Medical CentreLeidenThe Netherlands NIHR Academic Fellow in General Practice University College London and The Royal Free NHS Foundation Trust National centre for Rare Diseases Istituto Superiore di SanitaRomeItaly Department of Medical Informatics Amsterdam UMC location University of AmsterdamMeibergdreef 9AmsterdamThe Netherlands Amsterdam Public Health Methodology&Digital HealthAmsterdamThe Netherlands Health-Rl UtrechtThe Netherlands Semantics Services and Cybersecurity groupUniversity of TwenteEnschedeThe Netherlands Department of Medical Imaging Radboud Institute for Health SciencesRadboud University Medical CenterNijmegenThe Netherlands Department of Medical BioSciences Radboud University Medical CenterNijmegenThe Netherlands The Duchenne Parent Project VeneendaalThe Netherlands Departamento de Biotecnologia-Biologia Vegetal Escuela Tecnica Superior de Ingenieria AgronomicaAlimentaria y de BiosistemasCentro de Biotecnologia y Genomica de PlantasUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria-CSIC(INIA-CSIC).Campus MontegancedoPozuelo de Alarcon(Madrid)Spain Castor AmsterdamThe Netherlands Department of Bioinformatics-BiGCaT Maastricht UniversityMaastrichtThe Netherlands University Medical Center Groningen University of GroningenGroningenThe Netherlands Maastricht centre for Systems Biology(MaCSBio) Maastricht UniversityMaastrichtThe Netherlands Department of Computer Science Heriot-Watt UniversityEdinburghUK INSERM US14-OrphanetPlateforme Maladies RaresParisFrance centre for Digital Scholarship Leiden University LibrariesThe Netherlands Center for Molecular Medicine Cancer Genomics NetherlandsDepartment of GeneticsUniversity Medical Center UtrechtUniversity UtrechtThe Netherlands University of Aveiro DETI/IEETAAveiroPortugal GO FAIR Foundation LeidenThe Netherlands The Leiden Academic Center for Drug Research LeidenThe Netherlands Micel

Since 2014,"Bring Your Own Data"workshops(BYODs)have been organised to inform people about the process and benefits of making resources Findable,Accessible,Interoperable,and Reusable(FAIR,and the FAIRification process).The BYOD workshops'content and format differ depending on their goal,context,and the background and needs of ***-focused BYODs educate domain experts on how to make their data FAIR to find new answers to research ***-focused BYODs promote the benefits of making data FAIR and instruct project managers and policy-makers on the characteristics of FAIRification ***-focused BYODs gather software developers and experts on FAIR to implement or improve software resources that are used to support ***,these BYODs intend to foster collaboration between different types of stakeholders involved in data management,curation,and reuse(*** experts,trainers,developers,data owners,data analysts,FAIR experts).The BYODs also serve as an opportunity to learn what kind of support for FAIRification is needed from different communities and to develop teaching materials based on practical examples and *** this paper,we detail the three different structures of the BYODs and describe examples of early BYODs related to plant breeding data,and rare disease registries and biobanks,which have shaped the structure of the *** discuss the latest insights into making BYODs more productive by leveraging our almost ten years of training experience in these workshops,including successes and encountered ***,we examine how the participants'feedback has motivated the research on FAIR,including the development of workflows and software.

关键词： FAIR FAIRification FAIR expertise Bring Your Own Data Workshop BYOD

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Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

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Computers, Materials & Continua 2023年第5期75卷 2873-2888页

作者： Zhaoliang Wu Shanyu Tang Xiaoli Feng Jiajun Zou Fulian Yin College of Information and Communication Engineering Communication University of ChinaDingfuzhuang East Street No.1Beijing100024China cybersecurity and criminology centre University of West LondonSt.Mary’s RoadLondon W55RFLondonUK Department of Electronic Engineering Tsinghua UniversityShuangqing Road No.30Beijing100084China The State Key Laboratory of Media Convergence and Communication Communication University of ChinaDingfuzhuang East Street No.1Beijing100024China

Aspect-Based Sentiment Analysis(ABSA)is one of the essential research in the field of Natural Language Processing(NLP),of which Aspect Sentiment Quad Prediction(ASQP)is a novel and complete *** aims to accurately recognize the sentiment quad in the target sentence,which includes the aspect term,the aspect category,the corresponding opinion term,and the sentiment polarity of ***,existing approaches lack knowledge of the sentence’s syntax,so despite recent innovations in ASQP,it is poor for complex cyber comment ***,most research has focused on processing English text,and ASQP for Chinese text is almost *** usage is more casual than English,and individual characters contain more *** propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP *** this framework,part of speech(POS)and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text ***,we design a relation extraction module,which provides a bridge for the overall extraction of the framework.A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric *** experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

关键词： ABSA ASQP sentiment analysis Chinese comments

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Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

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Computers, Materials & Continua 2023年第10期77卷 491-515页

作者： Shadi Al-Sarawi Mohammed Anbar Basim Ahmad Alabsi Mohammad Adnan Aladaileh Shaza Dawood Ahmed Rihan National Advanced IPv6 centre(NAv6) University Sains MalaysiaGelugorPenang11800Malaysia Applied College Najran UniversityKing Abdulaziz StreetP.O.Box 1988NajranSaudi Arabia cybersecurity Department School of Information TechnologyAmerican University of Madaba(AUM)Amman11821Jordan

The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting *** Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the ***,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing *** paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power *** indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous *** UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed *** effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing ***,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment *** terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.

关键词： Internet of Things IPv6 over low power wireless personal area networks Routing Protocol for Low-Power and Lossy Networks Internet Protocol Version 6 distributed denial of service wireless sensor networks

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI cybersecurity

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Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

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Computers, Materials & Continua 2023年第11期77卷 2031-2047页

作者： Mudassir Khalil Muhammad Imran Sharif Ahmed Naeem Muhammad Umar Chaudhry Hafiz Tayyab Rauf Adham E.Ragab Department of Computer Engineering Bahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science Kansas State UniversityManhattanKS66506USA Department of Computer Science University of Management and TechnologyLahore54000Pakistan centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentST42DEUK Industrial Engineering Department Collage of EngineeringKing Saud UniversityPO Box 800Riyadh11421Saudi Arabia

Early detection of brain tumors is critical for effective treatment *** tumors in their nascent stages can significantly enhance the chances of patient *** there are various types of brain tumors,each with unique characteristics and treatment protocols,tumors are often minuscule during their initial stages,making manual diagnosis challenging,time-consuming,and potentially *** techniques predominantly used in hospitals involve manual detection via MRI scans,which can be costly,error-prone,and *** automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest *** research applies several data augmentation techniques to enhance the dataset for diagnosis,including rotations of 90 and 180 degrees and inverting along vertical and horizontal *** CIELAB color space is employed for tumor image selection and ROI *** deep learning models,such as DarkNet-53 and AlexNet,are applied to extract features from the fully connected layers,following the feature selection using entropy-coded Particle Swarm Optimization(PSO).The selected features are further processed through multiple SVM kernels for *** study furthers medical imaging with its automated approach to brain tumor detection,significantly minimizing the time and cost of a manual *** method heightens the possibilities of an earlier tumor identification,creating an avenue for more successful treatment planning and better overall patient outcomes.

关键词： Brain tumor deep learning feature extraction feature selection feature fusion transfer learning

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Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

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Computers, Materials & Continua 2022年第5期71卷 2543-2557页

作者： Ala’A.Eshmawi Hesham Alhumyani Sayed Abdel Khalek Rashid A.Saeed Mahmoud Ragab Romany F.Mansour Department of cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of SciencesTaif UniversityTaif21944Saudi Arabia centre for Artificial Intelligence in Precision Medicine King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram ***, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

关键词： Opinion mining sentiment analysis fuzzy neural network metaheuristics feature extraction classification

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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Computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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Artificial intelligence-based predictive model of nanoscale friction using experimental data

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Friction 2021年第6期9卷 1726-1748页

作者： Marko PERČIĆ Saša ZELENIKA Igor MEZIĆ University of Rijeka Faculty of Engineering51000 RijekaCroatia University of Rijeka Centre for Micro-and Nanosciences and Technologies&Center for Artificial Intelligence and Cybersecurity51000 RijekaCroatia University of California Santa Barbara Department of Mechanical EngineeringSanta BarbaraCA 93105USA

A recent systematic experimental characterisation of technological thin films,based on elaborated design of experiments as well as probe calibration and correction procedures,allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters,comprising normal forces,sliding velocities,and temperature,thus providing an indication of the intricate correlations induced by their interactions and mutual *** created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale *** to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual *** on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore *** results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical *** is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels,but the intrinsic nature of such algorithms prevents their usage in most practical *** programming-based artificial intelligence(AI)methods are consequently finally *** the marked complexity of the analysed phenomena and the inherent dispersion of the measurements,the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy,depending on the sample type,between 72%and 91%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process param

关键词： nanoscale friction thin films data mining machine learning(ML) predictive artificial intelligence(AI)-based model

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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

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Computers, Materials & Continua 2023年第8期76卷 2261-2279页

作者： Ayman Khallel Al-Ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin cybersecurity Research Group Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia National Advanced IPv6 centre(NAv6) Universiti Sains MalaysiaGelugorPenang11800Malaysia Department of Electronics and Communication Engineering Panimalar Engineering CollegePoonamalleeChennai600123TamilnaduIndia Department of Computer Science&Engineering Kebri Dehar UniversityKebri Dehar250Ethiopia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling *** proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the *** to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are ***,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central ***,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed ***,four scenarios are chosen to investigate the projected design’s *** all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

关键词： Transportation Artificial Intelligence(AI) data-driven Internet of Things(IoT)

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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