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Nano Research 2021年第11期14卷 3756-3772页

作者： sami Ullah Xiaoqin Yang Huy Q.Ta Maria Hasan Alicja bachmatiuk Klaudia Tokarska barbara Trzebicka Lei Fu Mark H.Rummeli College of Energy Soochow Institute for Energy and Materials InnovationsKey Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu ProvinceSoochow UniversitySuzhou215006China School of Energy and Power Engineering Xi’an Jiaotong UniversityXi’an710049China Institute for Complex Materials IFW Dresden20 Helmholtz StrasseDresden01069Germany NUTECH School of Applied Sciences&Humanities and Computer Engineering DepartmentNational University of TechnologyIslamabad42000Pakistan Centre of Polymer and Carbon Materials Polish Academy of SciencesM.Curie-Sklodowskiej 34Zabrze41-819Poland College of Chemistry and Molecular Science Wuhan UniversityWuhan430072China Institute of Environmental Technology VŠB-Technical University of Ostrava17.listopadu 15Ostrava70833Czech Republic

Graphene is a material with unique properties that can be exploited in electronics, catalysis, energy, and bio-related fields. Although, for maximal utilization of this material, high-quality graphene is required at both the growth process and after transfer of the graphene film to the application-compatible substrate. Chemical vapor deposition (CvD) is an important method for growing high-quality graphene on non-technological substrates (as, metal substrates, e.g., copper foil). Thus, there are also considerable efforts toward the efficient and non-damaging transfer of quality of graphene on to technologically relevant materials and systems. In this review article, a range of graphene current transfer techniques are reviewed from the standpoint of their impact on contamination control and structural integrity preservation of the as-produced graphene. In addition, their scalability, cost- and time-effectiveness are discussed. We summarize with a perspective on the transfer challenges, alternative options and future developments toward graphene technology.

关键词： high-quality transfer application-compatible substrate graphene technology

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software Requirement Ambiguity Avoidance Framework (sRAAF) for selecting suitable Requirement Elicitation Techniques for software Projects

Software Requirement Ambiguity Avoidance Framework (SRAAF) f...

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Current Development in engineering and Technology (CCET), IEEE International Conference on

作者： Ashok Kumar Gupta shams Tabrez siddiqui Khalid Ali Qidwai Agha salman Haider Haneef Khan Md Oqail Ahmad Faculty of Science and Technology Universiti Malaysia Terengganu Kuala Terengganu Malaysia Department of Computer Science Jazan University Jazan Saudi Arabia College of CS & IT Jazan University Jazan Saudi Arabia Department of Computer and Network Engineering Jazan University Jazan Saudi Arabia Department of Computer Application B.S Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India

Ambiguity is an intrinsic deficiency of natural language written requirement documents. Requirement Engineers face difficulties while communicating with stakeholders from different backgrounds and knowledge to extract requirements. The selection of a suitable requirement elicitation technique plays a critical role to improve the quality of elicited requirements. Differences among elicitation techniques are very large; it implies that some techniques may perform better than others for a particular situation. Generally, requirement engineers select elicitation techniques based on their interests, knowledge, and experience. The selection of the right technique is critical as all techniques can not fit into different situations. In addition, before starting the new project, there is no software available that can help in selecting suitable elicitation techniques based on the situation. This paper proposed a software-based selection of suitable elicitation techniques used in sRAAF (software Requirement Ambiguity Avoidance Framework). The tool focuses on the first phase of sRAAF, specifically the situational parameter based selection of effective elicitation techniques. These situational parameters are mapped onto the knowledge base for the selection process. The tool takes situational parameters as input related to three categories: project, requirement engineer, and stakeholders. The tool suggests a technique based on situational parameters under the current settings. If any new evidence related to situational parameters, elicitation technique or contextual value is originated, these values or information can be easily included in the tool.

关键词： Knowledge engineering Costs Natural languages Knowledge based systems software stakeholders Feeds

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Modelling the dynamics of fragmented vs. consolidated last-mile e-commerce deliveries via an agent-based model

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Transportation Research Procedia 2022年 62卷 155-162页

作者： Giovanni Calabrò Michela Le Pira Nadia Giuffrida Martina Fazio Giuseppe Inturri Matteo Ignaccolo Department of Civil Engineering and Architecture University of Catania Via S. Sofia 64 Catania 95125 Italy Spatial Dynamics Lab University College Dublin UCD Richview Campus D04 V1W8 Belfield Dublin Ireland Department of Physics and Astronomy University of Catania Via S. Sofia 64 Catania 95125 Italy Department of Electric Electronic and Computer Science Engineering University of Catania Via S. Sofia 64 Catania 95125 Italy

This paper proposes a new agent-based model (AbM) to explore different scenarios of e-commerce urban deliveries, comparing door-to-door deliveries with consolidation-based strategies. The AbM reproduces operation under different demand patterns and include the possible matching of customer systematic trips and collection/delivery points with small detour from the scheduled trip. several variables of the model can be changed in a parametric simulation environment, allowing to infer the level of convenience of consolidation strategies for the different actors involved. The model provides indicators able to take into account customer and logistics operator perspectives, and the impact of the service on the community. Results can give useful information to understand how to manage growing on-demand urban deliveries and to measure the impact of freight transport on city sustainability.

关键词： parcel deliveries e-commerce consolidation parcel lockers city logistics

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Detecting Depression in social Media Posts Using Machine Learning 2nd

Detecting Depression in Social Media Posts Using Machine Lea...

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2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018

作者： biradar, Abhilash Totad, s.G. Department of Computer Science B.V.B College of Engineering Hubli580031 India

ISBN: (纸本)9789811391866

The utilization of social Networking sites (sNs) like Twitter is expanding quickly and particularly by the more youthful age. The profit capacity of sNs enables us to express their interests, emotions and offer their day by day schedule. sNs sites such as Twitter allow for constant investigation of user behaviour. such examples are important for the psychological research network to comprehend the periods and area of most prominent interest. Worlds fourth biggest disease depression has turned out to be a standout amongst the most huge research subject. We propose a system which uses tweets as source of data and sentistrength sentiment analysis to create a training data for our system and a back Propagation Neural Network (bPNN) model to classify the given tweets into depressed or not depressed categories. © 2019, springer Nature singapore Pte Ltd.

关键词： sentiment analysis

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Machine Learning-based Detection of stress Levels in students with social Media Addiction

Machine Learning-Based Detection of Stress Levels in Student...

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Integrated Circuits and Communication systems (ICICACs), IEEE International Conference on

作者： Anuradha Parasar sujatha N sweta Priya Pramod s A. sasi Kumar Kogila Palanimuthu School of Liberal Education Galgotias University Dankaur India Department of Mathematics B. M. S. College of Engineering Bangalore India Amity School of Communication Amity University Patna India Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Applications (BCA & MCA) School of Science and Computer Studies (SSCS) CMR University Bangalore India Department of Pediatric and Child Health Nursing Institute of Health Sciences Dambi Dollo University Kellam Ethiopia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The epidemic has shown that social networks (sNs) are prevalent and necessary for human connections. However, sNs are easy to access, which may make them addictive, especially for kids. The study literature suggests several psychological and physiological risk factors for social media addiction. The suggested method includes preprocessing, feature selection, and model training. The content was preprocessed to remove all URLs to guarantee that web links would not interfere with the analysis. The mRMR method and Chi-square test were the most practical and effective feature selection methods. Training the model using the Attention-based GCN. The recommended model outperformed the popular Random Forest (RF) and established GCN models. Attention-based GCN boosted accuracy to 92.84%. It systematically identifies social media addiction risk factors using cutting-edge feature selection, model training, and preprocessing. Results suggest Attention-based GCN can overcome social media addiction identification challenges.

关键词： Training Radio frequency Accuracy Uncertainty social networking (online) Addiction Human factors Feature extraction Integrated circuit modeling stress

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Automated control and optimization of laser-driven ion acceleration

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High Power Laser science and engineering 2023年第3期11卷 32-40页

作者： b.Loughran M.J.v.streeter H.Ahmed s.Astbury M.balcazar M.borghesi N.bourgeois C.b.Curry s.J.D.Dann s.DiIorio N.P.Dover T.Dzelzainis O.C.Ettlinger M.Gauthier L.Giuffrida G.D.Glenn s.H.Glenzer J.s.Green R.J.Gray G.s.Hicks C.Hyland v.Istokskaia M.King D.Margarone O.McCusker P.McKenna Z.Najmudin C.Parisuaña P.Parsons C.spindloe D.R.symes A.G.R.Thomas F.Treffert N.Xu C.A.J.Palmer School of Mathematics and Physics Queen’s University BelfastBelfastUK Central Laser Facility STFC Rutherford Appleton LaboratoryDidcotUK Gérard Mourou Center for Ultrafast Optical Science University of MichiganAnn ArborMIUSA SLAC National Accelerator Laboratory Menlo ParkCAUSA Department of Electrical and Computer Engineering University of AlbertaEdmontonABCanada The John Adams Institute for Accelerator Science Imperial College LondonLondonUK ELI Beamlines Centre Institute of PhysicsCzech Academy of SciencesDolníBˇrežanyCzech Republic Department of Applied Physics Stanford UniversityStanfordCAUSA Department of Physics SUPAUniversity of StrathclydeGlasgowUK Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in PraguePragueCzech Republic Department of Mechanical Engineering Stanford UniversityStanfordCAUSA Institut für Kernphysik Technische Universität DarmstadtDarmstadtGermany

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear,multi-dimensional parameter *** limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation,although to-date this has been the accepted methodology due to low data acquisition *** repetition-rate(HRR)lasers augmented by machine learning present a valuable opportunity for efficient source ***,an automated,HRR-compatible system produced high-fidelity parameter scans,revealing the influence of laser intensity on target pre-heating and proton generation.A closed-loop bayesian optimization of maximum proton energy,through control of the laser wavefront and target position,produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60%of the laser *** demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

关键词： bayesian optimization high repetition-rate laser-target interaction laser-driven particle acceleration proton generation

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Prevention Of DDOs Attacks in Cloud Using Combinational Learning Approach

Prevention Of DDOS Attacks in Cloud Using Combinational Lear...

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Global Conference for Advancement in Technology (GCAT)

作者： Ruth Ramya Kalangi s Janardhanarao J sirisha Kayam saikumar Pagadala veeranjaneyulu M suman Department of C.S.E. Koneru Lakshmaiah Education Foundation Vaddeswaram India Institute of aeronautical engineering college Dundigal Hyderabad Dept of IT Prasad V. Potluri Siddhartha Institute of Technology Kanuru Vijayawada India Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Department of Computer science-CS-DS&AI&DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India

Individuals reinforce and staggering intensity of a clouds is an important task. It has delicate security data in perspective on the nonappearance of client-to-cloud controllability. To promise assurance, the data manager reorganizes encoded data before plaintexts. CP-AbE they developed Cipher text-Policy Attribute-based Encryption to arrange fine-grained and manager-motivated admittance power to present the blended records to various customers. In any case, this needs to be adequately protected besides dissimilar strikes. Different precedent strategies do not allow the cloud dealer the capacity to certify to be a downloader can unscramble. These reports are supposed to be available to everyone to unlock the passed-on accumulating. A compromising attacker is to download essential documents to send out EDos (Economic Denial of sustainability) attacks, which will, everything considered, gobble up the cloud benefit. Here the cloud association allows the cost. Additionally, the cloud supplier provides both the representative and the payee of preferred standpoint use charge without the straightforwardness to information proprietors. These burdens ought to be settled in authentic open passed-on storing. An action plan is proposed here for secure blended cloud holds from EDos ambushes, giving benefit operation commitment. It utilizes CP-AbE designs in a disclosure technique and agrees to the discretionary access approach of CP-AbE. Here we provide a fit scattered enlisting way to oversee and keep the DDos assaults utilizing dynamic character-based encryption.

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Retinal vessel segmentation using Deep Learning – A study

Retinal Vessel Segmentation using Deep Learning – A Study

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smart Electronics and Communication (ICOsEC), International Conference on

作者： Mohammed Mujahid Ul Islam M Indiramma Department of Computer Science & Engineering B.M.S College of Engineering Bengaluru India

ISBN: (数字)9781728154619

ISBN: (纸本)9781728154626

The Retinal vasculature conditions are important and reliable biomarkers for several cardiovascular and ophthalmologic diseases including Retinopathy of Prematurity, Diabetic Retinopathy, Glaucoma, Macular Degeneration, etc. The retinal-related diseases can be detected and diagnosed in the early stage before developing complexities and causing loss of sight by thoroughly understanding the vasculature condition. Analyzing these conditions is time consuming and tedious task. There have been many studies conducted to automatically extract retinal vessel information from fundus image and this is called retinal vessel segmentation. This paper provides a survey of the existing work that had been conducted to achieve this objective using Deep Learning methods. Deep Learning has become the state-of-the-art for solving computer vision problems. This paper briefly discusses U-Net based network architectures proposed for object segmentation and the usage of these networks in medical fields for vessel segmentation. It further compares the various architectures based on AUC and F1 score.

关键词： Image segmentation biomedical imaging blood vessels Convolution Retinal vessels Diseases

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Deep Learning Approaches for signal and Image Processing: state-of-the-Art and Future Directions

Deep Learning Approaches for Signal and Image Processing: St...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： s Deepan Jaideep Gera Anupam Pareek sheela Chinchmalatpure Gouri sankar Nayak Jagadeesh bodapati School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department Of Cse St Marys Group Of Institutions Guntur Chebrolu (V) Guntur (Dist) Faculty of Management of Commerce Poornima University IS-2027-2031 Ramchandrapura P.O. Vidhani Vatika Sitapura Extension Jaipur Rajasthan India AIDS Vishwakarma Institute of Technology Punez Maharashtra India Ai&Ds Vignan’s Institute Of Information Technology Duvvada Visakhapatnam Andhra Pradesh India Electronics and Communication Engineering BVC College of Engineering (A) Rajamundry

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has revolutionized signal and image processing by enabling the creation of complex algorithms with many applications. This study examines deep learning signal and image processing optimization and hardware acceleration strategies. Experimentally evaluating stochastic gradient descent (sGD) and Adam optimization algorithms determines their convergence speed and effectiveness. We also examine how GPUs may accelerate model execution and deep learning inference. Our study reveals that deep learning optimization methodologies and platforms provide several practical challenges and trade-offs. This work may help signal and image processing researchers and practitioners design scalable and efficient solutions. Additionally, their methods illuminate deep learning.

关键词： Deep learning Image processing signal processing algorithms Optimization methods stochastic processes streaming media Inference algorithms Hardware acceleration standards Convergence

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Retraction Note to: Local Directional Maximum Edge Patterns for facial expression recognition

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Journal of Ambient Intelligence and Humanized Computing 2022年第1期14卷 653-653页

作者： Uma Maheswari, v. varaprasad, G. viswanadha Raju, s. Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering B.M.S. College of Engineering Bangaluru India Department of Computer Science and Engineering JNTUHCEJ Jagityal Karimnagar India

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