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Neutrosophic Sets and Systems 2022年 51卷 824-839页

作者： Zulqarnain, Rana Muhammad Iampan, Aiyared Khalifa, Hamiden Abd El-Wahed Siddique, Imran College of Mathematics and Computer Science Zhejiang Normal University Zhejiang Jinhua321004 China Department of Mathematics School of Science University of Phayao Mae KaPhayao56000 Thailand Department of Mathematics College of Science and Arts Al-Badaya Qassim University Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Department of Mathematics University of Management and Technology Lahore Pakistan

A similarity measure is used to tackle many issues that include indistinct and blurred information, excluding is not able to deal with the general fuzziness and obscurity of the problems that have various information. In this paper, we study some basic concepts which are helpful for us to build the structure of the article, such as soft set, neutrosophic soft set, hypersoft set, neutrosophic hypersoft set (NHSS), and interval-valued NHSS, etc. The main objective of the present research is to develop a cosine similarity measure and set-theoretic similarity measure for an IVNHSS with their necessary properties. A decision-making approach has been established by using cosine and set-theoretic similarity measures. Furthermore, we used to develop a technique to solve multicriteria decision-making problems. Finally, the advantages, effectiveness, flexibility, and comparative analysis of the algorithms are given with prevailing methods. © 2022, Neutrosophic Sets and Systems. All Rights Reserved.

关键词： Decision making

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Identifying human values from goal models: An industrial case study

arXiv

引用

arXiv 2023年

作者： Iqbal, Tahira Taveter, Kuldar Strenze, Tarmo Hussain, Waqar Haggag, Omar Matthews, John Alphonsus Piirisild, Anu Institute of Computer Science University of Tartu Estonia Data 61 CSIRO Australia Faculty of Information Technology Monash University Australia

Human values are principles that guide human actions and behaviour in personal and social life. Ignoring human values during requirements engineering introduces a negative impact on software uptake and continued use. Embedding human values into software is admittedly challenging;however, early elicitation of stakeholder values increases the chances of their inclusion into the developed system. Using Pharaon, a research and innovation project of the European Union’s Horizon 2020 program, as a case study we analysed stakeholder requirements expressed as motivational goal models consisting of functional, quality, and emotional goals in three large-scale trial applications of the project. We were able to elicit 9 of 10 human values according to the theory of human values by Schwartz from the motivational goal models that represent the requirements for the three applications. Our findings highlight the dominant trend of stakeholder values being embedded in emotional goals and show that almost 45% of the identified values belong to the value categories of Security and Self-direction. Our research extends prior work in emotional goal modelling in requirements engineering by linking emotional goals to various stakeholder roles and identifying their values based on the Schwartz theory of human values. © 2023, CC BY.

关键词： Requirements engineering

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Leverage from Blockchain in Commodity Exchange: Asset-Backed Token with Ethereum Blockchain Network and Smart Contract 4th

Leverage from Blockchain in Commodity Exchange: Asset-Backed...

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4th International Conference on Smart Trends for Computing and Communications, SmartCom 2020

作者： Richard Heryadi, Yaya Lukas Trisetyarso, Agung Information Systems Department School of Information Systems Bina Nusantara University Jakarta11840 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara Univesity Jakarta11840 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (纸本)9789811552236

Ethereum is one of the most popular blockchain platforms with a high number of adoption in the blockchain world today. Ethereum token (ERC-20) can tokenize any real-world object while it is also possible to exchange the token across the network participants. This utility from the token drives many blockchain enthusiasts to create decentralized applications. ERC-20 token could be used to collect project funds, decision voting, or even can be used to provide investment platforms for people that want to hedge the volatility of the currency with buying an asset that is back to back with the crypto-token. This research shows the main characteristics of asset-backed token and its implementation on a complete end-to-end "digitalization assets system" (assets buy and assets redemption). The result shows that asset-backed token needs support from government and other legalizing parties to verify the legitimacy of the asset that backing the token itself. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Person Name Extraction from TV program Using Pre-trained Language Model and News Headline 10

Person Name Extraction from TV program Using Pre-trained Lan...

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10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Oda, Kazuki Sasaki, Minoru Major in Computer and Information Sciences Graduate School of Science and Engineering Ibaraki University Ibaraki Hitachi Japan Ibaraki University Faculty of Engineering Dept. of Computer and Information Sciences Ibaraki Hitachi Japan

ISBN: (纸本)9781665424202

In this study, we focus on extracting person names from text contained in from TV programs and TV-CMs broadcast in Japan. Person name extraction from TV programs is a challenging task because person names are often recognized with other entity names such as locations and organizations. To tackle this problem, we experiment with an existing named entity extraction method using a list of names extracted from Wikipedia. However, this method results in low precision in extracting the person names, which leads to the problem that the list of person names is not effective for TV program texts. In this paper, to solve this problem, we propose a person name extraction method using Conditional Random Fields (CRF) with ELMo pre-training language model. Also, we propose to use news headlines to construct the person name extraction model for effective extraction of person names performing on the TV programs. As a result of our experiments, the proposed model trained with training data and news headlines provides the highest precision. These results show that adding the news headlines as external information to TV program data is effective for person name extraction. © 2021 IEEE.

关键词： Extraction

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Experiments to Review Literature on Topic Trends in technology Development in Educational information Systems

Experiments to Review Literature on Topic Trends in Technolo...

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International Conference on information technology, information Systems and Electrical Engineering (ICITISEE)

作者： Irfan Santiko Tri Retnaningsih Soeprobowati Bayu Surarso Informatic Departement Faculty of Computer Science Universitas Amikom Purwokerto Doctor of Information System Departement School of Post Graduate Diponegoro University Semarang Indonesia Faculty of Science and Mathematic School of Post Graduate Diponegoro University Semarang Indonesia

The smart campus is a phenomenon that occurs in the industrial era 4.0. Many people understand that a smart campus is a campus where all resources can work and produce learning with high effectiveness and maximum efficiency. Everyone talks about elements of information technology that play a role in the sustainability of smart campuses. However, what actually makes the definition of a smart campus itself is not defined with certainty. This is because the world of education itself is experiencing major disruption. This causes innovation in the world of education, especially learning, to pay less attention to the actual aspects of education. This means that education has relied too much on technology which will actually cause a shift in the essence of education itself. In this paper, we will review how effective and efficient learning concepts actually are using information technology. The method used is an analysis of the literature review with an experimental survey and bibliometric analysis. As a result, it will be known that information technology that is in accordance with the essence of education is a matter that does not only focus on technical aspects but also needs to pay attention to aspects of methods, materials, and media. So that the results that are known will provide insight into the development of learning models on an ongoing basis according to the true essence of education.

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Denoising Medical Images Using Deep Learning in IoT Environment

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computers, Materials & Continua 2021年第12期69卷 3127-3143页

作者： Sujeet More Jimmy Singla Oh-Young Song Usman Tariq Sharaf Malebary School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Software Department Sejong UniversitySeoulKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21911Saudi Arabia

Medical Resonance Imaging(MRI)is a noninvasive,nonradioactive,and meticulous diagnostic modality capability in the field of medical ***,the efficiency of MR image reconstruction is affected by its bulky image sets and slow process ***,to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network(SANR_CNN)for eliminating noise and improving the MR image reconstruction *** proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality,and SARN algorithm is used for building a dictionary learning technique for denoising large image *** proposed SANR_CNN model also preserves the details and edges in the image during *** experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and mean squared error(MSE).The proposed SANR_CNN model achieved higher PSNR,SSIM,and MSE efficiency than the other noise removal *** proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.

关键词： Medical resonance imaging convolutional neural network denoising contrast enhancement internet of things rheumatoid arthritis

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Energy Efficiency Optimization for NOMA-based Multi-cell VLC System with Optical IRS

Energy Efficiency Optimization for NOMA-based Multi-cell VLC...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Zehao Yang, Fang Sun, Shiyuan Song, Jian Han, Zhu Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

Considering that optical intelligent reflecting surface (OIRS) can change the optical channel, the enhancement performance of OIRS on the energy efficiency (EE) of the non-orthogonal multiple access (NOMA)-based visible light communication (VLC) system is investigated. Specifically, a multi-cell VLC system is modelled with both line-of-sight (LoS) and OIRS-reflected paths, and then the problem is formulated to optimize the OIRS configuration and power allocation for the maximization of the overall EE while adhering to various constraints. Moreover, the original task is decomposed into two sub-problems, and is solved iteratively by the proposed algorithm. In addition, the convergence of the proposed algorithm and enhancement in EE achieved by OIRS are illustrated by simulation results, which offer beneficial insights on resource allocation for the NOMAbased VLC systems with OIRS. © 2024 IEEE.

关键词： Optical communication

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Identifying Inflammatory Bowel Disease-Associated Gene Ontology Groups Using Biological Knowledge-Based Machine Learning

Identifying Inflammatory Bowel Disease-Associated Gene Ontol...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Ersoz, Nur Sebnem Bakir-Gungor, Burcu Yousef, Malik Department of Bioengineering Graduate School of Engineering and Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Sciences Abdullah Gul University Kayseri Turkey Department of Information Systems Zefat Academic College Zefat Israel Zefat Academic College Zefat Israel

Inflammatory bowel disease (IBD) is a chronic inflammatory disease. Complex pathogenesis behind disease formation and progression necessitated the development of new approaches to identify disease related genes and affected gene ontology (GO) terms. In this study, via exploiting GeNetOntology method, we have reanalysed a gene expression data including Crohn’s Disease (CD) and Ulcerative colitis (UC) patients and controls. In order to identify IBD related genes and affected GO terms, GeNetOntology uses GO hierarchy as the biological domain knowledge while performing gene expression data analysis based on machine learning (ML). In the training part of GeNetOntology, genes annotated with selected ontology terms have been utilized to perform a two-class classification task which generates an important set of ontologies as an output. IBD data samples were obtained from peripheral blood and colon tissue. In order to investigate the effect of different collection sites, IBD data have been analysed under different scenarios;i.e., all samples, only tissue samples and only blood samples. Experimental findings indicate that GeNetOntology can successfully determine significant disease-related ontology terms. Performance of the model slightly differs according to the sample source. Via analysing the differences/commonalities between affected gene ontologies under different scenarios, we attempt to enlighten IBD development mechanisms. © 2025 by SCITEPRESS– science and technology Publications, Lda.

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How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception

arXiv

引用

arXiv 2024年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

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