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4th International Conference on Advanced Technology and Applied sciences, ICaTAS 2019

作者： Mostafa, M. Abdelwahab, A. Sayed, H.M. Department of Information System Faculty of Computer and Information Menoufia University Menoufia Egypt Department of Information Systems College of Computer Science and Information Technology King Faisal University Saudi Arabia

Twitter is a widespread supply for real-time news distribution between individuals. Furthermore, spammers could post any kinds of spam content to users, and a variant of incidents are committed on Twitter against users. These threats aren't restricted to the social media platforms however they threaten the safety of Twitter users. Most of the researches use deep learning techniques to detect Twitter spammer activities. The traditional solutions check the behavior of each account or campaign of similar purpose accounts. The number of solutions concentrate on detecting spam campaign based on URL only and ignoring text in a tweet. In this paper, the lightweight framework is proposed to take tweet text into consideration for optimizing spam campaign detection methods based on deep learning techniques. The main contribution of this work summarized in two key points. First one is to summarize text of the tweets to cluster them. The second one is to find similar tweets based on Siamese Recurrent Network. Experimental results show the ability of the proposed technique to extract the right candidate campaign and classify them as spam or not with high recall and precision. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches

Preferences for Using Canva Apps as a Presentation Media Cre...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Fairuz Iqbal Maulana Dian Lestari Agung Purnomo Rahman Arifuddin Vandha Pradwiyasma Widartha Puput Dani Prasetyo Adi Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga Surabaya 60132 Indonesia Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798350378573

ISBN: (纸本)9798350378580

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)" and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)" used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations.

关键词： Seminars Analytical models Technology acceptance model Accuracy Numerical analysis Media Mathematical models

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New crossover lumpy skin disease: Numerical treatments

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Partial Differential Equations in Applied Mathematics 2024年 12卷

作者： Sweilam, N.H. Kareem, Waleed Abdel Al-Mekhlafi, S.M. Abou Hasan, Muner M El-Ghareeb, Taha H Soliman, T.M. Department of Mathematics Faculty of Science Cairo University Giza Egypt Department of Mathematics Faculty of Science Suez University Egypt Department of Mathematics Faculty of Education Sana'a University Yemen Jadara University Research Center Jadara University Jordan Faculty of Mathematics and Data Science Emirates Aviation University Dubai United Arab Emirates The Higher Institute for Computer Science and Information System Fifth Settlement Egypt

This work expands on a novel piecewise mathematical model of Lumpy Skin Disease (LSD) in three time intervals by utilizing fractional stochastic derivatives and variable-order differential equations. The LSD model is developed into two crossover hybrid variable-order derivatives. This study's piecewise mathematical model representation of lumpy skin disease has revealed a property that has never been taken into account or seen in previous research employing mathematical models based on classical, different fractional derivatives and variable order fractional derivatives. The Caputo derivative and the Riemann-Liouville integral are merged linearly to produce the hybrid fractional order derivative. The variable-order fractional and hybrid fractional operators are approximated using the Grünwald-Letnikov approximation. We introduce the hybrid variable-order operator combined with the non-standard finite difference method. The stability, boundedness, positivity, and existence of the suggested model are examined. The effectiveness of the techniques and the validity of the theoretical results were verified through a number of numerical experiments. © 2024 The Author(s)

关键词： Crossover model Hybrid variable-order Lumpy skin disease Non-standard finite difference method Stability analysis

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Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

Emotion Recognition Using Bayesian Learning from a Multi-Lab...

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Computing, Networking, Telecommunications & Engineering sciences Applications (CoNTESA), International Conference on

作者： Bandi Swathi Madapuri Rudra Kumar Mohammad Riyaz Belgaum Zainab Alansari Computer Science and Engineering G. Pulla Reddy Engineering College Kurnool India Computer Science and Engineering G Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Information Technology University of Technology and Applied Science (UTAS) Muscat Oman

ISBN: (纸本)9798350398021

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies’ safety and security. Speech is a uniquely human trait used to express and communicate one’s point of view to others. Emotion recognition from speech audio signals is obtaining a presenter’s emotions from the presenter’s audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach’s performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers’ performance in making accurate detections with few false positives.

关键词： Training Emotion recognition Sensitivity Hidden Markov models Speech recognition Benchmark testing Feature extraction

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Adapting the User-Centered Cognitive Walkthrough (UC-CW) for Assessing the User Experience of Smart Regency Mobile-Apps Service in Indonesia 6

Adapting the User-Centered Cognitive Walkthrough (UC-CW) for...

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6th International Conference on Informatics and Computing, ICIC 2021

作者： Kisnu Darmawan, Aang Oranova Siahaan, Daniel Dwi Susanto, Tony Nizar Hidayanto, Achmad Subiyakto, A'Ang Yulianto, Tony System Universitas Islam Madura Department of Information Pamekasan Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia UIN Syarif Hidayatullah Jakarta Department of Information System Jakarta Indonesia Universitas Islam Madura Department of Mathematics Pamekasan Indonesia

ISBN: (纸本)9781665421553

Because the number of districts in Indonesia is four times that of cities, regency development requires serious consideration. However, very few studies are still available to explore districts from an ICT utility management perspective. This study aims to evaluate the user experience of a mobile-based smart regency application used in regencies. The method used is the User-Centered Cognitive Walkthrough (UC-CW), an enhanced Cognitive Walkthrough (CW) method. In this user experience evaluation method, the evaluator gives scenario-based tasks and several questions to mobile-App users to find problems that interfere with the learning process. This method is suitable for use on systems that have just been released because it can explore users' cognitive processes when using the system. Respondents in the study were ten users of mobile-based smart regency applications. The results show that many problematic features or functions cannot be used, or the data is still empty. This study contributes to the evaluation recommendations of user experience and provides input for application developers and policymakers to pay more attention to the factors that affect the user experience of smart regency application users in the regions. © 2021 IEEE.

关键词： Cognitive systems

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Analysing the Impact of COVID-19 outbreak and Economic Policy Uncertainty on Stock Markets in Major Affected Economies 6

Analysing the Impact of COVID-19 outbreak and Economic Polic...

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6th Smart Cities Symposium, SCS 2022

作者： Matuka, Adelajda Asafo, Shuffield Seyram Eweke, Gamaliel O. Mishra, Pradeep Ray, Soumik Abotaleb, Mostafa Makarovskikh, Tatiana Alhussan, Amel Ali Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Dutta, P.K. Chowdhury, Subrata University of Macerata Department of Economics and Law Italy Niger Delta University Department of Banking & Finance Nigeria J.N.K.V.V. College of Agriculture M.P. Powarkheda India Centurion University of Technology and Management Odisha Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog Egypt School of Engineering and Technology Amity University West Bengal Kolkata India AP Chittoor India

ISBN: (纸本)9781839538544

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Investments

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Light Fidelity and Internet of Things for Medical Applications

Light Fidelity and Internet of Things for Medical Applicatio...

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science and information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Puput Dani Prasetyo Adi Ros Sariningrum Rizky Rahmatullah Hessy Novita Tiar Lina Situngkir Priyo Hartono Adji Angela Mariana Lusiastuti Antonia Rahayu Rosaria Wibowo Yudi Prastiyono Tjahjo Adiprabowo Muhammad Yassir Fairuz Iqbal Maulana Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia Veterinary Research Center National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Management Faculty of Economics Singaperbangsa University Karawang Indonesia Research Center for Manuscripts Literature and Oral Traditions National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Electrical Engineering Faculty of Engineering Langlangbuana University Bandung Indonesia Department of System and Information System Institut Teknologi dan Bisnis Nobel Makassar Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This study focuses on transmitting data utilizing medical sensors that are vital for patients in hospitals and other settings. In this situation, Light Fidelity refers to transmitting medical sensor data or other data comprising the Transmitter (Tx) and the Receiver (Rx). If the Light Fidelity Principle is used, the Transmitter transmits LEDs or light to the Receiver. At the same time, the Receiver contains a receiver or light detection component known as the Light Dependent Resistor (LDR); using programming and this basic working principle, data can be sent from the LED Transmitter to the LDR Receiver using C++ Programming and its Library to send Medical sensor data. Sensors can include pulse sensors, like in prior research, and additional sensors such as SPO2 or blood pressure sensors. For medical data, a modest bit rate is sufficient, but LiFi applications can be created in such a way that they can be hybridized with WiFi and other RF devices such as LoRa and the Internet of Things.

关键词： Resistors Radio frequency Transmitters Receivers Programming Light fidelity Light emitting diodes Sensors Internet of Things Wireless fidelity

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Challenges in internet of things towards the security using deep learning techniques

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Measurement: Sensors 2022年 24卷

作者： Ravikumar, K.C. Chiranjeevi, Pandi Manikanda Devarajan, N. Kaur, Chamandeep Taloba, Ahmed I. Department of Computer Science Engineering Sridevi Women's Engineering College India Ace Engineering College Ghatkesar Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College Medchal - Malkajgiri District Telangana 500100 India Computer Science Department Jazan University Jizan Saudi Arabia Department of Computer Science College of Science and Arts in Qurayyat Jouf University Saudi Arabia Information System Department Faculty of Computers and Information Assiut University Assiut Egypt

Securing IoT devices and delivering end-to-end security in an IoT ecosystem presents a variety of issues. Regardless of the way that security concerns are not new with regards to data innovation, the qualities of numerous IoT arrangements give new and special security issues. It's anything but a main concern to resolve these issues and guarantee the security of IoT items and administrations. Clients should have certainty that IoT gadgets and related information administrations are secure, especially as this innovation develops more unavoidable and coordinated into our day by day lives. The goal of this article is to provide an overview of the Internet of Things and to go through all of the known security concerns that the Internet of Things faces today. All of the findings are based on publicly available documentation for essential Internet of Things components. Threats to IoT security, both inherent and newly developed, are discussed, as well as numerous potentials. The attack surfaces of the IoT system are examined, as well as the potential hazards associated with each surface. We then, at that point go over the benefits, weaknesses, and qualities of every Deep Learning approach for IoT security. We talk about the advantages and disadvantages of applying Deep Learning to IoT security. Future exploration headings could be founded on these chances and difficulties. © 2022

关键词： Internet of things

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Collaborative Data Anonymization for Privacy-Preserving Vehicular Ad-hoc Network

Collaborative Data Anonymization for Privacy-Preserving Vehi...

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2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020

作者： Nandy, Tarak Idris, Mohd Yamani Idna Bin Noor, Rafidah Md Bhattacharyya, Sananda Ghani, Norjihan Binti Abdul University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur Malaysia Maldives Business School Department of Information Technology Male Maldives University of Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9781728196732

Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may not be suitable in a multi-nodal environment such as a vehicular network. Alternatively, hiding sensitive attributes are essential alongside discreet data privacy. In this paper, we proposed a collaborative privacy-preserving data anonymization technique for the vehicular network. Moreover, the method is capable of achieving a desirable level of data sanitization in a group. The use of k-anonymity, l-diversity and t-closeness make the proposed technique more strong to protect data privacy in different stages of communication. Furthermore, the performed analysis of the proposed scheme ensures usability and practicality. Lastly, the future direction of the stipulated field shows the research path in collaborative data anonymization in the vehicular environment. © 2020 IEEE.

关键词： Vehicular ad hoc networks

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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