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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Garg, Bindu Kasar, Manisha Kotecha, Ketan Rahmani, Mohammad Khalid Imam University College of Engineering Pune India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031860713

This study addresses the burgeoning demand for website data collection and analysis in business operations, emphasizing the pivotal role of web analytics in providing crucial insights into customer behaviour. Despite the prevalence of various website traffic analysing tools, there exists a notable lack of comprehensive testing on the precision and reliability of these services. The research compares four key analytics measures from Google Analytics (GA) with SimilarWeb (SW) using average monthly statistics over a one-year period for 50+ websites across different nations and business verticals. Employing various statistical tests, including paired t-tests, Shapiro-Wilk tests, and correlation analysis, the study reveals statistically significant variations in key metrics between GA and SW. Notably, SimilarWeb tends to provide lower estimates for the number of visits and unique visitors compared to Google Analytics. However, the findings underscore the potential complementarity of using both tools in cases where direct access to a site’s analytics is not feasible, particularly for competitive intelligence and benchmarking purposes. The study’s extensive statistical analysis covers unique visitors, total visits, bounce rates, and average session duration, offering valuable insights into the nuanced variations and potential sources of error. There are statistically significant differences between the two web traffic tools in terms of unique visitors, total visits, average session time & bounce rates. In comparison to Google Analytics, SimilarWeb figures were on average, 40% less for unique visitors, 50% greater for average session time, 25% greater for bounce rate, and 20% lesser for total visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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Computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Data Mining and Text Mining Approach: Query based Information Retrieval using Genetic Algorithm 5

Data Mining and Text Mining Approach: Query based Informatio...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Ananthi, S. Venkateswaran, R. Vijaya, G. Arun, J. Sathya, R. Sri Eshwar College of Engineering Advanced Research Centre for Future Networks and Digital Twin Coimbatore India College of Computing & Information Sciences University of Technology and Applied Sciences Faculty-Cyber and Information Security Salalah Oman Sri Krishna College of Engineering and Tech Dept. of CSE Coimbatore India Sri Eshwar College of Engineering Dept. of ECE Coimbatore India Kongunadu College of Engg and Tech Department of IT Namakkal India

ISBN: (纸本)9798350367171

Information Retrieval system facilitates the retrieval efficiency of the user's requirement from a collection of information resources. In this research work, Genetic Algorithm (GA) with various fitness functions are implemented for retrieving the relevant document for the given textual query. To increase the efficiency of the retrieval system extended keywords are added to the retrieval system using query reformulation method. Furthermore, the selection procedure namely fitness proportionate selection is used to identify the best fit individual among the chosen population. This selection algorithm assumes that, for the chosen population, the fittest individuals have a greater chance of endurance than weaker ones. The performance of the proposed system is evaluated with three different datasets solely consists of 20,322 documents are analyzed using various matching functions and their results are examined. © 2024 IEEE.

关键词： Content based retrieval

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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ASYMPTOTICS OF LARGE DEVIATIONS OF FINITE DIFFERENCE METHOD FOR STOCHASTIC CAHN–HILLIARD EQUATION

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Acta Mathematica Scientia 2025年第03期 1078-1106页

作者：金典聪盛德瑞 School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Department of Applied Mathematics The Hong Kong Polytechnic University

In this work, we first derive the one-point large deviations principle(LDP) for both the stochastic Cahn–Hilliard equation with small noise and its spatial finite difference method(FDM). Then, we focus on giving the convergence of the one-point large deviations rate function(LDRF) of the spatial FDM, which is about the asymptotical limit of a parametric variational problem. The main idea for proving the convergence of the LDRF of the spatial FDM is via the Γ-convergence of objective functions. This relies on the qualitative analysis of skeleton equations of the original equation and the numerical method. In order to overcome the difficulty that the drift coefficient is not one-sided Lipschitz continuous, we derive the equivalent characterization of the skeleton equation of the spatial FDM and the discrete interpolation inequality to obtain the uniform boundedness of the solution to the underlying skeleton equation. These play important roles in deriving the Γ-convergence of objective functions.

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Exploring the Dynamics of Exchange Rates: An Empirical Analysis and Predictive Modelling

Exploring the Dynamics of Exchange Rates: An Empirical Analy...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Anand R N Peeta Basa Pati K. Deepa Jothibabu K Konidhala Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of Electrical & Electronics Engineering Amrita School of Engineering Bangalore Department of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman

In India, globalization and reforms in the financial sector have brought about a major overhaul of the financial landscape. The connection between financial markets and the tangible economy is now highly valued in the current scenario. Exchange rate affects a country's economy in several ways. It has a huge effect on international trade, economic growth, capital flows, interest rates and also inflation. Here, the aim is to understand the underlying factors that affects the exchange rate of US dollar with respect to Indian rupee. To accomplish this, a Vector Auto Regression (VAR) model, as well as other machine learning techniques such as multi-variable linear regression, polynomial regression, and regression tree, are employed to forecast exchange rates. Our findings indicate that Trade Balance is a significant factor which cause fluctuations in exchange rate of USD and regression tree model performs better from the side of all machine learning model applied for predicting the same.

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Structure Preserving PINN for Solving Time Dependent PDEs with Periodic Boundary

arXiv

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arXiv 2024年

作者： Hao, Baoli Braga-Neto, Ulisses Liu, Chun Wang, Lifan Zhong, Ming Department of Applied Mathematics Illinois Institute of Technology ChicagoIL United States Department of Electrical and Computing Engineering Texas A&M University College StationTX United States Department of Physics and Astrophysics Texas A&M University College StationTX United States

We present a structure preserving PINN for solving a series of time dependent PDEs with periodic boundary. Our method can incorporate the periodic boundary condition as the natural output of any deep neural net, hence significantly improving the training accuracy of baseline PINN. Together with mini-batching and other PINN variants (SA-PINN, RBA-PINN, etc.), our structure preserving PINN can even handle stiff PDEs for modeling a wide range of convection-diffusion and reaction-diffusion processes. We demonstrate the effectiveness of our PINNs on various PDEs from Allen Cahn, Gray Scott to nonlinear Schrödinger. © 2024, CC BY.

关键词： Deep neural networks

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Automatic modeling learner’s personality using learning analytics approach in an intelligent Moodle learning platform

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Interactive Learning Environments 2023年第5期31卷 2529-2543页

作者： Tlili, Ahmed Denden, Mouna Essalmi, Fathi Jemni, Mohamed Chang, Maiga Kinshuk Chen, Nian-Shing Smart Learning Institute of Beijing Normal University Beijing China Research laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE) Tunis higher school of engineering (ENSIT) University of Tunis Tunis Tunisia School of Computing and Information Systems Athabasca University Athabasca Canada University of North Texas Denton TX United States Department of Applied Foreign Languages National Yunlin University of Science and Technology Douliou Taiwan

The ability of automatically modeling learners’ personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional method of modeling personality is using self-reports, such as questionnaire, which is subjective and with several limitations. Therefore, this study presents a new unobtrusive method to model the learners’ personalities in an intelligent Moodle (iMoodle) using Learning Analytic (LA) approach with Bayesian network. To evaluate the accuracy of the proposed approach, an experiment was conducted with one hundred thirty-nine learners in a public university. Results showed that recall, precision, F-measure and accuracy values are in acceptance range for three personality dimensions including extraversion, openness, and neuroticism. Moreover, the results showed that the LA approach has a fair agreement with the Big Five Inventory (BFI) in modeling these three personality dimensions. Finally, this study provides several recommendations which can help researchers and practitioners develop effective smart learning environments for both learning and modeling. For example, it is needed to help identify more features of the hardest personality traits, such as agreeableness, using gamification courses. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： assessment learner modeling learning analytics Moodle Personality smart learning

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A Review of Android and iOS Operating System Security

A Review of Android and iOS Operating System Security

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Khan, Shahnawaz Yusuf, Ammar Haider, Mohammad Thirunavukkarasu, K. Nand, Parma Imam Rahmani, Mohammad Khalid Faculty of Engineering Design and Information and Communications Technology Bahrain Polytechnic Isa Town Bahrain Applied Science University College of Arts and Science Bahrain Saudi Electronic University College of Computing and Informatics Dammam Saudi Arabia Galgotias University Department of Computer Science and Engineering Greater Noida India Sharda University School of Engineering and Technology Greater Noida India Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Mobile devices are an inseparable part of our lives. They have made it possible to access all the information and services anywhere at any time. Almost all of the organizations try to provide a mobile device-based solution to its users. However, this convenience has arisen the risk of losing personal information and has increased the threat to security. It has been observed recently that some of the mobile device manufacturers and mobile apps developers have lost the private information of their users to hackers. It has risen a great concern among mobile device users about their personal information. Android and iOS are the major operating systems for mobile devices and share over 99% of the mobile device market. This research aims to conduct a comparative analysis of the security of the components in the Android and iOS operating systems. It analyses the security from several perspectives such as memory randomization, application sandboxing, isolation, encryption, built-in antivirus, and data storage. From the analysis, it is evident that iOS is more secure than Android operating system. However, this security comes with a cost of losing the freedom. © 2022 IEEE.

关键词： Android (operating system)

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Marie's ChemLab: A Mobile Augmented Reality Game to Teach Basic Chemistry to Children 16

Marie's ChemLab: A Mobile Augmented Reality Game to Teach Ba...

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16th European Conference on Games Based Learning, ECGBL 2022

作者： Arztmann, Michaela Alfaro, Jessica Lizeth Domínguez Blattgerste, Jonas Jeuring, Johan Van Puyvelde, Peter Department of Education Faculty of Social and Behavioral Sciences Utrecht University Utrecht Netherlands Department of Chemical Engineering Faculty of Engineering KU Leuven Leuven Belgium Mixed Reality Lab University of Applied Sciences Emden/Leer Electrical Engineering and Information Technology Emden Germany Department of Information and Computing Sciences Faculty of Science Utrecht University Utrecht Netherlands

ISBN: (纸本)9781914587511

Digital games are widely used in education to motivate students for science. In parallel, the role of augmented reality (AR) in education is increasing. This paper introduces Marie's ChemLab: a marker-less handheld AR application in which K-12 students learn about science. In Marie's ChemLab, users perform interactions in their physical environment using virtual AR objects displayed on a handheld device. The utilized interaction concept is based on the TrainAR framework (Blattgerste et al, 2021), which originally was envisioned for procedural AR training and layered feedback mechanisms. The original framework has been enhanced to support gamified interactions with virtual objects, specifically for K-12 students. In total, 239 early secondary school students played the final application as part of a larger study. The usability score indicated marginally acceptable usability, and the application was successfully implemented in a classroom setting. © 2022 Dechema e.V.. All rights reserved.

关键词： Augmented reality

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