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7th International Conference on E-Commerce, E-Business and E-Government, ICEEG 2023

作者： Ian, Vai-Kei Tse, Rita Tang, Su-Kit Pau, Giovanni Macao Meteorological and Geophysical Bureau Macao China Faculty of Applied Sciences Macao Polytechnic University Macao China Engineering Research Centre of Applied Technology on Machine Translation Artificial Intelligence of Ministry of Education Macao Polytechnic University Macao China Department of Computer Science and Engineering - Disi University of Bologna Bologna Italy Computer Science Department Ucla Los AngelesCA United States

ISBN: (纸本)9798400708398

Recently, with the rapid development and use of technology in network infrastructures, Macao is being transformed into a smart city progressively, even though it is recognized as a World heritage city for its architectural wonders and rich cultural traditions. The benefits of machine learning technology open up new avenues for environmental monitoring, such as weather forecasting, early extreme or severe weather alerts and safeguards, disaster mitigation enhancement, and technological revolution as part of overall smart city planning in Macao. This study focuses on the evolution of artificial intelligence, the important use of machine learning technology in the field of meteorology, and suggestions on the major future development trends in Macao to the migration of a smart *** discussed solutions tackling the problems in weather forecasting in Macao while creating advances in its transformation to be a smarter city, starting with the essence of machine learning and turning challenges into opportunities. © 2023 ACM.

关键词： Machine learning

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Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

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Journal of Communications and Information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

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High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

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Geoscience Frontiers 2022年第1期13卷 364-373页

作者： Guocheng Hao Juan Guo Wei Zhang Yunliang Chen David AYuen School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Department of Mathematics Duke UniversityDurhamNC 27708USA Department of Applied Physics and Applied Mathematics Columbia UniversityNew YorkNY 10027USA

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and *** change tendency may be related to the occurrence of earthquake *** of the underlying intensity trend plays an important role in the analysis of data and disaster *** chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field *** main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field *** verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data *** to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function ***,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.

关键词： Earth’s natural pulse electromagnetic field Chaos theory Radial Basis Function neural network Forecasting model

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An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence 2

An Automated Platform for Gathering and Managing Open-Source...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Kamrul Hasan, Mohammad Ajmal, Masood Ali, Muhammad Naseer, Iftikhar Ali, Atif Hamadi, Hussam Al Ali, Nasir Mutah University College of Engineering Department of Computer Engineering Jordan Center for Cyber Security Malaysia Riphah International University Islamabad Pakistan University of Central Punjab Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Cyberjaya Malaysia College of Engineering and IT University of Dubai United Arab Emirates School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide Information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial Information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present SecurityKG, an automated OSCTI collection and administration system. SecurityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, SecurityKG provides a user interface (UI) that supports multiple forms of interactivity. © 2023 IEEE.

关键词： User interfaces

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Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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computers, Materials & Continua 2022年第4期71卷 825-842页

作者： Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV ***-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different *** experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics

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Retraction Note: Firefly algorithm: an optimization solution in big data processing for the healthcare and engineering sector (International Journal of Speech Technology, (2021), 24, 3, (581-592), 10.1007/s10772-020-09783-y)

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International Journal of Speech Technology 2025年 1-1页

作者： Rahul, Kumar Banyal, Rohitash Kumar Department of Basic and Applied Science NIFTEM Sonipat131028 India Department of Computer Science and Engineering Rajasthan Technical University Kota324010 India

The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the Publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings, the Publisher no longer has confidence in the results and conclusions of this article. The authors have not stated whether they agree or disagree with this retraction. © Springer science+Business Media, LLC, part of Springer Nature 2025.

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Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Data accuracy

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Determining the financial performance of the firms in the Borsa Istanbul sustainability index:integrating multi criteria decision making methods with simulation

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Financial Innovation 2024年第1期10卷 3592-3635页

作者： Ahmet Kaya Dragan Pamucar Hasan Emin Gürler Mehmet Ozcalici Department of Banking-Finance Applied Sciences FacultyAkdeniz UniversityAntalyaTurkey Faculty of Organizational Sciences University of BelgradeBelgradeSerbia College of Engineering Yuan Ze UniversityTaoyuanTaiwan Department of Computer Science and Mathematics Lebanese American UniversityByblosLebanon Department of International Trade and Logistics Applied Sciences FacultyAkdeniz UniversityAntalyaTurkey Department of International Trade and Logistics Faculty of Economics and Administrative SciencesKilis 7 Aralık UniversityKilisTurkey

Regardless of the industry in which a company operates,evaluating corporate performance is one of the most critical and vital processes;the most essential and prominent performance evaluation is related to financial *** performance analysis is complex and critical for decision-makers in different financial performance factors;thus,a methodological framework is needed to solve such complex decision ***,this research aims to rank the companies included in the sustainability index(excluding banks)in Turkey by considering their financial *** criteria weights were determined using the full consistency method(FUCOM)by considering the evaluations of four *** firms were ranked using nine multi-criteria decision-making *** consensus among the nine rankings was ensured with the Copeland *** decision matrix includes financial ratios and the stock market performance of the firms;100,000 FUCOM weights were created with random evaluations to validate the *** results indicate that the most crucial criterion is the current ratio by considering expert *** simulation indicates that alternative 16(alternative 21)is superior(inferior)to the other alternatives,even though the weights are determined with random *** with expert evaluations is similar to the mean of the weight simulation *** results demonstrate that the proposed framework can be performed as a basis for financial performance ranking.

关键词： Financial performance BIST sustainability index Simulation MCDM techniques

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Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G Networks in Stochastic α-η-µ Fading Channels with Multi-Layer Selection Combining and Cross-Layer Interference Adaptation

Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G N...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Keshav Kaushik Priyanka Gaur Barga Mohammed Mujahid Mudassir Khan Elfatih Elmubarak Mustafa Amrita Singh Sharda School of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science Applied College Khamish Mushaiyt King Khalid University Saudi Arabia Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University Abha Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and Information Technology University of Bisha Saudi Arabia Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The authors proposed to use a meta-learningbased approach for QoS optimization in future 6G networks that are self-optimizing sections, focusing on stochastic α − η − µ fading channels. The proposed model utilizes information learned in the past so that it can rapidly adapt to these changes as they arrive, providing much better area adaptation, energy efficiency and interference mitigation than traditional learning approaches. You push the best and QoS, network throughput, latency abased simulation results are provided under high mobility in addition to severe fading conditions. The metalearning model showed accelerated convergence, adaptive resource allocation and improved interference mitigation which naturally led to QoS improvement up to 80% as well energy efficiency increased up to 65−90%, network reliability were enhanced. This step helps build green and robust communication mechanisms in the next-generation networks.

关键词： Fading channels 6G mobile communication Metalearning Adaptation models Prevention and mitigation Stochastic processes Quality of service Interference Energy efficiency Numerical models

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Recovering information from pixelized credentials 11

Recovering information from pixelized credentials

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Sicherheit, Schutz und Zuverlassigkeit: Konferenzband der 11. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft fur Informatik e.V. (GI), SICHERHEIT 2022 - Security, Protection and Reliability: Conference of the 11th Annual Meeting of the Security Division of the German Informatics Society (GI), SICHERHEIT 2022

作者： Garske, Viktor Noack, Andreas University of Applied Sciences Stralsund Department of Electrical Engineering and Computer Science Zur Schwedenschanze 15 Stralsund18435 Germany

ISBN: (纸本)9783885797173

Pixelation is a common technique to redact sensitive information like credentials in images. In this paper, we propose a system that is able to recover information from pixelized text. Our contribution consists of a neural network as well as a generic pipeline that generates a realistic training dataset considering flexible specifications including wordlists, fonts, font sizes and letter spacings. The contributed neural network is a composition of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN) using Long short-term memory (LSTM) and a Connectionist Temporal Classification (CTC) layer to decode sequences of characters. With our approach, we achieve a Label Error Rate (LER) under 50% when taking pixelation block sizes of up to 8 × 8 pixels on a 22pt font into account. Thereby, our results indicate that pixelation of sensitive data does not satisfy common privacy standards. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： computer vision

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