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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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Atomic layer deposition of zinc oxide onto 3D porous iron scaffolds for bone repair:in vitro degradation,antibacterial activity and cytocompatibility evaluation

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Rare Metals 2022年第2期41卷 546-558页

作者： Yu-Lei Li Jin He Hai-Xia Ye Can-Can Zhao Wei-Wei Zhu Xiong Lu Fu-Zeng Ren Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute of Applied Physics and Materials Engineering Faculty of Science and TechnologyUniversity of MacaoMacao 999078China Key Lab of Advanced Technologies of Materials Ministry of EducationSchool of Materials Science and EngineeringSouthwest Jiaotong UniversityChengdu 610031China

3D porous iron(Fe)scaffolds with interconnected open pores are promising candidates for bone ***,the bare 3D porous Fe scaffolds lack of antibacterial activity and the ability for cell ***,atomic layer deposition technique was used to deposit nanometer-thick zinc oxide(ZnO)layer onto the skeleton of 3D porous Fe scaffolds with interconnected open *** effect of ZnO thin film on the in vitro degradation behavior,antibacterial activity and cytocompatibility of 3D porous Fe scaffolds was systematically *** results showed that a dense ZnO thin film with a thickness of 76 nm was uniformly deposited on the skeleton of the porous Fe *** thickness of ZnO thin film could be easily controlled by the deposition *** deposited ZnO thin film significantly reduced the degradation rate of porous Fe scaffolds and the fabricated ZnO coated porous Fe scaffolds demonstrated strong antibacterial ability against both Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus,while did not significantly affect cytocompatibility and could also promote cell adhesion.

关键词： Atomic layer deposition Porous iron scaffolds ZnO Antibacterial activity

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

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computers, Materials & Continua 2023年第7期76卷 687-709页

作者： Muhammad Saad Maaz Bin Ahmad Muhammad Asif Muhammad Khalid Khan Toqeer Mahmood Elsayed Tag Eldin Hala Abdel Hameed College of Computing and Information Sciences Karachi Institute of Economics&TechnologyKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835Egypt Faculty of Computer and Information Systems Fayoum UniversityFaiyumEgypt Khaybar Applied College Taibah UniversityMedinaSaudi Arabia

Pandemics have always been a nightmare for humanity,especially in developing *** lockdowns are considered one of the effective ways to deal with spreading such ***,developing countries cannot afford such solutions because these may severely damage the country’s ***,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations *** novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled *** to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is *** use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding ***,this study also emphasizes information security,end-to-end solution,and experimental ***,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing ***,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication ***,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine *** step-by-step methodology and test results are proposed to ensure contact tracing and quarantine *** existing research studies,the security aspect is also considered in the realm of *** practical implementation of the proposed solution also obtains the *** results indicate the successful implementation of blockchain-enabled contac

关键词： Blockchain contact tracing distributed ledger technology geo-fencing internet of things industrial internet of things isolation management social distancing ultra-wideband

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Personalized Shopping Experiences: Leveraging Big Data Analytics in E-commerce for Enhanced Customer Engagement

Personalized Shopping Experiences: Leveraging Big Data Analy...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Raja, Abdullah Siddique, Fatima Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Altaf, Junaid Kousar, Shagufta University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University Putra Malaysia Researcher Cyber Security Center Malaysia Bahria University Lahore Campus Department of Computer Science Lahore Pakistan Gulab Devi Educational Complex Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (纸本)9798350369106

E-commerce systems have integrated big data analytics (BDA) as a core element, enabling personalized shopping experiences, crucial for customer loyalty and satisfaction. This research explores how intentional use of data analytics impacts customers and signals the combined effect of technology and market data in tailoring customer needs through innovation. We developed unique data mining processes from e-commerce archives to uncover patterns in customer behavior, aiming to simplify consumer analysis and identify factors influencing purchasing and loyalty barriers. Our research also tests personalization strategies on sample data to improve key performance indicators like conversion rates, average order value, and customer retention. Ethical data use and privacy are prioritized, ensuring trust and transparency between e-commerce platforms and consumers. This study is innovative, demonstrating the power of analytics to revolutionize e-commerce by prioritizing customer needs and sustainable growth. © 2024 IEEE.

关键词： Marketplaces

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical Engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Odd-even effects in the structure and properties of aryl-substituted aliphatic self-assembled monolayers

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Nano Research 2024年第5期17卷 4231-4243页

作者： Piotr Cyganik Andreas Terfort Michael Zharnikov Jagiellonian University Faculty of PhysicsAstronomy and Applied Computer ScienceSmoluchowski Institute of PhysicsŁojasiewicza 1130-348 KrakówPoland Institut für Anorganische und Analytische Chemie Universität FrankfurtMax-von-Laue-Straße 760438 FrankfurtGermany Applied Physical Chemistry Heidelberg UniversityIm Neuenheimer Feld 25369120 HeidelbergGermany

Self-assembled monolayers(SAMs)represent an important tool in context of nanofabrication and molecular engineering of surfaces and *** properties of functional SAMs depend not only on the character of the tail groups at the SAM-ambient interface,but are also largely defined by their *** its turn,the latter parameter results from a complex interplay of the structural forces and a variety of other factors,including so called odd-even effects,*** of the SAM structure and properties on the parity of the number(odd or even)of individual building blocks in the backbone of the SAM *** most impressive manifestation of the odd-even effects is the structure of aryl-substituted alkanethiolate SAMs on Au(111)and Ag(111),in which,in spite of the fact that the intermolecular interaction is mostly determined by the aryl part of the monolayers,one observes a pronounced dependence of molecular inclination and,consequently,the packing density of the SAM-forming molecules on the parity of number of methylene units in the alkyl *** we review the properties of the above systems as well as address fundamental reasons behind the odd-even effects,including the existence of a so-called bending potential,which is frequently disregarded in analysis of the structure-building *** generality of the odd-even effects in SAMs is additionally supported by the recent data for SAMs on GaAs,scanning tunneling microscopy data for SAMs on Ag(111),and the data for the monolayers with selenolate and carboxyl anchoring groups on Au(111)and Ag(111).The implications of these effects in terms of the control over the packing density and orientation of the tail groups at the SAM-ambient interface,structural perfection,polymorphism,temperature-driven phase transitions,and SAM stability toward such factors as ionizing radiation,exchange reaction,and electrochemical desorption are *** implications place the odd-even effects as an important tool

关键词： self-assembled monolayers molecular films surface and interface engineering odd-even-effects

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physics-informed neural networks for classical and quantum communication with multimode fibers

Physics-informed neural networks for classical and quantum c...

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2024 Frontiers in Optics, FiO 2024

作者： Zhang, Qian Sun, Jiali Rothe, Stefan Czarske, Jürgen Faculty of Electrical and Computer Engineering TU Dresden Dresden01069 Germany Department of Applied Physics Yale University New HavenCT06520 United States Institute of Applied Physics School of Science TU Dresden Germany

physics-informed neural networks provide mode decomposition based on intensity-only images in high-dimensional quantum communication using multimode fibers. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Multimode fibers

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