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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Lyu, Yun-Peng Zhu, Lei Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering SAR Macau China

ISBN: (纸本)9798350326277

In this talk, we present the co-design of phase shifters and antenna elements for series-fed arrays (SFA) to improve the performance, including the wider return loss bandwidth, lower sidelobe level (SLL), smaller gain variation, and larger achievable radiation range. We find the return loss and current distribution bandwidths of series-fed network (SFN) in SFA are dominated by its the input reactance slope. The ratio of phase shifter's impedance Z0 and antenna element's impedance ZL plays an important role in it. When the optimal Z0/ZL makes the input reactance slope equal to zero at the center frequency, the bandwidth of SFN can be highly extended by 24 times compared with non-optimal solutions. Herein, four classical types of SFNs are investigated and synthesized. The closed-form optimal formulas for these SFNs are further deduced under the specified element number N and current distributions I1, I2,... and IN. By virtue of the proposed co-design method, the designed SFA exhibits enhanced impedance and radiation bandwidths as predicted. © 2023 IEEE.

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The Simplex 3-partite Hypergraph-based Resource Allocation (S3H-RA) Method for Platooning Communications 25

The Simplex 3-partite Hypergraph-based Resource Allocation (...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Huang, Chung-Ming Lam, Dan-Nguyen Dao, Duy-Tuan National Cheng Kung University Dept of Computer Science and Information Engineering Tainan Taiwan The University of Danang-University of Science and Technology Faculty of Electronics and Telecommunications Engineering Viet Nam

ISBN: (纸本)9798350330014

This work proposed the Simplex 3-partite Hypergraph-based Resource Allocation (S3H-RA) method for multi-platoon. Using S3H-RA, the subchannels that Platoon Leader Vehicles (PLV) can use are assigned at first, for which these subchannels cannot be shared with other entities. Then, a 3-partite hypergraph, in which a 3-partite hyperedge links (1) a cluster of Platoon Member Vehicles (PMV) that use the same subchannel, (2) an individual cellular user and (3) a subchannel, was adopted to devise the PMV s' resource allocation algorithm. The simulation results shown that the proposed S3H-RA method can have the better performance in terms of spectral efficiency and the sum of transmission rate comparing with the other methods. © 2023 IEEE.

关键词： Autonomous vehicles

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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AI-Driven Digital Mirror technology for Securing IoT-Enabled Smart Infrastructures 2

AI-Driven Digital Mirror Technology for Securing IoT-Enabled...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Chaudhary, Jyoti Jain, Upma Sudarshan, N. Pai, Vasudeva Sutaria, Kamal Natrayan, L. Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering NMAM Institute of Technology NITTE Deemed to be University Karkala India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Mechanical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331504960

The expansion of the Internet of Things (IoT) is driven by the proliferation of interconnected wireless gadgets, made possible by ever-improving computer and Internet infrastructure. The IoT is an extensive system of interconnected sensors and gateways. In the course of their constant environmental interaction, IoT devices produce a wealth of data resources, including images, audio, video, digital signals, and more. Connectivity to the Internet allows all Internet of Things (IoT) equipment and apps to exchange data and resources more effectively. The proposed AI-Driven Digital Mirror technology effectively enhances the security of IoT-enabled smart infrastructures by integrating real-time monitoring, anomaly detection, and adaptive response mechanisms. By employing advanced AI algorithms such as Graph Neural Networks (GNNs) and Reinforcement Learning (RL), the technology mirrors IoT environments digitally, enabling dynamic modeling and threat assessment. The system demonstrated a significant improvement in threat detection accuracy, achieving a precision rate of 95.6% and a recall rate of 93.2% during simulations. Additionally, the adaptive response mechanism reduced the average response time to security breaches by 40%, ensuring minimal disruption to infrastructure operations. Comparative analysis with traditional security solutions highlighted the system's superiority in mitigating complex, multi-layered attacks, and its scalability makes it suitable for various smart infrastructure applications, including smart cities, industrial automation, and intelligent transportation systems. The results underscore the potential of AI-Driven Digital Mirror technology in securing IoT ecosystems against evolving cyber threats while maintaining operational efficiency and resilience. © 2024 IEEE.

关键词： Gateways (computer networks)

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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Blending Synthesized Strategies with Proven Practices for Achieving Business Success 6th

Blending Synthesized Strategies with Proven Practices for Ac...

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6th International Conference on information Systems and Management Science, ISMS 2023

作者： Parikh, Swapnil Pandeya, Megha Das, Bidyut Bikash haroon, Syed Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Gujarat Vadodara India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Commerce and Management Assam Down Town University Assam Guwahati India Karnataka Bangalore India

ISBN: (纸本)9783031664090

In today’s fast-paced, highly competitive business environment, it takes a well-planned combination of cutting-edge synthesized concepts and time-tested methods to succeed. In this research, we present an all-encompassing approach that balances these factors for peak efficiency. The suggested strategy integrates data-driven insights, a culture of continual learning, and a symmetrical focus on innovation and tradition. Innovation Incubators, Strategic Alliances, Agile Project Teams, Continuous Learning Culture, Data-Driven Decision Making, and a Customer-Centric Approach are some of the conventional techniques we contrast with this one. Our suggested technique is shown to be more efficient than the established methods by a wide margin in this comparison. We demonstrate our method’s advantage by graphically depicting the efficacy of competing strategies. In conclusion, the approach we suggest offers businesses a flexible, data-driven foundation for success in a dynamic market. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： and the capacity to adapt to new situations are all essential to the success of any business Strategy synthesized strategy

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Using Deep Learning to Unlock Data Science Insights: An Exploration of Techniques 15

Using Deep Learning to Unlock Data Science Insights: An Expl...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Samrat, Bhavuk Pandey, Megha Dushyanth Babu, V.R. Elackiya, R. Kulkarni, Omkaresh S. Srivastav, Vaibhav Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350370249

This paper explores deep getting-to-know-in records technology, introducing the numerous techniques that can be used to unlock information science insights. Deep learning is a subset of machine-gaining knowledge that specializes in using neural networks to increase representations of statistics. These representations can encode know-how about a machine or surroundings, making applying the representations to derive beneficial insights feasibly. The paper focuses on various deep getting-to-know strategies, inclusive of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative opposed networks (GANs). The paper also discusses the potential packages of deep getting-to-know statistics science, such as predicting time-collection statistics, detecting anomalies, and automated characteristic engineering. ultimately, the paper outlines how data technology professionals can leverage deep mastering to discover treasured records insights. © 2024 IEEE.

关键词： Automated engineering Generative Predicting Professionals

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Secure Image Communication Using Galois Field,Hyper 3D Logistic Map,and B92 Quantum Protocol

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computers, Materials & Continua 2024年第12期81卷 4435-4463页

作者： De Rosal Ignatius Moses Setiadi Nova Rijati Ahmad Rofiqul Muslikh Bonifacius Vicky Indriyono Aceng Sambas Faculty of Computer Science Universitas Dian NuswantoroSemarang50131Central JavaIndonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceUniversitas Dian NuswantoroSemarang50131Central JavaIndonesia Faculty of Information Technology Universitas MerdekaMalang65146East JavaIndonesia Faculty of Economics and Business Universitas STRADA IndonesiaKediri64133East JavaIndonesia Faculty of Informatics and Computing Universiti Sultan Zainal AbidinCampus BesutBesut22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTamansari GobrasTasikmalaya46196Indonesia

In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext ***,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased *** encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption *** hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based *** proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.

关键词： Galois field hyper 3D logistic map hyperchaotic map image security improved logistic map secure communication quantum security

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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Lightweight Convolutional Network For Automated Photovoltaic Defect Detection 9

Lightweight Convolutional Network For Automated Photovoltaic...

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9th International Conference on information technology Trends, ITT 2023

作者： Zahid, Arsalan Hussain, Muhammad Hill, Richard Al-Aqrabi, Hussain University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates

ISBN: (纸本)9798350327502

As the World moves towards renewable energy, photovoltaic modules are a fundamental option due to their green nature. However, the manufacturing process of solar cells is complex and vulnerable to discrepancies which can impact the overall performance of the system. Although human-led inspection is seen as the de-facto quality inspection protocol, issues pertaining to bias, cost and time can make it an expensive process. To this effect, this paper focuses on the development of a custom convolutional architecture that is lightweight, hence deployable within manufacturing facilities to assist with defective solar cell inspection. In addition, to address the issue of data scarcity, representative data augmentations are producing tailored towards enhancing the model's generalizability. The high efficacy of the proposed CNN and proposed augmentations can be gauged by the fact that 98% F1 score was achieved overall. © 2023 IEEE.

关键词： Convolutional neural networks

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