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2021 CIE International Conference on radar, radar 2021

作者： Liu, Zhongwei Gao, Hongwei Chen, Jie Zhou, dongming Li, Yingchun Sun, Shuyan Xiang, rongrong School of Electronic and Information Engineering Beihang University Beijing China Beijing Institute of Radio Measurement Radar R&d Center Beijing China Beijing Institute of Computer Technology and Application Department of Data Technology and Application Beijing China

ISBN: (纸本)9781665498142

Aiming at the problem of radar emitter individual recognition with unintentional modulation feature, a method of emitter fingerprint identification based on deep learning is proposed. In this method, the deep fingerprint features of radar emitter are extracted by deep Convolutional neural network, and the fingerprint feature library is constructed. Through similarity calculation, the individual recognition results of radar emitter signal are obtained. The results show that the new method can effectively identify both known and unknown emitter individuals. © 2021 IEEE.

关键词： deep learning Fingerprint features Individual identification radar emitter

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An In-depth Analysis of Electric Vehicle Charging Station Based on LSTM and SVM Hybrid Model 7

An In-Depth Analysis of Electric Vehicle Charging Station Ba...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： ramkumar, V. Nadaf, Akabarsaheb B Ghamande, Manasi Vyankatesh dabral, Amar Prakash Bharambe, Priti Mangesh Kumar, davinder Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Pune Abhijit Kadam Institute of Management and Social Sciences Solapur India E and AS Vishwakarma Institute of Information Technology Maharashtra Pune India Graphic Era Deemed to Be University Department of Hospitality Management Uttarakhand Dehradun India MIT Arts Commerce and Science College Alandi Computer Application Department Maharashtra Pune India Architect Micron Technology Telangana Raidurgam India

ISBN: (纸本)9798350340600

As part of their future smart city ambitions, many countries hope to increase environmental sustainability by electrifying their transportation networks. This means that the percentage of electric vehicles in any particular city's total vehicle population will rise rapidly. Electric vehicle (EV) batteries can be recharged in a number of ways, but charging stations will be given top priority. Charging stations should be dispersed across a city in such a way that all EVs may reach one within a reasonable period of time and then use that charge to travel the entire city. This data preprocessing phase makes use of normalized processing, time interval processing, and outlier processing. The features are derived using principal component analysis. The LSTM method entails a set of four gates (an input, a forget, a learn, and a remember), All of which contribute to the model's training. The models are tested using an LSTM-SVM method. When compared to LSTM and SVM, the proposed method produces higher-quality results. © 2023 IEEE.

关键词： Principal component analysis

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discussion on a new paradigm of endogenous security towards 6G networks

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Frontiers of Information technology & Electronic Engineering 2022年第10期23卷 1421-1450页

作者： Xinsheng JI Jiangxing WU Liang JIN Kaizhi HUANG Yajun CHEN Xiaoli SUN Wei YOU Shumin HUO Jing YANG Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China National Digital Switching System Engineering&Technological R&D Center Zhengzhou 450002China Purple Mountain Laboratories:Networking Communications and SecurityNanjing 211111China

The sixth-generation mobile communication(6G)networks will face more complex endogenous security problems,and it is urgent to propose new universal security theories and establish new practice norms to deal with theªunknown unknownºsecurity threats in *** paper first expounds the new paradigm of cyberspace endogenous security and introduces the vision of 6G cyberspace ***,it analyzes the security problems faced by the 6G core network,wireless access network,and emerging associated technologies in detail,as well as the corresponding security technology development status and the integrated development of endogenous security and traditional ***,this paper describes the relevant security theories and technical concepts under the guidance of the new paradigm of endogenous security.

关键词： 6G security New paradigm of endogenous security Core network Wireless access network

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FraudGuard Pro: An Ensemble-Based Feature Selection Algorithm for Credit Card Fraud detection 15

FraudGuard Pro: An Ensemble-Based Feature Selection Algorith...

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

作者： Bhardwaj, Shashank Baskar, S. Marakala, Vijaya Prasannan Gangodkar, durgaprasad dhole, Sumedh Vithalrao Dr. A.P.J. Abdul Kalam Technical University Department of Computer Application Lucknow India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of EE Tamil Nadu Chennai India University of Bisha College of Medicine Saudi Arabia Karpagam College of Engineering Department of Civil Engineering Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India College of Engineering Department of Electronics & Communication Engineering Maharashtra Pune India

ISBN: (纸本)9798350370249

In recent years, the exponential growth in data volume and sample sizes across various domains, including finance and online transactions, has presented significant challenges for machine learning and data mining algorithms. High-dimensional and imbalanced datasets, often tainted with noise, have impeded the performance and interpretability of fraud detection models. Feature selection has emerged as a pivotal technique to address these challenges by identifying the most relevant features while discarding redundant or irrelevant *** research explores the landscape of feature selection methods and confronts the existing challenges in credit card fraud detection. We propose the 'FraudGuard Pro Ensemble Feature Selection Algorithm,' designed specifically for credit card fraud detection tasks. Notably, our proposed model achieves an impressive accuracy rate of 99%, outperforming traditional models such as Logistic regression (95%), random Forest (98%), and Support Vector Machine (94%). This substantial improvement underscores the effectiveness of our algorithm in mitigating fraud while minimizing false positives, offering a promising solution for enhanced security in online transactions. © 2024 IEEE.

关键词： Support vector machines

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A study on porosity investigation of compacted bentonite in various densities by using micro-computed tomography images analysis

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Kerntechnik 2024年第3期89卷 327-339页

作者： Yang, Wudi Chen, Jui-Chi Li, Jing-Xing Yang, Song Shi, Yunfeng Chen, Chien-Jung Lu, Hui-Min Lu, Chien-Ping Tsai, Fang-Chang Lee, Chuan-Pin 12466 Xiamen University Xiamen361005 China Radioactive Waste Disposal Technology R&D Center 34881 National Tsing Hua Universi Hsinchu City Taiwan Department of Applied Chemistry National Chi Nan University Nantuo Taiwan School of Nuclear Sciences and Engineering 12394 East China University of Technology Nanchang330013 China Taiyuan030006 China Fundamental Science on Radioactive Geology and Technology Exploration Laboratory 12394 East China University of Technology Nanchang330013 China Department of Nuclear Medicine National Taiwan University Hospital College of Medicine National Taiwan University Taipei Taiwan Hubei Key Laboratory of Polymer Materials Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Collaborative Innovation Center for Advanced Organic Chemical Materials School of Materials Science and Engineering 12563 Hubei University Wuhan430062 China

Compacting a material means applying pressure so that the material itself loses its internal pores. It improves the homogeneity of the material itself and increases its density. The pore structure of compacted clay is also an important factor that affects its mechanical properties, permeability and water absorption. In this study, MX-80 bentonite clay was first compacted into round cakes of different densities and then subjected to tomography image analysis technique using micro-computed tomography (micro-CT) at millimeter scale of accuracy for 1.0-2.0g/cm3 compacted clay (thickness of 0.3cm bentonite compacted clay). In this paper, we use the latest commercial specifically for scientific visualization, materials science, non-destructive testing, numerical simulation of chromatographic imaging and other functions of a powerful visualization of the image analyzing method. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Bentonite

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Federated Learning-Based Intelligent Indoor Smoke and Fire detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1d for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable dL-CNN1d model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Leveraging Grey Wolf Optimizer for reliable data Transmission in Underwater Wireless Sensor Networks

Leveraging Grey Wolf Optimizer for Reliable Data Transmissio...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Umamahesh, G. Senthurya, S. Khatarkar, Poonam Alsalami, Zaid Kaur, Jaspal ramesh, P.S. Aditya Engineering College Department of Computer Science & Engineering India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies College of Technology Department of Electrical & Electronics Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350390759

This research investigates the application of the Grey Wolf Optimizer (GWO) to enhance reliable data transmission in Underwater Wireless Sensor Networks (UWSNs). UWSNs are critical for various applications, including environmental monitoring and underwater exploration, but face challenges such as high propagation delays, limited bandwidth, and energy constraints. The GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize routing paths, minimize energy consumption, and improve packet delivery ratio. The methodology includes simulating UWSNs with different network configurations using synthetic and real-world datasets, and implementing the GWO algorithm in Python within the NS-3 simulation framework. Comparative analysis with traditional routing protocols, AOdV and dSr, demonstrates that the GWO-based routing algorithm significantly reduces energy consumption, increases packet delivery ratio, lowers latency, and extends network lifetime. These results underscore the effectiveness of GWO in enhancing UWSN performance. The study concludes that GWO offers a robust and adaptable solution for reliable data transmission in UWSNs, with potential for further enhancements through hybrid optimization techniques and real-world validations. This research provides a comprehensive understanding of GWO's application in UWSNs, contributing to advancements in underwater communication technologies. © 2024 IEEE.

关键词： Wireless sensor networks

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Analysis of Lung Cancer for developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Mahaveerakannan, r. Murali dhar, M.S. Goswami, Sohan Krishna, P. Gopala Jayanthi, K. Kumar, M. Sathish The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Technique Polytechnic Institute Department of Computer Science and Technology West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Pollachi Institute of Engineering and Technology Department of Computer Science and Engineering Pollachi Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350300857

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method. © 2023 IEEE.

关键词： Internet of things

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Mutual Self‑regulation of d‑Electrons of Single Atoms and Adjacent Nanoparticles for Bifunctional Oxygen Electrocatalysis and rechargeable Zinc‑Air Batteries

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Nano-Micro Letters 2023年第4期15卷 40-57页

作者： Sundaram Chandrasekaran rong Hu Lei Yao Lijun Sui Yongping Liu Amor Abdelkader Yongliang Li Xiangzhong ren Libo deng College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060People’s Republic of China College of Chemistry and Bioengineering Guilin University of TechnologyGuilin 541004People’s Republic of China Shenzhen Key Laboratory of Special Functional Materials Shenzhen Engineering Laboratory for Advanced Technology of CeramicsCollege of Materials Science and EngineeringGuangdong Research Center for Interfacial Engineering of Functional MaterialsShenzhen UniversityShenzhen 518060People’s Republic of China Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials Institute of New Energy for VehiclesSchool of Materials Science and EngineeringTongji UniversityShanghai 201804People’s Republic of China Department of Design and Engineering Faculty of Science&TechnologyBournemouth UniversityPoole BH125BBDorsetUK

rechargeable zinc-air batteries(ZABs)are a promising energy conversion device,which rely critically on electrocatalysts to accelerate their rate-determining reactions such as oxygen reduction(Orr)and oxygen evolution reactions(OEr).Herein,we fabricate a range of bifunctional M-N-C(metal-nitrogen-carbon)catalysts containing M-Nx coordination sites and M/MxC nanoparticles(M=Co,Fe,and Cu)using a new class ofγ-cyclodextrin(Cd)based metal-organic framework as the *** the two types of active sites interacting with each other in the catalysts,the obtained Fe@C-FeNC and Co@C-CoNC display superior alkaline Orr activity in terms of low half-wave(E1/2)potential(~0.917 and 0.906 V,respectively),which are higher than Cu@C-CuNC(~0.829 V)and the commercial Pt/C(~0.861 V).As a bifunctional electrocatalyst,the Co@C-CoNC exhibits the best performance,showing a bifunctional Orr/OEr overpotential(ΔE)of~0.732 V,which is much lower than that of Fe@C-FeNC(~0.831 V)and Cu@C-CuNC(~1.411 V),as well as most of the robust bifunctional electrocatalysts reported to *** X-ray absorption spectroscopy and density functional theory simulations reveal that the strong electronic correlation between metallic Co nanoparticles and the atomic Co-N4 sites in the Co@C-CoNC catalyst can increase the d-electron density near the Fermi level and thus effectively optimize the adsorption/desorption of intermediates in Orr/OEr,resulting in an enhanced bifunctional electrocatalytic *** Co@C-CoNC-based rechargeable ZAB exhibited a maximum power density of 162.80 mW cm^(−2) at 270.30 mA cm^(−2),higher than the combination of commercial Pt/C+ruO2(~158.90 mW cm^(−2) at 265.80 mA cm^(−2))*** the galvanostatic discharge at 10 mA cm^(−2),the ZAB delivered an almost stable discharge voltage of 1.2 V for~140 h,signifying the virtue of excellent bifunctional Orr/OEr electrocatalytic activity.

关键词： Cyclodextrin Cd-MOF Single-atom catalyst Orr/OEr Zinc-air battery

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Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Workplace applications

Unveiling the Future: Exploring the Impacts and Challenges o...

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Advancement in technology (ICONAT), International Conference for

作者： S Pragith S Supriya S. Sasikala rayala Sateesh Hemanth Swamy P Karpagam Department of Computer Science Engineering Vellore Institute of Technology Vellore Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Tagore Engineering College Chennai Department of ECE MLR Institute of Technology Hyderabad Motorola Solutions Department of Cyber Security Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Society” embarks on a comprehensive journey through the transformative landscape shaped by Artificial Intelligence (AI). This abstract offers a nuanced examination of AI’s profound implications, ranging from its disruptive potential to its ethical complexities, and the societal imperatives it underscores. delving into the evolution of AI technologies, from foundational concepts to cutting-edge innovations, the abstract elucidates their growing significance across diverse sectors including healthcare, finance, transportation, and beyond. However, amidst the promises of efficiency and innovation, ethical concerns loom large. The abstract navigates through the ethical intricacies of AI, addressing issues of bias, accountability, and the erosion of privacy. Furthermore, it ventures into the societal ramifications of AI adoption, exploring its impact on employment dynamics, socioeconomic disparities, and the fabric of human interaction. By interrogating the dual narratives of promise and peril, this abstract offers a holistic understanding of AI’s role in shaping the future of society. It underscores the imperative for thoughtful governance, ethical stewardship, and inclusive decision-making to ensure that AI serves as a force for collective progress rather than exacerbating existing divides. Through its interdisciplinary lens, this abstract serves as a roadmap for stakeholders across academia, industry, and policymaking to navigate the complex terrain of AI’s integration into society, ultimately paving the way for a future where AI augments human capabilities while upholding fundamental values of fairness, equity, and justice.

关键词： Productivity Industries Ethics Technological innovation Navigation Employment distance measurement Stakeholders Artificial intelligence Socioeconomics

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