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检索条件"任意字段=Proceedings of the 6th International Conference on Algorithms, Computing and Systems"

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Double Walsh-Hadamard Transform for Improved Efficiency in OFDM-IM 12th

Double Walsh-Hadamard Transform for Improved Efficiency in O...

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12th international conference on Communications, Circuits, and systems (ICCCAS)

作者： Lian, Yuhao Wang, Yuhua Lin, Xinyu Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

ISBN: (纸本)9789819726387;9789819726363;9789819726356

Amidst the rapidly evolving landscape of B5G/6G wireless networks, the emergence of index modulation has marked a significant development, and one key technique gaining ground is orthogonal frequency division multiplexing-index modulation (OFDM-IM), which boosts both communication and signal transmission spectral efficiency. Although OFDM-IM is an innovative multicarrier modulation technique that builds on the conventional OFDM system. It still confronts a significant challenge related to the High Power Amplifier (HPA) cost, which arises from the distortion that OFDM-IM signals cause when passing through nonlinear devices. this distortion is driven by the considerable Peak-to-Average Power Ratio (PAPR) that characterizes the OFDM-IM signal. In this paper, we propose a novel waveform named Double WHT-OFDM-IM, which combines Double Walsh-Hadamard Transform (WHT) with orthogonal frequency division multiplexing-index modulation (OFDM-IM) to improve the system performance and combat the enormous PAPR of WHT-OFDM-IM in the low number of activated subcarriers scenarios. the proposed approach satisfies the demanding criteria of 6th-generation (6G) and beyond communication schemes, specifically with regard to low peak-to-average power ratio (PAPR), and low bit-error rate (BER).

关键词： B5G/6G wireless networks Low PAPR OFDM-IM Double WHT-OFDM-IM

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proceedings - 2024 IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

Proceedings - 2024 IEEE/ACM International Conference on Big ...

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11th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

ISBN: (纸本)9798350367300

the proceedings contain 57 papers. the topics discussed include: machine learning-based big data analytics in smart cities: a survey of current trends and future research directions;attribute-based semantic type detection and data quality assessment;exploring the knowledge mismatch hypothesis: hallucination propensity in small models fine-tuned on data from larger models;an explanation technique for yield prediction in smart farming;optimizing domestic energy consumption: a comprehensive plug for enhanced monitoring and efficiency;an accurate salary estimation scheme by using bigdata technique;a novel transfer learning approach for detecting partial shading in photovoltaic systems;extracting health evidence information from biomedical literature using large language models;semantic communications for healthcare applications: opportunities and challenges;plant diseases recognition using machine learning algorithms;unusual invoice detection using a permutation based genetic algorithm;an efficient iris recognition technique through minimal preprocessing and custom CNN;a cloud-agnostic serverless architecture for distributed machine learning;age-friendly trip planning using ant colony optimization;and temporal dynamics and anomaly detection in transactional networks.

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QL vs. SARSA: Performance Evaluation for Intrusion Prevention systems in Software-Defined IoT Networks 19

QL vs. SARSA: Performance Evaluation for Intrusion Preventio...

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19th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC)

作者： Moreira, Christian Miranda Kaddoum, Georges Ecole Technol Super Montreal PQ Canada Lebanese Amer Univ Cyber Secur Syst & Appl AI Res Ctr Beirut Lebanon

ISBN: (纸本)9798350333398

the resource-constrained IPV6-based low power and lossy network (6LowPAN) is connected through the routing protocol for low power and lossy networks (RPL). this protocol is subject to a routing protocol attack called a rank attack (RA). this paper presents a performance evaluation where leveraging model-free reinforcement-learning (RL) algorithms helps the software-defined network (SDN) controller achieve a cost-efficient solution to prevent the harmful effects of RA. Experimental results demonstrate that the state action reward state action (SARSA) algorithm is more effective than the Q-learning (QL) algorithm, facilitating the implementation of intrusion prevention systems (IPSs) in software-defined 6LowPANs.

关键词： 6LowPAN IoT Rank Attack Intrusion Prevention Collaborative Security Framework Software-Defined Network

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A Machine Learning Based Novel Approach of Predicting international Roughness Index(IRI) from Traffic Characteristics using Random Forest Regression 6

A Machine Learning Based Novel Approach of Predicting Intern...

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6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th Asia Digital Image Processing conference

作者： Abir, Abrar Rahman Bangladesh Univ Engn & Technol Dhaka Bangladesh

ISBN: (纸本)9798400716225

international Roughness Index (IRI) stands as a well-established metric for assessing pavement roughness and overall condition. Predicting IRI is crucial for maintaining pavement infrastructure. In this study, we present a novel approach to predict IRI using Random Forest regression, focusing exclusively on traffic characteristics as predictive variables. Existing studies considered a wide range of factors, including pavement materials, climate, structural attributes, and various pavement distress indicators alongside traffic data where we developed our model using only traffic characteristics. We have used Long-Term Pavement Performance Program (LTPP) dataset for training our models. We have compared our Random forest model with three other models (XGBoost, SVM regression, Gradient Boosting). R squared value and Mean Squared Error (MSE) were taken as performance evaluation metrics. Random forest showed R squared value of 0.70623 and MSE of 8.22 x 10-6 where Gradient Boosting, XGBoost and SVM had R squared value of 0.5737, 0.497, and 0.3455 *** also compared between two hyperparameter tuning methods(Random Search and Grid Search) used in our models and found Random search to perform better. We have also presented a comparative analysis of existing IRI prediction models with our model. Finally we present a SHAP(SHapley Additive exPlanations) analysis to interpret our model and find the contribution of each input feature on our model. We found Annual ESAL (Equivalent Single Axle Load) to be the most dominant factor to predict IRI from traffic characteristics.

关键词： Machine Learning Random Forest Regression Pavement Roughness

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Feynman Meets Turing: the Infeasibility of Digital Compilers for Gate-Based Quantum computing 59

Feynman Meets Turing: The Infeasibility of Digital Compilers...

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59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Boeck, Yannik N. Boche, Holger del Toro, Zoe Garcia Fitzek, Frank H. P. Tech Univ Munich Chair Theoret Informat Technol D-80333 Munich Germany Tech Univ Dresden Deutsch Telekom Chair Commun Networks CeTI Dresden Germany Tech Univ Dresden BMBF Res HUB Life TU Dresden 6G Dresden Germany

ISBN: (纸本)9781728190549

We consider the problem of computing gate-circuit approximations of quantum algorithms, i.e., unitary operators, from the perspective of computable (effective) analysis. the scientific community thinks the Solovay-Kitaev theorem a milestone in quantum compiling - the task of computing gate-circuit approximations - because it proves the existence of efficient quantum compilers in an analytic sense. However, since we cannot represent unitary operators in a mere analytical way on digital computers, contemporary digital implementations of quantum compiling resort to heuristic numerics and remain below the computational performance engineers hope to realize using the result of Solovay and Kitaev. this paper discusses quantum compiling within a framework of computable analysis, establishing a concept of computable unitary operators for digital computing based on the theory of Turing machines. Particularly, we prove that digital quantum compiling is uncomputable due to the underlying algebraic structure. Finally, we discuss several implications of our findings for heuristic digital implementations of quantum compiling, hinting toward possible research directions to thoroughly understand the relevant bottlenecks.

关键词： quantum computing quantum algorithm quantum compiler Solovay-Kitaev theorem Turing machine

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A Comparative Analysis of Food Habit and Nutrition on Food in Bangladesh using Machine Learning 6

A Comparative Analysis of Food Habit and Nutrition on Food i...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Ahmed, Md. Tofael Ferdaous, Jannatul Kamruzzaman, M. Khatun, Mst. Eshita Daffodil International University Department of CSE Dhaka Bangladesh

ISBN: (纸本)9781665410281

Day by day, humans change their way of life. Eating habits are one of them. this research mainly focuses on the eating food habits and nutritional status of the people in Bangladesh. For a healthy and beautiful nation, food habits must be appropriate. Just as good eating habits keep our body healthy and energetic, bad eating habits make us sick. For this research, A data set is gathered on eating habits to find and learn about the food tastes of the people. Collected 1200 statistics from human's everyday meals habits. Split the data into breakfast, lunch, snack, street food, dinner, and sweets. this research target is to provide the best knowledge about nutrition on food habits because it is quite difficult to develop a country without a healthy nation. this research is divided into two steps. (i) finding food habits (ii) synthesizing information and finding nutrition. An annotated reference list is included for ease is finding another helpful guideline. For this research, seven different types of machine learning algorithms such as Decision Tree, KNN ADA boosting classifier, Logistic Regression, Bagging Classifier, Gaussian Naive Bayes, Extra Trees Classifier have been applied. © 2022 IEEE.

关键词： Nutrition

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Few-shot Malicious Domain Detection on Heterogeneous Graph with Meta-learning 26

Few-shot Malicious Domain Detection on Heterogeneous Graph w...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Gao, Yi Yuan, Fangfang Cao, Cong Su, Majing Wang, Dakui Liu, Yanbing Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China The 6th Research Institute of China Electronic Corporations Beijing China

ISBN: (纸本)9798350331684

the Domain Name System (DNS), one of the essential basic services on the Internet, is often abused by attackers to launch various cyber attacks, such as phishing and spamming. Researchers have proposed many machine learning-based and deep learning-based methods to detect malicious domains. However, these methods rely on a large-scale dataset with labeled samples for model training. the fact is that the labeled domain samples are limited in the real-world DNS dataset. In this paper, we propose a few-shot malicious domain detection model named MetaDom, which employs a meta-learning algorithm for model optimization. Specifically, We first model the DNS scenario as a heterogeneous graph to capture richer information by analysing the complex relations among domains, IP addresses and clients. then, we learn the domain representations with a heterogeneous graph neural network on the DNS HG. Finally, considering that only few labeled data are available in the real-world DNS scenario, a meta-learning algorithm with knowledge distillation is introduced to optimize the model. Extensive experiments on the real DNS dataset show that MetaDom outperforms other state-of-the-art methods. © 2023 IEEE.

关键词： Learning algorithms

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5G/6G Technology Capabilities Designed for Secure Edge Network: Smart City Use Cases of Turkcell 6

5G/6G Technology Capabilities Designed for Secure Edge Netwo...

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6th international conference on Blockchain computing and Applications, BCCA 2024

作者： Kalem, Gokhan Kosu, Semiha Basaran, Mehmet Next-Generation R&D Network Technologies Turkcell Technology Istanbul34854 Turkey Information and Communications Research Group Istanbul Technical University Istanbul34469 Turkey 6GEN Lab. Next-Generation R&D Network Technologies Turkcell Istanbul34854 Turkey

ISBN: (纸本)9798350351538

there is no doubt that 5G and 6G technologies coupled with secure edge network capabilities play crucial roles in the evolution of Industry 4.0 and emerging Industry 5.0. In Industry 4.0, 5G enhances smart manufacturing by enabling ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), and enhanced mobile broadband (eMBB). these features support real-time monitoring, automation, edge computing, and data exchange across interconnected devices and systems to improve industrial efficiency and productivity. With Industry 5.0, which focuses on human-machine collaboration, 5G continues to be vital, but the advent of 6G will bring even greater capabilities. 6G is expected to offer a higher level of autonomy and advanced artificial intelligence integration by facilitating seamless interaction between humans, machines as well as intelligent systems via edge networking. this will enable more sophisticated applications, such as advanced robotics, virtual and augmented reality experiences, further connected driving innovations, and also the symbiosis between humans and technology, built on ubiquitous connectivity. From a leading mobile network operator perspective, in this paper, we delve into how 5G technology, empowered with secure edge networks, can transform XR communication, vehicular communication, and also UAV-based aerial communication while enabling safer, more reliable, and more efficient systems in the smart city applications on the road to 6G. © 2024 IEEE.

关键词： Smart manufacturing

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11th international Symposium on Applied computing for Software and Smart systems, ACSS 2024

11th International Symposium on Applied Computing for Softwa...

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11th international Symposium on Applied computing for Software and Smart systems, ACSS 2024

ISBN: (纸本)9789819797615

the proceedings contain 19 papers. the special focus in this conference is on Applied computing for Software and Smart systems. the topics include: CCD Array Spectrometer-Based FBG Bragg Wavelength Detection Using ANN Algorithm;performance Assessment of Sequential Models for Solar Radiation Forecasting Over Varying Forecast Horizon;advancing Smartphone Sensor-Based Keystroke Dynamics for Implicit and Active Authentication: Addressing Challenges and Enhancing Usability Control;a Prototype for Peer-to-Peer Transient Microservices with Wi-Fi Direct;comparison of the Effectiveness of Face Recognition algorithms in Terms of Photo Distortion Level;multi-class Histology Image Analysis Using Handcrafted Texture and Colour Features for Breast Abnormality Classification;fvFc-Net: Forged Video Frame Classification Network;semi-automated Ground Truth Generation System for Bangla Offline Handwritten Text Documents;SWFEM: Sparse Weighted Fine-Tuned Ensemble Model for Leaf Disease Detection;a Comparative Study of Hospital Length of Stay Prediction of Indoor Patients for Admission, Post Admission, and Discharge Stage Data Using Machine Learning algorithms;BEN-RS-ANN: An Innovative Approach for Revealing Emotion from Bengali Text with Exposure to Polysemy Resolution;Flood Detection in UAV Images of Urban Area Using Machine Learning and Deep Learning Techniques;supervised Classification Approach for Precise Cell Type Identification Improves Single Cell Data Analysis;quality-Driven Web Service Selection: Machine Learning Based Approach;contextual Correlation Inference in Multi-fleet Robotic systems;risk Assessment in Agile Software Development;Adaptive Cyber Defence: Leveraging GANs for Simulating and Mitigating Advanced Network Attacks in IoT Environments.

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K - Means Artificial Intelligence Clustering Technique for 6G Propagation Channels 5

K - Means Artificial Intelligence Clustering Technique for 6...

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5th international conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Aldossari, Saud Alhajaj Prince Sattam bin Abdulaziz University Department of Electrical Engineering Wadi Addwasir Saudi Arabia

ISBN: (纸本)9798350354690

the global contemporary revolution is accelerating in our modern environment. Mobile communications have undergone a generational transition at regular periods. While fifth-generation (5G) structures are presently beginning to be financially suitable and with the benefaction of high Mm Wave and terahertz (thz) frequencies, there is a great deal of interest in systems past 5G. It is known as the 6th generation (6G) remote system. In recent years, the development of Artificial Intelligence (AI) has rapidly grown, leading to its integration into various fields, including wireless communications. this integration of AI in wireless communication technologies can potentially revolutionize the future of 6G technology. the widespread use of new generation Information and Communications Technology (ICT) such as AI, blockchain innovation, virtual reality (VR), augmented reality (AR), etc. With the usage of high Mm Wave, the structure and propagation of the wireless signal is unknown. this paper studies the behavior of Channel State Information (CSI) via unsupervised learning method such as K-Means clustering methods in 145 GHz bands. © 2024 IEEE.

关键词： Terahertz waves

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