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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Ganesh, D. Sharma, Apurva Vignesh, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the ability to use herbal Language Processing (NLP) and Sentiment evaluation strategies to detect cybersecurity threats. We describe the traditional tactics and present-day tendencies in cybersecurity chance detection and discuss the demanding situations the technique confronts. We then present how NLP and Sentiment analysis can resource inside the detection technique, provide examples of ways NLP tools can assist in chance detection, and discuss how NLP can offer beneficial data in phrases of danger detection. We quickly review the strategies for classifying and verifying sentiment from textual statistics. Our findings propose that NLP and mawkish analysis can successfully discover cybersecurity threats and provide treasured insights for additional studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Predicting Groundwater Quality in Vietnam Using Artificial Intelligence Models 3rd

Predicting Groundwater Quality in Vietnam Using Artificial I...

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3rd International Conference on Advances in information and Communication technology, ICTA 2024

作者： Minh, Nguyen Hai Ngan, Tran Thi Giang, Nguyen Long Omar, Michael Chau, Hoang Thi Minh Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam International School Vietnam National University Hanoi Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Computing Department FPT University Greenwich University Hanoi Viet Nam University of Economics – Technology for Industries Hanoi Viet Nam

ISBN: (纸本)9783031809422

Various pollutants have endangered water quality over the past few years. As a result, predicting and modelling water quality has become crucial for reducing water pollution. Traditional techniques to evaluate water quality need a lot of expertise and time. Automated analysis using Artificial Intelligence (AI) models can effectively tackle increasingly complex issues and provide more accurate results. For this purpose, recent works use several AI algorithms to predict water quality. This study intends to assess the effectiveness of different machine learning (ML) models in estimating groundwater quality in Vietnam, including Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC). Firstly, the dataset was searched for and collected from the Ministry of Environment and Natural Resources, Vietnam. Data preprocessing is performed using different techniques to convert the data into meaningful form. K- Means, a clustering method, is applied to cluster the data into distinct groups based on which water quality needs to be evaluated. Afterwards, prediction is performed to classify the data into different categories by employing various AI models. Accuracy was used to assess how well the ML models performed in prediction. The results showed that all the models used performed well in predicting water quality, but GBC performed best and most accurately. Therefore, the results suggest that the AI models’ efficacy in water quality prediction with a high level of accuracy will further improve water quality management in Vietnam and other countries. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Towards Improving the Anti-attack Capability of the RangeNet++ 16th

Towards Improving the Anti-attack Capability of the RangeNet...

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16th Asian Conference on Computer Vision , ACCV 2022

作者： Zhou, Qingguo Lei, Ming Zhi, Peng Zhao, Rui Shen, Jun Yong, Binbin School of Information Science and Engineering Lanzhou University Tianshui South Road 222 Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW Australia

ISBN: (纸本)9783031270659

With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the point cloud segmentation model RangeNet++ in the field of autonomous driving, we propose novel approaches to improve the security and anti-attack capability of the RangeNet++ model. One is to calculate the local geometry that can reflect the surface shape of the point cloud based on the range image. The other is to obtain a general adversarial sample related only to the image itself and closer to the real world based on the range image, then add it into the training set for training. The experimental results show that the proposed approaches can effectively improve the RangeNet + +’s defense ability against adversarial attacks, and meanwhile enhance the RangeNet++ model’s robustness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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ULKNet:Rethinking Large Kernel CNN with UNet-Attention for Remote Sensing Images Semantic Segmentation 49

ULKNet:Rethinking Large Kernel CNN with UNet-Attention for R...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Liu, Weilin Wang, Lunqian Wang, Xinghua Ding, Hao Xia, Bo Zhang, Zekai The School of Information Science Linyi University Linyi China The School of Informatio Science Linyi University Linyi China Shandong Computing Center Qilu University of Technology Jinan China

ISBN: (纸本)9798350331820

Since the advent of the Transformer, the Vision Transformer (ViT) model has become the optimal solution for extracting semantic information in remote sensing image semantic segmentation tasks. However, recent research has discovered that using larger convolutional kernels to extract global information can achieve performance comparable to, or even better than the ViT models. This finding inspires us to redesign the convolutional neural network structure and innovatively construct a novel parallel global-local module based on large kernel convolution, in order to better extract semantic information and local contextual information. Following this design approach, we propose ULKNet, a pure CNN architecture that achieves performance comparable to the ViT model. Through experiments on the ISPRS Vaihingen and Potsdam datasets, We respectively achieve mIoU 82.7% and 86.0% mIoU scores. This finding demonstrates that a well-designed large kernel CNN network structure can provide a larger and more effective receptive field, thereby better extracting semantic information. © 2023 IEEE.

关键词： Semantic Segmentation

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High-efficiency multi-channel focusing and imaging enabled by polarization-frequency multiplexing non-interleaved metasurfaces

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光子学研究（英文） 2025年第4期13卷 976-986页

作者： XUNJUN HE MINGZHONG WU GUANGJUN LU YING ZHANG ZHAOXIN GENG School of Electrical and Electronic Engineering Harbin University of Science and TechnologyHarbin 150080China Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China College of Science Harbin University of Science and TechnologyHarbin 150080China School of Information Engineering Minzu University of ChinaBeijing 100081China

In microwave communication systems,focusing and imaging have attracted widespread attention due to their ap-plication prospects in the information processing and communication *** existing multi-channel focusing and imaging are implemented by interleaved ***,the disadvantages of their large size and low efficiency limit their practical applications in large-capacity and low-loss integrated *** solve these issues,here,we propose a non-interleaved polarization-frequency multiplexing metasurface for high-efficiency multi-chan-nel focusing and *** meta-atoms of the non-interleaved metasurface are composed of a metallic ground plate,two dielectric layers,a larger cross-shaped metal structure,and a smaller cross-shaped metal structure em-bedded by a circular metal *** altering the size of two cross-shaped structures,the designed meta-atom can obtain the independent complete 2π phase coverage with high reflection efficiency at two different frequency ranges for two orthogonal linear polarization(LP)incidences,realizing polarization multiplexing and frequency ***,two types of metasurfaces based on the above meta-atoms are designed to realize multi-channel focusing and imaging with high *** a proof,the focusing metasurface is fabricated and measured,and the measured results are well consistent with simulated ***,the proposed scheme has the advantages of high efficiency,multi-channel,and compact size,which possesses broad application prospects in low-loss and multi-channel communication integrated systems.©2025 Chinese Laser Press

关键词： imaging enabled focusing frequency high-efficiency metasurfaces multi-channel multiplexing non-interleaved polarization

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Research progress of quantum artificial intelligence in smart city

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Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

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A Review of Super Resolution technology for Extended-Range Precipitation Forecasting

A Review of Super Resolution Technology for Extended-Range P...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Chen, Jiahan Mao, Mingxuan Yuan, Chengsheng Zhong, Yuanzhi Tang, Zhenfei The Hong Kong Polytechnic University Department of Computing Hong Kong School of Automation Wuxi University Wuxi China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Fujian Huawang Information Technology Co. Ltd. Fujian China Fujian Provincial Climate Center Fujian China

ISBN: (纸本)9798350367560

In the field of meteorology, precipitation holds significant importance for human activities and environment. Extended-range precipitation forecasting, covering a timeframe of weeks to months, is crucial across various sectors, including water resource management, agriculture, and disaster prevention. However, the conventional meteorological forecasting models often fail to satisfy the precise demand for accurate and detailed precipitation predictions due to the inherent limitations in resolution and data collection frequency associated with these methods. Recently, super resolution technology has emerged as a promising solution with the potential to transform the resolution of meteorological data. By leveraging advanced algorithms and computational prowess, this technology can generate high-resolution images from low-resolution inputs. In this comprehensive review, we meticulously dissect the state-of-the-art super resolution techniques utilized in extended-range precipitation forecasting tasks. By classifying and analyzing the literature based on various methodologies, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, we uncover their performance characteristics, strengths, and limitations. Our in-depth exploration reveals that while these methods have shown promise, they also face significant challenges. For example, CNNs struggle with capturing ultrafine details, GANs exhibit training instability, and diffusion models require further optimizations in terms of their computational efficiency. Future research efforts should focus on enhancing model architectures, seamlessly incorporating physical constraints to ensure the physical consistency of the generated data, and optimizing the current data utilization strategies. Furthermore, it is crucial to develop comprehensive evaluation metrics, conduct extensive long-term and regional validation studies, and establish effective uncertainty quantification metho

关键词： Convolutional neural networks

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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Computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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Towards a Self-contained Data-driven Global Weather Forecasting Framework 41

Towards a Self-contained Data-driven Global Weather Forecast...

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41st International Conference on Machine Learning, ICML 2024

作者： Xiao, Yi Bai, Lei Xue, Wei Chen, Hao Chen, Kun Chen, Kang Han, Tao Ouyang, Wanli Department of Computer Science and Technology Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Qinghai University and Intelligent Computing Application Laboratory of Qinghai Province China School of Information Science and Technology Fudan University Shanghai China

Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i.e., analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in numerical weather prediction centers;it is accurate but computationally expensive. In this paper, we aim to couple the AI forecasting model, FengWu, with 4DVar to build a self-contained data-driven global weather forecasting framework, FengWu-4DVar. To achieve this, we propose an AI-embedded 4DVar algorithm that includes three components: (1) a 4DVar objective function embedded with the FengWu forecasting model and its error representation to enhance efficiency and accuracy;(2) a spherical-harmonic-transform-based (SHT-based) approximation strategy for capturing the horizontal correlation of background error;and (3) an auto-differentiation (AD) scheme for determining the optimal analysis fields. Experimental results show that under the ERA5 simulated observational data with varying proportions and noise levels, FengWu-4DVar can generate accurate analysis fields;remarkably, it has achieved stable self-contained global weather forecasts for an entire year for the first time, demonstrating its potential for real-world applications. Additionally, our framework is approximately 100 times faster than the traditional 4DVar algorithm under similar experimental conditions, highlighting its significant computational efficiency. Copyright 2024 by the author(s)

关键词： Data assimilation

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Trustworthy deep learning for encrypted traffic classification

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Soft computing 2025年第2期29卷 645-662页

作者： Li, Zheng Liu, Yanbei Zhang, Changqing Shan, Wanjin Zhang, Haifeng Zhu, Xiaoming School of Electronic and Information Engineering Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems Tianjin300387 China College of Intelligence and Computing Tianjin University Tianjin300072 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

Network traffic classification refers to the identification of collected network traffic data of various applications, which is widely used in research fields such as network resource allocation, traffic scheduling and intrusion detection systems. With the widespread application of encryption technology in the network, encrypted traffic classification has become a hot research topic. At present, most existing methods only focus on the accuracy of network traffic classification. Yet, few work studies the reliability of the classification model, which plays an important role in network regulation and network security. In this paper, we propose a novel traffic classification method based on trustworthy deep learning model, which can effectively improve the reliability of encrypted traffic classification models by correcting the confidence of model output. Specifically, we firstly perform data preprocessing on the original network traffic, and then adopt a ConvNet for feature learning and a ClassifyNet for traffic classification in the initial stage. At the same time, we utilize a trustworthy confidence criterion to design a ConfidNet trained according to the probability of the true class. The ConfidNet can provide a reliable confidence measure for the prediction of the classification model. Finally, we demonstrate the effectiveness of our framework through comprehensive experiments on two benchmark datasets ISCX VPN-nonVPN and USTC-TFC2016, and show that our method can improve the reliability of the classification model and has a good ability to identify misclassified samples compared with state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Network intrusion

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