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IEEE Transactions on Intelligent Vehicles 2024年第10期9卷 1-10页

作者： Dai, Xinguang Xie, Jun Zhang, Guoxin Chang, Kenglun Chen, Feng Wang, Zhepeng Tang, Chunming Artificial intelligence Tiangong University Tianjin China Lenovo Research Beijing China Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Department of Computer Science and Technology Tsinghua University Beijing China

Lane detection plays an important role in the autonomous driving system and attracts widespread interest in recent years. Despite the advantages of conventional work, such as segmentation-based and point-based methods, these models either practically require a large sum of anchors or only leverage the connection message of discrete points in the lane. Different from this, curve-based methods enable one to naturally learn the holistic representation of lanes based on the geometric semantics of curves and conduct end-to-end optimization conveniently, thus serving a promising direction. However, the existing curve-based methods encounter draws in: 1) modeling some complicated curves mathematically. 2) Simultaneously capturing geometric semantics within and between lane curves. 3) The endpoints of lane lines are susceptible to occlusion, a factor that is crucial for curve modeling. To tackle these issues, we revisit the curve-based methods and propose a novel model Dynamic NURBS Network (DBNet). Specifically, we introduce the NURBS curve to model lanes, enabling to theoretically fit various complicated curves and guarantee the robustness of local and global optimization. Based on this, we introduce three key modules to address lane detection challenges. Firstly, we propose Local Dynamic-Interaction (LDI) that adaptively exploits the geometric message of local at the feature level. Secondly, a Curve Fitting Enhancement (CFE) module to enhance the feature of both ends of the lane instance. Lastly, a Global Association-Sharing (GAS) module captures the global features of lanes, which can together promote semantic association and tackle the occlusion problem inside and outside the lane. Moreover, the proposed method achieves a new state-of-the-art performance on three public benchmarks and especially achieves 77.98% of the F1 score on the curve scenario in the CULane dataset. IEEE

关键词： Semantics

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WirelessLLM:Empowering Large Language Models Towards Wireless Intelligence

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Journal of Communications and information Networks 2024年第2期9卷 99-112页

作者： Jiawei Shao Jingwen Tong Qiong Wu Wei Guo Zijian Li Zehong Lin Jun Zhang Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed,configured,and managed. Recent advancements in large language models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM:knowledge alignment, knowledge fusion, and knowledge evolution. Then,we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.

关键词： large language models multi-modal models wireless communications power allocation spectrum sensing protocol understanding

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Integrating blockchain and machine learning for enhanced anti-money laundering system

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International Journal of information technology (Singapore) 2025年第4期17卷 2439-2447页

作者： Shafin, Khandakar Md Reno, Saha Department of Computer Science and Engineering CCN University of Science and Technology Cumilla 3503 Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka 1208 Bangladesh

Money laundering is a serious threat to global financial systems, causing instability and inflation, and especially hurting middle-class savings. This paper suggests a new way to tackle these problems by using blockchain technology and advanced machine learning models. We use hyperledger fabric to securely record transactions and advanced algorithms like autoencoders and neural networks to create a strong anti-money laundering (AML) system. This system can detect and predict illegal financial activities in real-time and includes continuous monitoring and alerts. These features improve financial transparency and stability while protecting the savings of the middle class. By identifying and reducing risks early, our solution not only ensures compliance with regulations but also strengthens the resilience of global financial systems against new threats. This research helps develop effective ways to fight financial crime, promoting a safer and more transparent financial world. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Hyperledger fabric Inflation Machine learning Money laundering Security

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ediction Model for a Good Learning Environment Using an Ensemble Approach

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computer Systems science & engineering 2023年第3期44卷 2081-2093页

作者： S.Subha S.Baghavathi Priya Department of Computer Science and Engineering Rajalakshmi Institute of TechnologyChennaiTamil NaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiTamil NaduIndia

This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification or prediction by the RF *** preprocessed data is normalized using minmax normalization often used before *** the input data or variables are measured at different scales,it is necessary to normalize them to contribute equally to the ***,the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation(k=10)is used to validate the *** proposed Prediction Model for Course Selection(PMCS)system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels,namely low,medium and *** is operated under two modes;locally and *** former considers the gender of the learner and the later does not consider the gender of the *** database comprises the learner opinions from 75 males and 75 females per category(low,medium and high).Thus the system uses a total of 450 samples to evaluate the performance of the PMCS *** show that the system’s performance,while using locally i.e.,gender-wise has slightly higher performance than the global *** RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%,whereas in the local system it is 97%(male)and 97.6%(female).The overall performance of the RF classifier with 75 trees is better than 25,50 and 100 decision trees in both local and global systems.

关键词： Machine learning ensemble learning random forest data mining prediction system

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Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques

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Multimedia Tools and Applications 2025年第16期84卷 16757-16788页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Agrawal, Akshat Rani, Uma Mohan, Anand Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering World College of Technology & Management Haryana Gurugram India Department of Physics Kunwar Singh College Bihar Darbhanga India

The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily functions. While thyroid nodules are predominantly benign, certain cases may manifest malignancy, leading to thyroid cancer. Despite the generally favorable prognosis, early detection and intervention remain paramount for optimal outcomes. Given the limitations in clinical data, this paper introduces an innovative approach employing a modified XGBoost model that integrates both clinical and molecular properties into a unified predictive model. Utilizing fine-needle aspiration biopsies and gene expression analysis, our model aims to enhance cancer prediction in thyroid nodules. We explore the influence of incorporating high-quality clinical data on the prioritized gene set, employing a nature-inspired method for hyperparameter optimization. The modified XGBoost model emerges as highly recommended for non-parametric feature selection, extraction, and prediction due to its remarkable accuracy. Our proposed model attains an impressive 94.6% accuracy, surpassing other machine learning models such as C5.0 (92.5%), CART (88.7%), CHAID (83.4%), Quest (81.5%), LSVM (77%), and random tree (24.1%). The real-time efficiency of our model is demonstrated in the results section. Experiments showcase the superior performance of combined molecular and clinical data-based prediction models. Emphasis is placed on the importance of scrutinizing the origins of clinical data and maintaining vigilance over variable quality. This study signifies a substantial advancement in thyroid cancer prediction, offering a robust and accurate model that integrates diverse data sources, ultimately contributing to improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

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computer Modeling in engineering & sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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StrideSense: Enriching Lower Extremity and Kinetics in ACLR Patients via Sonic Insights

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IEEE Internet of Things Journal 2025年第12期12卷 19549-19560页

作者： Ahmed, Awais Yang, Panlong Butt, Abdul Haleem Rizwan, Muhammad Angin, Pelin Khan, Taha Nanjing University of Information Science and Technology University of Science and Technology of China China University of Bologna Department of Biomedical and Neuromotor Sciences Italy Air University Islamabad Department of Creative Technologies Faculty of Computing and Artificial Intelligence Pakistan University of Science and Technology of China School of Computer Science and Technology China Middle East Technical University Department of Computer Engineering Turkey Detectivio AB Sweden

Tearing the anterior cruciate ligament requires repair and rehabilitation to restore lower limb functionality fully. This study outlines a method for monitoring rehabilitation after knee surgery by analyzing footstep sounds and applying deep learning techniques. The process involves examining gait sounds during the initial four weeks of rehabilitation. The suggested system, StrideSense, recognizes walking sounds, enabling seamless and ongoing monitoring of patients' gait rehabilitation. The research proposes a novel method for event detection by analyzing walking patterns using dynamic time-warping and sequential footstep duration. It leverages physiological data like gait sound and energy descriptors to develop a deep-learning model for gait improvement assessment, evaluated by a physiotherapist through lower extremity functional scores. The suggested model, AtdNet, which utilizes Densenet169 and attention mechanisms, achieves 96% accuracy in classifying walking on various post-surgical days. It also predicts lower extremity functional scores with a mean absolute error of 4.63%. A deeper analysis of bone-conducted footstep sounds enriched the acoustic sensing method. We assessed the suggested acoustic model alongside existing methods, showing that rehabilitation monitoring driven by acoustics outperforms traditional clinic-based approach assessments. Future efforts will focus on validating the model with a larger dataset and integrating it into smart homes. © 2014 IEEE.

关键词： Physiological models

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Research on Citrus Leaf Disease Recognition Using Class-Agnostic Contrastive Learning and Supervised Organizational Mapping

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IEEE Access 2025年 13卷 101592-101608页

作者： Xiao, Wen Shang, Jinxia Li, Fan Ao, Ou Wang, Xin Tian, Shiyi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Lab of Computer Technology Applications Kunming650500 China

Early detection of plant diseases is crucial for enhancing agricultural productivity and ensuring crop protection. While computer vision offers scalable alternatives to manual inspection, existing methods face two understudied challenges: (1) performance degradation in cluttered real-world conditions, and (2) ineffective feature separation for randomly distributed lesions with diverse symptoms. Notably, current research predominantly relies on controlled-environment datasets, lacking publicly accessible benchmarks with authentic field complexities. To address these, we present the first large-scale citrus leaf disease dataset (5,763 images, 6 categories) capturing complex field conditions including varying illumination, occlusion, multi-scale lesions, and deceptive backgrounds. Our key methodological innovations include a three-stage weakly supervised framework that systematically resolves background interference and feature redundancy: (i) Class-agnostic contrastive localization enabling precise lesion mapping without region annotations, (ii) Stable feature initialization through region-guided classification training, (iii) Sparse orthogonal mapping with experimentally-determined nodes introducing orthogonality constraints to enhance feature separation. Through feature alignment and feature fusion, an accuracy of 97.1% is achieved (+2.4% vs ResNet-18). Evaluations on citrus leaf and public datasets demonstrate superior accuracy and interpretable lesion localization, advancing plant disease diagnosis. © 2013 IEEE.

关键词： Citrus fruits

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A lightweight identity based mutual authenticated key agreement scheme for telecare medical information system

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Multimedia Tools and Applications 2025年 1-25页

作者： Banerjee, Ananya Sahana, Subhas Chandra Department of Computer Science & Engineering National Institute of Technology West Bengal Durgapur713209 India

The Telecare Medical information System (TMIS) faces challenges in securely exchanging sensitive health information between TMIS nodes. A Mutual Authenticated Key Agreement (MAKA) scheme is used to eliminate security flaws, ensure users’ authenticity and establish a shared session key between two communicating nodes. A Public Key Infrastructure (PKI) based traditional authenticated key agreement scheme in TMIS driven by resource-constrained devices requires extra processing power and storage space to perform different certificate management activities. This overhead can be eliminated adopting Identity-Based Cryptography. We propose a mutual authenticated key agreement scheme based on Identity-Based Cryptography (IBC) incorporating elliptic curves which make the scheme not only computation cost efficient but also communication cost efficient. The proposed scheme is secure against all attacks related to a key agreement protocol. Comparative analysis with existing authentication schemes and formal security simulation using the AVISPA toolset demonstrate that the proposed scheme outperforms similar types of existing established schemes in the TMIS literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Electronic health record

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