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FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Correlation leakage analysis based on masking schemes

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science China(Information sciences) 2022年第2期65卷 243-245页

作者： Jiawei ZHANG Yongchuan NIU An WANG Data Communication Science and Technology Research Institute School of Computer Science Beijing Institute of Technology

Dear editor,Masking is generally utilized to construct the first-order protection for cryptographic algorithms,but such protected designs are still susceptible to higher-order power analysis ***-order differential power analysis (DPA)[1–4]can break first-order masking countermeasures by combining the leakages of the two secret shares into a signal that is correlated with the target intermediate variable.

关键词： Correlation leakage analysis Correlation leakage model Second-order analysis Side-channel attack Masking schemes

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Classification recognition for traffic dangerous driving behavior

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第SPEICAL ISSUE期1卷 115-124页

作者： Yang, X.W. School of Information and Communication Engineering Shanxi Electronic Science and Technology Institute Linfen041000 China School of Data Science and Technology North University of China Shan Xi Taiyuan030051 China

The precise classification recognition of dangerous driving behaviors can effectively reduce traffic accidents and improve road safety. To address the challenges of complex and dynamic traffic environments where driving behaviors can vary significantly’ an innovative method for classification recognizing for dangerous driving behaviors has been developed’ utilizing an enhanced k-means clustering algorithm. Wavelet decomposition is aPPlied to denoise the image data of driving behaviors. The refined data is then effectively categorized using the improved k-means clustering technique to discern distinct driving actions. Convolutional neural networks are employed to extract and classify critical action areas’ enabling precise identification of hazardous driving behaviors. Test results demonstrate that this designed method achieves a maximum classification recognition rate of 98% for dangerous driving behaviors’ with shorter recognition processing time. © 2024, Aracne Editrice. All rights reserved.

关键词： K-means clustering

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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Empowering Tomorrow's Cities: Navigating Power Challenges through Deep Learning Solutions 8

Empowering Tomorrow's Cities: Navigating Power Challenges th...

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8th International Conference on Electronics, communication and Aerospace technology, ICECA 2024

作者： Priyadarsini, K. Karthik, S. Tamilarasi, R. Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Srm Institute of Science and Technology Vadapalani Campus Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

This review, titled 'Empowering Tomorrow's Cities: Navigating Power Challenges through Deep Learning Solutions,' explores the intersection of power management issues and the trans-formative potential of deep learning technologies. It addresses the growing challenges of energy consumption, grid reliability, and peak demand, emphasizing deep learning's vital role in optimizing power usage. The article provides a detailed overview of smart cities, highlighting the importance of efficient power management for their continued development. By presenting deep learning as a groundbreaking solution, it examines its applications in real-time monitoring, predictive analytics, and demand forecasting. Through case studies, the review showcases successful deep learning implementations in tackling specific power challenges. It also explores the integration of IoT with deep learning, emphasizing their synergy in advancing data-driven decision-making. Critical aspects like data security and privacy are discussed, ensuring a thoughtful approach to managing sensitive data for optimized power distribution. Looking ahead, the article envisions future trends and innovations, stressing the need for ongoing research to fully harness deep learning in addressing power challenges in smart cities. © 2024 IEEE.

关键词： Contrastive Learning

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Beyond pH Levels: A Comprehensive Survey on Ocean Acidification 2

Beyond pH Levels: A Comprehensive Survey on Ocean Acidificat...

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2nd International Conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Ramdas, Rithvik Patnaik, Balivada Srinivas Parmar, Ansh Tadepalli, Satya Kiranmai Vasanth, K. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798350328202

This survey explores the transformative role of machine learning in addressing the pervasive threat of ocean acidification on marine ecosystems. Focusing on its predictive capabilities, the paper examines machine learning's efficacy in forecasting pH trends, pinpointing vulnerable species, and assessing broader ecosystem impacts. By training on historical data, machine learning models offer valuable insights for identifying at-risk regions, prioritizing conservation efforts, and optimizing fisheries management. The survey accentuates the integral role of machine learning in unraveling the complexities of ocean acidification while acknowledging challenges like data quality and model robustness. Beyond its theoretical implications, the paper underscores the pragmatic application of machine learning in formulating effective mitigation and adaptation strategies. In doing so, it not only advances understanding of ocean acidification but also sets a roadmap for future research, emphasizing the need for refined data, robust models, and seamless integration of research outcomes into practical solutions for marine ecosystem preservation. Keywords: machine learning, ocean acidification, pH trends, marine ecosystems, vulnerability assessment, conservation strategies, adaptation planning. © 2024 IEEE.

关键词： Acidification

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Enhancing data sovereignty to improve intelligent mobility services in smart cities

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Urban Governance 2025年第1期5卷 20-31页

作者： Anthony, Bokolo Sarshar, Sizarta Department of Applied Data Science Institute for Energy Technology Halden 1777 Norway Department of Computer Science and Communication Østfold University College Halden Norway Department of Risk and Safety Institute for Energy Technology Halden 1777 Norway

Smart cities aim to provide more digitalized, equitable, sustainable, and liveable cities. In smart cities data evolves as an important asset and citizens data in particular is being used to provide data-driven mobility services. Likewise, in smart cities data is produced and used by individuals having no ownership or control over these data thus affecting their security and privacy. Also, actors such as mobility service providers, companies, third parties, etc. often leverage individuals’ data without regarding users’ autonomy. Accordingly, data sovereignty which entails the possibility to govern and keep control over owned data is gaining widespread attention. As a result, stakeholders in the mobility sector need to share data whilst keeping control to manage data access and usage. Although, existing initiatives are promoting sovereign participation using a federated infrastructure. However, achieving an effective governance usage control and access mechanisms can be challenging especially in the transportation sector. It is thus imperative for individuals to have control, ownership, and custody over their data. This has necessitated the notions of data sovereignty. Therefore, this article employs a systematic review to examine how data sovereignty can be achieved for individuals when they use urban mobility services in smart cities by enabling the secure sharing of data by giving control of data over to individuals. Additionally, this article designs a data control scheme that can be applied to realize data sovereign mobility services. Evidence from this study provides technical and non-technical requirements needed in realizing data sovereignty in smart cities. © 2025 The Author(s)

关键词： data access mechanisms data governance, data usage control data sovereignty data-driven mobility services Intelligent mobility services Smart cities

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Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Lv, Ying Liu, Zhi Li, Gongyang Chang, Xiaojun Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Engineering and Information Technology University of Technology Sydney Australia

Freespace detection holds a crucial role in autonomous driving technology, particularly in unstructured offroad scenarios that present additional challenges compared to structured road environments. Multimodal fusion methods are widely recognized as effective strategies for addressing these challenges. However, current fusion methods often overlook the critical issue of noise interference in multimodal data. To address this issue, we propose a novel Noise-Aware Intermediary Fusion Network for off-road freespace detection, named NAIFNet. This framework is specifically designed to mitigate noise interference during multimodal fusion. The key component of NAIFNet is the Noise-Aware Intermediary Interaction (NAII) module, which incorporates a denoising template as an intermediary during the fusion process. The NAII module employs multimodal features as query vectors, while the key and value vectors are derived from the search region features of the denoising template. At the same time, noise-aware interaction ensures effective denoising for data in each modality. Furthermore, in the decoding phase, we introduce the Denoising-Guided Decoder (DGD). Leveraging the denoising template, this decoder achieves more precise feature restoration and effectively mitigates the impact of noise. Extensive experiments on the popular benchmark of the offroad freespace detection dataset (ORFD) demonstrate that the proposed NAIFNet achieves state-of-the-art performance. IEEE

关键词： Noise abatement

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