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IEEE International Conference on Blockchain (Blockchain)

作者： Chengzu Dong Shantanu Pal Qi An Aiting Yao Frank Jiang Zhiyu Xu Jianhua Li Meiqu Lu Yangxu Song Shiping Chen Xiao Liu School of Information Technology Deakin University Melbourne Australia School of Computer Science and Technology Anhui University Hefei China Department of Engineering Technologies Swinburne University of Technology Melbourne Australia School of Artificial Intelligence Guangxi Minzu University Nanning China CSIRO Data61 Sydney Australia

This paper presents a novel architecture that utilizes the Zero Trust Principle to facilitate blockchain-enabled Unmanned Aerial Vehicle (UAV) delivery systems, emphasizing recipient privacy, verification, and continuous authentication. By integrating blockchain’s decentralization and transparency with a Zero Trust model, the architecture ensures robust security and access control. The blockchain-based architecture guarantees data integrity and resilience, while advanced cryptographic techniques, including homomorphic encryption, ensure long-term security. Ongoing security assessment and improvement are facilitated through the maturity model’s integration with blockchain technology. This innovative approach effectively mitigates cyber risks, safeguards recipient data privacy, it initiative promotes the secure and efficient use of UAVs in logistics. It emphasizes the importance of maintaining strict access controls and trust boundaries. Through adaptable security evaluations, the model addresses evolving threats, ensuring a secure environment for UAV integration into the modern supply chain while prioritizing recipient privacy and security.

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Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning

Enhancing Energy Efficiency in Wireless Sensor Networks usin...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： Sandeep Raskar Gulshan Dhasmana M. Lakshminarayana Harshal Patil Bhagya Shree S Natrayan L Department of Artificial Intelligence and Data Science Terna Engineering College Nerul Navi Mumbai Maharashtra India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Bengaluru Karnataka India School of Computer Science & Engineering IILM University Greater Noida Uttar Pradesh India Department of Physics Brindavan College of Engineering Bengaluru Karnataka India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations.

关键词： Measurement Wireless sensor networks Translation Scalability Deep reinforcement learning Routing Energy efficiency Real-time systems Synchronization Optimization

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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Computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

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Identifying Sources of Soil Pollution in Industrial Areas Using IoT data and Neural Networks

Identifying Sources of Soil Pollution in Industrial Areas Us...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Rajesh Kambattan Kovarasan Dr N V Shibu A. Sairam K. K. Manivannan M.V Ishwarya T. R. GaneshBabu Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering KCG College of Technology Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IOT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns.

关键词： Technological innovation Pollution Environmentally friendly manufacturing techniques Neural networks Pollution control Soil Internet of Things Stakeholders Soil pollution Contamination

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ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving

arXiv

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arXiv 2025年

作者： Feng, Renju Xi, Ning Chu, Duanfeng Wang, Rukang Deng, Zejian Wang, Anzheng Lu, Liping Wang, Jinxiang Huang, Yanjun Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China Department of Data and Systems Engineering The University of Hong Kong 999077 Hong Kong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Mechanical Engineering Southeast University Nanjing211189 China School of Automotive Studies Tongji University Shanghai201804 China

—This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods suffer from error propagation, while existing end-to-end models typically employ static one-shot inference paradigms that inadequately capture the dynamic changes of the environment. ARTEMIS takes a different method by generating trajectory waypoints sequentially, preserves critical temporal dependencies while dynamically routing scene-specific queries to specialized expert networks. It effectively relieves trajectory quality degradation issues encountered when guidance information is ambiguous, and overcomes the inherent representational limitations of singular network architectures when processing diverse driving scenarios. Additionally, we use a lightweight batch reallocation strategy that significantly improves the training speed of the Mixture-of-Experts model. Through experiments on the NAVSIM dataset, ARTEMIS exhibits superior competitive performance, achieving 87.0 PDMS and 83.1 EPDMS with ResNet-34 backbone, demonstrates state-of-the-art performance on multiple metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Advances in Clustering Algorithms for Large-Scale data Processing with AI

Advances in Clustering Algorithms for Large-Scale Data Proce...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sonal Sharma Vandana Singh Jeyalaxmi M Prasad B. Chaudhari Neeraj Das Avni Garg Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

Clustering is a widely used technique in statistics mining for exploring and reading records with AI. It is an unsupervised gaining knowledge of technique, which may be used for exploratory facts evaluation. Clustering primarily aims to group similar statistics factors in a given facts set. In recent years, diverse advances in clustering algorithms have been developed for massive-scale record processing with AI. Some of the maximum popular clustering algorithms are ok-means, hierarchical clustering, fuzzy clustering, and spectral clustering. This kind of algorithm was used with AI for massive-scale records processing. Okay-approach and hierarchical clustering algorithms were widely used in diverse information mining responsibilities. They permit one to discover and organize the maximum similar statistics points. Fuzzy clustering and spectral clustering algorithms have become famous recently because of their capability to duplicate human perception and questioning when clustering records. Lately, deep learning techniques have been used with clustering algorithms to enhance their overall performance and boom the accuracy of records evaluation. Deep mastering is a subset of machine mastering which allows computers to research from repeated exposure to big datasets robotically. It permits clustering algorithms to investigate big-scale facts faster and more as it should be. Clustering with AI is an effective method for processing and analyzing huge-scale records. Recent advances in clustering algorithms and deep mastering strategies have improved performance and accuracy. Moreover, these algorithms may be used in diverse applications, which include client segmentation, community evaluation, and anomaly detection.

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LG-Umer: UNet-like Network Integrate Local-Global Feature with Novel Attention for Road Extraction from Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Niu, Penghui Cai, Taotao Zhang, Yajuan Zhang, Ping Xu, Wenjia Gu, Junhua Han, Jungong Hebei University of Technology School of Artificial Intelligence Tianjin300401 China University of Southern Queensland Toowoomba487- 535 Australia Hebei Prospecting Institute of Hydrogeology and Engineering Geological Shijiazhuang050021 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China University of Sheffield Department of Computer Science S10 2TN United Kingdom

Road extraction from remote sensing images (RSIs) is a key research area in smart city development. While deep learning techniques have demonstrated remarkable effectiveness in this domain, existing approaches exhibit limitations: convolutional neural network (CNN)-based methods struggle to capture global contextual information for long-range road networks, vision transformer (ViT)-based methods fail to adequately extract multi-scale local features, and hybrid CNN-ViT architectures overlook the synergistic guidance between local and global features. To address these challenges, we propose LG-Umer, a UNet-like network that integrates Local-Global features with a novel attention mechanism, combining the complementary strengths of CNNs and ViTs within an encoder-decoder framework. Specifically, the encoder employs a Multi-scale Strip Deformational (MSD) module, which utilizes deformable convolutions to adaptively extract topological structures and variable-shaped local road features. In the decoder, a Multi-stage Gate Unit (MGU) module is introduced, incorporating a novel attention mechanism to model long-range dependencies by leveraging local features as attention operators for global feature refinement. Extensive experiments on three public benchmarks demonstrate the superiority of LG-Umer. It achieves IoU scores of 70.4%, 71.2% and 68.7% on the Massachusetts Road, DeepGlobe Road, and CHN6-CUG datasets, respectively, surpassing recent state-ofthe-art (SOTA) methods by 1.2%, 0.9%, and 1.1%. These results validate the effectiveness of our approach in balancing local detail preservation and global contextual modeling for road extraction tasks. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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An Analysis and Review on Batteryless Solutions for Handheld Devices using IoT

An Analysis and Review on Batteryless Solutions for Handheld...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Revathi R P Shanmugha Priya R K Chandrasekaran Raja Gowri S S Kaliappan Vinoth Kumar K Department of Applied Sciences New Horizon College of Engineering Bengaluru India Department of Information Technology Jai Shriram Engineering College Tirupur India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Artificial Intelligence & Data Science Sri Eshwar College of Engineering Coimbatore India Division of Research and Development Lovely Professional University Phagwara India Department of EEE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Batteryless Solutions refer to the elimination of the use of primary non-rechargeable batteries and replacing them with any kind of sustainable alternatives that combines circuits with long lasting rechargeable batteries or with alternative energy storage systems that provide a less polluting and a much eco-friendlier alternative. There is no specific limit for batteryless solutions and it just involves innovation and improvisation on existing rechargeable battery technologies and alternative energy storage systems so that they are long lasting, use fewer toxic chemicals as raw material, have good energy densities and are compatible with renewable energy sources like solar and wind and should also offer compatibility to hybridization and combination with other sources and they should have the necessary protection features integrated for smoother running. This mini project aims at building two prototypes which can interface Lithium-Ion batteries and Supercapacitors with power electronics and other charging circuits so as to act as a source of energy.

关键词： Lithium-ion batteries Technological innovation Renewable energy sources Social networking (online) Reviews Circuits Toxic chemicals Prototypes Supercapacitors Raw materials

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Malicious Traffic Classification in WSN using Deep Learning Approaches

Malicious Traffic Classification in WSN using Deep Learning ...

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Communication, Security and artificial intelligence (ICCSAI), International Conference on

作者： Chhote Lal Prasad Gupta Dinesh Rajassekharan Dilip Kumar Sharma Mohanraj Elangovan Varatharaj Myilsamy Kamal Upreti Dept. of Computer Science and Engineering Bansal Institute of Engineering and Technology Lucknow India Faculty of Computer Science Peninsula College Peninsula Malaysia Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science K. S. Rangasamy College of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST(Deemed to be University) Delhi-NCR Ghaziabad India

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data.

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