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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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AttentionFaultFormer: An attention-enhanced 3D CNN & transformer model for seismic fault detection

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Journal of Applied Geophysics 2025年 238卷

作者： Wang, Jing Ma, Siteng Liu, Yue Dong, Ruihai School of Computer Science and Engineering North China Institute of Aerospace Engineering China The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland

In seismic exploration, accurately identifying faults is fundamental for interpreting seismic data. Due to the highly sparse distribution of faults in the seismic volume and the heavy reliance on local features to reconstruct fault segmentation masks, predicting faults using 3D Vision Transformers (ViT) remains challenging. We propose a novel attention-enhanced 3D U-shaped CNN and ViT hybrid model, called AttentionFaultFormer, specifically for fault detection tasks. AttentionFaultFormer includes a transformer-based encoder, a residual-based decoder, and attention-enhanced skip connection. The skip connection integrates local feature extraction from convolutional layers with channel and spatial attention mechanisms to enhance the representation of detailed fault features. Furthermore, based on the geometric structure of faults, we designed the Multi-Axis Striped Convolution Attention (MASCA) module and incorporated it into the shallow skip connections to help the model delineate continuous long faults. We applied our model to three field seismic datasets (F3, Kerry3D, and Thebe). Comparative analysis against two CNN models and two ViT models reveals that our model predicts more continuous and interpretable faults. © 2025

关键词： Attention mechanism CNN Fault detection Seismic interpretation Vision transformer

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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computer Modeling in engineering & sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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A Study of Multimodal Identification Methods 6

A Study of Multimodal Identification Methods

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6th International Conference on computer Network, electronic and Automation, ICCNEA 2023

作者： Feng, Jianjian Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an710021 China

ISBN: (纸本)9798350305388

The purpose of this paper is to investigate unimodal determination and decision layer fusion methods in multimodal face recognition and to evaluate the effectiveness of decision layer fusion. Experiments are conducted on the "VidTIMIT"and "MOBIO"datasets using convolutional neural networks and MFCC feature extraction techniques. The performance of three feature fusion methods, front-end fusion, intermediate fusion and decision layer fusion, is compared. The experimental results show that the recognition accuracies are 94.9%, 94.7% and 94%, 94.1% for the face and voice models only, respectively. However, the accuracy decreases slightly in the case of front-end fusion. The best accuracy was achieved when the decision layer fusion method was used, reaching 95.7% and 95.6%, respectively. This indicates that training face and voice print data separately and fusing classifier output scores can effectively improve face recognition accuracy. This study explores feature fusion methods in the field of multimodal face recognition and experimentally demonstrates the advantages of decision layer fusion in improving accuracy, pointing to a new direction for future research. © 2023 IEEE.

关键词： Face recognition

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ParaBFT: An Efficient Parallel Asynchronous BFT Agreement

ParaBFT: An Efficient Parallel Asynchronous BFT Agreement

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lan, Yu Huang, Hui He, Qingming Wu, Caixia Wu, Shuaike Kong, Xiangzeng Minnan Normal University School of Computer Science Zhangzhou China Guangxi University School of Mathematics and Information Science Nanning China Fuzhou Institute of Technology School of Electronic Engineering Fuzhou China Fujian Agriculture and Forestry University College of Mechanical and Electrical Engineering Fuzhou China

ISBN: (纸本)9798350386226

With the increasing need for safeguarding confidential and secure healthcare information, Blockchain has become a pioneering approach to managing medical data due to its decentralized structure, resistance to tampering, and high level of transparency. Protocols for Asynchronous Byzantine Fault Tolerance (BFT) are fundamental for maintaining the depend-ability and uniformity of distributed systems. Nonetheless, these mechanisms still face challenges in terms of performance and efficiency. This work introduces an efficient parallel asynchronous BFT agreement (ParaBFT). First, we replace the traditional ABA with MVBA, enabling the processing of multiple values or proposals in each round of decision-making, which significantly enhances decision-making efficiency. Then, to optimize the message processing flow, ParaBFT incorporates a First-In-First-Out (FIFO) queue as a buffer pool, ensuring the simplicity and fairness of the processing logic. Finally, our experimental analysis reveals that ParaBFT offers enhanced throughput and reduced latency for managing large-scale transactions. © 2024 IEEE.

关键词： Blockchain

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Machine Learning Model for Sentimental Analysis of Amazon Reviews

Machine Learning Model for Sentimental Analysis of Amazon Re...

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electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari R.J. Pratishwaran M. Poojitha Reddy R. Yuvan Sankar Raj Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comments. Since there is such a massive amount of data on the Amazon platform, it was perfect for the analysis. Our methodology is platform-agnostic and can be easily extended to other comparable datasets from other web platforms with comparable guarantees in achieving wide applicability. Our primary motivation is improving the dependability of online shopping. Since product reviews are currently widely used by many consumers as guides in making decisions to buy products, such reviews ought to be fair representations of the actual dependability and quality of the products. The goal of our effort is to identify patterns in the language and ratings of such reviews that would directly reflect the real customer sentiment and also the reliability of the product. Our approach has yielded already a rate of about 92%, which shows it has the potential greatly to raise the legitimacy of online reviews. We'd like to make our customers feel more confident and better equipped with information to make better and safer purchases on a website like Amazon and others. In the final analysis, we strive to make online shopping for our customers worldwide safer and more trustworthy.

关键词： Analytical models Sentiment analysis Reviews Stacking Stars Predictive models Boosting Ensemble learning electronic commerce Random forests

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Fast Moving Consumer Goods Object Detection Using YOLOV8

Fast Moving Consumer Goods Object Detection Using YOLOV8

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electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Umamageswari Mukund. P.U Abhishek. V.B Y. Devi Vaishnavi Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering With Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management; together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability.

关键词： YOLO Training Accuracy Scalability Refining Streaming media Logic gates Real-time systems Mobile handsets Monitoring

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： M.S. Minu Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system’s scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce.

关键词： Text mining Machine learning algorithms Scalability Systems architecture Machine learning Chatbots User experience Real-time systems electronic commerce Time factors

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Adaptive Pixel Pair Evaluation Method for Image Matting 15

Adaptive Pixel Pair Evaluation Method for Image Matting

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15th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Huang, Qiping Liu, Yi Feng, Fujian Liang, Yihui School of Computer Science and Technology Guangdong University of Technology Guangzhou510000 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Image matting is an ill-posed problem that aims to extract the opacity of foreground objects in an image. Pixel-pair-optimization-based (PPO-based) image matting approaches are widely adopted in natural image matting, whereby the alpha value is estimated by choosing the optimal pixel pair according to a pixel pair evaluation (PPE) function. Multiple PPE criteria are employed to improve the accuracy of PPE, resulting in the weight setting problem of PPE criteria. Existing PPE functions use fixed weight PPE criteria, which cannot provide the accuracy of PPE on the little transparent images due to the satisfaction degree of PPE criteria related to the type of the image. To address this shortcoming, in this work, an adaptive weight criteria PPE method is presented, which adaptively adjusts the contribution of chromatic distortion and spatial closeness criteria to the PPE function by analyzing the type of the image. Experimental results show that the proposed adaptive weight criteria PPE method provides accurate PPE compared with existing PPE methods, especially on the little transparent Images. © 2022 IEEE.

关键词： Pixels

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Microsphere-assisted super-resolution polarized light microscopy for polarization-sensitive materials

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Optics Letters 2025年第3期50卷 908-911页

作者： Ye, Ran Zhang, Xiangyu Zhi, Lingjie Cao, Yurong Melinte, Sorin Wang, Zengbo Yang, Songlin School of Computer and Electronic Information Nanjing Normal University Nanjing210046 China Institute of Information and Communication Technologies Electronics and Applied Mathematics Université Catholique de Louvain Louvain-la-Neuve1348 Belgium School of Computer Science and Engineering Bangor University BangorLL57 1UT United Kingdom

Polarization sensitivity is a fundamental phenomenon observed in nature, and its application is vital for advancing scientific discoveries. Here, we present a microsphere-assisted polarized light microscopy method that enables high-quality, label-free super-resolution imaging of polarization-sensitive materials. We investigated the imaging performance of this method using various samples, including colloidal crystals and grating structures. Both simulations and experiments were performed to explore the underlying physical mechanisms driving the enhancement in imaging quality. The results of this work have potential values for the fields of polarized light microscopy and super-resolution imaging. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Microspheres

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