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检索条件"任意字段=International Conference on Computer Engineering and Technology"

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Analyzing the Effect of Eco-Friendly Branding on Consumer Purchasing Decisions on Sustainability in Marketing using Data Analytics 3

Analyzing the Effect of Eco-Friendly Branding on Consumer Pu...

引用

3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mageswaran, J. Tamilselvan, P. Tamilarasu, J. Sivasubramani, K. Devi, Tharani Sah, Raja Ram School of Management Studies Bannari Amman Institute of Technology Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu India Nandha Engineering College Department of Management Studies Tamil Nadu India M.Kumarasamy College of Engineering Department of Management Studies Tamil Nadu India Gobi Arts and Science College Department of Management Studies Tamil Nadu India Government Engineering College Department of Computer Science and Engineering Bihar Jehanabad India

ISBN: (纸本)9798331542375

Background: Consumer buying behaviour is increasingly influenced by eco-friendly branding, thus sustainable practices are starting to take front stage in contemporary marketing plans. Companies under increased demand to link their brands with sustainable ideas as consumers become more conscious of the effects they have on the environment. Contribution: This study investigates how environmentally friendly branding influences purchasing decisions keeping customer attitudes as well as behaviour changes in mind. Findings: A comprehensive survey was given to 1,500 persons total throughout the duration of the research and statistical analysis was done with assistance from a simulation tool in order to evaluate changes in consumer sentiment and purchase behaviour. Results: Based on the findings, 48 percent of the participants were ready to pay extra for environmentally friendly products and 72 percent showed an increased tendency to buy from companies deemed to be such. Moreover, sixty-four percent of respondents claimed that environmental regulations greatly affected their brand loyalty. These findings emphasise the significance of adding sustainability into marketing plans to establish confidence in a company and to establish long-term ties with contented consumers. © 2025 IEEE.

关键词： Environmental regulations

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NewsCap: Audio-guided Context-aware Network for News Video Captioning 24

NewsCap: Audio-guided Context-aware Network for News Video C...

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Zeng, Zijian Zheng, Yue Li, Yali Wang, Shengjin Beijing National Research Center for Information Science and Technology Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798400717482

As a task connects computer vision and natural language processing, video captioning aims to generate natural language descriptions of video content. Because of the rapid development of deep learning, recent research has greatly promoted the progress in video captioning. Due to the limitations of datasets and application scenarios which mainly focus on the visual-centric content, most methods concentrate on how to better extract visual information, while ignoring other modal information such as audio. However, in our view, these methods may be limited in the news scenario. Because in addition to visual information, news videos are often accompanied by audio information, such as voice, background music or ambient sounds. These audio elements provide an additional modality for the semantic description of videos, which makes captions richer and more comprehensive. In this paper, we propose a method called Audio-guided Context-aware Network for news video captioning (NewsCap). It consists of two modules named context-aware module which considers comprehensive audio information and audio-guided module which exploits audio information to guide the generation of captions. We conduct extensive experiments on News-11k, a multi-modal video captioning dataset based on news video, and results demonstrate the effectiveness and priority of NewsCap, which outperforms the existing methods by 2.3 in CIDEr-D. © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learning Unet for Cloud Detection

GEMD-UNet: Graph Structure Enhanced Multi-dimensional Learni...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Jianing Chen, Chuhao Yang, Junze Li, Wei Yadav, Rahul Zheng, Wenqi College of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Cloud detection (CD) in remote sensing images is commonly used in satellite imaging and laser communication. UNet-based methods with multi-level feature caching and interaction learning, are popular for superior CD performance. However, most current CD methods focus on spatial feature enhancement through global feature learning, which often overlooks the detailed perception of color, shape, and boundaries due to insufficient channel interactions. Additionally, these methods tend to increase computational requirements. To ensure flexible feature attention, enhancing both structural and detailed understanding, while maintaining low computational complexity. In this paper, we propose Graph Structure Knowledge Enhanced Multi-Dimensional Learning U-shaped Network (GEMD-UNet) for CD. Specifically, we utilize tensor dimension transformations to cleverly employ two fundamental learnable parameters alternately for spatial/channel relationship learning. Furthermore, to enhance feature knowledge capacity and focus more on local detail, we employ local window partitioning to divide hidden features into multiple independent learning groups and inject two different physically meaningful graph structures knowledge in a non-Euclidean space into features. Experimental results demonstrate that GEMD-UNet outperforms previous SOTA methods on both HRC-WHU and L8SPARCS datasets. Code can be available at: https://***/mxx123321/cloud_model. © 2025 IEEE.

关键词： Cloud detection (CD) Deep Learning Graph Neural Network UNet

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Semi-Circular Microchannel Embedded in D-Shaped Photonic Crystal Fiber Plasmonic Biosensor with Improved Sensitivity

Semi-Circular Microchannel Embedded in D-Shaped Photonic Cry...

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international conference on Electrical, computer and Communication engineering (ECCE)

作者： Anik Saha Tanvir Ahmed Department of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This study describes a semi-circular microchannel-equipped modified D-shape PCF SPR biosensor, designed to recognize a vast span of refractive indices in unidentified analytes. The technique of finite elements is applied to numerical analysis to fully examine the sensor’s optical *** gold-based plasmonic sensor exhibits a refractive index detection range of 1.33–1.44, enabling sensitive analysis of different samples. It exhibits a remarkable sensitivity to wavelengths at 72,000 nm/RIU and a maximum amplitude sensitivity of 3975 RIU −1 . In addition, the sensor has a noteworthy figure of merit and sensor length, rendering it the ideal choice for the recognition of different analytes in living systems and biochemicals.

关键词： Sensitivity Photonic crystal fibers Refractive index Plasmons Rendering (computer graphics) Surface plasmon resonance Finite element analysis Structural engineering Biosensors Microchannels

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FedHKD: A Hierarchical Federated Learning Approach Integrating Clustering and Knowledge Distillation for Non-IID Data 1

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2nd international conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Hu, Shiwen Wang, Changji Li, Yuan Liu, Zhen Liu, Ning Gan, Qingqing School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Guangdong Engineering Research Center of Data Security Governance and Privacy Computing Guangzhou China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Federated learning allows decentralized model training while preserving data privacy. However, Non-IID data poses significant challenges, leading to performance degradation and increased communication overhead. This paper introduces FedHKD, a hierarchical FL algorithm that integrates client clustering and knowledge distillation to address Non-IID challenges. By clustering clients with similar data distributions and transferring knowledge across clusters, FedHKD improves model accuracy while reducing communication costs. Experimental results on Fashion-MNIST and CIFAR-10 datasets demonstrate up to 14.24% improvement in accuracy and a substantial reduction in communication overhead, proving its effectiveness in Non-IID environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Weakly Aligned Multi-spectral Pedestrian Detection via Cross-Modality Differential Enhancement and Multi-scale Spatial Alignment 27th

Weakly Aligned Multi-spectral Pedestrian Detection via Cros...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Shao, Zhenzhou Chen, Yongxin Zou, Yibo Zhang, Jie Guan, Yong The College of Information Engineering Beijing Key Laboratory of Light Industrial Robot and Safety Verification Capital Normal University Beijing100048 China The School of Optoelectronic Science and Engineering Soochow University Suzhou China The College of Information Science and Technology University of Chemical Technology Beijing China

ISBN: (纸本)9783031781124

Multi-spectral pedestrian detection has attracted extensive attention in recent years. In particular, the combination of RGB and thermal infrared images allows the around-the-clock applications, even in the poor illumination conditions. Considering the fact that RGB and thermal infrared (RGB-T) image pairs are not well aligned, it leads to the inaccuracy of pedestrian detection. To this end, this paper proposes a Multi-scale Alignment and Differential Enhancement Network (MADENet) for multi-spectral pedestrian detection, consisting of Cross-Modality Differential Enhancement Module (CDEM) and Multi-scale Spatial Alignment Module (MSAM). CDEM module is embedded in the backbone to suppress the redundant features and extract complementary information between modalities, and MSAM module is designed to align the RGB-T features by the transformation of thermal features using features of RGB image as the reference. The proposed network is evaluated on the public KAIST dataset across different scenarios. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. Miss rate using all test set can reach 8.01. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Potential of Vision Transformer in Leukemia Detection 3

Potential of Vision Transformer in Leukemia Detection

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ahmed, Rayan Nadeem, Laraib Shafique, Maryam Saleem, Zameera Iqbal, Ayesha Iqbal, Hafsa National University of Sciences and Technology (NUST) School of Electrical Engineering and Computer Science (SEECS) Islamabad Pakistan Shaukat Khanum Memorial Cancer Hospital & Research Center Lahore Pakistan

ISBN: (纸本)9798331523893

Leukemia, a bone marrow-related disease, arises from the abnormal proliferation of cells. It is the predominant form of childhood cancer, representing about one-third of cases annually. This study focuses on incorporating AI models to enhance the early detection of Leukemia. A public dataset is employed for testing purposes which comprises of 15,114 images in total. Advancing the field, this research employs vision transformer techniques, previously unexplored in leukemia detection, showing promising efficiency for such applications. Emphasizing the critical role of precision in medical imaging, particularly in the context of Leukemia detection, this study contributes to the ongoing efforts to improve the diagnostic accuracy and patient outcomes. © 2025 IEEE.

关键词： artificial intelligence automated diagnosis image analysis leukemia Machine learning medical imaging vision transformer

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IOT Based Multicrop Soil Moisture Control and Monitoring System

IOT Based Multicrop Soil Moisture Control and Monitoring Sys...

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AIML-Applications for engineering & technology (ICAET), international conference on

作者： Madhusudan Barhate Devam Desai Gaurav Mishra Gargi Phadke Siuli Das Supriya Yogesh Bhuran Dept. of Instrumentation Engg Dept. of Computer Engg Dept. of Computer Science and Engg Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Navi Mumbai India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Agriculture is crucial for the economy, with China and India leading in production. Conventional irrigation methods require precise timing and quantity, varying for different crops. This paper proposes an IoT-enabled intelligent irrigation system to enhance crop productivity. The system uses a moisture sensor to activate the pump motor, and IoT technology enables remote monitoring through a web interface or mobile app. The system stores moisture data customized to specific crop requirements, reducing human intervention while ensuring optimal water supply.

关键词： Productivity Irrigation Soil moisture Moisture Crops Motors Control systems Timing Mobile applications Remote monitoring

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Egyptian Hieroglyphics Handwriting Character Classification Using CNN with Explainable AI: SHAP and LIME

Egyptian Hieroglyphics Handwriting Character Classification ...

引用

international conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir M D. Shaek Ahmed Nurul Bashar Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Egyptian hieroglyphics, one of the oldest writing and human communication systems. In our modern life, it's challenging to interpret this language due to its complex visual symbols. In this study, we propose a Convolutional Neural Network (CNN) model for classifying Egyptian hieroglyphic handwriting characters. The dataset provides 18 different class of handwritten hieroglyph character images. To enhance model interpretability, we apply explainable AI techniques, specifically SHAP and LIME, to identify regions that influence model predictions. The results that our custom CNN model achieves train accuracy 90.62%, validation accuracy 88.25%, and test accuracy of 84.5%, with specific characters showing high classification performance. Also, AUC score reflects 0.99 to 1.00 for each of the classes.

关键词： Support vector machines Visualization Accuracy Translation Explainable AI Computational modeling Symbols Writing Predictive models Convolutional neural networks

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Venue crowd recognition in dense smoke environment: a recognition method based on attention mechanism fusion segmentation instances 4

Venue crowd recognition in dense smoke environment: a recogn...

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2024 4th international conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Sun, Xu Liu, Yuan Qie, Kun Wang, Jianyu Wang, Baohui Chang, Chengkai Sun, Ao Beijing Laboratory of General Aviation Technology Beijing University of Civil Engineering and Architecture Beijing100044 China YICUN Transportation Shaanxi Xixian New Area 712000 China

ISBN: (纸本)9781510686304

In densely populated regions, the identification of evacuating pedestrians remains a significant challenge, with profound implications for disaster management and public safety. Here, we present a novel approach to identifying evacuating pedestrians under such challenging conditions. Our method integrates attention mechanisms with segmentation techniques to accurately identify individuals amidst the dense smoke and haze of fires. Through extensive experimentation and validation, we demonstrate the robustness and effectiveness of our approach in real-world scenarios. This study not only advances the field of pedestrian recognition but also provides valuable insights for improving emergency response strategies in fire-affected areas. We propose a computer vision approach by augmenting target pedestrian recognition with a Mask branch on top of object detection algorithms, utilizing channel attention mechanisms to enhance target pedestrians in the environment while reducing the weighting of the environment in images. We introduce a YOLOv8-Mask model which effectively enhances pedestrian recognition in hazy scenes typical of fire-smoke environments, facilitating efficient rescue operations for trapped individuals. Finally, comparative experiments conducted on self-collected datasets with five established object detection models demonstrate the superiority, robustness, and effectiveness of our proposed model in crowd recognition under such hazy conditions and real-world scenarios. The research outcomes presented herein contribute to assisting rescue efforts in densely populated venues amidst fire-smoke conditions, thereby providing technological support for the development of resilient *** model of this study has been made available for public access and can be found on this website https://***/xifangfff/YOLOv8Mask/tree/main © 2025 SPIE.

关键词： Risk assessment

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