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共 33538 条记录，以下是41-50 订阅

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State Monitoring and Fault Diagnosis of Power Internet of Things Devices based on Artificial intelligence 1

State Monitoring and Fault Diagnosis of Power Internet of Th...

引用

1st international conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Yao, Weiqing Luo, Xian Li, Xiang Hu, Bingyao He, Xin Branch of Hubei Electric Power Co. Ltd State Grid Information and Communication Wuhan China Wuhan Diameter Technology Co. Ltd Wuhan China

ISBN: (纸本)9798331529246

In the context of the development of the power industry towards digitalization and intelligence, it is of great importance to monitor the status and diagnose faults of equipment in the power Internet of Things (PIoT). This article intends to combine the theory and methods of Artificial intelligence (AI), and use real-time data analysis and deep learning methods to conduct online monitoring and early fault diagnosis of various devices in the PIoT. In the monitoring of the status of PIoT devices, the monitoring time is 7 days, and the error rate is not higher than 1.25%. Only the first day has the highest error rate, and the fifth day has an error rate of 0.37%. This article significantly improves the fault diagnosis accuracy of PIoT equipment by applying and optimizing new artificial intelligence algorithms, laying a theoretical and technical foundation for its further research and application. © 2025 ieee.

关键词： artificial intelligence equipment status monitoring fault diagnosis power internet of things

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Investigating Hyperparameter Effects on U-Net for Oral Epithelial Layer Segmentation 1st

Investigating Hyperparameter Effects on U-Net for Oral Epith...

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2024 international conference on computational intelligence and Generative AI

作者： Rahman, Taibur Mahanta, Lipi B. Das, Anup Kumar Ahmed, Gazi Naseem Inst Adv Study Sci & Technol Math & Computat Sci Div Gauhati Assam India Acad Sci & Innovat Res AcSIR Ghaziabad India Arya Wellness Ctr Gauhati Assam India North East Canc Hosp Res Inst Gauhati Assam India

ISBN: (纸本)9783031766121;9783031766107;9783031766091

Oral cancer (OC) is a prevalent malignancy in India, ranking sixth nationally and thirteenth globally. The ICMR National Cancer Repository Program forecasts 15.7 lakh cancer cases by 2025, up from 14.6 lakh in 2014. India's diverse cancer occurrence patterns pose significant challenges to prevention and treatment. Regular health checks and increased risk factor awareness are crucial for improving OC survival rates. In this study, we explore the field of oral epithelial dysplasia pathology by using deep learning (DL)-based models to segment 900 carefully curated images of the histopathology of oral epithelial dysplasia at a 100x magnification from the biopsy slides. We use a two-pronged training strategy that includes the Vanilla U-Net model and a fine-tuned U-Net variation. Our results show significant differences in performance, with the optimized U-Net model performing better. To be more precise, the modified U-Net model produces the following results: Intersection over Union (IoU): 95.24%;Precision: 98.77%;Recall: 98.19%;and F1_Score: 97.55%. Additional evaluations utilizing additional native histopathology pictures of OC validate the resilience of our training models, resulting in the effective segmentation of epithelial layers. This study adds to our understanding of the pathophysiology of OC and highlights the potential of DL methods for accurate diagnosis and treatment.

关键词： U-Net Artificial intelligence Deep learning Oral dysplasia Medical imaging Segmentation

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Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language 31

Semi-Automated Construction of Sense-Annotated Datasets for ...

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31st international conference on computational Linguistics, COLING 2025

作者： Riley, Jai Hauer, Bradley Hriti, Nafisa Sadaf Luo, Guoqing Mirzaei, Amirreza Sheikhi, Ali Rafiei Hadi Siavashpour, Mahvash Tavakoli, Mohammad Shi, Ning Kondrak, Grzegorz Alberta Machine Intelligence Institute Department of Computing Science University of Alberta Edmonton Canada

ISBN: (纸本)9798891761964

High-quality sense-annotated datasets are vital for evaluating and comparing WSD systems. We present a novel approach to creating parallel sense-annotated datasets, which can be applied to any language that English can be translated into. The method incorporates machine translation, word alignment, sense projection, and sense filtering to produce silver annotations, which can then be revised manually to obtain gold datasets. By applying our method to Farsi, Chinese, and Bengali, we produce new parallel benchmark datasets, which are vetted by native speakers of each language. Our automatically-generated silver datasets are of higher quality than the annotations obtained with recent multilingual WSD systems, particularly on non-European languages. © 2025 Association for computational Linguistics.

关键词： WSDL

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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

作者： Liang, Yu Zhang, Sheng Wu, Jie School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China State Key Laboratory for Novel Software Technology Nanjing University China Center for Networked Computing Temple University United States

ISBN: (纸本)9798350368741

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach. © 2025 ieee.

关键词： online optimization resource allocation Video analytics

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Skin Lesion Prediction and Classification using Deep Learning methods 2

Skin Lesion Prediction and Classification using Deep Learnin...

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2nd international conference on computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Paulraj, Betty Arumugam, Rekha Dhir, Saru Singh, Aryan Department of CSE Amity University Uttar Pradesh Noida India Department of Artificial Intelligence University of Ulster Northern Ireland Belfast United Kingdom Department of IT Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798331530389

In the past decade, The Skin Cancer is observed to be a most common disease. Eventually it is not considered as a fatal disease if properly treated. The early diagnosis is more important for the proper cure. As per the reports, it is said over 20% of the population in Canada, US are mostly affected. It is also reported that, with respect to the age, the chance of getting affected by the skin cancer is more. The better recovery is seen once the early diagnosis is done. As per the available medical records, the recovery rate is huge and the fatal rate is less. In this research, investigations on identifying the suitable methodology in deep learning technique is carried out The research gave the optimum solution of efficient skin cancer prediction with an accuracy of 99.6 %. The EfficientNetBO methodology with the Adam optimizer is observed with computationally improved in performance and accuracy when compared with the state of the art methods. © 2025 ieee.

关键词： Lung cancer

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Performance Optimization of U-Net Network in Segmentation of Nerves Image Dataset

Performance Optimization of U-Net Network in Segmentation of...

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2025 international conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Sreeja, G Veera Nagaraju, M. Theja, C.P. Sai Agarwal, Vikas Telangana Hyderabad India

ISBN: (纸本)9798331509828

This research explores the performance and optimization of U-Net architectures for segmenting nerve images. U-Nets are prevalent in medical image analysis due to their effectiveness in capturing detailed features and spatial relationships. Our focus is on enhancing U-Net models by fine-tuning hyperparameters, applying data augmentation techniques, and utilizing advanced loss functions to boost segmentation accuracy and computational efficiency. The study compares various U-Net variants, evaluating their effectiveness in preserving the delicate structures of nerve tissues. The results indicate that optimized U-Nets deliver high accuracy with reduced processing times, making them ideal for real-time medical applications. This research provides valuable insights into the practical use of U-Nets for nerve image segmentation and suggests avenues for future improvements. © 2025 ieee.

关键词： computational Modelling Data Augmentation Hyperparameter Tuning Medical Image Analysis Nerve Disease Segmentation

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Social Group Human-Robot Interaction: A Scoping Review of computational Challenges 20

Social Group Human-Robot Interaction: A Scoping Review of Co...

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20th Annual ACM/ieee international conference on Human-Robot Interaction, HRI 2025

作者： Nigro, Massimiliano Akinrintoyo, Emmanuel Salomons, Nicole Spitale, Micol Politecnico di Milano Milan Italy Imperial College London United Kingdom

ISBN: (纸本)9798350378931

Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015-2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges. © 2025 ieee.

关键词： Emotional intelligence

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A Comprehensive Analysis of Intrusion Detection System in Cloud computing using Artificial intelligence Technique 1

A Comprehensive Analysis of Intrusion Detection System in Cl...

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1st international conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Kuthuru, Shravya Ranjeeth Kumar, M. Sr University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798331529246

Intrusion Detection Systems (IDS) comprise of devices utilized for network monitoring to detect unusual activities that deviate from normal functionality and lead to policy violation. However, ensuring both privacy and security is a challenging task. Monitoring security operations becomes complex due to the presence of a large number of CC devices, their heterogeneity, and limitations in technical specifications, which lead to inefficiencies in security operations. To address this issue, Intrusion Detection Systems (IDS) are introduced to ensure a secure network against various vulnerabilities and threats. The integration of robust Machine Learning (ML) and Deep Learning (DL)-based techniques becomes crucial to address various security challenges. ML and DL-based IDS methods are fundamental in securing cloud environments and networks. The IDS monitors network traffic and system activities, effectively mitigating security threats caused by the rapid growth in network traffic. Additionally, the ML and DL techniques enhance the detection of known and unknown attacks, including zero-day attacks, thereby reducing false positive rates. The performance of ML and DL models is typically evaluated in terms of the metrics of accuracy, recall, precision, and F1-Score. © 2025 ieee.

关键词： Network intrusion

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Impact of Artificial intelligence Readiness on Employee Experience in IT Sector 6

Impact of Artificial Intelligence Readiness on Employee Expe...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kirupa Priyadarsini, M. Bashir, Firdaus PSG Institute of Management PSG College of Technology Coimbatore India

ISBN: (纸本)9798331522667

Adopting Artificial intelligence (AI) in the Information Technology (IT) sector has led to meaningful transformation, especially how the workplace functions. The present study identifies employee experience as significant for better outcomes, but how AI shapes employee experience calls for further research. To narrow this gap, a study on the relationship between AI readiness and employee experience has been conducted. Data were collected using convenience sampling from employees working in IT companies located in Tamil Nadu. Later, these data were analyzed for structural equation modelling using the statistical software called JAMOVI. This study revealed a significant relationship between AI readiness and employee experience among those working in the IT sector. When employees are ready to incorporate AI into their work tasks, the employee experience improves enhancing their capability required to excel in an ever-changing dynamic business world. © 2025 ieee.

关键词： Artificial intelligence employee experience employee readiness informational technology sector

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Enhancing Emotional Text-to-Speech Controllability with Natural Language Guidance through Contrastive Learning and Diffusion Models

Enhancing Emotional Text-to-Speech Controllability with Natu...

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

作者： Jing, Xin Zhou, Kun Triantafyllopoulos, Andreas Schuller, Björn W. Technical University of Munich Munich Germany Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany GLAM - Group on Language Audio & Music Imperial College London United Kingdom Alibaba Group Singapore

ISBN: (纸本)9798350368741

While current emotional text-to-speech (TTS) systems can generate highly intelligible emotional speech, achieving fine control over emotion rendering of the output speech still remains a significant challenge. In this paper, we introduce ParaEVITS, a novel emotional TTS framework that leverages the compositionality of natural language to enhance control over emotional rendering. By incorporating a text-audio encoder inspired by ParaCLAP, a contrastive language-audio pretraining (CLAP) model for computational paralinguistics, the diffusion model is trained to generate emotional embeddings based on textual emotional style descriptions. Our framework first trains on reference audio using the audio encoder, then fine-tunes a diffusion model to process textual inputs from ParaCLAP's text encoder. During inference, speech attributes such as pitch, jitter, and loudness are manipulated using only textual conditioning. Our experiments demonstrate that ParaEVITS effectively control emotion rendering without compromising speech quality. Speech demos are publicly available. © 2025 ieee.

关键词： Affective computing computational Paralinguistics Contrastive Learning Diffusion Emotional TTS

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