检索结果-内蒙古大学图书馆

Advanced Computing Technologies (ICoACT), International Conference on

作者： L. Sanker Magadevan N. Shanmugalakshmi M. Giridhar M. Balaji R. Saravanakumar S. Jeya Lakshmi Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi Tamil Nadu India Department of Artificial Intelligence & Data Science Vel Tech High Tech Dr. Rangarajan and Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The increasing demand for high-capacity and dynamic services in optical networks necessitates intelligent and adaptive provisioning mechanisms. This study investigates the application of machine learning (ML) techniques for traffic-driven service provisioning in optical networks, addressing challenges such as traffic prediction, resource allocation, and fault management. The proposed framework employs supervised and unsupervised learning models, including Long Short-Term Memory (LSTM) networks, Random Forests, and K-Means clustering, to analyze traffic patterns and optimize service deployment. The system leverages real-time and historical traffic data to predict demand, enabling proactive resource allocation and dynamic wavelength assignment. Furthermore, reinforcement learning is explored to automate decision- making for adaptive routing and spectrum management under varying network conditions. Performance evaluation on simulated and real-world datasets demonstrates that ML-based approaches significantly improve network efficiency, reduce latency, and enhance the quality of service (QoS). This research highlights the potential of machine learning in revolutionizing service provisioning in optical networks, offering scalable, data-driven solutions to meet the growing demands of next-generation communication systems.

关键词： Reviews Buildings Machine learning Quality of service Optical fiber networks Elastic optical networks Resource management Monitoring Long short term memory Random forests

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

引用

arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

来源：评论

学校读者我要写书评

暂无评论

Machine learning and internet of things in industry 4.0: A review

引用

Measurement: Sensors 2023年 28卷

作者： Rahman, Md. Sazzadur Ghosh, Tapotosh Aurna, Nahid Ferdous Kaiser, M. Shamim Anannya, Mehrin Hosen, A.S.M. Sanwar Institute of Information Technology Jahangirnagar University Dhaka1342 Bangladesh Department of Computer Science and Engineering United International University Dhaka1212 Bangladesh Division of Information Science Nara Institute of Science and Technology Nara Japan Department of Artificial Intelligence and Big Data Woosong University Daejeon Korea Republic of

Machine learning (ML), sensors networks, and Internet of Things (IoT) are the most important contributor in the newest revolution in the industry. It is going towards a fully automated industrial environment where all the components including post production, pre production, supply chain and quality control would be automatically managed. Human will work more with brain, where all the physical efforts would be replaced by ML enabled intelligent robots that will perform all the tasks, where sensor networks will collect live information from the environment. All the decision would be taken on the fly from the previous records. In this work, we have tried to shed some light on the current involvement of ML and IoT in industry 4.0 environment. 28 articles were reviewed in this work which were selected through a selection process and were published between 2017 and 2022. Different tools, protocols, algorithms and the latest technologies used in the industry 4.0 environment have been analytically discussed in this work. Research gaps were tried to found out and some recommendations were provided which can be a pathway to research advancement related to industry 4.0. © 2023 The Authors

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

arXiv

引用

arXiv 2024年

作者： Zhu, Lipeng Ma, Wenyan Xiao, Zhenyu Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore School of Electronic and Information Engineering Beihang University Beijing100191 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum signal-to-noise ratio (SNR) among users is derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Virtual Palette: An Efficient Object Tracking Tool

Virtual Palette: An Efficient Object Tracking Tool

引用

Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： R. Madhura S. Nikkath Bushra Devi P. P Sasigresa P Department of Computing Technologies SRM Institute of Science and Technology Kattangulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering (Data Science) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Virtual Palette is a cutting-edge tool designed to enhance audience participation by providing an alternative to conventional jamboards for educators. Leveraging object tracking, a fundamental component of computer vision, it facilitates seamless and intuitive interactions with the audience. The video analysis process comprises three primary steps: object detection, object tracking across frames, and object behavior analysis. This sophisticated approach necessitates meticulous consideration of elements such as accurate object representation, feature selection for tracking, object recognition, and object tracking. One of the primary advantages of Virtual Palette over traditional mouse or touchpad devices is its ability to support clear and legible drawing and effective interaction in virtual environments. This feature is particularly beneficial for individuals with hearing impairments, aiding in the identification of patterns and drawings. The development of Virtual Palette involves overcoming challenges such as precise three-dimensional finger movement tracking and optimizing the technology for virtual backgrounds. Overall, Virtual Palette offers a dynamic and interactive teaching experience, akin to the advantages provided by screen recording for creating tutorials and virtual documentation.

关键词： Fingers Virtual environments Tutorials Transforms Object detection Green computing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

FairTP: A Prolonged Fairness Framework for Traffic Prediction 39

FairTP: A Prolonged Fairness Framework for Traffic Predictio...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Traffic prediction is pivotal in intelligent transportation systems. Existing works focus mainly on improving overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some urban areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents’ life. Existing fairness machine learning models struggle to maintain fair traffic prediction over prolonged periods. Although these models might achieve fairness at certain time slots, this static fairness will break down as traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introducing two novel fairness metrics, i.e., region-based static fairness and sensor-based dynamic fairness, tailored to fairness fluctuations over time and across areas. An innovative prolonged fairness traffic prediction framework, namely FairTP, is then proposed. FairTP achieves prolonged fairness by alternating between “sacrifice” and “benefit” the prediction accuracy of each traffic sensor or area, ensuring that the number of these two actions are balanced over time. Specifically, FairTP incorporates a state identification module to discriminate whether the traffic sensors or areas are in a “sacrifice” or “benefit” state, thereby enabling prolonged fairness-aware traffic predictions. Additionally, we devise a state-guided balanced sampling strategy to select training examples to further enhance prediction fairness by mitigating the performance disparities among areas with uneven sensor distribution over time. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing significant accuracy degradation. Copyright © 2025, Association for the Advancement of Ar

关键词： Traffic control

来源：评论

学校读者我要写书评

暂无评论

LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments

LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Soc...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Sovon Chakraborty Muhammad Borahn Uddin Talukdar Muhammed Yaseen Morshed Adib Sowmen Mitra Md. Golam Rabiul Alam Department of Computer Scinece and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Department of Artificial Intelligence Hebei University of Technology Beijing China

Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%.

关键词： Chaos Economics Sentiment analysis Analytical models Social networking (online) Computational modeling Deep architecture

来源：评论

学校读者我要写书评

暂无评论

Advancing NICU Monitoring with IoT and Real-Time AI Analytics

Advancing NICU Monitoring with IoT and Real-Time AI Analytic...

引用

Visual Analytics and data Visualization (ICVADV), International Conference on

作者： N. Vini Antony Grace S Balakrishna Reddy D Sahaya Lenin N. Mohankumar L.M. Merlin Livingston M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering DMI College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In neonatal intensive care units (NICUs), the prompt and accurate observation of sick and premature infants is of the most critical significance. Using fuzzy logic and the Internet of Things (IoT), this research demonstrates an innovative method for NICU monitoring. Our system constantly records data on critical factors, including temperature, oxygen saturation, and heart rate, via an IoT sensor network. To facilitate proactive management of newborn health, this data is analysed using Artificial Intelligence (AI) algorithms based on fuzzy logic. The results of this processing include predictive analytics and real-time insights. To provide more adaptable and precise decision-making assistance, fuzzy logic is used to deal with physiological data's inherent uncertainties and unpredictability. The proposed approach aims to have a more responsive NICU staff, better patient outcomes, and fewer critical events. This integrated strategy is useful for improving NICU operations and providing the best care for premature newborns via clinical trials and simulations.

关键词： Fuzzy logic Pediatrics Uncertainty Visual analytics Decision making Real-time systems Internet of Things Artificial intelligence Predictive analytics Monitoring

来源：评论

学校读者我要写书评

暂无评论

Automatic combination strategy search in segmentation of Cardiac T1 mapping image 4

Automatic combination strategy search in segmentation of Car...

引用

4th International Conference on Advanced Electronic Materials, computers and Software engineering, AEMCSE 2021

作者： Wangchen, Xingyu Shen, Wenfeng Takasumi, Hideaki Seino, Shinya Zhu, Xin Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University School of Computer Engineering and Science Shanghai Institute for Advanced Communication and Data Science Shanghai China Fukushima Medical University Hospital Department of Radiology Fukushima Japan The University of Aizu Biomedical Information Engineering Lab Fukushima9658580 Japan

ISBN: (纸本)9781665415965

Fusion of multiple medical images may improve image quality and ease feature extraction for the diagnosis and therapy of diseases. In cardiac magnetic resonance (MR) images T1 mapping, multiple MR images are reconstructed for the estimation of T1 mapping;therefore, heart myocardium should be segmented from images for further analysis using T1 values. Traditionally, segmentation is performed for each image separately, and the per-image segmentation results are combined in a complicated way to get a final segmentation result. Traditional combination methods require the segmentation results of all images satisfy certain criteria, and therefore often fail because of low image quality. However, it is very difficult to design a combination strategy to integrate the information of multiple images for segmentation. In this study, we designed the Segfuse network based on convolutional neural networks to get strategies for improving the segmentation performance of T1 mapping images even when some images were of low quality. After validating with 112 images from 42 subjects, the segmentation accuracy based on the Segfuse had a mean dice of 80.2%, nearly equivalent to that of a traditional method. © 2021 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

DAAP: privacy-preserving model accuracy estimation on unlabeled datasets through distribution-aware adversarial perturbation 24

DAAP: privacy-preserving model accuracy estimation on unlabe...

引用

Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Guodong Cao Zhibo Wang Yunhe Feng Xiaowei Dong School of Cyber Science and Engineering Wuhan University P. R. China The State Key Laboratory of Blockchain and Data Security Zhejiang University P. R. China and School of Cyber Science and Technology Zhejiang University P. R. China Department of Computer Science and Engineering University of North Texas

ISBN: (纸本)9781939133441

In the dynamic field of deep learning, accurately estimating model performance while ensuring data privacy against diverse and unlabeled test datasets presents a critical challenge. This is primarily due to the significant distributional shifts between training and test datasets, which complicates model evaluation. Traditional methods for assessing model accuracy often require direct access to the entire test dataset, posing significant risks of data leakage and model theft. To address these issues, we propose a novel approach: Distribution-Aware Adversarial Perturbation (DAAP). This method is designed to estimate the accuracy of deep learning models on unlabeled test datasets without compromising privacy. Specifically, DAAP leverages a publicly available dataset as an intermediary to bridge the gap between the model and the test data, effectively circumventing direct interaction and mitigating privacy concerns. By strategically applying adversarial perturbations, DAAP minimizes the distributional discrepancies between datasets, enabling precise estimation of model performance on unseen test data. We present two specialized strategies for white-box and black-box model contexts: the former focuses on reducing output entropy disparities, while the latter manipulates distribution discriminators. Overall, the DAAP introduces a novel framework for privacy-preserving accuracy estimation in model evaluation. This novel approach not only addresses critical challenges related to data privacy and distributional shifts but also enhances the reliability and integrity of model performance assessments. Our extensive evaluation on the CIFAR-10-C, CIFAR-100-C, and CelebA datasets demonstrates the effectiveness of DAAP in accurately estimating performance while safeguarding both data and model privacy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：