检索结果-内蒙古大学图书馆

Medical Information Processing and Analysis (SIPAIM), International Symposium on

作者： Farzaneh Dehghani Nikita Malik Joanna Lin Sayeh Bayat Mariana Bento dept. Biomedical Engineering University of Calgary Calgary Canada dept. Information Technology Manipal Institute Of Technology MAHE Manipal India dept. Computer Science University of Calgary Calgary Canada dept. Biomedical Engineering dept. Geomatics Engineering University of Calgary Calgary Canada dept. Biomedical Engineering dept. Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331528652

ISBN: (纸本)9798331528669

With the wide employment of Artificial Intelligence-based systems in healthcare, there is an increasing need to further quantify and analyze potential biases in data and algorithms, mitigating health disparities. We aim to investigate publicly available healthcare datasets to identify potential biases, and investigate the impact of these biases on the predictive performance of the models. The results indicate that bias in these datasets exerts a negative impact on the fairness of the learned models. In the context of a highly imbalanced dataset, we further investigate the impact of various imbalance handling techniques on both the performance and fairness of the models. This study underscores the importance of early detection of bias to mitigate the risk of introducing such biases in real-world applications, particularly within a sensitive domain, such as healthcare.

关键词： Measurement Prevention and mitigation Decision making Pipelines Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding

arXiv

引用

arXiv 2025年

作者： Chen, Tianyu Fu, Xingcheng Gao, Yisen Qian, Haodong Wei, Yuecen Yan, Kun Zhou, Haoyi Li, Jianxin SKLCCSE School of Computer Science and Engineering Beihang University China School of Software Beihang University China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University China Institute of Artificial Intelligence Beihang University Beijing China

Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a galaxy scale for understanding astronomical phenomena, the integration of spherical space for planetary orbits and hyperbolic spaces for black holes raises two formidable challenges. a) The current pretraining model is confined to Euclidean space rather than a comprehensive geometric embedding. b) The predominant architecture lacks suitable backbones for anisotropic physical geometries. In this paper, we introduced Galaxy-Walker, a geometry-aware VLM, for the universe-level vision understanding tasks. We proposed the geometry prompt that generates geometry tokens by random walks across diverse spaces on a multi-scale physical graph, along with a geometry adapter that compresses and reshapes the space anisotropy in a mixture-of-experts manner. Extensive experiments demonstrate the effectiveness of our approach, with Galaxy-Walker achieving state-of-the-art performance in both galaxy property estimation (R2 scores up to 0.91) and morphology classification tasks (up to +0.17 F1 improvement in challenging features), significantly outperforming both domain-specific models and general-purpose VLMs. © 2025, CC BY.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

A Multi-objective Optimization Approach for Secure Communications Based on Collaborative Beamforming in UAV Networks

A Multi-objective Optimization Approach for Secure Communica...

引用

IEEE Conference on Wireless Communications and Networking

作者： Xinrong Guo Fang Mei Geng Sun Tingting Zheng College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Electrical and Computer Engineering University of Waterloo Waterloo Canada

With the rapid development of wireless communication, unmanned aerial vehicle (UAV) networks have received extensive attention and been applied in many fields, but some challenges exist in their applications, such as the issue of security when implementing communication. In this paper, a virtual antenna array (VAA) is formed in multiple UAV units using collaborative beamforming (CB) technology. Under the interference of multiple eavesdroppers, secure communication with the ground base station (BS) is achieved. To achieve better performance, we formulate a multi-objective optimization problem for UAV network security communication (SCMOP), and jointly optimize the positions and excitation current weights of UAVs to set the null values in the direction of known eavesdroppers, reduce the sidelobe levels (SLLs) and enhance the directivity of the main lobe (ML). Since the formulated SCMOP is an NP-hard problem, we propose an improved the third non-dominated sorting genetic algorithm (IMNSGA-III) with chaos operator and crossover and mutation operators to solve the problem in this paper. The simulation results show that the IMNSGA-III can solve the SCMOP well and obtain the best results compared with other benchmark algorithms.

关键词： Chaos Wireless communication Array signal processing Null value Collaboration Autonomous aerial vehicles Statistics

来源：评论

学校读者我要写书评

暂无评论

The Capability of Wavelet Convolutional Neural Network for Detecting Cyber Attack of Distributed Denial of Service in Smart Grid 18

The Capability of Wavelet Convolutional Neural Network for D...

引用

18th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2021

作者： Monday, Happy Nkanta Li, Jian Ping Nneji, Grace Ugochi Yutra, Abel Zenebe Lemessa, Bona Debela Nahar, Saifun James, Edidiong Christopher Haq, Amin Ul University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu Sichuan 611731 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan 611731 China University of Missouri School of Computer and Information Systems St LouisMO United States

ISBN: (纸本)9781665413640

The electrical system's dependability, security, and efficiency are all improved through smart grid technologies. Its dependence on digital communication technology, on the other hand, introduces new risks and vulnerabilities that should be examined for the purpose to providing effective and trustworthy service delivery. This study presents a method for the detection of distributed denial of service (DDoS) attacks on smart grid infrastructure. Continuous wavelet transform (CWT) is used in the suggested approach to convert one-dimensional traffic data to two-dimensional time-frequency domain scalogram as the input to the wavelet convolutional neural network (WavCovNet) to detect anomalous behavior in the data by distinguishing attack features from normal patterns. Our results demonstrate that the proposed approach detects DDoS attacks with a high rate of detection and with a very low rate of false alarm. © 2021 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

ActivityMamba: A CNN-Mamba hybrid neural network for efficient human activity recognition

引用

IEEE Transactions on Mobile Computing 2025年

作者： Luo, Fei Li, Anna Jiang, Bin Khan, Salabat Wu, Kaishun Wang, Lu Great Bay University School of Computing and Information Technology Dongguan China Lancaster University School of Computing & Communications United Kingdom University of Petroleum College of Oceanography and Space Informatics Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Guangzhou China

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN's local and Mamba's global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition. © 2002-2012 IEEE.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting

arXiv

引用

arXiv 2023年

作者： Zhu, Huilin Yuan, Jingling Zhong, Xian Yang, Zhengwei Wang, Zheng He, Shengfeng School of Computer Science and Artificial Intelligence Wuhan University of Technology China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computing and Information Systems Singapore Management University Singapore

Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. However, existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset, leading to additional learning ambiguities. These domain-agnostic factors, e.g., density, surveillance perspective, and scale, can cause significant in-domain variations, and the misalignment of these factors across domains can lead to a drop in performance in cross-domain crowd counting. To address this issue, we propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains. The DAOT consists of three steps. First, individual-level differences in domain-agnostic factors are measured using structural similarity (SSIM). Second, the optimal transfer (OT) strategy is employed to smooth out these differences and find the optimal domain-to-domain misalignment, with outlier individuals removed via a virtual "dustbin" column. Third, knowledge is transferred based on the aligned domain-agnostic factors, and the model is retrained for domain adaptation to bridge the gap across domains. We conduct extensive experiments on five standard crowd-counting benchmarks and demonstrate that the proposed method has strong generalizability across diverse datasets. Our code will be available at: https://***/HopooLinZ/DAOT/. © 2023, CC0.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

AI-based Bonsai Species Classification and Care Recommendation System using YOLO

AI-based Bonsai Species Classification and Care Recommendati...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Derry Jeswyn S K Ramalakshmi S. Shirly R Venkatesan G Naveen Sundar Saravana Kumar C S Division of CSE Karunya Institute of Technology and Sciences Coimbatore Department of CSE & IT Alliance University Bangalore Computer Science and Engineering Karunya University Division of CSE Karunya University Coimbatore Global Tech Park Bosch Global Software Technologies Coimbatore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Bonsai, an ancient art form that blends horticultural precision with artistic expression, poses unique challenges to both beginners and seasoned practitioners due to its species-specific care requirements and intricate maintenance processes. This research introduces an innovative AI-based solution combining YOLOv8, an advanced object detection model, and Google Gemini LLM, to simplify bonsai species classification and care recommendations. The system is designed to accurately identify bonsai species and provide personalized, context-aware guidance on essential care aspects such as watering, pruning, and pest management, while dynamically adapting to environmental and seasonal variations. Featuring an intuitive interface with sub-100ms response times, the proposed solution enhances accessibility and engagement for users of all expertise levels. Testing affirms its capability to improve understanding, usability, and appreciation of bonsai cultivation. Beyond its application in bonsai care, this hybrid AI integration demonstrates the potential for broader adoption in fields requiring precise classification and tailored insights, showcasing a seamless blend of tradition and technology.

关键词： YOLO Accuracy Scalability Horticulture Natural language processing Internet Time factors Usability Recommender systems Testing

来源：评论

学校读者我要写书评

暂无评论

Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges

arXiv

引用

arXiv 2022年

作者： Abeysekara, Prabath Dong, Hai Qin, A.K. School of Computing Technologies RMIT University Melbourne Australia Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the network edge. The opportunity to combine these two domains of technologies to power interconnected devices with intelligence is likely to pave the way for a new wave of technology revolutions. Embracing this technology revolution, in this article, we present a novel computing vision named Deep Edge Intelligence (DEI). DEI employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc. aiming to provision reliable and secure intelligence services to every person and organisation at any place with better user experience. The vision, system architecture, key layers and features of DEI are also detailed. Finally, we reveal the key enabling technologies and research challenges associated with it. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bioelectric Signal Acquisition and Design of Portable ECG for Medical Internet of Things Applications 1st

Bioelectric Signal Acquisition and Design of Portable ECG fo...

引用

1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Ashraf, Abrar Sahar, Sadia Iqbal, Muhammad Waseem Irfan, Danish Hamza, M. Ameer Joseph, Sylvester Iqbal, Muhammad Tassawar Department of Computer Science Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Information Technology Superior University Lahore Pakistan Trondheim Norway COMSATS University Islamabad Wah Campus Islamabad Pakistan

ISBN: (纸本)9783031776199

In contemporary times, heart diseases have emerged as the leading cause of untimely deaths. To tackle this alarming situation, engineers have developed a range of medical devices for diagnosing and scrutinizing various diseases. Numerous patients suffer from heart problems that pose critical threats to their lives, thereby necessitating continuous monitoring via traditional monitoring systems such as Electrocardiography (ECG). However, this technique is available only in hospitals, making it prohibitively expensive and inaccessible to remote patients. The advent of wireless technologies has paved the way for building a network of connected devices through the internet. To address this issue, a proposed ECG monitoring system comprises an AD8382 ECG sensor for reading patient data, an Arduino Uno, an ESP32 Wi-Fi module, and an IoT Blynk application. Implementing this healthcare system would enable doctors to remotely monitor patients using the IoT Blynk application installed on their smartphones, which processes and visualizes the patient's ECG signal. This monitoring process can be carried out at any time and from any location, without requiring patients to visit the hospital. To conclude, the compact and integrated system design could revolutionize the healthcare industry by making it more accessible and affordable to patients. By providing real-time monitoring, this system could help doctors diagnose and treat heart diseases more effectively. Therefore, it is imperative to promote such innovative technologies to improve healthcare services worldwide. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrocardiography

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：