检索结果-内蒙古大学图书馆

Iraqi Journal for Computer science and Mathematics 2023年第4期4卷 99-110页

作者： Azam, Nurulain Nusrah Mohd Ismail, Mohd Arfian Mohamad, Mohd Saberi Ibrahim, Ashraf Osman Jeba, Shermina Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan 26600 Malaysia Center of Excellence for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Gambang26300 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medicine and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah 88400 Malaysia Department of Computing Muscat College Muscat Oman

The COVID-19 virus had easily affected people worldwide through direct contact. Individuals diagnosed with positive COVID-19 virus may be affected with many symptoms, such as fever, tiredness, dry cough, difficulty in breathing, sore throat, chest pain, nasal congestion, runny nose, and diarrhea. An individual can also be diagnosed with COVID-19 even when he does not have any symptoms or be in contact with an infected person. data classification was required due to the size of COVID-19 data that will be analyzed for future countermeasures determination. Some problems in data classification occurred due to unorganized data, such as time consumption, human error in complexity of symptom features and the diagnosis process data needed expert knowledge. This study aimed to use the artificial neural network (ANN) approach, which was multilayer perceptron (MLP) to classify the COVID-19 data by using patient symptom data. The MLP process involved data collection, data normalization, MLP design, MLP training, testing, and MLP verification. From the experiments, the MLP method was able to obtain an accuracy rate of 77.10%. In conclusion, the MLP method could classify the COVID-19 data and achieve a high accuracy rate. © 2023 College of Education, Al-Iraqia University. All rights reserved.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Potholes Detection Using Yolo Algorithms and Their Integration Into Gazebo Simulation 9

Potholes Detection Using Yolo Algorithms and Their Integrati...

引用

9th International Conference on Control, Robotics and Cybernetics, CRC 2024

作者： Hassan Tanveer, M. Fatima, Zainab Rehman, Tanazzah Siddique, Moaaz Shah, Arbaz Voicu, Razvan Cristian Farrukh Shahid, M. Kennesaw State University Department of Robotics and Mechatronics Engineering Marietta30060 United States NED University of Engineering and Technology Department of Software Engineering Karachi Pakistan FAST University of computing and Emerging Science Department of AI and Data Science Karachi Pakistan

ISBN: (纸本)9798331517489

Road cavities, often called potholes, pose a major problem for our transportation infrastructure, causing accidents, costly repairs, and traffic jamming. This study shows using a computer vision technique called You Only Look Once (YOLO) to find potholes on roads. YOLO is proficient and efficient for realtime object detection. The researchers want to see which version of YOLO is more effective in object analysis in pictures and videos. We'll be comparing YOLOv5, YOLOv7, and YOLOv8, thereby also setting up the system in Gazebo simulated environment. By carefully analyzing the results, we aim to understand the advantages and disadvantages of each YOLO version. This will help us see the most useful ones for finding potholes when managing and maintaining roads. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Near-optimal Active Regression of Single-Index Models

arXiv

引用

arXiv 2025年

作者： Li, Yi Tai, Wai Ming School of Physical and Mathematical Sciences College of Computing and Data Science Nanyang Technological University Singapore

The active regression problem of the single-index model is to solve minxf(Ax) − bp, where A is fully accessible and b can only be accessed via entry queries, with the goal of minimizing the number of queries to the entries of b. When f is Lipschitz, previous results only obtain constant-factor approximations. This work presents the first algorithm that provides a (1 + Ε)-approximation solution by querying Õ(dp2 ∨1/Εp∨2) entries of b. This query complexity is also shown to be optimal up to logarithmic factors for p ∈ [1, 2] and the Ε-dependence of 1/Εp is shown to be optimal for p > 2. © 2025, CC BY.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

An Extractive Automatic Summarization Method for Chinese Long Text 9th

An Extractive Automatic Summarization Method for Chinese Lon...

引用

19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Jizhao Duan, Wenyu Yu, Naitong Pan, Xinlong Fan, Chunlong Shenyang Aerospace University Shenyang China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China Naval Aviation University Yantai China

ISBN: (纸本)9783031466632

The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods utilize deep learning technology to treat automatic summarization as a binary classification task. However, the effectiveness of automatic summarization for Chinese long text is limited by the maximum input length of the model, and it requires a large amount of training data. This paper proposes an unsupervised extractive automatic summarization method which solves the long text encoding problem by incorporating contextual semantics into sentence-level encoding. Firstly, we obtain the semantic representation of sentences by using the RoBERTa model. Secondly, we propose an improved k-Means algorithm to cluster sentence representations. By defining sparse and dense clusters, we improve the accuracy of summary sentence selection while preserving maximum semantic information from the original text. Experimental results on the CAIL2020 dataset show that our method outperforms baselines by 6.64/7.68/7.14% respectively on ROUGE-1/2/L. Moreover, we further enhance the automatic summarization results by 4.5/5.36/3.24% by adding domain rules tailored to the dataset’s characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation of Federated Learning-Assisted Mobile Augmented Reality Systems in the Metaverse via Uplink and Downlink NOMA

引用

IEEE Transactions on Wireless Communications 2025年

作者： Zhou, Xinyu Li, Yang Zhao, Jun Nanyang Technological University College of Computing and Data Science Singapore Nanyang Technological University Interdisciplinary Graduate Programme Singapore

Metaverse has become a buzzword recently. Mobile augmented reality (MAR) is a promising approach to providing users with an immersive experience in the Metaverse. However, due to limitations of bandwidth, latency, and computational resources, MAR cannot yet be applied on a large scale in the Metaverse. Moreover, federated learning, with its privacy-preserving characteristics, has emerged as a prospective distributed learning framework in the future Metaverse world. This paper proposes a federated learning-assisted MAR system via non-orthogonal multiple access (NOMA) for the Metaverse. Additionally, to optimize a weighted sum of energy and model accuracy, a resource allocation algorithm is devised by setting appropriate transmission power, CPU frequency, and video frame resolution in the uplink and downlink NOMA for each user. Besides, time complexity and convergence analysis of the resource allocation algorithm are provided. Experimental results demonstrate that our proposed algorithm achieves an overall good performance from the following three aspects: 1) under different system parameters, 2) comparisons with baseline algorithms, and 3) adjustment of the weight parameter to balance accuracy and energy consumption. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Neurosymbolic AI for Mining Key Aspects of Socially Responsible Investing

Neurosymbolic AI for Mining Key Aspects of Socially Responsi...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Wihan Van Der Heever Ranjan Satapathy Erik Cambria Rick Siow Mong Goh College of Computing and Data Science Nanyang Technological University Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Environmental, Social, and Governance (ESG) factors have become critical for assessing corporate sustainability and ethical responsibility. However, the vast volume of unstructured data available across corporate reports, social media, and news sources poses a challenge for systematic ESG analysis. This paper explores the application of neurosymbolic AI, which combines neural networks’ pattern recognition capabilities with the structured reasoning of symbolic AI, to mine key aspects of ESG from large-scale, diverse data sources. By leveraging SenticNet for concept parsing and deep learning for sentiment analysis, we extract relevant ESG metrics, classify corporate practices, and identify trends. This hybrid approach enhances both the interpretability and scalability of ESG analysis, providing more accurate insights into corporate behaviors and their impact on sustainability goals. Results demonstrate that neurosymbolic AI not only improves the extraction of meaningful ESG aspects but also enables real-time monitoring, supporting data-driven decision-making for investors, regulators, and stakeholders.

关键词： Ethics Technological innovation Sentiment analysis Systematics Decision making Companies Real-time systems Stakeholders Artificial intelligence Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Blockchain and IoT's role in Agri Food Supply Chain Traceability 9

Blockchain and IoT's role in Agri Food Supply Chain Traceabi...

引用

9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2022

作者： Subashini, B. Hemavati, D. Department of Data Science and Business Systems School of Computing. SRM IST Tamilnadu Kattankulathur603203 India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

In this work, we show how blockchain can be used to manage the supply chain for non-perishable agricultural food (NPAF). Keeping product quality high across the supply chain is a major challenge for the food business. Traditional foods are severely affected. The system's major purpose is to make product history accessible. At each stage of the supply chain, the product can be checked for quality and cleanliness. We created a trust less environment by utilising Blockchain, RFID, and IoT. Encryption and the network are more trusted than public or private institutions. This research suggests an execution model for the supply chain management of non-perishable agricultural foods that addresses these challenges by combining Ethereum smart contracts with IPFS, RFID, and IoT technologies. These technologies were combined in an effort to find a solution to these problems. The technology will give consumers a clear view of the complete process from raw material to the finished product and make it simple for a controlling authority to conduct operational examinations on the excellence of the goods and decent working practises. © 2022 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Sentiment Detection through Emotion Classification Using Deep Learning Approach for Chinese Text 15

Sentiment Detection through Emotion Classification Using Dee...

引用

15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023

作者： Huang, Yuxin Jusoh, Shaidah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia School of Electrical Engineering and Artficial Intelligence Xiamen University Malaysia Selangor Sepang Malaysia

ISBN: (纸本)9798350321388

Emotion classification and sentiment analysis represent crucial research areas within the field of Natural Language Processing. Previous studies have primarily focused on conducting sentiment classification and emotion classification as separate tasks. Only a limited number of researchers have delved into exploring the relationship between these two and invested efforts in deriving one from the other. This study aims to determine sentiment by employing emotion classifications. Specifically, we utilise the ERNIE Tiny deep learning model to classify emotions in Chinese texts, and detect sentiments through our devised rules. For instance, if emotions such as 'happiness' or 'like' are present, the sentiment is classified as positive. Conversely, emotions like 'sadness', 'disgust', 'anger', or 'fear' classify the sentiment as negative. The experimental results demonstrate the F1 score of 93.00% and 90.14% for positive and negative sentiment, respectively, in Chinese song reviews. These findings substantiate the validity and feasibility of utilising emotions to extract sentiment © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

引用

31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

引用

8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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：