检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Jia, Xibin Zhang, Wang Wang, Luo Yang, Zhenghan Yang, Dawei Hong, Min Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Friendship Hospital The Capital University of Medical Sciences Beijing100050 China Department of Computer Software Engineering Soonchunhyang University Asan31538 Korea Republic of

Semi-supervised learning methods have gained significant attention in recent years due to the challenge of obtaining a large amount of labeled data. While leveraging unlabeled data has shown promise in improving segmentation performance, accurate predictions in ambiguous boundary regions remain a challenge in medical image segmentation. To address this issue and effectively leverage unlabeled data, we propose a unified cross-task and cross-model consistency (CTCMC) regularization framework for boundary-aware semi-supervised medical image segmentation. Our framework consists of two identical sub-models, each performing the main foreground segmentation task and an auxiliary boundary segmentation task to obtain a foreground map and a boundary map, respectively. To promote consistency between two tasks and enable effective exploitation of unlabeled data, we introduce a novel differential implementation of the morphological gradient to convert boundary map to foreground map, which serves as the basis for the cross-task consistency loss. Furthermore, the cross-model consistency is put forward to regularize the two identical sub-models, where two types of pseudo labels from one perturbed sub-model are used to supervise the other sub-model with corresponding tasks, and vice versa. The joint training of boundary segmentation, cross-task consistency and cross-model consistency facilitates discriminative representation learning, enhancing boundary information, and ultimately improving medical image segmentation performance. We conduct comprehensive experiments on two public datasets to evaluate the effectiveness of our proposed method. The experimental results demonstrate that our method outperforms current state-of-the-art semi-supervised medical image segmentation methods and achieves better segmentation performance with more accurate boundaries. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

引用

International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

引用

Intelligent Automation & Soft Computing 2024年第2期39卷 135-155页

作者： Abeer Abdullah Alsadhan Abdullah A.Al-Atawi Hanen karamti Abid Jameel Islam Zada Tan N.Nguyen Computer Science Department Imam Abdulrahman Bin Faisal UniversityDamam32232Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk47512Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences and Information Technology Hazara UniversityMansehraPakistan Faculty of Computing Department of Software EngineeringInternational Islamic UniversityIslamabadPakistan Department of Architectural Engineering Sejong UniversitySeoul05006Korea

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military *** are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing *** this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of *** trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM *** exposure was not previously conducted on these *** newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

关键词： Malware malicious code code obfuscation IoT machine learning deep learning

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support

arXiv

引用

arXiv 2024年

作者： Liu, Yongkun Chen, Jiachi Bi, Tingting Grundy, John Wang, Yanlin Yu, Jianxing Chen, Ting Tang, Yutian Zheng, Zibin School of Software Engineering Sun Yat-sen University China The The University of Western Australia Australia The Faculty of Information Technology Monash University Australia The School of Artificial Intelligence Sun Yat-sen University China The School of Computer Science and Engineering University of Electronic Science and Technology of China China University of Glasgow United Kingdom

Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has began to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools, and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers’ discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering. Copyright © 2024, The Authors. All rights reserved.

关键词： Life cycle

来源：评论

学校读者我要写书评

暂无评论

EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

The:ater 4uality 2nline 0onitoring 6ystem %ased on Wireless Sensor Network 13

The:ater 4uality 2nline 0onitoring 6ystem %ased on Wireless ...

引用

13th International Symposium on Computational Intelligence and Design, ISCID 2020

作者： Bai, Qiuchan Wu, Jiahao Jin, Chunxia Faculty of Automation Huaiyin Institute of Technology Huaian China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781728184463

Due to the poor real-time performance of water quality monitoring, secondary pollution, high cost and other issues, this paper proposes to use wireless sensor network technology to design a water quality monitoring system. The system has strong real-time, online monitoring functions, and acquires multiple parameter data that affect water quality, timely and accurately monitor water quality information, prevent water environmental pollution, reduce the impact of water pollution, and meet the requirements of efficient and intelligent water quality. ©2020 IEEE

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Cloud computing methods for leaking detection of halogen pipeline based on distributed terminals

Cloud computing methods for leaking detection of halogen pip...

引用

2020 International Conference on Robots and Intelligent Systems, ICRIS 2020

作者： Xu, Min Zhao, Jianyang Ye, Qiwei Ding, Weihong Zhu, Xiaoqi Zhang, Qin Huaiyin Institute of Technology Huaian Faculty of Automation China Huaiyin Institute of Technology Huaian Faculty of Computer and Software Engineering China

ISBN: (纸本)9780738124070

In order to ensure the safe transmission of the brine pipeline, this paper designs a leakage detection terminal based on STM32F7. This terminal uses GPS to added time stamps to the collected data. And each distributed terminal uses second pulse signal of GPS to realize the function of synchronously collecting signals. Finally, the data is transmitted to the cloud computing platform through the 4G wireless transmission module system. This paper uses the S transform algorithm to analyze the characteristics of these transmitted data. S transform algorithm fully extracts the characteristics of the data in the time-frequency-mode domain. The simulation results show that the data generates high-frequency signals at default point. Through the whole system, it can effectively realize real-time monitoring. © 2020 IEEE.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

7He monitoring system of aquaculture environment 13

7He monitoring system of aquaculture environment

引用

13th International Symposium on Computational Intelligence and Design, ISCID 2020

作者： Jin, Chunxia Bai, Qiuchan Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China Faculty of Automation Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781728184463

According to the needs of the aquaculture industry, this article proposes to design an aquaculture environment monitoring system for collecting temperature, PH, and dissolved oxygen parameters that affect fishery water quality environment, real-time tracking and monitoring, remote transmission and abnormal handling, etc. The system has played an important role in the application of fish breeding industry, providing technical support for the development of the breeding industry, and bringing greater economic profits to farmers. ©2020 IEEE.

关键词： Aquaculture

来源：评论

学校读者我要写书评

暂无评论

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

引用

International Conference on Emerging Technologies, ICET

作者： Tafreed Ahmed Ahmad Maaz Danyaal Mahmood Zain ul Abideen Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

引用

Automated software engineering 2024年第1期32卷 1-11页

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

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：