检索结果-内蒙古大学图书馆

2024 1st International Conference on Logistics (ICL)

作者： Marriam Nawaz Tahira Nazir Ali Javed Momina Masood Hussain Dawood Department of Software Engineering University of Engineering and Technology Taxila Pakistan Department of Computing Riphah International University Islamabad Pakistan Skyline University College Sharjah United Arab

ISBN: (数字)9798331518844

ISBN: (纸本)9798331518851

Melanoma is a malicious form of skin cancer and can develop wounds on the entire human body which can lead to the victim's demise at the advanced level. The timely and accurate detection of melanoma moles can prevent the victims from painful treatment and surgeries. However., immense similarity among the healthy and diseased areas and massive variations in the mass, contour., and shades of the melanoma moles complicate the recognition process. Here., we have employed a deep learning (DL) framework named the CornerNet model for the localization and categorization of skin lesions. The Hourglass-oriented CornerNet approach is used for deep features calculations which are later grouped by the one-step object recognizer network of the employed solution. The approach is tested on a standard data sample called the ISIC-2016 database and secured the average mAP and IOU values of 0.916., and 0.921 with an accuracy value of 90.08%. The model testing confirmed that our solution is vigorous for melanoma mole detection and cataloging.

关键词： Accuracy Databases Object oriented modeling Surgery Melanoma Wounds Feature extraction Skin Lesions Testing

来源：评论

学校读者我要写书评

暂无评论

Age-Based Differences in Drone Control Gestures: An Exploratory Study 23

Age-Based Differences in Drone Control Gestures: An Explorat...

引用

Proceedings of the 35th Australian Computer-Human Interaction Conference

作者： Julia Hermann Aysegül Dogangün Marc Hesenius Institute of Positive Computing Hochschule Ruhr West - University of Applied Science Germany Institute of Positive Computing Hochschule Ruhr West Germany Institute for Software Engineering University of Duisburg-Essen Germany

ISBN: (纸本)9798400717079

In addition to cultural background and professional context, age can also influence user preferences for gestures when controlling drones. In this paper, we present an exploratory study of age-based differences in gesture selection and design for drone control with 24 participants aged 18 to 69. We investigated the impact of age on the shape, type, body parts used, and number of partial gestures employed for drone direction and interaction commands. Firstly, we quantified the number and type of gestures utilized by each age group for specific commands, revealing significant disparities between older and younger participants. Additionally, we explore the qualitative aspects of gesture control by analyzing participants’ descriptions and verbalized thoughts, uncovering further differences and similarities. Overall, our paper contributes to the growing body of research on Human-Drone Interaction by highlighting the significance of age in gesture control and its consequences for the individual User Experience. By incorporating these findings into future design practices, we can advance the field and promote inclusive drone interfaces that accommodate users of all ages.

关键词： age groups drones gestures human-drone interaction uav

来源：评论

学校读者我要写书评

暂无评论

A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

引用

16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey of Techniques for Lung Cancer Diagnosis and Prediction

A Comprehensive Survey of Techniques for Lung Cancer Diagnos...

引用

Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Danish Jamil Syed Mehr Ali Shah Fatma Mahfoodh Mohammed Al-Jarwani Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan College of Computing and Information Sciences University of Technology and Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

Despite significant advancements, lung cancer remains a formidable global health challenge, necessitating effective diagnostic and prognostic methodologies. This survey paper examines current literature to identify and evaluate methods and models utilized for lung cancer diagnosis and prognosis, aiming to enhance early detection strategies for improved patient outcomes. The research addresses critical questions: What are the current methods and models employed for lung cancer diagnosis and prognosis, their strengths, limitations, and potential for future improvement? It investigates specific applications of deep learning architectures such as CNN, GoogleNet, VGG-16, U-Net, and machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models in lung cancer detection. Methodologically, a systematic review across key databases using relevant keywords was conducted to synthesize findings. The study highlights the strengths and limitations of existing approaches and identifies gaps in model interpretability, real-world validation, and integration of diverse techniques. Conclusions underscore the transformative potential of advanced computational methods in enhancing lung cancer diagnosis and prognosis, proposing avenues for future research to address current challenges and improve patient care.

关键词： Surveys Deep learning Machine learning algorithms Accuracy Computational modeling Transfer learning Lung cancer Medical services Prognostics and health management Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Self-Bidirectional Decoupled Distillation for Time Series Classification

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4101-4110页

作者： Xiao, Zhiwen Xing, Huanlai Qu, Rong Li, Hui Feng, Li Zhao, Bowen Yang, Jiayi Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China University of Nottingham School of Computer Science NottinghamNG7 2RD 455356 United Kingdom Xi'an Jiaotong University School of Mathematics and Statistics Xi'an710049 China Tongji University School of Software Engineering Shanghai201804 China

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model's performance usually depends on the quality of the semantic information extracted from lower and higher levels within the representation hierarchy. Efficiently promoting mutual learning between higher and lower levels is vital to enhance the model's performance during model learning. To this end, we propose a self-bidirectional decoupled distillation (self-BiDecKD) method for TSC. Unlike most self-distillation algorithms that usually transfer the target-class knowledge from higher to lower levels, self-BiDecKD encourages the output of the output layer and the output of each lower level block to form a bidirectional decoupled knowledge distillation (KD) pair. The bidirectional decoupled KD promotes mutual learning between lower and higher level semantic information and extracts the knowledge hidden in the target and nontarget classes, helping self-BiDecKD capture rich representations from the data. Experimental results show that compared with a number of self-distillation algorithms, self-BiDecKD wins 35 out of 85 University of California, Riverside (UCR) 2018 datasets and achieves the smallest AVeraGe (AVG)_rank score, namely 3.2882. In particular, compared with a nonself-distillation baseline, self-BiDecKD results in 58/8/19 regarding 'win'/'tie'/'lose.' © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning

Enhancing Topic Interpretability for Neural Topic Modeling T...

引用

International Conference on Data engineering

作者： Xin Gao Yang Lin Ruiqing Li Yasha Wang Xu Chu Xinyu Ma Hailong Yu Software Engineering Institution School of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs.

关键词： Training Measurement Contrastive learning Coherence Knowledge discovery Data engineering Data models

来源：评论

学校读者我要写书评

暂无评论

R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Severity Prediction Model

R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Seve...

引用

IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Albe Bing Zhe Chai Bee Theng Lau Mark Kit Tsun Tee Chris McCarthy Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

The tremendous impact of road traffic accidents (RTAs) remains a global concern, especially in low- and middle-income countries (LMICs). While most LMICs have limited budget to afford advanced traffic monitoring systems, it is necessary to develop feasible solutions for enhancing road safety in these regions. Recent studies have extensively explored the applicability of machine learning (ML) models to predict RTA severity in different regions. However, there is limited exploration of training and evaluating the performance of ML model from a multi-region perspective. Further investigation is also necessary to formulate a cost-effective implementation strategy for LMICs to deploy ML models for real-time prediction. Hence, this paper contributes by proposing R-TAP, which deploys a TabNet-based model on the cloud for multi-region RTA severity predictions. A web application is also developed to receive inputs and visualise the prediction results. The TabNet model is trained on a multi-region dataset consisting of 42 features, produced by combining the individual RTA datasets collected from Australia, Brazil, Ethiopia, New Zealand, the UK, and the US. Evaluation with random forest and multinomial logistic regression showed that TabNet can achieve performance comparable to random forest. It also has the highest area under the receiver operating characteristics at 0.8857. R-TAP is also simulated using testing samples as real-time input, with an overall execution time of 4.104s per prediction. It is considered satisfactory and is expected to be useful for emergency responders to make informed decisions. This solution is expected to support LMICs in their roadmap towards enhanced road safety. Future research is essential to improve the predictive power and include datasets from more regions for a more inclusive RTA severity prediction.

关键词： Training Visualization Receivers Predictive models Road traffic Road safety Real-time systems Random forests Accidents Testing

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review on Test Case Prioritization in Combinatorial Testing 21

A Systematic Literature Review on Test Case Prioritization i...

引用

Proceedings of the 5th International Conference on Algorithms, computing and Systems

作者： Muhammad Syafiq Abdul Manan Dayang Norhayati Abang Jawawi Johanna Ahmad Software Engineering School of Computing Universiti Teknologi Malaysia Malaysia

ISBN: (纸本)9781450385084

In recent years, regression testing is widely used and performed in industries during the maintenance phase of software system development. The increases of software regression test cases resulted in a higher complexity of the software system, due to the rapid change in the software development. Thus, many researchers had proposed the test cases prioritisation technique to overcome the raised issues. However, most of the applications needed the combinatorial testing due to the enormous size of the software inputs. The problem arises when the small t-way values were selected in combinatorial testing, which resulted in the ineffectiveness of the fault detection ability. This work presents the results of a systematic literature review on the test case prioritisation and combinatorial testing that report the main approach, problems, and evaluation metric that were being used from existing studies. This paper aims to identify the aspects of the test case prioritisation and combinatorial testing that have been studied before, provide a basis for the improvement, and evaluate the current trends of this research area. Apart from the recent works that were published in the last ten years, 20 papers were selected for further study in the systematic literature review. The results show that this area of study is open for improvement especially to efficiently generate the test cases, which minimise the number of test cases generated, and effectively prioritise the test cases that increase the fault detection ability in software testing. However, the use of both techniques still needs more enhancements for the existing proposed approach that could result in the increasing of the fault detection ability.

关键词： software testing Systematic literature review Test case prioritization Combinatorial testing

来源：评论

学校读者我要写书评

暂无评论

Socially-Critical software Systems: Is Extended Regulation Required? 1

引用

29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Dagg, Niall Kostick, Conor Fallon, James O’Neill, Alex Yilmaz, Murat Messnarz, Richard Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey ISCN the International Software Consulting Network Graz Austria Lero the Science Foundation Ireland Research Center for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Data has become a prevailing aspect of our daily lives, becoming ever more present since the beginning of the 21st century. It is a commodity in today’s world and the amount of data being produced has increased enormously. One of the major ways data is produced and collected is from the use of websites and web-based applications. This data is later used for many different purposes. This paper presents findings from a multivocal literature review, exploring the methods of how this data is collected, what the data is used for once it has been collected, the ethics of data and its collection, and the future of data collection. Among the possible futures, we introduce the concept of socially-critical applications, where data harvesting in web-based applications might require pre-market disclosure and evaluation by notified bodies (instructed by regulation) as a means to break the existing cycle of technology companies outpacing under-resourced and ill-equipped regulators. Rather than regulators continually falling short of enacting laws to satisfy the common good, a new class of socially-critical application could be created in law to permit pre-market evaluation of applications (or versions of applications) that could undermine or interrupt the common good. © 2022, Springer Nature Switzerland AG.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Operator Placement for IoT Data Streaming Applications in Edge computing Environment 22nd

Operator Placement for IoT Data Streaming Applications in ...

引用

22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Sixin Tang, Bing Yang, Qing Liu, Yuanyuan School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan411201 China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou510725 China

ISBN: (纸本)9783031226762

With the rapid development of IoT applications, how to process large and continuous data streams in real time for IoT applications has become a challenge in recent years. This paper presents a model to predict the Maximum Sustainable Throughput (MST) of data streaming applications at the edge, and then proposes GACP, a constraint programming-base operator placement strategy to maximize MST using the genetic algorithm. We evaluated the proposed operator placement strategy in terms of MST, the number of edge servers in usage, and the percentage of communications within edge servers. Experimental results show that compared with the existing heuristic algorithms, the proposed GACP strategy can achieve better MST, which is 2–5 times higher than existing algorithms. © 2023, Springer Nature Switzerland AG.

关键词： Edge computing

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：