检索结果-内蒙古大学图书馆

20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Akalin, Neziha Arnelid, Maria Harrison, Katherine School of Engineering Jönköping University Dept. of Computer Science and Informatics Jönköping Sweden Linköping University Unit of Gender Studies Dept. of Thematic Studies Linköping Sweden

ISBN: (纸本)9798350378931

This paper emphasizes the importance of interdisciplinary collaboration in exploring the concept of robot gender within Human-Robot Interaction (HRI). It draws on a case study of the authors' own collaboration, where interdisciplinary discussions on the nature of gender informed the design of a multimethod study. This approach allowed us to avoid assigning a binary gender to Pepper robot, while still encouraging participants to reflect on their own gendering practices during interactions with robots. Additionally, we invite readers to consider alternative ways of conceptualizing robot gender in HRI - specifically, as fluid and performative rather than binary, which relies on stereotypical cues. After describing how our discussions on gender influenced and reshaped the study design, we offer practical advice on fostering interdisciplinary collaborations. These suggestions focus on communication strategies, mindset, and the practical setup of collaborative studies. We hope that these recommendations will inspire other researchers to continue exploring new and interdisciplinary ways of approaching robot gender in HRI. © 2025 IEEE.

关键词： Social robots

来源：评论

学校读者我要写书评

暂无评论

Transforming Synthesized Netlists into a Feasible & Compliant Functional Bookshelf Format 24

Transforming Synthesized Netlists into a Feasible & Complian...

引用

Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： George Kranas Kosmas Kariotis Antonios Dadaliaris George Stamoulis Athanasios Kakarountas Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece Dept. of Informatics & Telecommunications University of Thessaly Lamia Greece Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

引用

2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： deep learning DenseNet-201 Kellgren-Lawrence grading Knee osteoarthritis knowledge distillation.

来源：评论

学校读者我要写书评

暂无评论

Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images

引用

Procedia computer Science 2025年 252卷 108-117页

作者： Umme Sara Utpol Kanti Das Juel Sikder Sudipta Roy Chowdhury Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar-4500 Rangamati Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%.

关键词： Skin Lesion Cancer-CNN (SLC-CNN) Skin Cancer Deep Learning Classification Segmentation

来源：评论

学校读者我要写书评

暂无评论

Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201

Knee Osteoarthritis detection and classification using a cus...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： N. Shirisha K. Shilpa CH. Meghana Mohammed Abdul Rahman Dept. of Computer Science and Engineering MLR Institute of Technology Hyderabad India Dept. of Computer Science and Engineering MLR Institute of Technology Nalgonda India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Training Deep learning Accuracy Computational modeling Magnetic resonance imaging Computed tomography X-rays Feature extraction Osteoarthritis Testing

来源：评论

学校读者我要写书评

暂无评论

2D Numerical Modeling of Pavements on Expansive Soil in Extreme Climates

2D Numerical Modeling of Pavements on Expansive Soil in Extr...

引用

Geotechnical Frontiers 2025: Geotechnics of Natural Hazards

作者： Agarwal, Ekansh Zhang, Xiong Luo, Ning Dept. of Engineering College of Engineering and Computer Science Texas A&M Univ.–Corpus Christi Corpus ChristiTX United States Dept. of Civil Architectural and Environmental Engineering Missouri Univ. of Science and Technology RollaMO United States

ISBN: (数字)9780784485996

This study focuses on modeling a rigid pavement slab on expansive soil in extreme climates to understand the interaction between weather, pavement, and soil. Lightweight superstructures such as pavements are particularly prone to damage due to the differential settlement of expansive soils. In the context of climate change, the occurrence of severe droughts followed by intense rainfall has risen, causing severe impacts on crack initiation and development, and thus infiltration patterns in the structures built on expansive soils. Pavements are typically designed for a limited lifespan, and early failure may occur if climate change is not considered during design. This is especially important in coastal areas when coastal flooding becomes more frequent. The interplay between climates, soils, and pavements leads to a spatial and temporal change in soil moisture and even soil structure, causing significant variations of volume change. Despite the dominant impact of expansive soils on pavement systems, a deep understanding of their role in pavement structural behavior remains challenging to both the researchers and the practitioners. This paper addresses this gap through a comprehensive numerical study using two-dimensional (2D) finite element modeling. We adopted the unsaturated soil constitutive relationship to understand the stress changes during the expansion and shrinkage of expansive soil. We employed four user-defined subroutines in ABAQUS to perform a coupled hydro-mechanical stress analysis. The study uses historical climate events as a reference to assess the impact of climate events on pavements. Mechanical, hydraulic, and soil-vegetation-atmosphere (SVA) boundary conditions are considered. The numerical findings highlight the vulnerability of pavements founded on expansive soils to climate change-induced events and indicate that a coupled hydro-climate-geotechnical analysis is necessary. Unprecedented climatic events lead to more uneven settling of pavements

关键词： Subroutines

来源：评论

学校读者我要写书评

暂无评论

AlertCam-An Effective Anomaly Detection System using computer Vision

AlertCam-An Effective Anomaly Detection System using Compute...

引用

Emerging Smart Computing and informatics (ESCI), Conference on

作者： Santushti Betgeri Piyush Lodha Aneesh Oak Aryan Parikh Ayush Lodha Dept. Of Computer Engineering Vishwakarma Institute of Technology Pune

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

With the rising incidents of harassment and violence in India, particularly against women, this paper presents a real-time violence detection system using YOLO image detection models (YOLOv7, YOLOv8, and YOLOv9). The models were trained on a carefully curated and manually annotated dataset using Roboflow, ensuring robust detection across diverse scenarios. Transfer learning and hyperparameter tuning were applied to improve accuracy. The system was evaluated using key performance metrics, including mean average precision (mAP), Intersection over Union (IoU), precision, and recall, achieving 80% precision and 50–60% recall. Extensive manual testing on real-world surveillance footage and additional video sources further validated its effectiveness. Results indicate that YOLOv8 outperforms other versions in balancing speed and accuracy. This system has strong potential for deployment in law enforcement, public surveillance, and safety-critical applications.

关键词： YOLO Accuracy Surveillance Scalability Transfer learning Real-time systems Safety Tuning Standards Testing

来源：评论

学校读者我要写书评

暂无评论

Decentralization, Customization, and Emergency Assistance: The Next Generation of Ride-Sharing

Decentralization, Customization, and Emergency Assistance: T...

引用

Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Avani Acharya Pradeep Kanchan Ashlin Dsouza Chaithra M Dept. of Computer Science and Engineering NMAM Institute of Technology

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mobile apps for taxi-hailing and carpooling have become very popular to tackle city problems like traffic jams, pollution, and high costs. This review of research aims to spot the main trends in this field and look at different approaches across key areas. We dig into models that don't have a central control using blockchain tech safe options with anonymous IDs and encryption based on traits quick systems to match rides in real-time, ways to manage how well-liked services are to build trust, plans to get more people carpooling, and how ride-sharing affects various groups of users and rules. On top of these current trends, we suggest a new feature of smart contract customization to give ride-sharing platforms more flexibility and the ability to adapt. This customization could allow users and platforms to adjust contracts for specific situations, like changing prices, insurance choices, or extra services. We also support adding real-time ride-matching systems and emergency help options to make users safer and more comfortable. By looking at many studies, we spot promising areas to develop in the future and suggest ways to explore further to improve ride-sharing tech and make it more available to different groups of people.

关键词： Technological innovation Privacy Pollution Shared transport Reviews Urban areas Smart contracts Market research Real-time systems Blockchains

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Phishing Website Detection System - Natural Language Processing Approach 2

Machine Learning Based Phishing Website Detection System - N...

引用

2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Naik, S. Vinay Harika, K. Bahadure, Nilesh Bhaskarrao Verma, Kamal Kant Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

Natural language processing (NLP) methods can be used to identify phishing websites in addition to static and dynamic features. Phishing sites frequently include certain phrases, misspellings, or misleading text patterns in their material, as well as other visual cues and language that match those of authentic websites. Through the examination of textual components such anchor texts, meta-descriptions, and homepage titles. The proposed model is able to comprehend the linguistic traits that are frequently seen in phishing websites using NLP-based features like sentiment analysis, keyword matching, and phrase frequency to increase model's sensitivity. In addition, we use real-time URL parsing to detect possible dangers as users come across them, guaranteeing prompt notifications and lowering the possibility that phishing attempts would be successful. Our study emphasizes how crucial it is to keep improving and upgrading phishing detection models because attackers usually change their strategies. © 2025 IEEE.

关键词： Phishing

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：