检索结果-内蒙古大学图书馆

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Sukhvinder Jasim, L.H. Dari Dhote, Vijay Suseela, D. Venkatasubramanian, R. Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study explores innovative Privacy-Preserving Data Mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big Data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world. © 2023 IEEE.

关键词： Analytically Valuable Patterns Data Privacy Data Repositories Homomorphic Encryption and Utility Privacy-Preserving Data Mining Sensitive Information

来源：评论

学校读者我要写书评

暂无评论

Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational Networks 2

Cross-Temporal Snapshot Alignment for Dynamic Multi-Relation...

引用

2022 2nd International Conference on Electrical, Electronics and Computing Technology, EECT 2022

作者： Chen, Lvjia Liang, Shangsong School Of Computer Science And Engineering Sun Yat-sen University Guangzhou China Department Of Machine Learning Mohamed Bin Zayed University Of Artificial Intelligence Abu Dhabi United Arab Emirates

A dynamic network is often represented as a sequence of snapshots evolving over time. In certain real-world scenarios, the identities of nodes in snapshots of a dynamic network are unknown and need to be figured out. To deal with such a challenge, recently, the task of cross-temporal snapshot alignment for dynamic networks is proposed, which aims to match equivalent nodes across temporal snapshots of a dynamic network given a small set of identified nodes. However, in many dynamic multi-relational networks like temporal knowledge graphs, the relation type information of edges, which can be useful for the alignment task, is neglected by existing methods. In this paper, we focus on a similar task but pay special attention to dynamic multi-relational networks. We propose a Relation-Aware Cross-Temporal Snapshot Alignment model (RCTSA) that incorporates time-dependent topological structure information into temporal node embeddings and temporal relation embeddings. To disentangle time-dependent information and time-independent information of the dynamic multi-relational network, RCTSA maintains a time embedding for each snapshot to preserve the temporal information which is incorporated into entity embeddings and relation embeddings to get the temporal embeddings. After training, the learned entity embeddings of unidentified nodes together with time embeddings can be used for the alignment task. Experimental results on real-world dynamic multi-relational networks demonstrate the effectiveness of our model. © Published under licence by IOP Publishing Ltd.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Biomaterials and Their Applications

Biomedical Materials and Devices

引用

Biomedical Materials and Devices 2025年 1-16页

作者： Menagadevi, M. Nirmala, M. Thiyagarajan, D. Somasundram, D. Department of Computer Science and Engineering School of Engineering Malla Reddy University Hyderabad India Department of Biomedical Engineering Dr. N G P Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Hyderabad India Department of Micro & Nanoelectronics School of Electronics Engineering Vellore Institute of Technology Vellore India

A biomaterial is a biocompatible substance engineered to interact with biological systems for therapeutic and diagnostic, such as pacemakers, dental implants, vascular stents, artificial joints, drug delivery systems, tissue engineering, biosensors, image contrast agents in computer Tomography, Magnetic Resonance Imaging, optical imaging, and ultrasound. Biomaterials are also used as assays in point-of-care testing devices. Based on the organs’ biological properties, the biomaterials are mainly classified into metals, polymers, ceramics, and composite biomaterials. The success rate of the implant depends on the physical, chemical, and mechanical properties of the selected material. Each biomaterials have unique properties, so it is used in both soft and hard tissue replacements. In this review, various properties and applications of biomaterials are discussed. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Applications Biocompatibility Biomaterials Properties Types

来源：评论

学校读者我要写书评

暂无评论

A Framework for Rainfall Forecasting based Crop Analysis using Deep learning 9

A Framework for Rainfall Forecasting based Crop Analysis usi...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gokuldev, S. Selvapandian, D. Satheshkumar, K. Deepa, T. INurture Education Solutions Zonal Head South - IT Academics Bengaluru India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Learning Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Erode Sathyamangalam India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350397376

More than half of India's arable land is susceptible to drought, making the country's agriculture perpetually insecure. Crop productivity and agricultural investment are both negatively impacted by the monsoon's poor performance. Literature states that the existing statistical and numerical procedures often fail to meet the present requirements and strategies for recognizing the important input data for designing efficient rainfall and crop yield forecasting models. In this research, Adaptive Moment Optimization (AMO) based Long Short-term Memory Neural Networks (LSTM-NN) are developed for accurate seasonal rainfall forecasting. The AMO is used to find the relevant features for rainfall prediction and LSTM is used to predict the rainfall. The AMO has a higher learning rate and selects the relevant features that help to progress the performance of the model. The investigational outcomes demonstrated that the proposed strategy accomplished nearly 95% of accuracy in all cases. © 2023 IEEE.

关键词： Rain

来源：评论

学校读者我要写书评

暂无评论

Automating the Selection of Proxy Variables of Unmeasured Confounders 41

Automating the Selection of Proxy Variables of Unmeasured Co...

引用

41st International Conference on machine learning, ICML 2024

作者： Xie, Feng Chen, Zhengming Luo, Shanshan Miao, Wang Cai, Ruichu Geng, Zhi Department of Applied Statistics Beijing Technology and Business University Beijing China School of Computer Science Guangdong University of Technology Guangzhou510006 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Probability and Statistics Peking University Beijing China

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach. Copyright 2024 by the author(s)

关键词： Higher order statistics

来源：评论

学校读者我要写书评

暂无评论

Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

引用

IEEE/SICE International Symposium on System Integration

作者： Svetlana Seliunina Artem Otelepko Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available.

关键词： Reflectivity Adaptation models Laser radar Three-dimensional displays System integration Robot sensing systems Encoding Sensor systems Sensors Safety

来源：评论

学校读者我要写书评

暂无评论

Towards Conscious Service Robots

arXiv

引用

arXiv 2025年

作者： Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Deep learning’s success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain’s System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem solving

来源：评论

学校读者我要写书评

暂无评论

Real-Time Weapons Detection System using computer Vision 3

Real-Time Weapons Detection System using Computer Vision

引用

3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Nale, Pranav Gite, Shilpa Dharrao, Deepak Symbiosis Institute of Technology Department of Information Technology Maharashtra Pune412115 India Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Maharashtra Pune412115 India Symbiosis Institute of Technology Department of Computer Science and Engineering Maharashtra Pune412115 India

ISBN: (纸本)9798350370867

The growing use of Closed-Circuit Television (CCTV) systems in modern security applications has driven the need for automated surveillance through computer vision. The primary aim is to reduce human intervention while enhancing early threat detection and real-time security assessments. Although advanced surveillance technologies have facilitated monitoring, constant human oversight remains challenging. This has prompted a quest for models capable of identifying unlawful activities with minimal human involvement. Real-time weapon detection, despite advancements in deep learning algorithms and dedicated CCTV cameras, remains a formidable challenge, especially with varying angles and potential obstructions. Existing detection systems are often expensive and require specialized tools, necessitating a cost-effective and reliable alternative that minimizes false positives. This research focuses on creating a secure environment by utilizing real-time resources and deep-learning algorithms for identifying dangerous weapons. Without a predefined dataset for real-time detection, the researchers compiled one from diverse sources, including camera shots, internet images, movie data, YouTube CCTV recordings, and Roboflow computer Vision Datasets. The proposed weapon detection system employs a hybrid model of Detectron2 and YOLOv7, emphasizing precision and recall in object detection, particularly in challenging conditions like lowlight environments. This research contributes to developing an effective, reliable real-time weapon detection system tailored for diverse scenarios. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

learning Dynamic Similarity by Bidirectional Hierarchical Sliding Semantic Probe for Efficient Text Video Retrieval 39

Learning Dynamic Similarity by Bidirectional Hierarchical Sl...

引用

39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Liu, Yang Huang, Shudong Xiong, Deng Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu China Department of Mechanical Engineering Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)157735897X

Text-video retrieval is a foundation task in multi-modal research which aims to align texts and videos in the embedding space. The key challenge is to learn the similarity between videos and texts. A conventional approach involves directly aligning video-text pairs using cosine similarity. However, due to the disparity in the information conveyed by videos and texts, i.e., a single video can be described from multiple perspectives, the retrieval accuracy is suboptimal. An alternative approach employs cross-modal interaction to enable videos to dynamically acquire distinct features from various texts, thus facilitating similarity calculations. Nevertheless, this solution incurs a computational complexity of O(n2) during retrieval. To this end, this paper proposes a novel method called Bidirectional Hierarchical Sliding Semantic Probe (BiHSSP), which calculates dynamic similarity between videos and texts with O(n) complexity during retrieval. We introduce a hierarchical semantic probe module that learns semantic probes at different scales for both video and text features. Semantic probe involves a sliding calculation of the cross-correlation between semantic probes at different scales and embeddings from another modality, allowing for dynamic similarity computation between video and text descriptions from various perspectives. Specifically, for text descriptions from different angles, we calculate the similarity at different locations within the video features and vice versa. This approach preserves the complete information of the video while addressing the issue of unequal information between video and text without requiring cross-modal interaction. Additionally, our method can function as a plug- and-play module across various methods, thereby enhancing the corresponding performance. Experimental results demonstrate that our BiHSSP significantly outperforms the baseline. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All

关键词：

来源：评论

学校读者我要写书评

暂无评论

Investigation on Digital Forensic Using Graph Based Neural Network with Blockchain Technology 2

Investigation on Digital Forensic Using Graph Based Neural N...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Bhujang, Raghavi K. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. Information Technology University of the Cumberlands United States Ms Ramaiah Institute of Management Department of Entrepreneurship Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science and Engineering Bms Institute of Technology and Management Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Digital forensics has evolved to focus on leveraging social media evidence, offering substantial support for investigating various crimes. However, scrutinizing social media data for evidence presents significant challenges for authorities. This article proposes employing blockchain infrastructure and natural language processing (NLP) techniques as foundational elements in digital forensic investigations. Specifically, it advocates for a Graph Convolutional Neural Network (GCNN) based approach to multi-class classification. NLP is integral to various stages of this process, including data collection, feature selection, classifier assessment, and vectorization. Moreover, to enhance data security against hackers and network attacks, a blockchain-based system is utilized. Real-world dataset experimentation demonstrates the efficacy of the proposed system. The findings reveal significant improvements in GCNN classification performance when incorporating topology knowledge. Furthermore, optimizing the model necessitates considering various input factors, such as document length, training data representation, and network architecture. This structured approach to digital forensic investigation holds promise for bolstering law enforcement efforts. © 2024 IEEE.

关键词： Graph neural networks

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：