检索结果-内蒙古大学图书馆

Smart office automation via faster R-CNN based face recognition and internet of things

Measurement: Sensors 2023年 27卷

作者： Rajeshkumar, G. Braveen, M. Venkatesh, R. Josephin Shermila, P. Ganesh Prabu, B. Veerasamy, B. Bharathi, B. Jeyam, A. Department of Information Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu 638 401 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Information Technology PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tamil Nadu Chennai India Department of Electrical and Electronics Engineering University College of Engineering Dindigul Tamilnadu India Department of ECE Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu 626126 India Tamilnadu Coimbatore641109 India Computer Science and Engineering Lord Jegannath College of Engineering and Technology Tamilnadu 629402 India

Many constraints limit the accuracy level of classification of a face recognition system in smart office automation application, and these limitations make mask face recognition an important research area. In this research, a novel deep learning based Faster R-CNN which integrates with Internet of Things (IoT) to overcome the security issues in the office. The images of existing employees were gathered in a database and these images are pre-processed to train the neural network. Faster R-CNN employs VGG-16 as the foundation of its architecture to extract the features from pre-processed pictures. The recent development in Internet of Things (IoT) and deep learning have made it possible to addressing the difficulties of face recognition with deep neural network. Based on the feature classification, when a member of an organization approaches the door, it instantly opens. The door remains locked if it is an unknown individual. The images of a both authorized and unauthorized person were stored in a cloud and send it to the office manager for monitoring. The proposed Faster R-CNN model attain the accuracy range 99.3% better than the existing system. The proposed Faster R-CNN improves the overall accuracy ranges of 2.06%, 5.63%, 9.36%, and 3.54% better than Deep CNN, SVM, LBPH, and OMTCNN respectively. © 2023

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants

Automatic Concrete Surface Crack Recognition Using Efficient...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： A. Rama Robertas Damaševičius S. Arunmozhi Mazin Abed Mohammed Ragheed Hussam V. Rajinikanth Department of CSE Saveetha School of Engineering SIMATS Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Department of Electronics and Communication Engineering Manakula Vinayagar Institute of Technology Puducherry TN India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar Iraq Medical instruments engineering techniques Al-farahidi University Baghdad Iraq Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai TN India

Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Lower Limb Muscle State Transition Control Based on Complex Network

Lower Limb Muscle State Transition Control Based on Complex ...

引用

Chinese Control Conference (CCC)

作者： Junjie Geng Lingling Chen Xin Guo Rui Chen Mengge Wang School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Intelligent Rehabilitation Device and Detection Technology Engineering Research Centre of the Ministry of Education Tianjin China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. China School of Electrical Engineering Hebei University of Technology Tianjin China

Muscle activation cannot achieve the activation state experienced during normal activity throughout the rehabilitation process, and effectiveness of rehabilitation will be enhanced if the muscle state can be controlled from the “zero EMG” state to the normal motion state. It is challenging to precisely regulate the muscle state during the model establishment process because the lower limb muscle group is a highly interconnected complicated system. Therefore, this study provides a muscle state transfer control method based on complex network control theory. A linear time-invariant functional myoelectric network model is modeled, and the structural controllability of the network is demonstrated using the PBH rank criterion to determine the optimal set of control nodes. Functional Electrical Stimulation is used as an exogenous input to drive state transfer between muscles. The results suggest that the method we propose is effective in controlling muscle state transitions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Toward multi-target self-organizing pursuit in a partially observable Markov game

arXiv

引用

arXiv 2022年

作者： Sun, Lijun Chang, Yu-Cheng Lyu, Chao Shi, Ye Shi, Yuhui Lin, Chin-Teng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Artificial Intelligence CIBCI Lab Faculty of Engineering and Information Technology University of Technology Sydney Australia College of Computer and Information Science Southwest University China School of Information Science and Technology ShanghaiTech University China

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm-fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100% capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent agents

来源：评论

学校读者我要写书评

暂无评论

AI Technological Interference in Court Proceedings: Right to Fair Trial Decision

AI Technological Interference in Court Proceedings: Right to...

引用

Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Santosh Kumar Kapil Joshi Sumeshwar Singh Harishchander Anandaram Kawerinder Singh Sindhu Satish Kumar Mahariya Vishal Kaushik Department of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida UP India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Coimbatore India Uttaranchal University Dehradun India GSSS Mahawa Neemkathana Sikar Rajasthan India School of Computer Science UPES India

The given study focuses on the influence of artificial intelligence (AI) on adjudicatory procedures mostly on observance of such right to an impartial trial in several law-related fields with respect to existing decisions. The consideration of such substance of the right to an impartial trial serves as the beginning stage for the debate, with a special focus on the autonomy and impartiality of the judiciary. Following that, the article analyses the prospect of minimizing the individual role in court processes in charge of making decisions using information technologies. This paper analyses the admissibility of using AI tools in the judiciary and contains considerations on ethical aspects of AI application in judicial proceedings and whether an AI system can take over the role of a decision-maker in judicial proceedings, thereby replacing or supporting the judge. The paper with a view of its legal compliance, non-discrimination, transparency, and efficiency of legal proceedings. The study employs the method of analyzing the law in force and investigating theories of law, taking into account the critical analysis of the literature at the decision level.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

arXiv

引用

arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A transformer based multi task learning approach to multimodal hate speech detection

Natural Language Processing Journal

引用

Natural Language Processing Journal 2025年 11卷

作者： Prashant Kapil Asif Ekbal Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar India School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur Rajasthan India

Online hate speech has become a major social issue in recent years, affecting both individuals and society as a whole. Memes are a multimodal kind of internet hate speech that is growing more common. Online memes are often entertaining and harmless. The seemingly innocent meme, on the other hand, transforms into a multimodal form of hate speech—a hateful meme—when specific types of text, graphics, or combinations of both are used. The spread of these harmful or undesirable memes has the potential to disrupt societal peace. Therefore, it is vital to limit inappropriate memes on social media. Multimodal hate speech identification is an inherently difficult and open question. It necessitates collaborative language, visual perception, and multimodal reasoning. This line of research has been progressed in this work by building a multi-task learning-based multimodal system for detecting hateful memes by training four hateful meme data sets concurrently. This MTL framework, which consists of Contrastive Language Image Pretraining (CLIP), UNiversal Image-TExt Representation Learning (UNITER), and BERT, was trained collaboratively to transfer common knowledge while simultaneously training four meme datasets. The results show that the recommended strategy outperforms unimodal and multimodal approaches on four multilingual benchmark datasets, with considerable AUC-ROC, accuracy, and F1-score. The ablation studies are undertaken to emphasise the impact of the sub-component in the MTL model. The confusion matrix is shown as quantitative analysis.

关键词： Multi-task learning UNITER CLIP AUC-ROC F1 score

来源：评论

学校读者我要写书评

暂无评论

Quantifying the Impact of AI and Machine Learning on data Access Optimization

Quantifying the Impact of AI and Machine Learning on Data Ac...

引用

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Mahesh TR M. Rajeswari Prranjali Jadhav Mansingh Meena Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Quantifying the impact of synthetic intelligence (AI) and machine getting to know (ML) on information access Optimization (DAO) and its ability to enhance overall performance and performance inside organizations has been a topic of research for decades. This newsletter investigates how such technology is essential in optimizing records access and its effect on the rate and accuracy of operations. We speak about how cutting-edge advances in AI and ML can be utilized to detect patterns in facts, reduce latency, and improve the general quality of data. Furthermore, we discuss how that technology can reduce manual techniques and get rid of errors due to human mistakes, hence enhancing performance and productiveness. Sooner or later, we talk about the main challenges associated with enforcing AI and ML within a company and identify capacity areas of development.

关键词： Performance evaluation Machine learning algorithms Machine learning Companies Manuals Reliability Object recognition

来源：评论

学校读者我要写书评

暂无评论

Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network

Predictive Maintenance in IoT Networks Using a Temporal Atte...

引用

Innovation in Technology (ASIANCON), Asian Conference on

作者： Naga Venkata Ramakrishna Guduri R.M. Dilip Charaan Kalaiyarasi R Samuthira Pandi V D.Laxma Reddy Selvaprabhu. T Faculty of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of CSE Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types.

关键词： Adaptation models Temperature distribution Technological innovation Attention mechanisms Accuracy Predictive models data models Real-time systems Convolutional neural networks Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

引用

arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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：