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Comparing Artificial intelligence Algorithms in Computer Vision: The Weapon Detection Benchmark 3rd

Comparing Artificial Intelligence Algorithms in Computer Vis...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Dentamaro, Vincenzo Giglio, Paolo Impedovo, Donato Pirlo, Giuseppe University of Bari "Aldo Moro" Bari Italy

ISBN: (纸本)9783031090363

The following work proposes a benchmark of performances of state of art AI algorithms for the weapons detection. Particularly, it is aimed to test three CNN based models on the task of detecting specific types of weapons. In order to accomplish this goal, four datasets are employed. Additionally, due to the lack of rich amounts of well-structured datasets in these field of research, new labeled data are produced as a new resource to test specific hypotheses about their impact on the performances of the models: different transfer-learning approaches are studied to understand how specific types of data could increase the generalization of the trained algorithms, with a peculiar attention to realistic scenarios. The whole work is designed with the intention to select the state-of-art algorithms that truly could be employed for realistic applications. © 2022, Springer Nature Switzerland AG.

关键词： Computer vision

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3D surface reconstruction of transparent objects using structured light with PMP-LIF method 3

3D surface reconstruction of transparent objects using struc...

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Zhang, Xinming Gu, Haorong Jiang, Hao Fu, Weijie School of Mechatronic Engineering and Automation Foshan University Foshan528001 China School of Mechanical and Electrical Engineering Changchun University of Technology Changchun130022 China Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing Changchun130022 China

ISBN: (纸本)9781510681880

Acquiring high-quality 3D information from transparent materials, which exhibit weak light scattering and absorption characteristics, presents a formidable challenge. In this paper, we introduce a novel three-dimensional reconstruction method called PMP-LIF (Phase Measurement Profilometry with Laser-Induced Fluorescence). This approach uses machine vision and incorporates structured light technology with laser-induced fluorescence to enhance the reliability and robustness of surface profile data for transparent objects by improving their reflectivity. In our study, we accomplished a three-dimensional reconstruction of surface profiles for transparent materials by employing a structured light pattern with a 544 nm wavelength, projected using a DLP (Digital Light Processing) projector. The image data was captured using a binocular vision system. Any distorted information within the image data was processed using image processing techniques, such as a phase unwrapping algorithm. The adoption of the PMP-LIF method for image data acquisition streamlines the complexity of the image acquisition system while preserving the accuracy of surface profile information. We conducted three-dimensional reconstruction tests on various objects, including human face shapes, on the surfaces of static liquids and transparent aspheric mirror blanks. The results reveal root-mean-square errors of 0.34 mm and 1.67 mm for the registration of the reconstructed point cloud information with fitting planes, respectively. © 2024 SPIE.

关键词： Binocular vision

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Streamlining Information Capture and Retrieval with AI-Enabled Systems 3

Streamlining Information Capture and Retrieval with AI-Enabl...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sudhamsu, Gadug Agarwal, Ankita Dhivrani, Rekha Bhanu, K.S. Jeyalaxmi, M. Nawadkar, Ashwini R. Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation. © 2023 IEEE.

关键词： information organizational predictive processing security

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The application of image Style Conversion Network in the design of Zhuang brocade pattern derivation 3

The application of image Style Conversion Network in the des...

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3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Meng, Kailin Shi, Zhuo Zhang, Mengxue Xiong, Biao Su, Qianli School of Art and Design Guilin University of Electronic Technology Guangxi Guilin541004 China School of Computer Science and Information Security School of Software Engineering Guangxi Guilin541004 China

ISBN: (纸本)9781510671485

Object: With the help of style conversion Network, the design of the Zhuang brocade pattern is derived to explore the new direction of inheritance and development of the Zhuang brocade pattern in the current era. Methods: The cultural connotation and design value of Zhuang brocade patterns are analyzed, and the selected Zhuang brocade patterns are used as content maps and different style maps to conduct style conversion Network experiments. The experimental results are analyzed and evaluated to find the optimal conversion solution. Investigate the effect of the conversed Zhuang brocade pattern in the derivative design. Result: By using style conversion Network, different styles of patterns can be generated quickly and can produce good derivative design effects in many products, thus achieving the purpose of integrating Zhuang brocade patterns with modern products. Conclusion: The addition of the style conversion Network algorithm makes the style simulation process significantly reduced, saving time for subsequent derivative designs and providing the possibility of mass production. The designs produced by the style conversion Network retain the original pattern characteristics of Zhuang brocade, with both auspicious symbolic meaning and national spirit, and realize the interaction between traditional crafts and the art field, in which the cultural heritage can also be shown, promoting Zhuang culture and providing new possibilities for the modern development of Zhuang brocade patterns. © 2023 SPIE.

关键词： Product design

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An Overview of 3GPP Release 17 & 18 Advancements in the Context of V2X Technology 5

An Overview of 3GPP Release 17 & 18 Advancements in the Cont...

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5th international conference on Artificial intelligence in Information and Communication (ICAIIC)

作者： Saad, Malik Muhammad Tariq, Muhammad Ashar Seo, Junho Kim, Dongkyun Kyungpook Natl Univ Sch Comp Sci & Engn Sangju Si South Korea

ISBN: (纸本)9781665456456

The 3rd Generation Partnership Project (3GPP) for the first time introduced the NR-V2X standard in its Rel.16, which is progressively improved by the researchers and industrial verticals to provide ubiquitous connectivity to the Internet-of-Vehicles (IoV). The 3GPP recently finalized NR-V2X standardization in its Rel.17. This paper overviews the recent technological advancements made in Rel. 17. Moreover, this paper summarizes the work items proposed for the 3GPP Rel. 18 and identified the potential research opportunities. The work items included in 3GPP Rel. 18 are primarily focused on integrating artificial intelligence/machine learning (AI/ML) into the core radio access network (C-RAN) to boost network performance. The 3GPP plenary consensus on Rel. 18 as an evolution to 5G-Advanced. This paper presents in-depth insights related to NR-V2X and its evolution to 5G-Advanced V2X. Energy efficiency, beamforming management, relaying User Equipment (UE), sidelink innovations, and Reduced device Capabilities (RedCAP) are the key aspects of the 5G-Advanced V2X.

关键词： NR-V2X 5G-Advanced Re-evaluation mechanism NR-V2X Mode 2 Resource Allocation

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Optimized Algorithm Design for Text similarity Detection Based on Artificial intelligence and Natural Language Processing 3

Optimized Algorithm Design for Text similarity Detection Bas...

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3rd international conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Zhe Zhu, Jiajia Cheng, Xinyun Lu, Qingqiang State Grid Jiangsu Elecric Power Co. Ltd Information and Telecommunication Branch Jiangsu Nanjing210024 China Co. Ltd Jiangsu Nanjing210000 China

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection. © 2023 The Authors. Published by Elsevier B.V.

关键词： Natural language processing systems

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Tabular Data Interpretation on COVID-19 Through Explainable machine Learning 3rd

Tabular Data Interpretation on COVID-19 Through Explainable...

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3rd international conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Jo, Ashly Ann Paul, Caina Rose Arundhathi Nair, S. Thomas, Jeena Raj, Ebin Deni Department of Computer Science and Engineering Indian Institute of Information Technology Valavoor—Chakkampuzha Rd Kottayam686635 India

ISBN: (纸本)9789819939626

Over the last decade, several AutoML techniques have been introduced for developing fully automated ML pipelines, from preprocessing to final deployment. Researchers have shown that consumers do not have trust in AutoML-generated models because of data gaps and errors. Despite the fact that such autonomously synthesized ML pipelines are capable of achieving a comparable performance. The lack of explanations for the built-in ML pipelines and the transparency of AutoML systems affect the clinical decisions in Healthcare. The paper builds on a substantial body of related work in the fields of trust in artificial intelligence (AI), XAI, AutoML, and explaining the decision-making of AutoML systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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EFFICIENT DDoS ATTACK DETECTION USING machine LEARNING TECHNIQUES 3

EFFICIENT DDoS ATTACK DETECTION USING MACHINE LEARNING TECHN...

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3rd IEEE international Power and Renewable Energy conference (IPRECON)

作者： Nazarudeen, Fathima Sundar, Sumod TKM Coll Engn Ctr Artificial Intelligence Kollam Kerala India

ISBN: (纸本)9781665491754

Distributed Denial-of-Service (DDoS) attacks are deliberate attempts to interrupt the regular traffic of a specific server, network, organization, by flooding the victim or its neighbouring servers with network traffic. Identification of such attacks using various models is challenging due to the substantial modifications in their regular pattern and traffic rates. An automated detection approach is used to mitigate this issue, by limiting the feature space, which minimizes the model's overfitting and computational time. The CICDDoS2019 data set containing extensive DDoS attacks are used to train and access the proposed methodology in a cloud-based context. The relevant features are extracted using the Extra Tree classifier and they are fed to the Decision Tree, XGBoost, and Random Forest. Consequently, the proposed model can be used to detect DDoS attacks effectively.

关键词： CICDDoS2019 dataset DDoS attacks Data preprocessing Feature selection Classification

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BotNet Attack Detection Using MALO-Based XGBoost Model in IoT Environment 3rd

BotNet Attack Detection Using MALO-Based XGBoost Model in Io...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Alzubi, Omar A. Faculty of Artificial Intelligence Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819726707

The Internet of Things (IoT) has flourished and found numerous practical applications thanks to the advent of energy-aware sensor devices, as well as autonomous and brainy systems. But the Internet of Things gadgets are especially vulnerable to botnet assaults. To counteract this risk, it may be possible to design an anomaly-based detection method that is both lightweight and capable of creating profiles for both malicious and benign behaviors on IoT networks. machine learning (ML) procedures may also be used for the mountain of data produced by IoT devices. Various tactics have been used to identify the Botnet's initial point of entry. However, the work remains challenging due to the limited sum of characteristics encompassed in Botnet datasets. Nine industrial-grade IoT devices were hacked in this study, and XGBoost and B models were constructed to identify and categorize common IoT botnet threats including Mirai and BASHLITE. Rigid hyperparameter tuning using a variant of the ant lion optimization algorithm (ALOA) formed the basis of the model development process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bot (Internet)

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SHARP: Segmentation of Hands and Arms by Range Using Pseudo-depth for Enhanced Egocentric 3D Hand Pose Estimation and Action recognition 27th

SHARP: Segmentation of Hands and Arms by Range Using Pseu...

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27th international conference on pattern recognition, ICPR 2024

作者： Mucha, Wiktor Wray, Michael Kampel, Martin Computer Vision Lab TU Wien Favoritenstr. 9/193-1 Vienna1040 Austria University of Bristol Bristol United Kingdom

ISBN: (纸本)9783031783531

Hand pose represents key information for action recognition in the egocentric perspective, where the user is interacting with objects. We propose to improve egocentric 3D hand pose estimation based on RGB frames only by using pseudo-depth images. Incorporating state-of-the-art single RGB image depth estimation techniques, we generate pseudo-depth representations of the frames and use distance knowledge to segment irrelevant parts of the scene. The resulting depth maps are then used as segmentation masks for the RGB frames. Experimental results on H2O Dataset confirm the high accuracy of the estimated pose with our method in an action recognition task. The 3D hand pose, together with information from object detection, is processed by a transformer-based action recognition network, resulting in an accuracy of 91.73%, outperforming all state-of-the-art methods. Estimations of 3D hand pose result in competitive performance with existing methods with a mean pose error of 28.66 mm. This method opens up new possibilities for employing distance information in egocentric 3D hand pose estimation without relying on depth sensors. The code is available under https://***/wiktormucha/SHARP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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