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Gait recognition Algorithm GCGait based on Skeleton

Gait Recognition Algorithm GCGait based on Skeleton

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Information Technology,Big Data and Artificial intelligence (ICIBA), international conference on IEEE international conference on

作者： Ru Ma Jikai Zhang Mengyu Jia Xiaoqi Lv School of Information Engineering Inner Mongolia University of Science and Technology Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China School of Information Engineering Inner Mongolia University of Technology Hohhot China

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method.

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Automating Big Data Analysis Using Artificial intelligence 3

Automating Big Data Analysis Using Artificial Intelligence

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

作者： Bhimanpallewar, Ratnmala N. Jangid, Satish Kumar Yadav, Rakesh Kumar Arunkumar, D.T. Dehankar, Pooja Geetha, B. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India School of Engineering Ajeenkya D. Y. Patil University Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai128 India

ISBN: (纸本)9798350319125

Huge information evaluation, the use of synthetic intelligence, is a manner of using AI structures to automate the system of extracting meaningful insights from massive sets of statistics. A combination of AI-driven algorithms and hardware may be used to quickly perceive styles in unlabeled information that could otherwise take vast quantities of time to extract. This paper presents a method and framework for Automating Big Data Analysis using Artificial intelligence (AI). With the increasing volume and complexity of big data, traditional manual analysis methods are becoming inefficient and often result in inaccurate or incomplete insights. To address this challenge, we propose a systematic approach that leverages AI techniques such as machine learning and natural language processing to automate the process of data analysis. Our framework includes measures for data preprocessing, feature selection, model building, and result interpretation, and adapts to different types and sources of big data. We demonstrate the effectiveness of our approach through experiments on real-world datasets and compare the results with manual analysis methods. The proposed automated approach not only improves the speed and accuracy of big data analysis, but also reduces the human effort and expertise required. This has the potential to greatly enhance decision-making processes in various industries and domains that deal with large amounts of data. These talents can be used to permit higher choice-making and create extra efficient, automatic approaches to conducting huge-scale data analysis. © 2023 IEEE.

关键词： Permit Processing Quantities Similarly Visualization

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Application of Artificial intelligence Algorithm in Indoor Virtual Display System 3

Application of Artificial Intelligence Algorithm in Indoor V...

引用

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

作者： Wang, Ye Li, Yanmin School of Art Design and Media Sanda University Shanghai201209 China Institute of Applied Research on Intelligent Science and Technology Jiangsu and Chinese Academy of Sciences Jiangsu Changzhou213164 China

In recent years, virtual exhibition technology has been continuously developed and extended. Indoor virtual exhibition is assisted by various information technologies, and artificial intelligence algorithms are more and more used. Indoor virtual exhibition can gradually create a more infectious and personalized exhibition space, and through this space, the contents to be displayed are displayed to the audience in a purposeful and planned way. With the continuous evolution and development of artificial intelligence, the space and application fields of indoor virtual display are becoming more and more extensive. From various science and technology museums, museums, exhibition halls to game venues, fashion design, interior design and many other fields, we can see the integration of artificial intelligence and indoor virtual display. This paper studies and analyzes the development of artificial intelligence algorithm, focusing on its integration with indoor virtual display system, hoping to provide some reference for the development of indoor virtual display technology. © 2023 The Authors. Published by ELSEVIER B.V.

关键词： international trade

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An Open Framework for Nonverbal Communication in Human-Robot Interaction 15th

An Open Framework for Nonverbal Communication in Human-Robot...

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15th international conference on Ubiquitous Computing and Ambient intelligence

作者： Lozano, Ernesto A. Sanchez-Torres, Carlos E. Lopez-Nava, Irvin H. Favela, Jesus CICESE Comp Sci Carretera Ensenada Tijuana Ensenada 22860 Baja California Mexico UABC Comp Sci Km 106Carretera Tijuana Ensenada Ensenada 22860 Baja California Mexico

ISBN: (纸本)9783031486418;9783031486425

Nonverbal communication plays a vital role in human interaction. In the context of Human-Robot Interaction (HRI), social robots are designed primarily for verbal-based communication with humans, making nonverbal communication an open research area. We present a flexible, open framework designed to facilitate nonverbal interactions in HRI. Among its components is a P2P Browse-rBased Computational Notebook, leveraged to code, run, and share reactive programs. machine-learning models can be included for real-time recognition of gestures, poses, and moods, employing protocols such as MQTT. Another key component is a broker for distributing data among different physical devices like the robot, wearables, and environmental sensors. We demonstrate this framework's utility through two interaction scenarios: (i) the first one employing proxemics and gaze direction to initiate an impromptu encounter, and (ii) a second one incorporating object recognition and a Large-Language Model to suggest meals to be cooked based on available ingredients. These scenarios illustrate how the framework's components can be seamlessly integrated to address new scenarios, where robots need to infer nonverbal cues from users.

关键词： Human-Robot Interaction Nonverbal communication Broker architectural pattern Langchain ChatGPT Computational Notebook

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A Novel Optimization Strategies for Detecting and Preventing Distributed Denial of Service Attacks in Wireless Networks 3

A Novel Optimization Strategies for Detecting and Preventing...

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

作者： Kumar, Saurabh Ratna Raja Kumar, Jambi Mondal, Dipannita Hemelatha, S. Geetha, B. Diwakar, Mandar P. Mangalayatan University Department of Commerce Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Dr. D. Y.Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

allotted Denial of carrier (DDoS) assaults have end up a prime difficulty in trendy wireless networks. these assaults aim to disrupt the regular operation of networks by means of overwhelming them with excessive traffic from more than one assets. traditional DDoS detection and prevention strategies have established to be useless in wireless networks due to the dynamic and unpredictable nature of the wireless surroundings. To deal with this problem, novel optimization techniques were proposed to enhance the potential of wireless networks to discover and prevent DDoS *** of the key techniques is the use of wise algorithms which includes machine learning and synthetic intelligence. these algorithms can analyze and study from network site visitors patterns to pick out strange behavior this is indicative of a DDoS attack. by way of continuously tracking the network and adapting to converting traffic patterns, these algorithms are capable of as it should be locate and mitigate DDoS assaults in actual-time. any other method is the deployment of robust and dynamic defense mechanisms. those mechanisms include the usage of anomaly detection, traffic filtering, and traffic shaping. Anomaly detection strategies rent statistical techniques to hit upon patterns that deviate from ordinary network conduct. traffic filtering involves figuring out and blockading malicious site visitors, even as site visitors shaping manages and controls the flow of legitimate visitors to prevent congestion and enhance community overall performance. © 2023 IEEE.

关键词： filtering legitimate ordinary robust strategies

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Methods to Detect an Event using Artificial intelligence and machine Learning 3

Methods to Detect an Event using Artificial Intelligence and...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Singh, Arjun Srivastava, Arun Pratap Bhardwaj, Garima Jain, Akshat Singh, Arun Kumar Ritika G L Bajaj Institute of Technology and Management Department of IT Greater Noida India Amity University Uttar Pradesh Greater Noida India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

machine Learning can improve and automate how anomalous events can be detected and reported faster than before. Some of the ways events can be easily detected through the use of Artificial intelligence and machine Learning Models are, using big data analysis on Twitter and Facebook which are examples of social media sites. to get huge event data and a machine learning model such as SVM to process them to segregate and identify an event as anomalous or not, using CNN ( Convolutional neural network ). This network performs feature extraction and classification in a very optimum way and detects fire hazards at a higher pace. While Convolution Neural Networks are good at extracting 2D localised features, a new 3D feature extraction Model called T-CNN or Tube Convolutional Neural Network can also be used for Event Detection. This paper looks at various methods mentioned above and explains the best way to detect Events using machine Learning. © 2022 IEEE.

关键词： Convolutional neural networks

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Vehicle Theft Detection Using Neural pattern recognition

Vehicle Theft Detection Using Neural Pattern Recognition

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： J. Jane Rubel Angelina S.J Subhashini Anil Kumar Rapuri Vennela Ravva Raghavendra Rasala Sai Dcepak Ray Adurgam Dept of Computer Science and Engineering Kalasalingam Academy Of Research And Education Krishnankoil TamilNadu India Dept of Computer Science and Engineering SRM Madurai College for Engineering and Technology Sivagangai TamilNadu India

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge.

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Research on machine Translation (MT) System Based on Deep Reinforcement Learning 3

Research on Machine Translation (MT) System Based on Deep Re...

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3rd international conference on Computer Science and Management Technology, ICCSMT 2022

作者： He, Junchen Fuzhou University of International Studies and Trade School of Foreign Languages Fujian Fuzhou350202 China University of Perpetual Help System DALTA Graduate School Las Pinas Philippines

ISBN: (纸本)9798350320404

With the deepening of globalization and the rapid development of human society and economic society in the world, the demand for machine translation in human society is also increasing rapidly, and the progress of artificial intelligence technology has put forward new requirements for the quality of machine translation. At the same time, the development of machine translation research has set a benchmark for other fields of natural language processing. Therefore, the research on machine translation not only has high practical value, but also can promote the progress of the theoretical research on natural language processing. Based on the idea of deep learning, this paper selects English language features as feature parameters, uses fine-tuning Python model training technology and feature-based deep learning method, and conducts deep reinforcement learning on Anglo-German machine translation model. The comparative experiment of Indian English-German neural machine translation model shows that, The machine translation system based on deep learning is obviously better than traditional machine translation, and the translation effect is obviously improved. © 2022 IEEE.

关键词： Reinforcement learning

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Artificial intelligence Augmenting Human Teams. A Systematic Literature Review on the Opportunities and Concerns 3rd

Artificial Intelligence Augmenting Human Teams. A Systematic...

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3rd international conference on Artificial intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Khakurel, Jayden Blomqvist, Kirsimarja LUT Univ LUT Sch Business & Management Yliopistonkatu 34 Lappeenranta 53850 Finland

ISBN: (纸本)9783031056437;9783031056420

Artificial intelligence (AI) that can augment human intelligence in teamwork has been addressed in many studies;however, the state-of-the art of scholarly knowledge of the topic itself is missing. Thus, this paper provides a systematic review of the current knowledge on AI in augmenting human teams. The systematic literature review shows that AI working as teammate could augment human teams in important ways in enhancing team coordination, enhancing knowledge sharing and learning, supporting decision making, as well as evaluation and team performance. Further, the review also reveals that there are concerns related with social and machine teammate interaction, design, privacy, and ethics, that need further research to unleash AI technologies' benefits in increasingly knowledge-intensive and diverse team collaboration.

关键词： Artificial intelligence Humans Teams AI-human collaboration AI-augmenting human intelligence (HI) AI-human teamwork (HT)

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3rd international conference on Artificial intelligence Logic and Applications, AILA 2023

3rd International Conference on Artificial Intelligence Logi...

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3rd international conference on Artificial intelligence Logic and Applications, AILA 2023

ISBN: (纸本)9789819978687

The proceedings contain 36 papers. The special focus in this conference is on Artificial intelligence Logic and Applications. The topics include: An Abstraction Neural Network Generator for Efficient Formal Verification;a Fault Diagnosis Method of Discrete Event System Based on Binary Decision Diagram;Comparison and Implementation of ROS-Based SLAM and Path Planning Methods;a Diagnostic Model Generation Method Based on Clustering;a Heterogeneous Multicore Co-scheduling Algorithm Based on Multi-characteristic Fuzzy Cluster;conceptual Clustering Based on Linguistic-Valued Layered Concept Lattice;clause and Literal Selection Strategies Based on Complementary Pair Distribution for Contradiction Separation Deduction;an Improved Genetic Programming Based Factor Construction for Stock Price Prediction;dual Channel Graph Neural Network for Fraud Detection;non-negative Matrix Factorization Method Based on Mixed Gaussian Kernels;satisfiability Verification of Integrity Constraints for Spatial Linked Data;Direct Limits of EMV-Algebras;weighted Consistent Multi-view Discriminant Analysis in Unreliable Labeling Environment;nonlinear Manifold Learning via Graph Curvature;cross-Lingual Entity Alignment via Two-Hop Neighbour Sampling and Distinguishable Relation Embedding;GRU-Attention Interpretable Knowledge Tracking Model with Forgetting Law for Intelligent Education System;a Multi-Label Feature Selection Method Based on Label Granulation;regularized Loose Coupled Deep Non-negative Basis Matrix Factorization for Low-Resolution Face Image recognition;CDFSIP Feature Selection Algorithm Based on ADA-DPC;person Re-identification Based on Fusion of Shared Feature Branches;granular Computing Measures for the Classical Formal Concepts and Intuitionistic Fuzzy Formal Concepts;fine-Grained Mushroom Image Classification by Resnet Combined with Attention Mechanism;link Prediction Based on Pairwise Proximity Preserving Graph Neural Networks;hierarchical Time Series Forecasting Based on Tempo

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