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machine learning Techniques for Pose Based Human Activity recognition

Machine Learning Techniques for Pose Based Human Activity Re...

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Disruptive Technologies (ICDT), international conference on

作者： Ravindra Singh Koranga Arun Kumar Rai Lalit Mohan Shubham Singh Pritika Priya School of Computer Science and Engineering Galgotias University Greater Noida India CSE Birla Institute of Applied Sciences Bhimtal India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Activity recognition is the task of identifying human actions and physical activities. It can have applications in broad areas including medical, sports and fitness, healthcare, behaviour analysis and security. Activity recognition is a widely sought after area of research in both machine learning (ML) and smart devices. The activity recognition plays an important role in the field of machine learning. There are several machine learning techniques which can be applied for pose based activity recognition. These techniques involve training machine learning models for the dataset. The dataset is obtained by using OpenPose framework. This framework provides keypoints from an input image. These keypoints represent the raw data determining the pose of a human. The machine learning model can predict the pose after training on the dataset. models

关键词： Training Pose estimation machine learning Medical services Predictive models Security Object recognition Human activity recognition Smart devices Sports

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English Teaching Evaluation Algorithm and Modeling based on machine learning and data mining Technology

English Teaching Evaluation Algorithm and Modeling based on ...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Wei Sun Heilongjiang International University Harbin China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

Traditional English teaching evaluation has problems such as subjectivity, limitations, quantitative bias, and lacks feedback and support. The evaluation algorithm based on data analysis technology provides an accurate, comprehensive, and personalized evaluation method for teachers and students, which can understand their learning needs and ability levels by collecting and analyzing students' learning data. This study adopts a method based on data analysis technology to develop English teaching evaluation algorithms. Firstly, this paper collected students' learning data, including information on language expression, learning behavior, and academic performance. Then, machine learning and data mining techniques are used to analyze and model these data to predict students' English proficiency level and learning needs. The research results indicate that the F1 value range of the algorithm in this paper is 0.85-0.94, and the English teaching evaluation algorithm based on data analysis technology has achieved significant results in evaluating students' English proficiency.

关键词： machine learning algorithms Accuracy data analysis Education machine learning learning (artificial intelligence) Predictive models Prediction algorithms data models data mining

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Spatial data Analytics for Regional Planning Using GIS and RS Technologies 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Dahiya, Mamta Khushpal Kumar, Narender MRIIRS Faridabad India SGT University Gurugram India Geo Solutions Gurgaon India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

The modern tools and technologies, i.e., GeoAI, Drones, DGPS, and GIS platforms are being used to digitize all the villages of whole India and will be digitally mapped very soon. The process is in continuation and various organizations are playing a vital role to be a part of this Digital India project and continuously working in their respective areas to achieve it. These digital maps will be integrated with all the spatial and non-spatial information about our nation. Spatial data analytics is a technique that is used to analyze, predict, and plan geospatial data for multiple purposes by adding time and location stamps. It can be related to the demographic assessment, land-use and land cover pattern analysis, road networks, agriculture, weather analysis, forestry, and many more. Currently, in this paper, we are focusing on how spatial analytics is helpful for planning and decision-making at different levels of stake holders. Research work is carried out through a case study of Nalanda regional area using GIS technologies. Different land-use and land cover pattern are identified over the time and are superimposed on satellite images of that area to check the quality and accuracy of prediction that will be used for regional planning of study area. This work also emphasis on, "how this technology is strongly contributed to the project Digital India with speed and accuracy" particularly for regional or urban planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data accuracy

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PR-GB-NET: Probabilistic Graph Bridging Network with data Resampling for Scene Graph Generation 3rd

PR-GB-NET: Probabilistic Graph Bridging Network with Data Re...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Lu, Hongyu Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

Scene graphs are structured and powerful representations of images that extract semantic information from images to enhance visual understanding and image reasoning. To improve the utilization of predicate prior knowledge in scene graphs, we use a probability mass function (PMF) for the predicate, given a subject-object pair during the initialization of the graph neural network. The PMFs are updated through a gating function during message passing of the graph neural network. To mitigate the effects of long-tailed distributions in datasets, we employ a data resampling strategy. During each training iteration, we try to balance the head, body and tail predicates as much as possible. Experiments show that the proposed methods achieve competitive performance compared to previous methods on dataset we created for home environment, demonstrating their effectiveness and generality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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A Method to Detect the Onsets and Ends of Paroxysmal Atrial Fibrillation Episodes Based on Sliding Window and Coding 3

A Method to Detect the Onsets and Ends of Paroxysmal Atrial ...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Jia, Haijun Liu, Sen Wang, Ya'nan Wang, Daomiao Yang, Cuiwei Fudan University Center for Biomedical Engineering School of Information Sci. & Tech. Shanghai China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide and it significantly impacts patients' morbidity, mortality and quality of life. AF is usually divided into paroxysmal AF (AFp), persistent AF (AFf), and permanent AF. It's vital to detect the onsets and ends of AFp episodes accurately for treatment of patients. However, the detection of the onsets and ends of AFp episodes is not concerned in existing detection algorithms for AFp. In order to solve the problem, a method to detect the onsets and ends of AFp episodes based on sliding window and coding is proposed in this paper. Before using the method to detect the onsets and ends, it's required to train the classifier which is used to detect whether AF attacks in windows with fixed-number of RR intervals. The method based on sliding window and coding offers a systematic and effective method to detect which heart beat is the onset or end of AFp episodes. The classifier achieves accuracy of 0.968, sensitivity of 0.975 and specificity of 0.960 respectively. The proposed method achieves recall rate of 0.818 and score U of 1.746. The results show that the method based on sliding window and coding can reduce the impact of erroneous prediction offered by the classifier. In addition, real-time detection is achieved because both the trained classifier and proposed method have low time complexity. © 2022 IEEE.

关键词： machine learning

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3rd Workshop on Online and Adaptive Recommender Systems (OARS) 23

3rd Workshop on Online and Adaptive Recommender Systems (OAR...

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29th ACM SIGKDD conference on Knowledge Discovery and data mining (KDD)

作者： Cui, Xiquan Dave, Vachik Su, Yi Al-Jadda, Khalifeh Kumar, Srijan McAuley, Julian Yet, Tao Guo, Stephen D. Huyen, Chip Home Depot Atlanta GA 30301 USA Walmart Labs Sunnyvale CA USA Univ Calif Berkeley Berkeley CA USA Google New York NY USA Georgia Inst Technol Atlanta GA 30332 USA Univ Calif San Diego San Diego CA USA Amazon San Francisco CA USA Indeed San Francisco CA USA Claypot AI San Francisco CA USA

ISBN: (纸本)9798400701030

Recommender systems (RecSys) play important roles in helping users navigate, discover, and consume large and highly dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only feasible way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user's intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. A RecSys should adapt in real time to meet user needs and be robust against distribution shifts. The online and adaptive recommender systems (OARS) workshop offers a focused discussion of the study and application of OARS, and will bring together an interdisciplinary community of researchers and practitioners from both industry and academia. KDD, as the premier data science conference, is an ideal venue to gather leaders in the field to further research into OARS and promote its adoption. This workshop is complementary to several sessions of the main conference (e.g., recommendation, reinforcement learning, etc.) and brings them togethe

关键词： data science recommender system machine learning artificial intelligence

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Security Risk Management System for the Construction and Operation of Smart Port Area Based on BP Neural Network Algorithm 3

Security Risk Management System for the Construction and Ope...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Cui, Di Sun, Guoqing Zhan, Xiaotiao Safety and Environmental Protection Division China Waterborne Transportation Research Institute Beijing China

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the BP neural network algorithm. By establishing and analyzing a safety risk management system model based on the BP multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority! © 2023 Elsevier B.V.. All rights reserved.

关键词： data mining

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The Integration of Stream data Models in Modern Transportation Networks 3

The Integration of Stream Data Models in Modern Transportati...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naval, Preeti Jain, Sandeep Kumar Krishna, K. Gopala Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Electrical Engineering Jaipur India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka562112 India

ISBN: (纸本)9798350343274

The study proposal proposes an innovative strategy for managing modern transport networks, with an emphasis on the coordinated use of analytics, real-time data processing, and dynamic decision-making utilizing stream data models. To create a solid infrastructure for real-time data analysis, it is required to use a diverse set of data sources, such as sensors, GPS devices, traffic cameras, and mobile application software. A strategy for reaching this goal is described below. The real-time system that integrates transportation models, artificial intelligence, and data mining is at the core of this strategy. In addition to monitoring and assessment, one of the key responsibilities of this system is to develop the transportation network. Without this framework as a foundation, the method would be worthless. This collaborative effort provides proactive traffic control, efficient route planning, and event prediction in real time. The application of machine learning in predictive analytics and traffic analysis, which together give solid traffic forecasts, prompts the creation of tactics that cut wait times and enhance productivity. Event detection algorithms are critical for ensuring commuter safety and reducing wait times in the event of an emergency. Using cloud-hosted data processing technologies, the idea manages massive amounts of real-time data. This is critical for data collection, processing, and storage methods. Finally, we believe that the development of an intelligent transportation networkone that can continually learn, adapt, and drive itselfwill result in less traffic, more safety, shorter travel times, and environmentally friendly legislation and practices. To address the ever-increasing demands placed on dynamic urban settings, it is believed that the technique developed would transform current transport networks into more responsive, user-friendly, and efficient systems. © 2023 IEEE.

关键词： Real time systems

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A Real-time Visual Target Detection and Automatic Tracking Algorithm Based on YOLOv3 and DWSiamRPN 3

A Real-time Visual Target Detection and Automatic Tracking A...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Zhong, Yexiu Qiu, Senhui Zhao, Zhipeng He, Fuyun Guilin Institute of Information Technology School of Electronic Engineering Guilin China Guangxi Normal University School of Electronic Engineering Guilin China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The current single-target trackers cannot detect the categories of the targets, and multi-object trackers have low performances due to the missed and false detections. Most of these trackers do not have the capabilities for the interesting target selections. To overcome these problems, a real-time visual target detection and automatic tracking algorithm is proposed. First, the video image is detected by using YOLOv3. Then the interesting target is obtained by the proposed intelligent selection strategy. Finally, the selected target is tracked in real-time by the Deeper and Wider Siamese Networks (DWSiamRPN). Experiments show that the proposed YOLOv3-DWSiamRPN method can effectively detect, automatically obtain and track targets in real-time (26 frames per second), with 55.5 mean Average Precision (mAP) on COCO dataset, 0.296 Expect Average Overlaprate (EAO) on VOT-2017 and 0.66 Area Under Curve (AUC) on OTB-2015, respectively. Furthermore, it also addresses the problems of short-term and long-term target losses. © 2022 IEEE.

关键词： Target tracking

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SmartBlend: Smart Video Recommendations on YouTube

SmartBlend: Smart Video Recommendations on YouTube

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Kulvinder Singh Aditya Kumar Maurya Drishti Solanki Dept of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal data mining and data Synthesis to develop a Smart System for learning Video Recommendations. With a focus on Personalized learning and Adaptability, the methodology employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. SmartBlend introduces a novel approach to personalized learning video recommendations on YouTube, leveraging Multimodal data mining and data Synthesis. The SmartBlendEdu framework employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials.

关键词： Video on demand Social networking (online) Semantics Education learning (artificial intelligence) Tagging data mining

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