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Research on human pose and action detection algorithm based on YOLOv8 2

Research on human pose and action detection algorithm based ...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Cheng, Weizhong Wang, Xuefei School of Information Engineering Suihua University Heilongjiang Suihua China School of Electrical Engineering Suihua University Heilongjiang Suihua China

ISBN: (纸本)9781510689053

Human pose and action detection technology has significant applications in areas such as smart security, sports monitoring, and human-computer interaction. The development of deep learning has driven the demand for real-time and efficient detection algorithms in this domain. Although existing human action detection algorithms have improved accuracy, their model complexity and computational demands hinder practical use in portable devices and edge computing platforms. To address this, the paper presents a lightweight human pose and action detection algorithm based on the YOLOv8 framework, designed to meet the high-precision, low-latency needs of edge computing and portable devices. In the feature extraction phase, GhostNet's lightweight structure is used to reduce model parameters, while the CBAM attention mechanism, applied in the feature fusion phase, enhances the model's attention to critical features. This approach significantly decreases computational load while maintaining high accuracy. Experimental results indicate that the algorithm achieves a real-time detection rate exceeding 40 frames per second on edge computing platforms, demonstrating high accuracy and robustness across various human action scenarios. This offers strong technical support for human pose detection in smart security and wearable devices. © 2025 SPIE.

关键词： Portable equipment

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A Meta-learning Framework with Model Stacking for Depression Detection Through Sentiment Classification

A Meta-Learning Framework with Model Stacking for Depression...

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2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Remya, K. Ranjana, P. Hindustan Institute Of Technology And Science Department Of Information Technology Chennai India Hindustan Institute Of Technology And Science Department Of Cse Chennai India

ISBN: (纸本)9798350364828

In the proposed system, named AdaptiveSentinel, introduces a cutting-edge approach for depression detection depends on sentiment classification. Leveraging the power of meta-learning frameworks and model stacking, AdaptiveSentinel which aims to automate the intricate process of selecting the most effective machine learning-Driven models, preprocessing techniques, and featured representations for accurate sentiment analysis in the context of depression disorder. The system will begins by preparing a dataset with labeled textual samples, by emphasizing sentiment distinctions get related to depression severity. It then employs a meta-learning driven framework to autonomously explore a diverse range of machine learning-Driven models and configurations. Proposed system intelligently will navigates through options such as Gradient supported Boosting simultaneously optimizes hyper parameters through cross-validation. By Following model optimization, the system may engages in feature engineering, extracting of valuable information from the textual data and incorporating meta features such as user demographics and posting patterns. The Core part of AdaptiveSentinel lies in its model stacking mechanism, where it formatted a powerful ensemble by combining of predictions from the selected models. This stacked ensemble not only captures patterns in the data, also mitigates over fitting, resulting in a more robust and accurate depression detection system. The Evaluation metrics may includes accuracy, precision, and recall, validates the effectiveness of the ensemble model. Once get validated, the system can be deployed for real-world applications, continuously get monitored, with periodically retrained to adapt to evolving patterns in sentiment. It will be done by providing a dynamic and reliable tool for depression detection. AdaptiveSentinel also represents a significant stride towards automated, efficient, and precise sentiment-depending depression identification, holding prom

关键词： Sentiment analysis

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AI-Enabled Edge computing Models: Trends, Developments, and Future Implications 2

AI-Enabled Edge Computing Models: Trends, Developments, and ...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Satyam Geetha, P. Shashikala, K.S. Kumar, N. Ashok School of Engineering Mohan Babu University Department of Ece Tirupati517102 India New Horizon College of Engineering Department of Ai&ml Bangalore560103 India

ISBN: (纸本)9798350347579

This research study examines the ways in which AI-enabled edge computing (AIEC) models are being used across various industries to improve operational efficiency and enable faster decision-making. The study discusses the key trends and developments of shaping the future of these next generation computing models, including the increased adoption of EC, the growing use of IoT devices, the emergence of 5G networks, advancements in machine learning algorithms, and the focus on data security. The research study also highlights case studies from different industries to illustrate the benefits of AIEC models and the potential impacts on businesses. The findings of this research work suggests that these computing models have the potential to transform the way organizations operate, leading to increased efficiency and productivity, better customer experiences, and more informed decision-making. © 2023 IEEE.

关键词： Machine learning

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Self-organizing interval type-2 fuzzy neural network based on singular value decomposition and QR decomposition

Self-organizing interval type-2 fuzzy neural network based o...

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2023 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2023

作者： Wang, Panchao Zhao, Taoyan Liaoning Petrochemical University School of Information and Control Engineering Fushun China

ISBN: (纸本)9781510666474

In this paper, a self-organizing interval type-2 fuzzy neural network based on singular value decomposition and QR decomposition with column pivoting (SVD-QR)method is proposed to solve the identification problem of nonlinear systems. The network model can realize both structure learning and parameter learning. Firstly, in the aspect of structure learning, error criterion and completeness criterion of fuzzy rules are used to judge whether the rules are growing or not, and at the same time, SVD-QR method is used to find the less active rules to delete. Secondly, a sliding-window second-order algorithm with forgetting factor is used to optimize the parameters. Finally, the proposed model is applied to one typical nonlinear examples for identification. The experimental result shows that the proposed model can produce a relatively compact network structure and has the high identification and the forecast precision. © 2023 SPIE.

关键词： Singular value decomposition

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Enhancing Deep learning Performance Through Parallel Processing: A Comprehensive Research Study 6

Enhancing Deep Learning Performance Through Parallel Process...

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6th international conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Thakur, Rudraditya Goel, Naman Bhavesh Vijaykumar, Vaidehi Vellore Institute of Technology Chennai School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350394702

The computational demands of training complex deep learning models have hindered the widespread adoption of this transformative technology. Parallel processing techniques have emerged as a promising approach to address this challenge by distributing the computational workload across multiple processors. This research delves into the multifaceted dimensions of enhancing deep learning performance through parallel processing. It explores the theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact of parallel processing in deep learning. © 2023 IEEE.

关键词： Application programs

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Machine learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

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2024 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Bhatt, Pragya Obaidat, Mohammad S. Dangwal, Gagan Das, Ashok Kumar Wazid, Mohammad Sadoun, Balqies Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248 002 India University of Science and Technology Beijing Beijing100083 China SRM University Kattankulathur603203 India Amity University Noida201301 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy. © 2024 IEEE.

关键词： Phishing

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The Impact of Federated learning on IoT Security: An Empirical Analysis 7

The Impact of Federated Learning on IoT Security: An Empiric...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mehta, Shiva Sharma, Ajay Dogra, Ayush Hariharan, Shanmugasundaram Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350372816

This paper presents how Federated learning (FL) will be applied in multiple areas, including smart homes, work environments, and healthcare systems, to enhance the security level of Internet of Things (IoT) systems. The rising number of Internet of Things devices has put focus on obscuring and managing these connected systems as a severe security issue. Traditional centralized classifiers frequently lack the required cyber-threat vulnerability, scalability, and data protection attributes. In this way, federated learning fills the communication gap between users (aka data producers) and servers by processing data locally, and on-device study demonstrates that the FL models get a significant advantage in the performance of the false positive reduction and the threat detection accuracy in comparison with the conventional models of operation. FL, though, may not be giving 92% accurate prediction in detecting the hazards as traditional models do with 88% correct prediction. Also, FL excelled in prior models by scoring 90% in industrial settings and 89% in the hospital setting, with an accuracy of 95% in the industrial setting and 93% in a ***, the recognition rate for ties grew from 5% to 3%, with 2% for correctly matched tie types compared to the traditional methods at 10% for correctly matched ties and 8% for corresponding tie types. The contribution is made using a mixed-methods approach comprising simulations and experimental sets to quantify the performance of FL. INTERPRETATION: FL has proved to be the best (from the results) among other security measures because it reduces data exposure and compliance with data privacy laws (strictness). © 2024 IEEE.

关键词： Federated learning

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Greensense: IoT-Enabled Smart Monitoring And Early Detection System For Optimal Plant Health In Greenhouse

Greensense: IoT-Enabled Smart Monitoring And Early Detection...

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2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Venkatesh, R. Aadhira, K.R. Kathiravan, M. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

The emergence of Agriculture 4.0 signifies a transformative shift in traditional farming practices, integrating cutting-edge technologies like IoT, machine learning, and robotics to support productivity and sustainability. This era leverages real-time data and automation for precision farming, predictive analytics, and resource optimization, crucial for addressing global food security challenges while minimizing environmental impact. GreenSense represents progress within Agriculture 4.0. It seeks to revolutionize greenhouse management by integrating IoT and advanced machine learning algorithms. Through sensors, actuators, and a Raspberry Pi 4 board, this system offers a comprehensive solution for monitoring and optimizing plant health in controlled environments. The motivation behind this system stems from the imperative to enhance agricultural practices sustainably in the face of population growth. By enabling real-time monitoring and swift anomaly detection through application, this paper empowers proactive intervention to mitigate risks and optimize growing conditions. Integration of the YOLOv5 algorithm with 97% accuracy enhances precision in leaf disease and pest detection using OpenCV, reducing crop losses effectively. Beyond greenhouse management, this paper contributes to advancing sustainable agriculture. Its scalable and adaptable nature promotes resource-efficient practices and environmental care. Finally, a user-friendly interface is tailored to facilitate efficient data collection from sensors, enabling comprehensive monitoring of crop conditions, including pest and disease detection. © 2024 IEEE.

关键词： learning algorithms

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Cloud And Rain Streak Image Analysis For Rainfall Prediction Using Deep learning Approaches 15

Cloud And Rain Streak Image Analysis For Rainfall Prediction...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gothai, E. Sasipriyaa, N. Natesan, P. Kavinkumar, S. Kongu Engineering College Department of CT-PG Erode India Kongu Engineering College Department of CSE Erode India Kongu Engineering College Department of It Erode India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350370249

Rainfall prediction plays a pivotal role in weather forecasting and environmental management. It has significant implications for agriculture, water resource management, disaster preparedness, and many other sectors. The use of deep learning techniques for predicting rainfall based on cloud image and rain streak image analysis has been gaining more and more importance lately. The study highlights the importance of having accurate rainfall forecasts and introduces a new data preprocessing methodology that aims to improve the precision of rainfall predictions. Day/Night discrimination based on HSV (Hue, Saturation, Value), resizing images to a standardized dimension of 128 × 128, and introducing random image rotations contribute to the robustness of the dataset used for training deep learning models. Effective preprocessing is essential for enabling accurate rainfall predictions by helping models learn and generalize effectively from visual cues in cloud images. The study evaluated the performance of five deep learning architectures for rainfall prediction using cloud image analysis: CNN, RNN (LSTM), DNN, DenseNet-169, and ResNet-50. To train and test the deep learning models, rainfall data from four publicly available datasets, namely Hindustan Times and The Weather Channel, Swimcat, and Unsplash, was used. The DenseNet-169 architecture achieved the best accuracy of 98.5%, followed by CNN at 94.5%. DNN architecture had an accuracy of {8 4%}, followed by the ResNet-50 architecture with an accuracy of 77%, and RNN architecture with an accuracy of 75%. These results suggest that DenseNet-169 is the most promising architecture for rainfall prediction using cloud image analysis. However, further research is needed to improve the accuracy of RNNs and other architectures for this task. © 2024 IEEE.

关键词： Rain

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The Framework for Training and Validation of Healthcare System using Accurate Classifier Model 4

The Framework for Training and Validation of Healthcare Syst...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Sivapriyan, R. Sarkar, Rashel Reddy, Y. Pavan Kumar Loganayagi, S. Kareem, Doaa Saadi Meassar, Al-Hussain Sir Mvit R & D Centre Department of Eee Bengaluru India The Assam Royal Global University Computer Science and Engineering Assam India Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science and Engineering Tamil Nadu Thandalam India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Our novel strategy uses IoT-cloud technology to present a cognitive healthcare platform. This framework utilizes deep learning algorithms in conjunction with smart sensor integration to facilitate intelligent decision-making in the setting of smart cities. Our intelligent and cognitive framework provides accurate, efficient, and economical healthcare services by continually monitoring patients' vital signs in real-time. Here, we provide experimental results on deep learning-based EEG pathology categorization to verify the efficacy of our proposed system. We record and track many healthcare data modalities using a range of smart sensors, such as EEG sensors. Patients' EEG data are effortlessly sent to the cloud via Internet of Things devices, where a cognitive module processes and analyzes them. Through the analysis of sensor data, including voice, gestures, motions, and EEG signals, this system assesses the patient's state. Real-time decisions, crucial for determining the appropriate course of action, are made by the cognitive module. Additionally, EEG signals are processed by a deep learning module to classify them as pathological or normal. The insights gleaned from patient monitoring and EEG signal analysis are then shared with healthcare providers. This helps them to precisely evaluate the patient's state and quickly offer emergency care if needed. Notably, our proposed deep learning model outperforms existing systems in terms of accuracy and reliability, underscoring its potential for transforming healthcare delivery. © 2024 IEEE.

关键词： Cloud technologies EEG pathology classification IoT Healthcare framework machine learning

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