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CART Decision Tree Based Human State Estimation Algorithm and Research 4

CART Decision Tree Based Human State Estimation Algorithm an...

引用

4th International Conference on Robotics and computer Vision, ICRCV 2022

作者： Ma, RongFei Xu, Wenxia Yu, Baocheng Zhang, Min Wu, Jing Zhu, Huizhi Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Human state estimation is an important technical tool in the field of elderly mobility assistance. In order to further improve the accuracy of human state recognition, a CART decision tree based human state classification algorithm was proposed for the auxiliary support system. Firstly, the human inertial sensor posture data was pre-processed, the human body link model was used to calculate the human body center of gravity for posture detection of sitting and standing motion process, extract features and training samples, then the dimensionality of the posture data was reduced by PCA method to get the best subset of data with good reflection of human body posture, the motion process includes four consecutive states: sitting, rising, standing and falling, and the state transition stages were subdivided into another four categories, and finally the CART decision tree algorithm was used to train the samples to generate CART rule trees to classify the human posture. The experimental results show that the classification accuracy of the human posture detection system under the constructed CART decision tree is up to 98%, which is better than the SVM classification results, and the estimation is correct in the vicinity of the state transition, and the accuracy of the state estimation is reasonable. © 2022 IEEE.

关键词： Decision trees

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Direction Based-Improved Ant Based Routing Algorithm

Direction Based-Improved Ant Based Routing Algorithm

引用

2022 International Conference on Industry 4.0 technology, I4Tech 2022

作者： Kanshi, Avaneesh Vinod Melarkode, Navneet Agarwal, Varun Niraj School of Computer Science Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781665471961

ISBN: (纸本)9781665471961

High-efficient routing is a significant component of a network which forms an indispensable part of designing Wireless Sensor Networks (WSNs). A typical WSN environment limits its resources with respect to the power it transmits, the overhead it carries and the energy it is boarded along with. Subsequently, when these WSNs are employed in large numbers to gather data from solitary destinations and targets, few constraints are enforced by itself owing to the fact that sensor nodes in WSNs carry limited energy, a limited computation power and a highly exhaustive storage capacity among themselves. With due credit to this very fact, Swarm Intelligence (SI) algorithms have been brought on the research scene to help administrate and govern the sensor nodes when in action. In this research study, we analyze and weigh the existing advancements in Energy efficiency routing protocols that utilize SI for WSNs and propose our own modification to the Ant Based Routing Protocol, Direction Based-Improved Ant Based Routing Algorithm (DB-IABR). Since the legacy protocol was found to be the trendsetter in terms of Energy efficiency, it serves as our benchmark for evaluations that succeed today's innovations. This study uses standard metrics like energy, path hops and the time as the evaluation parameters. The algorithm is compared with its legacy root algorithm Ant Based Routing and Improved Ant Based Routing Protocols. © 2022 IEEE.

关键词： Swarm intelligence

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Systematic Survey on security issues in Cognitive Wireless Sensor Networks (CWSN) 20

Systematic Survey on security issues in Cognitive Wireless S...

引用

20th OITS International Conference on Information technology, OCIT 2022

作者： Chatei, Roshan Kumar Das, Rudrashish Chatterjee, Pinaki Sankar School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9781665493482

Cognitive wireless sensor networks, also known as CWSN, are a cutting-edge technology that could one day provide a solution to the issues that have plagued wireless networks in the past. These issues have plagued conventional wireless networks for a number of years. Today, CWSNs are faced with a whole new and significant task, which is to ensure network safety. A conventional wireless network is not the same as a CWSN since it does not have the same capabilities and does have additional constraints. In this article, we go over the many different attacks that may be made against CWSNs, how they are categorised, and the many different security measures that can be put in place to prevent these attacks from happening. The difficulties that lie ahead in the future are also discussed. © 2022 IEEE.

关键词： Wireless sensor networks

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Based on FCOS and HSV Recognition Algorithms for Oil Level and Implementations of Train Hydraulic Brake 2

Based on FCOS and HSV Recognition Algorithms for Oil Level a...

引用

2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Chen, Qiuyang Pan, Kaixin Zhao, Jianyang Ding, Weihong School of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

An important application in the field of Digital Image Processing is reading water level recognition. Currently, the automatic recognition method of train hydraulic brake oil level is uncommon, mostly by eye observation oil level position which can cause much errors and poor efficiency. Our project is about how to realize the automatic recognition of train hydraulic brake oil level reading. In this regard, we propose a model algorithm based on FCOS and HSV algorithm. © 2022 IEEE.

关键词： Water levels

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Machine Learning Techniques for Parkinson's Disease Detection 2

Machine Learning Techniques for Parkinson's Disease Detectio...

引用

2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Sanjay, V. Swarnalatha, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665460477

A neurological disease is Parkinson's disease. It causes trembling in the hands, trouble walking, losing balance, and coordination. In the high-level stage, there is no access to medical care. Blood test reports, CT scan results, and X-ray reports are not accessible early enough. Early Parkinson's disease detection is crucial to implement effective treatment. The purpose of the proposed effort was to identify Parkinson's disease in early prediction using clinical imaging and machine learning technologies. Despite the fact that there are numerous methods for detecting Parkinson's disease, using MRI scan images still it is a big challenge. In this study, an Adaboost classifier is used with a hybrid PSO algorithm to propose a novel technique for detecting Parkinson's disease. Adaboost acted as the best classifier among other classifiers. Initially, MRI image best features are extracted and identified by the curvelet transform and principal component analysis. This Ad boost classifier receives optimal features as input. Finally, Adaboost classifieds the MRI images and gave excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems. © 2022 IEEE.

关键词： Adaptive boosting

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning 2

Revolutionizing Machine Learning Security: The Role of Quant...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Random Forest Algorithm in Prediction of Protein Subcellular Localization

Random Forest Algorithm in Prediction of Protein Subcellular...

引用

2022 International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2022

作者： Zhao, Qing Lei, Tengfei Liu, Yuan School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (数字)9781665454704

ISBN: (纸本)9781665454704

Proteins perform their function in subcellulars. Only in specific places, proteins can play their specific functions, and maintain the normal life activities. It is very important to determine the subcellular location of proteins accurately. Protein feature extraction and prediction classification are two key steps in protein subcellular location prediction. In this study, Pseudo Amino Acid Composition model, Amino Acid Physicochemical Property Composition model and Entropy Density model were used to extract feature. Combining the random forest classification model to complete the prediction of protein subcellular localization, the prediction accuracy was greatly improved. © 2022 IEEE.

关键词： Physicochemical properties

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Pedestrian Re-identification after Enhancing Textural Features Based on Parallel Residual Network 5

Pedestrian Re-identification after Enhancing Textural Featur...

引用

5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Shen, Zhiyu Zhao, Tongzhou Liu, Yang Wuhan Institute of Technology School of Computer Science & Engineering Wuhan China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Aiming at the availability of global features learned by residual network when it was trained to classify images, but was not sensitive to the classification of fine-grained images, a pedestrian re-identification method with enhancing textural features based on parallel residual network was proposed. First, a sub-hard example method was used to deal with the dataset of market1501, by this way the model could converge whiling maintaining good performance. Then structure a parallel network which was used to strengthen textural features, making the network paid more attention to the textural features of images and mixed features from multiple layers for final classification. Experimental results show that sub-hard examples method can improve the accuracy rate of model about 0.2% without changing its parameters, while the value rises to 1.2% if the parallel network is used jointing texture enhancement, with which increasing a few parameters. Improvements were also seen in CUHK03 and MSMT17, two large datasets, at 0.6% and 1.0%, respectively. © 2022 IEEE.

关键词： Image classification

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CT Intensity Segmentation of Lungs 2

CT Intensity Segmentation of Lungs

引用

2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Jayakumar, K. Deendayal, Namdev Parth Dhindsa, Gurnehmat Kaur Nagrani, Agrim Bali, Vinay Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

The early diagnosis and treatment of lung diseases is a very critical procedure and it requires the use of Computed Tomography (CT) imaging for the segmentation of lungs. Segmentation of the lung helps in the analysis of the lesions. The project proposes a CT lung and vessel segmentation model without any labels which is based on medical image processing using Python. This would assist the medical practitioners and scientists who are working in the field of CT intensity segmentation of lungs. It would make the diagnosis process easier and more convenient for patients, especially in pandemic situations like COVID. © 2022 IEEE.

关键词： Python

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Evaluation of New Energy Accommodation Capability Based on Renewable Energy Power Prediction under "Double Carbon" Goal 5

Evaluation of New Energy Accommodation Capability Based on R...

引用

5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Ji, Xiu Cheng, Qi Wang, Mingyue Meng, Xiangping Qi, Chenglong Guo, Shuanghao School of Electrical Engineering Changchun Institute of Technology Changchun China Graduate School Changchun Institute of Technology Changchun China

ISBN: (纸本)9781665434256

Under the goal of "double carbon", the installed capacity of renewable energy except hydraulic will show a higher growth trend. In the future, new energy will become the main power source of power system, and it is particularly important to evaluate the accommodation capability of power grid for renewable energy. An evaluation method of new energy accommodation capability based on renewable energy power prediction is proposed. Based on the renewable energy output value predicted by the Generative Adversarial Networks (GAN) for the next day, considering factors such as load characteristics, tie-line power and carbon emissions, the start-stop status of conventional units is optimized, the carbon emissions of thermoelectric units are limited, and the accommodation of renewable energy and carbon emissions are calculated. The practicability of the proposed evaluation method is verified by an example analysis of the new energy accommodation capability in a certain area of China. © 2021 IEEE

关键词： Generative adversarial networks

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