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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

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Improvement of GM (1,1) model optimization based on the hill-climbing algorithm and its application in vehicle traffic accident studies 3

Improvement of GM (1,1) model optimization based on the hill...

引用

3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Geng, Caiqi Wu, Dayu Li, Qinfu Qin, Yuanyuan China Acad. of Electronics and Information Technology China Electronic Technology Group Corporation Beijing China National University of Defense Technology College of Military Management Beijing China

ISBN: (纸本)9798350357707

In order to accurately predict the change trend of the number of vehicle traffic accidents, this paper proposes an improved GM (1,1) model based on the hill-climbing method to solve the problem that the traditional GM (1,1) model has low accuracy in predicting data with large fluctuations. The heuristic method is used to optimize the parameter calculation method of the original model, improve the accuracy of curve fitting, and reduce the average relative residual error of the model prediction. On this basis, using the multiple sets of data of the number of real historical vehicle traffic accidents in the past ten years, the accident prediction accuracy of the traditional GM (1,1) model and the improved GM (1,1) model is compared. The experimental results show that the improved GM (1,1) model based on hill-climbing method proposed in this paper has higher fitting accuracy, and the average relative residual is lower than that of the traditional model. It is less affected by the fluctuation of data sequence and can better meet the needs of practical engineering. It can provide some support for the development of related application research and has certain practical value. © 2023 ieee.

关键词： Heuristic methods

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Encrypted video identification - Normalization and activation function perspective 3

Encrypted video identification - Normalization and activatio...

引用

3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Bukhari, Syed M. A. H. Afandi, Waleed Maqsood, Tahir Khan, Muhammad U. S. Comsats University Islamabad Department of Computer Science Abbottabad Pakistan Comsats University Islamabad Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350332391

Video identification in encrypted network traffic has become a challenge for researchers in the past few years. However, the accuracy of the identification depends on various factors. This paper highlight the impact of data normalization techniques and the activation function of the hidden and dense layer on accuracy. For this purpose, we tried different normalization techniques for data normalization. Upon selecting the best data normalization technique, we first modified the activation functions of hidden layers. After selecting the most suitable activation function for hidden layers, we modified the activation function of the dense layer. The results show that the Min-Max data normalization technique, the ReLU function on hidden layers, and the Soft-max activation function on dense layer is the most suitable combination for video identification in network traffic. The configuration achieves an accuracy of 96.38%. © 2023 ieee.

关键词： Convolutional neural networks

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Multimodal data Fusion and Machine Learning for Comprehensive Management of Parkinson's Disease in Healthcare 3

Multimodal Data Fusion and Machine Learning for Comprehensiv...

引用

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

作者： Ganesh, D. Gautam, Anupam Kumar Bhambu, Pawan Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350343274

Parkinson's disease is a neurological ailment that affects millions of individuals throughout the world. Successful treatment will greatly enhance the quality of life for those who have it. To effectively treat Parkinson's disease, this study investigates how multimodal data and machine learning technology might be combined. To improve illness diagnosis, symptom severity prediction, and medication efficacy evaluation, the suggested technique draws from a broad variety of data sources such as clinical records, medical imaging, and sensor data. In this study, novel data fusion and analytic techniques are used to generate a comprehensive picture of the patient's health. Classification and prediction can be accomplished using either the Random Forest, Convolutional Neural Network, or Long Short-Term Memory Network machine learning techniques. data fusion and feature extraction, the first steps in building reliable models, are graphically shown by a set of mathematical equations. data presented in the tables of comparison show how much of an improvement the proposed remedy represents over the status quo. This strategy has the potential to significantly alter the way doctors treat Parkinson's disease, leading to better outcomes for patients and setting a new standard for healthcare in general. © 2023 ieee.

关键词： Neurodegenerative diseases

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Research on Feature Learning Algorithm of Social Media based on Deep Network 3

Research on Feature Learning Algorithm of Social Media based...

引用

3rd ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Bai, Haoming Wu, Linjiao Chengdu University of Technology College of Earth Science Sichuan Chengdu China University of Shanghai for Science and Technology College of Communication and Art Design Shanghai Shanghai China

ISBN: (纸本)9798350317022

Deep learning is a branch of machine learning, which has been used to solve many problems in the fields of computer vision, speech recognition, natural language processing and so on. Deep neural networks are trained by inputting large amounts of data into them, and then they learn how to recognize patterns from these data. This process is called supervised training because the network learns from tagged examples (such as images) or other inputs (such as audio files or text documents). The social media based performed by two models such as Deep network structure feature learning and Feature learning of social media based on deep network. The suggested approach achieved the better transmission of social media by M10, DBLP and Cora of values about 0.70, 0.75 and 0.80 respectively. The problem with deep learning is that it is difficult to train these networks on new tasks without accessing a large amount of tag data for each task. In addition, there are many challenges in teaching the knowledge learned in the learning process. © 2023 ieee.

关键词： deep learning learning algorithm social media features

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Research on the application of SPN in Power Comm unication Network 3

Research on the Application of SPN in Power Comm unication N...

引用

3rd international Symposium on computer Technology and Information science, ISCTIS 2023

作者： Yao, Hao Liang, Demin Liang, Zhensheng Li, Xiao Guangzhou Electric Power Design Institute Guangzhou China

ISBN: (纸本)9798350325386

Energy revolution and digital revolution are jointly promoting the transformation and upgrading of traditional power grid to digital power grid. The digital power grid is facing the demand for two-way transmission of widely distributed, large connections and massive data, which poses a great challenge to the power communication infrastructure. SPN is new generation of end-to-end hierarchical switching network technology system, which based on multilayer convergence, which is able to carry out comprehensive bearing of high-quality private line services and realize comprehensive service transmission with large bandwidth, low latency and high efficiency, and has good applicability to power communication networks. From the technical architecture and key technologies of SPN, combined with the existing architecture and business characteristics of power communication network, this paper puts forward the scheme of applying SPN network in power industry from two aspects of service bearing and networking mode. © 2023 ieee.

关键词： Packet networks

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A non Invasive Brain-computer-Interface for Service Robotics 3

A non Invasive Brain-Computer-Interface for Service Robotics

引用

3rd ieee international conference on Artificial Intelligence, ICAI 2023

作者： Ahmed, Fatima Iqbal, Hashim Nouman, Ahmed Maqbool, Hafiz Farhan Zafar, Saqib Saleem, Muhammad Khurram University of Engineering & Technology Mechatronics Engineering Department Lahore Pakistan AASS Computer Science Department Orebro University Örebro Sweden University of Engineering & Technology Mechatronics Engineering Department Taxila Pakistan

ISBN: (纸本)9798350322125

A Brain-computer Interface (BCI) enables individuals to control a system solely through their brain activity, without relying on physical movement. These interfaces have numerous applications, particularly in assisting individuals with paralysis. Our research paper details a BCI interface that can classify and control seven wheelchair movements: forward, backward, left, right, stair climbing upwards, stair climbing downwards, and stop. We collected raw signal data using the electroencephalog-raphy (EEG) technique from healthy volunteers, which we then filter before feeding into the feature extraction and classification stages. We evaluated our approach using three classification algorithms: Convolution Neural Network (CNN), Support Vector Machines (SVM), and Random Forest Classifier, and compared their performance. Our experimental results demonstrate that our proposed approach is highly promising for implementing BCI, with a classification accuracy of 99% using a Random Forest Classifier. © 2023 ieee.

关键词： Brain computer interface

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Cotton Care: Harnessing Transfer Learning for Real-Time Health Assessment with Mobile Devices 23

Cotton Care: Harnessing Transfer Learning for Real-Time Heal...

引用

23rd ieee/ACIS international conference on computer and Information science, ICIS 2023

作者： De Lemmus, Emerson A. McAntosh, Brendan P. Islam, Abm Rezbaul Cell Signaling Technology Bioinformatics Group Boston United States Sam Houston State University Department of Computer Science Huntsville United States

ISBN: (纸本)9798350345841

Cotton frequently experiences substantial yield reduction due to disease afflictions. This paper introduces a proof-of-concept mobile application that employs transfer learning with pre-trained MobileNetV3 and NASNetMobile models to detect cotton plant diseases. The system addresses a four-class problem, identifying the health status of cotton plants or leaves. Using TensorFlow, these models are refined with publicly available data and deployed on an iOS app. This study also provides a performance assessment of both models, previously unexplored in this specific context. The methodology, showcasing significant potential for real-world implementations, recorded overall accuracies of 97.7% for NASNetMobile and 96.7% for MobileNetV3. © 2023 ieee.

关键词： Cotton

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Comparative Analysis of Oversampling Techniques on Small and Imbalanced datasets Using Deep Learning 3

Comparative Analysis of Oversampling Techniques on Small and...

引用

3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Ul Sabha, Saqib Assad, Assif Din, Nusrat Mohi Ud Bhat, Muzafar Rasool Islamic University of Science and Technology Computer Science and Engineering Kashmir India Islamic University of Science and Technology Computer Science Kashmir India

ISBN: (纸本)9798350320749

In imbalanced datasets, certain classes have a larger number of samples compared to others, leading to an unequal distribution of samples across the classes. Since many crucial real-world classification problems, like medical diagnosis, involve imbalanced data, the research community places a high priority on understanding how to use this data. If machine learning is performed directly on the imbalanced data, the disparity between the majority and minority classes will cause bias towards the majority class and lead to inaccurate results. There is growing interest in this field of study, and several algorithms have been created. This study aims to evaluate the effectiveness of five oversampling strategies that are intended to address data imbalance, namely random oversampling, SMOTE, borderline SMOTE, ADASYN, and Deep SMOTE. A comparative analysis is carried out, and the effectiveness of each strategy is examined in terms of evaluation metrics. Experimental results demonstrated that DeepSMOTE outperformed all other oversampling techniques on small and imbalanced datasets. © 2023 ieee.

关键词： Classification (of information)

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Web-Based BIM Platform for Building Digital Twin 3

Web-Based BIM Platform for Building Digital Twin

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3rd ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Furuta, Satoru Nakazato, Jin Tsukada, Manabu The University of Tokyo Graduate School of Information Science and Technology Tokyo Japan

ISBN: (纸本)9798350318470

Building digital twin (BDT) is utilized throughout the lifecycle of a building. It serves for efficient operations during the design and construction phases, and during the operational and maintenance phases, it's used for asset management and as field maps for robots. Building Information Modeling (BIM), which contains both semantics and geometry data of building elements, holds promise as a data source for BDT. We have extracted four key technical challenges of the digital twin, particularly vital during the operational and maintenance phases: software, visualization, update, and real-scene reconstruction. Due to the exclusive and static nature of BIM, these challenges also pose significant issues in the context of BDT. To address these challenges, we designed and implemented a Web-based BIM platform. The implemented application shows not only geometry data but also semantics data, enables easy overlay with the latest indoor conditions, and provides updating functionality. The developed system is essential for the continuous operation of BDT in dynamic indoor environments. We evaluated the application through a questionnaire survey. © 2023 ieee.

关键词： Machine design

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A Density-Aware Voxeliztion for 3D Object Segmention in Point Clouds 3

A Density-Aware Voxeliztion for 3D Object Segmention in Poin...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Cui, Lindong Meng, Zhaohui College of Computer and Information Hohai University Nanjing211100 China

ISBN: (纸本)9798350331387

para>In the segmentation of large-scale driving-scene LiDAR data, state-of-the-art methods typically achieve impressive results through the subdivision of point clouds into small voxels, followed by the application of 3D convolutions. While this approach yields competitive segmentation performance in general, it has limitations when segmenting small objects due to ignoring the inherent spatial density of the point cloud. To improve segmentation accuracy for small objects, we propose a new framework called Density-Aware Voxelization (DAV) that divides the point cloud into voxels of varying sizes process the feature based on local spatial density. This captures the underlying density distribution more accurately and helps distinguish between small dense objects and large sparse objects. Our method has achieved a significant improvement of 3% in small object detection and outperforms existing methods on nuScenes, about 1%. © 2023 ieee.

关键词： Optical radar

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