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共 3357 条记录，以下是441-450 订阅

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Auto-commissioning Technology of Engineering Equipment Driven by machine learning 4

Auto-commissioning Technology of Engineering Equipment Drive...

引用

4th international conference on Informatics Engineering and Information Science (ICIEIS)

作者： Rao, Youfu Liu, Yi Zou, Guotong Zhang, Zuozhi Wang, Yun Yao, Zhiheng SANY Automobile Mfg Co Ltd Changsha 410000 Peoples R China

ISBN: (数字)9781510651999

ISBN: (纸本)9781510651999;9781510651982

the auto-commissioning system of engineering equipment requires real-time identification of the working mode with the whole product. Determine whether there is any abnormality in the commissioning process according to the parameter standards of each working mode, to evaluate the compliance rate of the commissioning. In this article, the working mode of engineering equipment is determined by Toeplitz Inverse Covariance-based Clustering (TICC) and the time series data are segmented and clustered. Each mode in the TICC is defined by a Markov Random Field (MRF), which characterizes the interdependence between different factors in the mode. On this basis, highlight the characteristics of the model and the importance of each factor. By using the TICC method to commissioning the concrete pump truck, the results of automatic identification show that the method has high accuracy in the recognition of the working mode of engineering equipment. the successful application of the online commissioning and monitoring system of the whole machine based on this technology provides a new idea for the development of the industry.

关键词： Multivariate analyses pattern recognition time-series clustering machine commissioning

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A Framework for Daily Living Activity recognition using Fusion of Smartphone Inertial Sensors data 4

A Framework for Daily Living Activity Recognition using Fusi...

引用

4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Khan, Sheharyar Shah, Syed M. Adnan Noorani, Sadam Hussain Arsalan, Aamir Ehatisham-Ul-Haq, M. Raheel, Aasim Ahmed, Wakeel University of Engineering and Technology Taxila Pakistan Fatima Jinnah Women University Rawalpindi Pakistan Air University Islamabad Pakistan

ISBN: (纸本)9798350335316

Recent years have seen rapid advancements in the human activity recognition field using data from smart sensor devices. A wide variety of real-world applications can be found in different domains, particularly health and security. Smartphones are common devices that let people do a wide range of everyday tasks anytime, anywhere. the sensors and networking capabilities found in modern smartphones enable context awareness for a wide range of applications. this research mainly focuses on recognizing human activities in the wild for which we selected an in-the-wild extra-sensory dataset. Six human activities i.e., lying down, sitting, standing, running, walking, and bicycling are selected. Time domain features are extracted and human activity recognition is performed using three different machine learning classifiers i.e., random forest, k-nearest neighbors, and decision trees. the proposed human activity recognition scheme resulted in the highest classification accuracy of 89.98%, using the random forest classifier. Our proposed scheme outperforms the state-of-the-art human activity recognition schemes in the wild. © 2023 IEEE.

关键词： Smartphones

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Integration of Vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle's Departure and Arrival Times 23

Integration of Vision IoT, AI-based OCR and Blockchain Ledge...

引用

8th international conference on machine learning Technologies (ICMLT)

作者： Sichinga, Marumbo Nizeyimana, Eric Gatare, Ignace Nsenga, Jimmy Univ Rwanda African Ctr Excellence Internet Things ACEIoT Kigali Rwanda

ISBN: (纸本)9781450398329

Vehicle based logistics are hinged on their ability to timely deliver goods, services, and people. the classical expression of "time is money" comes alive in the logistics industry yielding potentially huge financial and health consequences in case of missing deadlines. this is especially the case for time sensitive pharmaceuticals, delivery of perishable goods, delivery of people travelling, delivery of services in fault fixing/recovery sector. All these use cases motivate the need for an immutable, secure, and immortalized process of tracking time. To solve this challenge, this paper presents prototype-based research that integrates the 4th industrial revolution technologies of vision Internet of things (IoT), Artificial Intelligence (AI)-based Optical Character recognition (OCR) and blockchain. the developed prototype features a Raspberry-PI board embedding a camera, an Artificial Intelligence (AI) model to recognize plate letters from the image and a crypto wallet to sign the logging of plate number and time events on the NEAR blockchain, an emerging sharded, proof-of-stake, layer-one blockchain that is simple to use, secure and scalable. the effective operation of the developed prototype has been validated inside a campus parking and shows an accuracy of 80%. the benefits of transparency, security, and immutability of the blockchain combined with the intelligence, data capture, and processing of IoT will enable to develop accountability solutions trusted by all different logistic stakeholders.

关键词： Web3 Blockchain Dapp NEAR IoT YoloV4 OCR

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Research on Image Classification Based on Extreme learning machine and Convolutional Neural Network 4

Research on Image Classification Based on Extreme Learning M...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Sun, ChangXu Zhang, Rui Shenyang Jianzhu University China

ISBN: (纸本)9781510666412

In image classification and recognition, in order to obtain higher classification accuracy, it is necessary to extract more accurate and more expressive features of image semantic information[1]. this paper proposes an improved image classification model based on convolutional neural network. By improving and optimizing AlexNet from both the network architecture and the internal structure, we can further improve the expression ability of features. By introducing the extreme learning machine in the full connection layer, not only the classification ability and classification time of the network are improved, but also the structure has better data processing ability. © 2023 SPIE.

关键词： Image classification

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Opinion mining-Based Fake Review Detection Using Deep learning Technique 1

引用

the 4th international conference on data Science, machine learning 2022

作者： Pal, Koustav Poddar, Sayan Jayalakshmi, S.L. Choudhury, Madhumita Saif Ahmed, S.K. Halder, Soumyajit Department of Computer Science Pondicherry University Puducherry India

ISBN: (数字)9789819920587

ISBN: (纸本)9789819920570

Recently, the field of text mining gained more attention due to its enormous opportunities and challenging problems in the exponential growth of unstructured textual data. People frequently express their thoughts in complicated ways, making automated labelling of textual data challenging. the usage of mislabelled data sets reduced the efficiency of automated labelling tasks. In this paper, we proposed an opinion mining-based fake review detection using deep learning technique to express word semantic sentiment. SentiWordNet and WordNet lemmas are used to retrieve word synsets and sentiment scores. the Amazon shoes review data set is used for implementation. the proposed model achieved 92% accuracy for the Amazon shoes review data set. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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3D Shape Estimation from RGB data Using 2.5D Features and Deep learning 4

3D Shape Estimation from RGB Data Using 2.5D Features and De...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Ashfaq, Hamid Jalal, Ahmad Air University Dept. of Creative Technologies Islamabad Pakistan

ISBN: (纸本)9781665463775

Creation of 3D models from a single RGB image is challenging problem in image processing these days, as the technology is in its early development stage. However, the demands for 3D technology and 3D reconstruction have been rapidly increasing nowadays. the traditional approach of computer graphics is to create a geometric model in 3D and try to reproduce it onto a 2D image with rendering. the major aim of the study is to create 3D models from 2D RGB image using machine learning techniques to be less computationally complex as compared to any deep learning algorithm. the proposed model has been based on three different modules such as: 2.5D features extraction, mesh generation, and 3D boundary detection. the ShapeNet dataset has been used for comparison. the testing results has shown an accuracy of 90.77 % in the plane class, 85.72% in the chair class, and 72.14% in the automobile class. the proposed model could be applicable to problems where reconstruction of 3D models is required such as: variations in geometric scale, mix of textured, uniformly colored, and reflective surfaces. © 2023 IEEE.

关键词： data mining

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4th international conference on machine learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031200984

the proceedings contain 152 papers. the special focus in this conference is on machine learning for Cyber Security. the topics include: machine learning Based Security Situation Awareness Method for Network data Transmission Process;multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling Problem;High Voltage Power Communication Network Security Early Warning and Monitoring System Based on HMAC Algorithm;Large Scale Network Intrusion Detection Model Based on FS Feature Selection;Privacy Preserving CSI Fingerprint Device-Free Localization;Research on Intelligent Detection Method of Automotive Network data Security Based on FlexRay/CAN Gateway;adversarial Attack and Defense on Natural Language Processing in Deep learning: A Survey and Perspective;a Novel Security Scheme for Mobile Healthcare in Digital Twin;Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining data;spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain;Diverse Web APIs Recommendation with Privacy-preservation for Mashup Development;network Security Evaluation Method of College Freshmen Career Counseling Service Based on machine learning;FedTD: Efficiently Share Telemedicine data with Federated Distillation learning;increase Channel Attention Based on Unet++ Architecture for Medical Images;distributed Power Load Missing Value Forecasting with Privacy Protection;A Novel Blockchain-MEC-Based Near-Domain Medical Resource Sharing Model;differentially Private Generative Model with Ratio-Based Gradient Clipping;differential Privacy Protection Algorithm for data Clustering Center;improved Kmeans Algorithm Based on Privacy Protection;symmetry Structured Analysis Sparse Coding for Key Frame Extraction;data Reconstruction from Gradient Updates in Federated learning;natural Backdoor Attacks on Speech recognition Models;boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Co

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4th international conference on machine learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031200953

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Improving Image Compression Performance by Spatial-Channel Context Adaptive Model 4

Improving Image Compression Performance by Spatial-Channel C...

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4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Wang, Hao Wang, Huifen Xue, Junda Lu, Enmin Wang, Hanming Wu, Zijun Song, Yunlong Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

ISBN: (纸本)9798350314496

the significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding) to achieve high compression efficiency. Our method is evaluated on the classic OpenImage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. the results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression. © 2023 IEEE.

关键词： Image compression

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4th international conference on machine learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031201011

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