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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1791 条记录，以下是371-380 订阅

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Automated Proctoring System to Detect Anomalous Behavior in E-learning During Times of Crisis 4th

Automated Proctoring System to Detect Anomalous Behavior in ...

引用

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

作者： Manjunathswamy, B.E. Rathod, Ranjeet Managave, Padmaraj D. Sagar, Karthik S. Gowda, R. Rakshith Don Bosco Institute of Technology Bengaluru India

ISBN: (纸本)9789819920570

A boom in remote learning has happened recently. However, there has not been a good answer for academic exams. Some institutions use online copy-and-paste assignments, while others use remote proctoring, where a human proctor maintains an eye on students’ activities. there must be a solution as if way we live is to become the new norm. In this essay, we have put out a plan for creating an integrated AI-based system that can aid in discouraging exam cheating. the method recognizes fraud and records the evidence. this technology will be both affordable and secure. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Comparative Analysis of Convolutional and Long Term Short Memory Architectures in machine learning 1

引用

4th international conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Venugopal, Anita Kapil, Anil Kumar Dhofar University Dhofar Oman Surajmal University Uttarakhand India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

Training machines to develop intelligence is one of the popular studies of computer science. As long as machines with human intelligence is used for the benefits of humans, this technology will flourish civilization. Digital data capturing may process numerous challenges as documents may contain a combination of multiple unstructured data types. Such documents are easily readable to a human but machines may be unable to interpret the data as humans do. Neural network approaches like multilayer perceptron, CNN, RNN and LSTN are effective recognition methods as it can effectively deal with complex computing problems related to training networks and in the process of automating and producing predictive results. In deep neural networks, machines can detect prominent features of a given object without human interventions. the classification and recognition rate of all present algorithms depend chiefly on input images. this paper presents two approaches of deep learning for document recognition problem, convolutional neural network and the result of CNN is continued to RNN and LSTM approaches for synthesizing document. the study attempts to compare the results of CNN vs CNN-LSTM in terms of precision rate. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Proceedings - 2023 international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

Proceedings - 2023 International Conference on Advances in E...

引用

4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

ISBN: (纸本)9798350308082

the proceedings contain 159 papers. the topics discussed include: cloud edge interaction protocol detection system for distribution IoT based on metadata definition;a data mining method based on natural language processing and machine learning;simulation and intelligent fault diagnosis of bidirectional LLC resonant converter;an autonomous exploration method integrating local multi-point TOF data and edge following;regional integrated energy system control strategy considering orderly charging and discharging of electric vehicles;photogrammetric method for calculating spatial coordinates of transmission line galloping;analysis of the mechanism of soft fault in cables based on transmission line theory;short-term photovoltaic power prediction based on similar daily clustering and ISSA optimized neural network;and leveraging profiling for facial recognition optimization.

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Analysis of User's Power Consumption Behavior Based on K-means 4

Analysis of User's Power Consumption Behavior Based on K-mea...

引用

4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Wang, Xinmeng Li, Haiqi Yi, Xiaoguang Kong, Jing Wang, Xinling State Grid Shandong Electric Power Company Heze Power Supply Company Marketing Department Shandong China

ISBN: (纸本)9798350333947

With the continuous development of energy Internet and smart grid technologies, the potential value of power big data is constantly being mined. User behavior of power consumption is of great significance to power companies, consumers and power systems. In this paper, through the mining and analysis of the data on the power consumption side, the K- means clustering method is used to search the similarity of the samples, and the users are classified according to the characteristics of the power consumption behavior. Finally, four different types of users are obtained, and the family structure composition and economic status of related users are analyzed. through this method, the user's power consumption behavior pattern is analyzed, which provides a decision-making basis for the grid management side, and improves the service quality of electricity sellers to users. © 2022 IEEE.

关键词： data mining

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Deep learning and machine Vision based Robot for Fire Detection and Control 4

Deep Learning and Machine Vision based Robot for Fire Detect...

引用

4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Tephila, M.Benedict Aswini, P.M. Abhinandhan, S. Arjun, K.K. Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Robotics and deep learning algorithms have emerged as a crucial part of our everyday life. Most of the recent image recognition innovations use deep learning, big data analysis, and artificial intelligence as their core platform. Hence in this work, a firefighting robot that uses deep learning is introduced to detect fire. On detection, the type of fire is also identified with the help of a combination of Imagenet and Alexnet. A Raspberry Pi is used to execute the proposed work and is programmed to function efficiently to detect and classify fire. Based on the results obtained, it is concluded that classification accuracy stands at 92%, while the detection of fire is accurate up to 97.75%. the proposed work shows a dynamic improvement in the accuracy of detecting fire with the help of robots and further classifying them using machine vision. © 2022 IEEE.

关键词： Fires

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Pallet Detection and 3D Pose Estimation via Geometric Cues Learned from Synthetic data 4th

Pallet Detection and 3D Pose Estimation via Geometric Cues...

引用

4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Beleznai, Csaba Reisinger, Lukas Pointner, Wolfgang Murschitz, Markus Assistive & Autonomous Systems AIT Austrian Institute of Technology Vienna Austria AGILOX Services GmbH Neukirchen bei Lambach Austria

ISBN: (纸本)9789819787043

Vision-based object recognition is an important enabler for automating specific workflows in production and transportation scenarios. Locating and manipulating pallets as common functional objects represents a relevant robotic task in these domains. However, learning accurate neural models to estimate the location and pose of such objects is non-trivial. the main complexity stems from the underlying diversity of representing pallet objects: varied viewing conditions, frequent occlusions, self-occluded parts, diverse pallet materials jointly span a vast space of possible appearances. We present a solution which tackles the data diversity problem and the issue of occlusions. On one hand, we demonstrate how to rely on synthetic image pairs to compute geometry-encoding stereo disparity images, highly independent from appearance variations and exhibiting a small synthetic-to-real data domain gap. On the other hand, we introduce a novel point- and line-segment-based voting scheme, yielding a strong support for object presence in case of occlusions and up to distances of 8 m. We provide a quantitative evaluation of recognition accuracy for several network architectures using a manually fine-annotated multi-warehouse data-set. Based on the presented pallet recognition scheme, we also describe an automated forklift demonstrator, able to perform automated pallet pick-up and drop-off operations under diverse observation conditions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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Classification of Inflammatory Gene Expression patterns with machine learning Models

Classification of Inflammatory Gene Expression Patterns with...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yeonglong Ay Yit Hong Choo Asim Bhatti Chee Peng Lim Department of Neurology and Neurological Science Stanford University Stanford U.S.A Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

Dementia is a complex neurological disorder characterized by a progressive decline in cognitive functions. Recent studies have suggested a link between neuroinflammation and the development of dementia, particularly in Alzheimer’s disease. this study focuses on the analysis of inflammatory gene expression patterns in the parietal cortex (PCx) and temporal cortex (TCx) from a human brain RNA sequence data set, aiming to derive insights into the underlying genetic link associated with dementia, as well as prediction of dementia. Using five machine learning and statistical methods, our study reveals better detection and classification of dementia patients using PCx-related gene patterns, as compared with those from the TCx. Our findings support the hypothesis that the PCx plays a significant role in neuroinflammatory responses associated with dementia. Based on this preliminary study, machine learning models offer a useful approach to recognize inflammatory gene expression patterns and providing a valuable biomarker analysis and prediction for dementia diagnosis.

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Implementation of machine learning Methods on data to Analyze Emotional Health 4th

Implementation of Machine Learning Methods on Data to Analyz...

引用

4th international conference on Recent Trends in Advanced Computing - Computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Jadala, Vijaya Chandra Pasupuleti, Sai Kiran Hrushikesava Raju, S. Gole, Srihari Babu Ravinder, N. Sreedhar, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Greenfields Vaddeswaram Guntur522502 India

ISBN: (纸本)9789811971686

the design and implementation of the intelligent model based on machine learning methodologies on datasets to analyze emotional and mental health, majorly concentrating to measure, cognize, simulate, and react to human emotions. the data captured by the apparatus is always available to the user and provides contact to interact with patients in case of Emergency. It stores data for future analysis and for pattern identification, Experts will be able to use Artificial Intelligent based machines to augment the human capacity to detect, diagnose, and treat mental health issues. machines have a significantly higher computational capacity than humans, creating assessments from large pools of patient’s data that will eventually make informed clinical decisions. this article will examine AI applications on emotional and mental health, It also highlights the implications of using emotional AI in identifying, diagnosing, and improving mental health. We focused on the conceptual and simulation model for result analysis where data stored in the cloud is retrieved as per the requirement and analyzed. Different machine learning algorithms are implemented on the dataset and results are identified through graphical analysis. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Research on preprocessing and feature extraction methods for handwriting images based on Gabor texture learning

Research on preprocessing and feature extraction methods for...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Guzalnur Amrurla Nurbiya Yadikar Kurban Ubul Institute of Information Science and Engineering Xinjiang University Urumqi China

Abstract: Handwriting recognition is a science and technology that uses machines to analyze the differences in handwriting styles to determine the identity of the writer. In the fields of computer vision and pattern recognition, handwriting recognition technology has also received increasing attention and research from experts and scholars. At the same time, the Uyghur handwriting recognition technology is still in its early stages, and the research on Uyghur handwriting recognition methods is of great significance. In this paper, research work was mainly concened with off-line Uyghur handwriting identification method, with special attention is given basically in handwriting image preprocessing and feature extraction concerning special handwriting character structure using Gabor transform method. Main objective is to evaluate and achieves Uyghur handwriting identification related algorithms and techniques and promote automation of the process.

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Assessment and Prediction of Corrosion Rate of Marine Railway Bridges Based on Ridge Regression Model

Assessment and Prediction of Corrosion Rate of Marine Railwa...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Huan Wang Zhengbin Chen Rail Transit Branch of Sinohydro 7th Engineering Bureau Co. LTD Chengdu China

this investigation employs ridge regression analysis to assess and forecast the corrosion rate of maritime railway bridges. We have gathered and organized pertinent data, devising a regression model encompassing multiple attributes, intending to scrutinize seawater’s influence on railway bridge corrosion. the model’s performance is appraised using cross-validation techniques, and the significance of each point is measured via ridge regression coefficients. Experimental findings demonstrate that the ridge regression model excels in predicting the corrosion rate of maritime railway bridges, exhibiting minimal discrepancies between predicted and actual values. Upon analysis of the model’s attributes, we discern that salinity and sulfide content in seawater are critical determinants of railway bridge corrosion rates. the findings of this study provide valuable reference points for marine engineering and railway bridge construction while offering novel insights into the application of ridge regression models for predicting and analyzing maritime environmental issues.

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