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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Deep learning Based Road Traffic Sign Detection and recognition 3

Deep Learning Based Road Traffic Sign Detection and Recognit...

引用

3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Swapna, Y. Reddy, Mekala Saketh Sai, Jagini Venkat Krishna, Nawathe Sri Sai Teja, Madugula Varun Vignan Institute of Technology and Sciences Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9780738146270

In order to observe and recognize road signs, it is necessary to prepare a system that is mainly based on visibility. The traffic sign recognition system collects traffic sign data and allows the driver. Traffic signs from a series of images with inaccurate color data. Color-based unit area segmentation techniques are used to detect traffic signs. To improve the targeting performance, it is highly required to use products with the highest hue and saturation coefficients. To achieve higher shape classification performance, a linear support vector machine with raster space options are used for fractional blobs. Area unit recognition of traffic signs is applied to non-linear support vector multiple classifiers with the desired pixels that are connected to edges. © 2021 IEEE.

关键词： Traffic signs

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Deep learning-Based Plant Species Identification using VGG16 and Transfer learning: Enhancing Accuracy in Biodiversity and Agricultural Applications

Deep Learning-Based Plant Species Identification using VGG16...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： K Kiran Arpanpreet Kaur School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model's ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks.

关键词： Deep learning Accuracy Plants (biology) Biological system modeling Transfer learning data augmentation data models Convolutional neural networks Biodiversity Environmental management

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Key Technologies for Dynamic Early Warning of BIM Power Monitoring data in Bridge Operation and Maintenance

Key Technologies for Dynamic Early Warning of BIM Power Moni...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Yu Peng Tian Wang Chongqing College of Electronic Engineering Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

Bridge operation and maintenance is an important solution to how to ensure the safe operation of bridges and extend their service life. In this paper, a dynamic early warning technology of BIM (Building Information Modeling) power monitoring data was proposed to supervise the safety of bridges. Through the layout of sensors and monitoring equipment, the power data of the bridge was obtained in real-time. The quality control and data correction of the collected power data were carried out, and data cleaning, data correlation and other technologies were used to process the power data and extract useful characteristic information. An early warning model was also constructed, and machine learning, data mining and other methods were used to analyze and model power data to realize dynamic early warning of bridge power systems. After experiments and analysis, it was concluded that the stability of the method in this paper was maintained at 89%-98%. Through the dynamic early warning of BIM power monitoring data, real-time monitoring and fault diagnosis of the bridge power system can be realized and abnormal conditions and potential problems can be found in a timely manner.

关键词： Bridges Power system dynamics Power system stability data models Stability analysis Real-time systems Maintenance Safety data mining Monitoring

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Post-COVID Cardiovascular Risk Assessment Using an Efficient machine learning Model

Post-COVID Cardiovascular Risk Assessment Using an Efficient...

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Advancement in Technology (ICONAT), international conference for

作者： D Srinuvasa Rao Desidi Narsimha Reddy Lova Naga Babu Ramisetti M S Radha Manga Mani Harikrishna Pathipati Siva Kumar Pathuri Mohana Prasad Mendu Dept of CSE GMR Institute of Technology Rajam AP India Soniks Consulting LLC 101 E park blvd Plano TX USA Information Technology 10333 Harwin Dr. #375e Houston TX Department of Information Technology SRKR Engineering College Bhimavaram AP India Information Technology ITG Technologies10998 S Wilcrest Dr Houston TX USA Dept of CSE Koneru Lakshmaih Education Foundation Vaddeswaram AP India Dept of CSE Malineni Suseelamma Womens’s Engineering College PrakasamDst Singarayakonda AP India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Individuals who were exposed to COVID-19 in different parts of their bodies, including heart attacks, faced major consequences. The frequency of cardiovascular diseases is increasing in the healthcare industry. This illness most commonly affects people aged 20 to 48. In supervised learning, data mining mainly depends on categorization algorithms. Using various combinations of calculations and algorithms, the information obtained from hospital data analysis is used for early-stage prediction of post-Covid cardiovascular disease. machine learning is one of the strange inventions that have been widely used in a variety of fields, including the application of medical services to disease prediction. We examined the accuracy of machine learning algorithms that can be used in this study to predict heart disease analyzes and predict overall risk. One of the most current data mining methods is classification. The classification method’s primary goal is to keep the data in the correct class. In this work, classification methods such as Logistic Regression Classifier, Naive-Bayes, and Ensemble Cardiovascular Prediction Classification Algorithm were used (ECVDPCA) and the dataset is taken from Kaggle repository. The goal of this study is to find the most exact classification approach and to improve accuracy in predicting Post-Covid Cardio-Vascular illness. Python was used to get the ideal solution in this case to predict the accuracy of the classifier in which the proposed classifier gave the best accuracy when compared with the other two classifiers i.e., 95

关键词： COVID-19 Technological innovation Accuracy machine learning algorithms Supervised learning Stacking Predictive models Prediction algorithms Classification algorithms data mining

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Human Pose Estimation Based on GAN and DCGAN Models 3

Human Pose Estimation Based on GAN and DCGAN Models

引用

3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Ma, Jiaming Li, Jiaqi Feng, Shenglu Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin Peoples R China Wuhan Univ Sch Elect Engn Wuhan Peoples R China

ISBN: (纸本)9781665417907

As an important application based on the field of computer vision, the various human movements and gestures can be reconstructed by detecting the key articulation points of the human body. It is mainly used in human behavior recognition, human-computer interaction, and attitude tracking. However, the current human pose estimation models have many challenges, such as difficulty detecting the non-typical articulation points of the human body and inaccurate locating of the extremities. They are prone to error or lack of information in complex situations. This paper proposed GAN and DCGAN models to tackle this issue, which can improve the accuracy of human posture prediction. This paper mainly focuses on the contribution of Generative Adversarial Network to the detection of human key articulation points, revising the original model posture and obtaining a model that is closer to the real posture of the human body. The experimental results demonstrated that the model's accuracy is improved to a certain extent after using the DCGAN model. Furthermore, we note that in most cases, the performance of the proposed model is superior to others.

关键词： Human pose estimation Key articulation points detection DCGAN model Experimental analysis

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Implementation of Process mining to Discover Student learning patterns using Fuzzy Miner Algorithm (Case Study: learning Management System (LMS) Telkom University) 3

Implementation of Process Mining to Discover Student Learnin...

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3rd international conference on Electronics Representation and Algorithm, ICERA 2021

作者： Valensia, Larasati Andreswari, Rachmadita Fauzi, Rokhman Telkom University Information System Department Bandung Indonesia

ISBN: (纸本)9781665434003

A learning management system is a system that supports student learning with several components. Telkom University is an institution that has an LMS called CeLOE LMS (Center for e-learning and Open Education learning Management System). Using LMS supports students' learning process, and lecturers can monitor what activities are carried out by their students. The pattern of learning by students can affect the success of learning in certain situations. The influence of these learning patterns makes lecturers need to pay attention to differences in student learning patterns. The data that will be used in this research are Website Application Development and Enterprise Systems courses. This study uses a process mining method with fuzzy miner algorithms to overcome the "spaghetti-like"process model into a more straightforward and easier-To-understand form. The resulting process model will then be analyzed using the graph simplification process method to obtain an accurate process model that describes the event log. In the Website Application Development course, conformance for all students Event logs is 99.93%, while conformance for students with index A event log is 99.94%. In the Enterprise System course, both event logs show the best conformance of 100%. The modeling produced by the two courses shows that the resulting model has a close alignment between the fuzzy model and the event log to reflect the behavior observed in the event log. © 2021 IEEE.

关键词： Miners

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Recommendation System with Exploratory data Analytics using machine learning 3

Recommendation System with Exploratory Data Analytics using ...

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Panwar, Manu Wadhwa, Amit Pippal, Sanjeev GL.Bajaj Institute of Technology Computer Science and Engineering Gr.Noida India

ISBN: (纸本)9781665438117

Digital data is generated on a vast scale nowadays and has been for the past two decades. This phenomenon implies shift in how data is managed, and conclusions drawn from it. Furthermore, artificial intelligence approaches and procedures for new ideas of analyzing Big data, Sentiment Analysis (SA), also known as Opinion mining (OM), has been a hot topic in recent, because of its ability to extract value from data, it has been around for a long time. It is, however, a topic that has gotten more attention in the fields of engineering and linguistics. As a result, the goal of this research is to provide insights into the field of exploratory data analysis, as well as an orientation toward the study of a recommendation system employing big data in the context of an e-commerce system, through the use of machine learning. probable machine learning based model. Initially the research contribution briefly summarizes discussion of the strategies and processes currently used in Sentiment Analysis and further we work upon the analysis of a recommendation system with e- commerce data using LSTM Model. © 2021 IEEE.

关键词： Sentiment analysis

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recognition of Mild Cognitive Impairment in the Elderly Based on machine learning 3

Recognition of Mild Cognitive Impairment in the Elderly Base...

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3rd IEEE international conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Guo, Shuhan Liu, Yanru Wang, Pengyun Zhang, Sen Han, Buxin Li, Juan Xiao, Wendong Center on Aging Psychology Cas Key Laboratory of Mental Health Institute of Psychology Beijing China University of Chinese Academy of Sciences Department of Psychology Beijing China School of Automation Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9780738112152

As the prodromal stage of Alzheimer's disease, effective recognition of mild cognitive impairment can reduce the prevalence of Alzheimer's disease. At present, most of the research on the recognition of mild cognitive impairment is carried out through biomarkers and neuroimaging, which is not conducive to large-scale analysis and research. Based on neuropsychological evaluation and life habits questionnaires, this article applies machine learning methods to the recognition of mild cognitive impairment and conducts experimental research. A questionnaire survey of the elderly was conducted to obtain raw data, including demographic variables, daily habits and neuropsychological data. Feature selection is carried out through filter screening method and the influence of factors such as lifestyle, physical health and learning ability on the morbidity of mild cognitive impairment is analyzed. The classifier mainly uses three methods: artificial neural network, support vector machine and random forest. The experimental results show that the random forest classification effect is the best and the accuracy rate is as high as 92%. Artificial neural network has strong generalization ability with 91% accuracy rate. Support vector machine has inferior effect. © 2021 IEEE.

关键词： Support vector machines

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

3rd International Conference on Neural Computing for Advance...

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

ISBN: (纸本)9789811961410

The proceedings contain 77 papers. The special focus in this conference is on Neural Computing for Advanced Applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis;a Perception Method Based on Point Cloud Processing in Autonomous Driving;many-Objective Artificial Bee Colony Algorithm Based on Decomposition and Dimension learning;LCSW: A Novel Indoor Localization System Based on CNN-SVM Model with WKNN in Wi-Fi Environments;large Parallax Image Stitching via Structure Preservation and Multi-matching;traffic Congestion Event mining Based on Trajectory data;many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition;a New Unified Control Approach for Finite-/Fixed-Time Synchronisation of Multi-weighted Dynamical Networks;aperiodic Sampling Based Event-Triggering for Synchronization of Nonlinear Systems;two-Stream 3D MobileNetV3 for Pedestrians Intent Prediction Based on Monocular Camera;Research on Non-intrusive Household Load Identification Method Applying LightGBM;design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm;an Ensemble Deep learning Model Based on Transformers for Long Sequence Time-Series Forecasting;multi-layer Integrated Extreme learning machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker;wind Power Forecast Based on Multiple Echo States;UAV-Assisted Blind Area Pedestrian Detection via Terminal-Edge-Cloud Cooperation in VANETs;a Survey of Optimal Design of Antenna (Array) by Evolutionary Computing Methods;a Span-Based Joint Model for Measurable Quantitative Information Extraction;bayesian Optimization Based Seq2Seq Network Models for Real Estate Price Prediction in Hong Kong;ensemble learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects;Cage Mass Center Capture for Whirl Analysis Using an Improved MultiResUNet from the Multimodal Biomedical Image Segmentation;master Multiple Real-Time Strategy Game

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

3rd International Conference on Neural Computing for Advance...

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

ISBN: (纸本)9789811961342

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