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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是551-560 订阅

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A Novel Approach to learning Motivation Classification: Fusion of LSTM and Neural Ordinary Differential Equations 3

A Novel Approach to Learning Motivation Classification: Fusi...

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2023 IEEE 3rd international conference on Data Science and computer application, ICDSCA 2023

作者： Xiaoqin, Lai Siyun, Zhou Jiangao, Zuo Jianzhen, Li Yuan, Lei Caiyun, Jiang Guangxi Science & Technology Normal University Laibin545004 China The Institute of Education Sciences Yiyang413000 China Yiyang Open University English Department Yiyang413000 China Guangxi Suhang Firefighting Technology Co. Ltd. Nanning530000 China

ISBN: (纸本)9798350341546

With the rapid expansion of online education and continuous advancements in information technology, in-depth analysis and understanding of students' learning motivation have become increasingly critical. Students' learning motivation may affect their participation and achievements, making accurate identification and classification of learning motivation significant. This study proposes an advanced integrated model that combines Long Short-Term Memory networks (LSTM) with Neural Ordinary Differential Equations (Neural ODEs) for the classification and analysis of learning motivation. The LSTM component focuses on capturing long-term dependencies in time series data, while the Neural ODEs part is responsible for modeling continuous dynamic processes. We delve into the mathematical properties of the model and provide comprehensive theoretical analysis through theorems, reasoning, and inference. Moreover, we designed a series of rigorous experiments using real online education datasets from the Tianchi platform for validation. The experimental results show that the integrated model significantly outperforms standalone LSTM, Neural ODEs, and the baseline Random Forest model on multiple performance metrics such as accuracy, recall, and F1 score. We also deeply analyze the contribution of different model components and discuss various implementation details and hyperparameter selections. The main findings and methods of this study not only offer new perspectives and tools for analyzing learning motivation in the field of online education but also provide valuable insights for other time series and continuous data classification tasks. © 2023 IEEE.

关键词： Integrated Model learning Motivation Classification Long Short-Term Memory networks Neural Ordinary Differential Equations Online Education Performance Evaluation

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A comparative study of different algorithms using contrived failure data to detect robot anomalies 3

A comparative study of different algorithms using contrived ...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Wescoat, Ethan Kerner, Scott Mears, Laine Clemson Univ 4 Res Dr Greenville SC 29607 USA

Unexpected downtime remains a costly, preventable burden in manufacturing. To mitigate and eliminate unexpected downtime, manufacturers have incorporated machine learning to diagnose equipment faults and determine the equipment remaining useful life. However, such models suffer from a lack of failure data and context knowledge surrounding data gathered from production (i.e. rich labeled sets). The purpose of this paper is to conduct an algorithm comparison study over a previously collected contrived anomaly data set from an industrial robot. The goal of the study is to measure the effectiveness of algorithms to eliminate bias and variance from the classification results. The tested system is a 6-DOF collaborative robot from Universal Robots, on which an anomalous condition is artificially induced on a robot to simulate robot overload. The different algorithms are assessed based on their accuracy in determining the overloading case on the robot. From the analysis of data-driven machine learning and deep learning models, a deep learning regression was determined as the best model from the assessment of the data both qualitatively (low overfitting and bias) and quantitatively (98% overall accuracy). As part of the future research direction, different failure cases should be created based on wear applied to specific robot joint components and an adaptability assessment carried out for the model to other robot paths. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Deep learning Condition Monitoring Physical Twin

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Covid-19 and Diabetic Retinopathy (Dr) Detection Using Ai and Deep learning 3

Covid-19 and Diabetic Retinopathy (Dr) Detection Using Ai an...

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3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Srivastava, Abhishek Singh, Gaurav Shukla, Manoj K. Majumdar, Rana Amity University Uttar Pradesh Amity School of Engineering and Technology Noida201313 India Sister Nivedita University Professor School of Engineering Kolkata India

ISBN: (纸本)9781665453196

Artificial intelligence (AI), deep learning (DL), and neural networks (NN), though these words sound flashy and may leave you perplexed, represent powerful technologies that have the capabilities to transform the world. It is just now emerging how valuable these machine learning-based techniques are and how they can solve many real-world problems ranging from fraud detection, resource management to driver-less *** such field where the application of AI systems is progressively growing is in medical diagnosis. A lot of research is going on to enhance computer-Aided diagnosis and detection of diseases. Recent world events have tested the healthcare systems all around the world. Suppose we have sophisticated deep learning systems (DLS) that could help in faster and efficient disease detection and diagnosis;how beneficial it would be to assist both medical professionals and *** study explores how AI and machine learning techniques could be used for disease detection, giving COVID-19 and Diabetic Retinopathy detection examples. We present two deep learning (DL) models, one to detect COVID-19 from chest x-ray image scans and the other to detect Diabetic Retinopathy at various stages of the disease from retinal fundus images. With reasonably high accuracy, >95% for the COVID-19 detection model and >80% for the Diabetic Retinopathy detection model, these results highlight AI and deep learning potential to assist general practitioners. © 2022 IEEE.

关键词： COVID-19

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Video Chat using WebRTC with In-built Sign Language Detection 3

Video Chat using WebRTC with In-built Sign Language Detectio...

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3rd international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Sendhan, A. Saran, J.U. Vidhya Sagar, P.V. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350306989

Many research efforts into video conferencing technology have focused on audio-only interactions. Text chat, on the other hand, has many uses and is often used in conjunction with the audio channel in multimedia conferencing systems because of the many benefits it offers to users in a variety of contexts. Video chat on the other hand has been providing participants to participate not only through voice but also through visual clues, creating a richer and more immersive communication experience. With the development of technology, video chat has transformed how people cooperate and communicate across distances by becoming more accessible and user-friendly. This study details a prototype program that uses video chat with in-built sign language identification to help the hearing-impaired and the speech-impaired. The prototype is constructed from WebRTC to provide realtime communication for the application, on top of which the object detection model from Tensor Flow is integrated to provide hand sign detection and recognition for the user. We focus our testing on the bandwidth and sign language detection needs of customers in order to provide improved support for video chat. We then talk about how this evaluation's findings can inform the evolution of video chatting software. © 2023 IEEE.

关键词： Object detection

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application of machine learning in Healthcare: An Analysis 3

Application of Machine Learning in Healthcare: An Analysis

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Mana, Suja Cherukullapurath Kalaiarasi, G. Yogitha, R. Helen, L Suji Senthamil Selvi, R. Sathyabama Institute of Science and Technology Department of Cse Chennai India Saranathan College of Engineering Department of Cse Tamil NaduEdamalaipatti Pudur India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Health care field is facing a lot of challenges due to the huge volume of people need medical support. The pandemic situation has created a lot of challenges to the healthcare field. This paper analyses how advancement in machine learning can be best utilized in improving health care services. machine learning techniques are based on the idea of how systems can learn from the already existing data and can work with minimal human supervision. Thus machine learning has huge scope in healthcare. machine learning algorithms can be effectively utilized for disease prediction, disease detection, providing personalized healthcare etc. These models can effectively predict the presence of diseases and also helps in detecting the diseases at earlier stage itself. Both supervised and unsupervised algorithms will be helpful in this field. Personalized healthcare applications aim to provide patient oriented healthcare services. machine learning in combination with internet of things technologies made the personalized health care possible. The data collected from wearable devices and sensors can be effectively processed using machine learning algorithms and effective predictions can leads to quality of life improvements. In this chapter authors studies some of the existing applications of machine learning in healthcare field. Authors also propose a model that will add value to the existing applications. © 2022 IEEE.

关键词： Forecasting

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Research on BP neural network model based on feature engineering 3

Research on BP neural network model based on feature enginee...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Yang, Lei Zhang, Longqing Fan, Yong Tian, Liwei Chen, Yungui School of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510661240

This research used the machine learning algorithm of the BP neural network to predict a data set. In order to verify the performance of the prediction model, we introduce the confusion matrix and F1 score to evaluate the effect of machine learning. In order to optimize the BP neural network model, we use feature engineering to process the data set and apply the BP neural network model to this new data set. The experimental results show that the machine learning performance of the BP neural network model based on feature engineering is improved. © 2022 SPIE.

关键词： machine learning

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Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using Deep learning Deployment in Effective application 3

Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using D...

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3rd IEEE international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Hamim, Md Abrar Tahseen, Jeba Hossain, Kazi Md. Istiyak Akter, Nasrin Asha, Umme Fatema Tuj Daffodil International University Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350335262

Finding rotten fruits and vegetables has been important, especially in the agricultural industry. computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent. © 2023 IEEE.

关键词： Vegetables

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Design and Implementation of College Students’ Innovation and Entrepreneurship Experience System Based on Deep learning 3rd

Design and Implementation of College Students’ Innovation a...

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3rd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Yang, Yi Jiangxi Institute of Applied Science and Technology Nanchang China

ISBN: (纸本)9783031631290

Cultivating students’ Set up a company leader ability is one of the main teaching tasks in universities. On the basis of encouraging teachers and students to participate in Set up a company leader teaching, students’ comprehensive quality and practical ability are improved, and more outstanding talents are trained for the society. According to the actual needs in students’ credit recognition of create a company, a fully functional experience system in students’ create a company based on B/S structure is constructed based on Java language. According to the support vector machine of big data analysis method, the risk assessment model in students’ entrepreneurship is established. At last, the functions of the system are realized by ASRNET technology, and the system is tested comprehensively from three aspects of interface, function and performance, and the errors found are corrected, so as to realize the platform and development of the whole system. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Students

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A machine Vision-Based Person Detection under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System 3

A Machine Vision-Based Person Detection under Low-Illuminanc...

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3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Susa, Julie Ann B. Militante, Sammy V. Acoba, Aimee G. Evangelista, Ryan Soriente Lacatan, Luisito Lolong Lavina, Charlemagne G. Tanguilig, Bartolome T. Southern Luzon State University Dept. of Computer Engineering Quezon Lucban Philippines College of Engineering and Architecture University of Antique Antique Philippines Technological University of the Philippines Electronics Eng'g Technology Department Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan Ng Cabuyao Laguna Philippines

ISBN: (纸本)9781665456647

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance. © 2022 IEEE.

关键词： learning algorithms

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machine learning - Based Diagnosis of Covid-19 using Clinical Data 3

Machine Learning - Based Diagnosis of Covid-19 using Clinica...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Gowda, Mona N Singh, Dalwinder Rakhra, Manik Lovely Professional University Computer Science and Engineering Punjab Phagwara India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Coronavirus (COVID-19) is a worldwide pandemic caused by SARS Coronavirus 2. (SARS-CoV-2). The COVID-19 epidemic has put global healthcare systems in jeopardy. This study's purpose is to develop and evaluate an automated COVID-19 infection detection system using machine learning and chest x-ray images. Early diagnosis and treatment may help avert major illness and even death. It is presently the most favoured and accurate approach for COVID-19 diagnosis. X-ray imaging of the chest may be used instead of the rRT-PCR test to look for early COVID-19 symptoms. A new machine learning (ML)-based analytical framework for automated COVID-19 diagnosis is created utilizing chest X-ray pictures of likely patients. The proposed framework for COVID-19 disease diagnosis using X-ray images has a 99 percent accuracy for Covid and a 92 percent accuracy for Non-covid in two-class categorization. The investigation suggests the COVID-19 detection framework is better. © 2022 IEEE.

关键词： COVID-19

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