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

共 3244 条记录，以下是361-370 订阅

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The Analysis and Prediction Mode of Students' Academic Performance and Social Behavior in the Background of Big data 24

The Analysis and Prediction Mode of Students' Academic Perfo...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Qiao, Shaoping Humanities and Design College Chengdu Technological University Sichuan Chengdu China

ISBN: (纸本)9798400712692

The popularization of digital campus brings convenience to the life of teachers and students, and provides rich data for educational data mining (EDM). EDM uses data mining and machine learning techniques to analyze student data, aiming to provide quality services. This paper uses big data to analyze students' behavior, constructs the academic performance prediction model, and provides a reference for decision-making for teaching assistants. The Dis_GAT network based on graph neural network is proposed and compared with traditional methods to prove its effectiveness. By preprocessing and analyzing student data, the behavioral characteristics were extracted, and identifying abnormal students using K-means and isolated forest algorithms to help the counselors fully understand the student situation. Finally, the Dis_GAT network prediction framework is proposed, transforming the academic performance prediction problem into the node classification problem in the graph structure, introduces the concept of graph neural network, improves the graph attention network, and considers the influence of early performance similarity on later academic performance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Consumer behavior

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Staying Ahead of the Curve: Early Prediction of Academic Probation among First-Year CS Students 3

Staying Ahead of the Curve: Early Prediction of Academic Pro...

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3rd international conference on Applied Artificial Intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Neda, Barbara Martinez Wang, Max Singh, Amanjeet Gago-Masague, Sergio Wong-Ma, Jennifer Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9798350328929

Application processes in higher education, particularly in fields like Computer Science (CS), have become more competitive in recent years. Additionally, at the University of California, Irvine (UCI), 57% of students who were admitted and enrolled in the CS program between Fall 2011 and Fall 2015 failed to maintain satisfactory academic standing and entered academic probation. Of these probation students, 41% entered probation during their first year. The objective of this study is to explore the feasibility of using machine learning tools to identify the academic probation status of first-year CS students at an early stage, with the ultimate aim of providing timely support to those who are at the greatest risk of entering academic probation. We trained various classifiers on a dataset containing demographic and academic performance variables for CS students at UCI, and performed feature analysis to identify the most influential variables. Since it is our priority to identify students who enter academic probation, our focus was on maximizing recall. The best-performing classifiers were Linear Support Vector machines (recall of 0.838) and Logistic Regression (recall of 0.812). Our feature analysis revealed that ethnicity, student region, and having taken the Advanced Placement CS exam had a greater impact on the classification task than GPA and summer course enrollment. Future work could focus on expanding the dataset to include academic data available after enrollment to improve our current results. Our findings could also be applied to assess their impact in strategies for student retention in CS. Overall, this study demonstrates the potential for machine learning tools to assist in identifying at-risk students in CS, and highlights the importance of considering a range of demographic and academic variables in this process.

关键词： academic performance prediction computer science education educational data mining academic probation academic probation detection machine learning first-year probation

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Specific Emitter Identification Method Based on Aggregation Model with Metric learning in Complex Field 3rd

Specific Emitter Identification Method Based on Aggregation ...

引用

3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

作者： Xu, Siyang Wang, Ruokun Zhu, Qinghao Wu, Xiaodong China Shipbuilding Industry Corporation No. 722 Institute Wuhan430200 China

ISBN: (纸本)9789819721993

In the field of Specific Emitter Identification (SEI), due to the time-varying characteristics of the ionosphere, the radio short-wave communication channel is a complex variable parameter channel, and there are multi-path fading, polarization rotation, Doppler shift and other phenomena, resulting in the short-wave radiation source individual fingerprint features are covered by the channel features, and the recognition algorithm based on a single machine learning model or deep learning model is often difficult to achieve good recognition results. To solve this problem, based on the idea of metric learning and ensemble learning, this paper proposes an aggregation model of metric complex value CNN (AM-MCVCNN) for SEI and tests it on 2G-ALE signal. We generate multiple metric learning models, which can be used for SEI, and use a sort cumulative aggregation method (SCAM) to generate aggregation model. The experimental results show that the recognition accuracy of AM-MCVCNN can reach 89%, compared with the single model, the recognition accuracy is improved by 10–15%. Testing AM-MCVCNN on the data after one week, AM-MCVCNN still has a high accuracy (89.56%). For the data after two weeks, the accuracy of AM-MCVCNN decreased slightly (81.23%). For data collected after four months, some individuals still have a high identification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： learning systems

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Enhancing Sign Language Classification through Reservoir Computing and Depth Camera Technology 3

Enhancing Sign Language Classification through Reservoir Com...

引用

3rd international conference on Image Processing and Robotics, ICIPRoB 2024

作者： Thongking, Witchuda Premachandra, Chinthaka Wiranata, Ardi Maeda, Shingo Shibaura Institute of Technology Department of Engineering and Science Tokyo Japan Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan University of Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Research Group Tokyo Japan

ISBN: (纸本)9798350374766

The depth camera has emerged as an efficacious tool for facilitating interactions between individuals and machines. Its advantageous features, including a lightweight design, high durability, completeness, superior image quality, and versatility, have prompted researchers to explore its integration into advanced applications. Currently, the prevailing methods for classifying sign language exhibit certain limitations, such as suboptimal accuracy and inefficiency in gesture recognition technologies. In this study, we conduct a comprehensive examination of hand language characteristics rooted in American Sign Language (ASL), utilizing gesture-based interactions and augmenting sensing performance through preprocessing and the application of reservoir computing in the training model. We conduct a comprehensive evaluation of the gesture characteristics captured by the depth camera, elucidating a methodology encompassing preprocessing, machine learning, and the technical prediction of salient gesture features derived from conventional depth camera video footage. This endeavor aims to establish a robust signal model for enhanced understanding and representation of the analyzed gestures. Our machine-learning models adeptly predict American Sign Language signs. Correlation values derived from the depth camera data demonstrate a pronounced alignment with naturally occurring variations in metrics observed within specific gestures. Our methodologies for quantifying gestures with depth cameras contribute to heightened accessibility in quantitative motion analysis. The outcomes of the reservoir computing implementation exhibit the successful classification of three hand language signs, thereby manifesting high precision and recall. The robust performance of the classification is pivotal for practical applications and underscores the efficacy of our approach. © 2024 IEEE.

关键词： machine learning

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2023 3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

2023 3rd International Conference on Smart Generation Comput...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

ISBN: (纸本)9798350319125

The proceedings contain 441 papers. The topics discussed include: evaluating the impact of image restoration on digital image processing using compression technique;blockchain's transcendent evolution: uncovering its impact across industries and cutting-edge applications;analysis of the construction of digital education resource sharing in universities from the perspective of blockchain;examining the impact of data mining on decision-making processes;the synchronous dislocation scheduling algorithm: cloud-based performance improvement;the multicarrier NOMA- assisted full duplex IoT networks for robust resource allocation using lightweight secure transmission;research on English teaching quality evaluation based on fuzzy comprehensive evaluation based on k-means clustering algorithm;and predicting life expectancy using machine learning approach through linear regression and decision tree classification techniques.

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machine learning Based Cardiovascular Disease Prediction 3

Machine Learning Based Cardiovascular Disease Prediction

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Saraswathi, P. Prathiksaa, J.G. Prabha, M. Jhoshitha, N.A. It Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350330724

Heart disease is an illness that affects the heart and manifests itself in a variety of ways, including blood vessel issues, irregular heartbeats, and diseases of the heart muscle or valves. In recent years, researchers have found that machine learning can help to detect heart problems by analyzing digital electrocardiogram (ECG) data. By using this technology, doctors can detect heart issues earlier and save lives. The process involves analyzing large sets of medical data, which includes information about patients' medical history. Logistic Regression, Support Vector machine (SVM), and Random Forest are most widely utilized machine learning techniques to perform classification. Also, it predicts whether a patient has heart ailments or not. In this research, Decision Tree Classifiers are modeled along with the mentioned techniques which predict the heart related issues. A performance measure like accuracy of each technique is analyzed and the best one is identified to guess the heart disease. © 2023 IEEE.

关键词： Electrocardiograms

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Hand Gesture recognition Using machine learning for Bionic Applications: Forearm Case Study 2nd

Hand Gesture Recognition Using Machine Learning for Bionic A...

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3rd international conference on Digital Technologies and Applications

作者： Lamsellak, Oussama Benlghazi, Ahmad Chetouani, Abdelaziz Benali, Abdelhamid ORAS Team LaMAO Lab Oujda 60000 Morocco Mohammed First Univ Oujda Elect & Syst Lab ENSA Oujda Oujda 60000 Morocco

ISBN: (纸本)9783031298561;9783031298578

The ubiquitous use of accelerometers and gyroscopes can provide meaningful knowledge about the recognition of human limb movements and offers significant potential for the design of human-machine interaction and the control of bionic systems. Our research puts an emphasis on linking acceleration data produced by hand movements to surface electromyograms (sEMG) collected in the arm in order to actively participate in robotic prosthesis control and the development of digital healthcare systems. The goal of this work is to demonstrate how to model a flexible system of hand gesture recognition using a dual-sensor accelerometer and gyroscope as a first stage, with the ability to evolve and improve its efficiency by incorporating other signals in subsequent stages of feature extraction and processing. Our research is primarily focused on identifying the triaxial signal generated via the accelerometer and gyroscope that appears when the hand is performing six gestures. Based on amassive gesture library collected over a long period of time by nine individuals belonging to three age categories and different genders, a forearm gesture classification model has been successfully developed, implementing a novel set of measurement criteria and statistical characteristics by virtue of five reputed machine-learning classifiers with an overall accuracy of 97.9% for training and 88.0% for testing, which are employed to differentiate and learn the forearm direction classes and interpret the performances.

关键词： Gesture recognition machine learning Accelerometer EMG signals

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Classification of Devanagari Characters dataset Using Deep learning Methods 3rd

Classification of Devanagari Characters Dataset Using Deep L...

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3rd international conference on Recent Trends in machine learning, IoT, Smart Cities and Applications, ICMISC 2022

作者： Brunda, S. Soumya, C.S. Akarsha, D.P. Manjunatha, C. Nikshep, C.V. Kumar, Samuel Roushan Nijaguna, G.B. Nitte Meenakshi Institute of Technology BSF Campus Karnataka Yelahanka Bengaluru560064 India

ISBN: (纸本)9789811960871

Deep learning is a growing set of approaches for extracting useful information and knowledge from large amounts of data. Deep learning research and tools have focussed on commercial sector applications. Only a fewer Deep learning research have focussed on scientific data. This paper highlights the machine learning techniques applied to handwriting character recognition using network models built by using Deep learning Techniques. Handwriting recognition is now getting attention for researchers to assist technology for visually impaired, blind and human-robots for business documents. We have experimented Devanagari Character database with two different architectures GoogLeNet and AlexNet to evaluate the performance and achieve highest accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Object Detection in Images using machine learning 3

Object Detection in Images using Machine Learning

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nagarajan, G. Piratla, Abhiram Golla, Karthikeya Department of CSE Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331527549

This study develops a scalable, effective, and user-friendly solution to tackle the problem of real-time object detection in photos. The suggested approach makes use of YOLOv5 (You Only Look Once version 5) for deep learning-based object detection and Flask for managing REST API queries. Users can submit base64-encoded photos to the system, which will analyze them using the YOLOv5 model to identify and categorize objects, label them with bounding boxes, and then return the base64-formatted images. High-speed performance is ensured by using PyTorch for model inference, while Non-Maximum Suppression (NMS) increases object localization accuracy by removing redundant detections. Because of its lightweight architecture and ease of integration with online applications, this system can be used for a variety of purposes, including surveillance and e-commerce. The study's output is a reliable, approachable object detection API that can effectively manage real-time image processing and deliver precise item recognition, showcasing the potential for implementing deep learning models in intuitive and scalable web-based settings. © 2025 IEEE.

关键词： Image annotation

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ProductGuard: Empowering Informed Choices for Safe Living 3

ProductGuard: Empowering Informed Choices for Safe Living

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vadlamudi, Sandyarani Shashikala, K.S. Patil, Suyash Ganesh Shetty, Rajath U. Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331527549

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices. © 2025 IEEE.

关键词： Bottles

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