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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 3121 条记录，以下是361-370 订阅

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Parallel Selective Meta-Heuristic Algorithms for Real-Time Non-Convex Optimization in Big data Analytics 3

Parallel Selective Meta-Heuristic Algorithms for Real-Time N...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ghosal, Shreya Shankar, Amar Agarwal, Trapty Suchithra, R. Raj, Nimesh Hansda, Prity Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

With big data in modern analytics becoming more common, a lot of it is real-time optimization, which is an essential problem. This is because big data requires efficient and accurate optimization methods, leading to developing parallel selective viable meta-heuristic algorithms for nonconvex optimization in large analytics. They can be used to find optimal solutions rapidly by merging the complementary advantages of meta-heuristics and parallel computing. Algorithms do this using an optimization process, but not always for the whole data set used, saving computational performance. As information is updated in real-time, its approach will constantly be improved, resulting in a better and more accurate solution. A parallel implementation of these algorithms makes them effective for real-time optimization using large datasets. However, the performance of big data analytics may be significantly improved by implementing these algorithms because they have achieved satisfactory results in a number of applications, including machine learning, data mining, and pattern recognition. © 2025 IEEE.

关键词： Large datasets

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Spam recognition Model Based on TextCNN 3

Spam Recognition Model Based on TextCNN

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Yixuan, Gao Yuanyuan, Zhang Ye, Cheng Fuzhou University Maynooth International Engineering College Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement of modern Internet technology, email is widely used in people's daily lives and has become one of the common communication tools. However, at the same time, email-based spam has also been widely spread, flooding people's lives and not only wasting public resources but also infringing on people's legitimate rights and interests. Based on this, we propose a Spam recognition model based on TextCNN by combining Convolutional Neural Network and Natural Language Processing, where natural language processing techniques are used to pre-process the text in the spam corpus to obtain text features, which are then imported into the constructed Convolutional Neural Network analysis model. A CNN spam recognition model is then designed and analyzed hierarchically using the TensorFlow framework. After extracting the features from the email dataset, the model was fed into the convolutional neural network again for several iterations of training. The results indicated that the model achieved an accuracy of 97.7% in spam recognition, which is an exceptionally significant improvement in accuracy and stability compared to the SVM model used previously, demonstrating good filtering performance. © 2022 IEEE.

关键词： Convolutional neural networks

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recognition of Leukemia Sub-types Using Transfer learning and Extraction of Distinguishable Features Using an Effective machine learning Approach 3

Recognition of Leukemia Sub-types Using Transfer Learning an...

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3rd international conference on Electrical, Computer and Communication Engineering, ECCE 2023

作者： Sajon, Tahsen Islam Chowdhury, Maria Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Mahedy Hasan, S.M. Sayeed, Abu Minhazur Rahman, A.F.M. Department of Cse Ruet Rajshahi Bangladesh

ISBN: (纸本)9798350345360

Although extensive research has been conducted on leukemia, the disease still accounts for more than 350,000 fatalities annually. Automated Leukemia diagnosis may alter the situation because actions can be taken immediately;as a result, accurate detection of Leukemia has been a subject of interest for researchers. As statistics grow and expand, the need for precise leukemia identification continues to increase. In this study, we investigated a dataset of leukemia that used the WHO classification scheme. We developed a modified DenseNet201 design that achieved an overall accuracy of 99.69% without relying on data augmentation. Additionally, we identified and validated key features for leukemia classification by utilizing three feature extraction approaches (i.e., hu moments, haralick texture and parameter-free threshold adjacency statistics) and several machine learning classifiers (i.e., Gaussian Process, Support Vector machine, K-Nearest Neighbor or KNN, Extra Trees Classifier, and Logistic regression) that outperformed earlier feature extraction-based techniques. © 2023 IEEE.

关键词： Feature extraction

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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...

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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

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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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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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