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共 3332 条记录，以下是161-170 订阅

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A Comparative Analysis of Various machine learning Algorithms for Handwritten Digit recognition 4

A Comparative Analysis of Various Machine Learning Algorithm...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Akshat, Abhi Tripathi, Kritika Malik, Devanshi Fatima, Hoor Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

One of the most fundamental uses of machine learning is the identification of handwritten digits. there has been a lot of compelling research in this field. Since everyone has different handwriting, it becomes very important for the algorithm to recognize all styles and ways of writing. With the advancement in science and technology, digitization of all forms of documents has become prominent. Since there are still many documents that are yet to be digitized, these applications come in handy. MNIST and SKLearn load digits datasets are prominent for the recognition process and they have a large set of data points. Many Algorithms are developed over the years that are capable of performing recognition of digits. the accuracy and performance metrics of the methods Support Vector machine (SVM), Random Forest, Gaussian Naive Bayes, and Convolutional Neural Networks (CNN) are analyzed in this study. © 2023 IEEE.

关键词： Support vector machines

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Minimal Electrode EEG for BCI Emotion Detection 4

Minimal Electrode EEG for BCI Emotion Detection

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4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Li, Yuxin Fang, Hao Liu, Wen Cheng, Chuantong Chen, Hongda Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China The University of Edinburgh Institute for Imaging Data and Communications School of Engineering EdinburghEH9 3FG United Kingdom Chinese Academy of Sciences State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Beijing China Jiangsu Industrial Technology Research Institute Brain Machine Fusion Intelligence Institute Suzhou China

ISBN: (纸本)9798350394375

Electroencephalography (EEG)-based emotion recognition is a potential research direction in the field of brain-computer interfaces (BCIs). However, its deployment on wearable devices still suffers from the challenges of low accuracy, heavy computation, and complex electrode placement. this study focuses on advancing the efficiency and cost-effectiveness of EEG-based BCIs for emotion recognition. Our approach begins with an investigation of electrode placement in relation to emotion detection, leveraging the SEED dataset to identify an optimal configuration that uses a minimal number of electrodes while maintaining high recognition accuracy. Employing a variety of machine learning and deep learning algorithms, we compare detection accuracy across different electrode combinations. through these experiments and subsequent analysis, we identify an effective combination of two electrodes, T7 and T8, with the SVM method achieving an impressive 92.8 % accuracy. this finding laid the foundation for the design of our wearable, closed-loop BCI device with EEG-based emotion recognition capability. © 2024 IEEE.

关键词： Emotion recognition

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Research on Radar PRI Modulation pattern recognition Based on Recurrent Neural Network 4

Research on Radar PRI Modulation Pattern Recognition Based o...

引用

4th international conference on Computer Vision, Image and Deep learning, CVIDL 2023

作者： Qu, Wei Yao, Gang Meng, Lei Space Engineering University Department of Electronics and Optics Beijing China Beijing Institute of Tracking and Telecommunications Technology Beijing China Beijing Remote Sensing Information Research Instituteline Beijing China

ISBN: (纸本)9798350326444

In this paper, the Recurrent Neural Network (RNN) model is introduced into the identification of radar emitters, which verifies the feasibility of RNN application to radar emitter identification, and the design process is simplified and the radar emitter identification method is easy to apply in engineering. Based on the radar emission source recognition model, the Pulse Repetition Interval(PRI) modulation pattern recognition research based on recurrent neural network was carried out. Firstly, aiming at the problems of gradient vanishing and gradient explosion in RNN, a PRI modulation pattern recognition method based on LSTM is proposed, and the recognition method process is introduced in detail. According to the characteristics of pulse train data, a data preprocessing method suitable for recurrent neural network is proposed. Finally, simulation verifies the effectiveness of the proposed method and tests the performance of the algorithm under different interference conditions. © 2023 IEEE.

关键词： Long short-term memory

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Ensemble learning with Mixup Style for Ultrasound Image Classification 4th

Ensemble Learning with Mixup Style for Ultrasound Image Clas...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Alblwi, Abdalrahman Barner, Kenneth E. Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9783031821493;9783031821509

Early detection of breast cancer has become an essential medical procedure to reduce mortality rates in patients. Although various deep learning methods enhance cancer analysis and detection, including feature extraction techniques, transfer learning, image fusion, and data augmentation, challenges such as limited data and class imbalance impede progress in early cancer detection. this study introduces a new approach that employs a dual ensemble learning and MixUp data techniques to simultaneously (1) tackle unbalanced classes and (2) improve diversity in ultrasound images. the proposed training methodology involves various strategies, integrating original and augmented datasets to maintain model training and increase performance. through comprehensive evaluations using benchmark breast ultrasound images (BUSI), the proposed approach demonstrates a substantial improvement and feasibility in the multi-classification of breast cancer.

关键词： Ensemble learning Breast cancer Class imbalance, Multi-Classification MixUp

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AI Empowers data mining Models for Financial Fraud Detection and Prevention Systems 4

AI Empowers Data Mining Models for Financial Fraud Detection...

引用

4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Ziyue Shen, Qinyan Bi, Shuochen Fu, Chengqian New York NY10012 United States Jersey City NJ07302 United States Boston MA02110 United States Baltimore MD21211 United States

With the rapid development of Internet finance, the problem of financial fraud has become increasingly prominent, which has brought severe challenges to the security and stability of the financial industry. this paper aims to use artificial intelligence (AI) technology to empower data mining models to build an efficient financial fraud detection and prevention system. First of all, through the analysis of the development trend of Internet finance and the current situation of financial fraud, the limitations of traditional prevention and control measures in the face of big data and complex fraud modes are revealed. Secondly, the paper introduces the application prospect of AI technology in the financial field, especially the advantages and potential in data mining and fraud detection. then, the data mining techniques adopted are discussed in detail, including the application of machine learning-based model recognition and artificial intelligence algorithms, as well as data preprocessing and feature engineering in big data environment. Further, the paper describes the system architecture design and key technology implementation, including model training and optimization, real-time monitoring and early warning solutions. Finally, through the in-depth analysis and evaluation of the experimental results, the paper verifies the remarkable effect and feasibility of the system in financial fraud detection and prevention, and provides a new technical guarantee and risk management strategy for the financial industry. the experimental results show that the model has achieved good results in the actual data in the financial field, and provides effective technical guarantee and support for financial security. © 2024 the Authors.

关键词： Decentralized finance

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YOLO-based obstacle detection under few-shot learning 4

YOLO-based obstacle detection under few-shot learning

引用

4th IEEE international conference on data Science and Computer Application, ICDSCA 2024

作者： Liu, Chengyu Yang, Yuan School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350368239

the obstacle detection industry has been developing rapidly in recent years. Obstacle detection technology has grown by leaps and bounds. Few-shot obstacle detection has a profound significance, which can help us to solve the problem of small samples under high speed conditions in target detection. Due to the limitation of the shooting environment, the sample size about the obstacles is small today. the goal of small sample learning is not for the machine to recognize images in the training set and generalize to the test set;the goal of small sample learning is for the machine to learn to learn on its own. the purpose of learning is for the model to understand the similarities and differences between things and learn to distinguish between them. In this paper, we help with this process by adding a parameter reweighting module. the models will fall into overfitting when a small number of samples are available. In this paper, we propose a small-sample detection method based on improved YOLO-v8. For the problem of small sample size, we improve the generalization ability of the model by adding an attention mechanism at the detection header, and for the problem of model overfitting, we update the model parameters by adding a feature reweighting module to improve the model effect. Finally, the proposed method is validated based on the track area dataset, and the experimental results show that the model can realize the accurate recognition of small sample obstacles. ©2024 IEEE.

关键词： Zero-shot learning

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Interpretable Ensemble learning Model for Enabling an IDS to Detect DNS Attacks 4th

Interpretable Ensemble Learning Model for Enabling an IDS to...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mahmoud, Loreen Pillai, Sreedev Gangopadhyay, Sugata IIT Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9783031821523;9783031821530

Malicious domains pose a significant threat to internet security, with cyber-criminals exploiting the Domain Name System (DNS) to deceive users and host malicious content. the DNS services are very significant, and hence, the DNS traffic cannot be blocked. this situation is exploited by establishing a covert tunnel for communicating commands by the malicious server, thereby taking over the control of the compromised machine. Traditional methods of detecting malicious domains, such as blacklisting, have limitations in detecting newly generated domains. In this paper, we suggest a DNS intrusion detection system using ensemble learning, where we also use the Local Interpretable Model-agnostic Explanations (LIME) to further understand the predictions of the model. For evaluating the model, the CIC-Bell-DNS2021 dataset was used. To validate the generalizability of the model, we also check it on the UNSW-NB15 dataset. the Experiments show that the proposed method outperforms the state-of-the-art results on two datasets, CIC-Bell-DNS2021 and UNSW-NB15.

关键词： Malicious Domain DNS LIME technique Ensemble learning Intrusion Detection System Bagging Model Classification

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Traffic pattern recognition Method with XGBoost Based on Multi-scale Features 4

Traffic Pattern Recognition Method with XGBoost Based on Mul...

引用

4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Song, Yunlong Wang, Hao Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

ISBN: (纸本)9798350314496

Traffic pattern recognition belongs to a branch of scene recognition and has become a hot research field. Correctly identifying the transportation mode used by users to travel plays a vital role in promoting the development of situational recognition. In the field of traffic pattern recognition research, many recognition methods use GPS, the combination of GPS and WiFi, and the combination of GSM and WiFi to obtain data, and use LR, SVM and deep learning models to make predictions. However, these methods have some disadvantages, such as poor WiFi signal in some outdoor environments, resulting in failure to obtain user data, GPS being interfered by the external environment, resulting in inaccurate data acquisition and other issues, and the models they use have problems such as low prediction accuracy or prolonged prediction consumption. In response to these problems, this paper uses multi-source sensors in mobile phones to obtain data, and preprocesses sensor data through statistics and signal processing methods to generate features at different scales, and finally uses XGBoost to identify various traffic modes. Extensive experiments are carried out on the method proposed in this paper, and the effectiveness of the proposed method is demonstrated by comparing with two state-of-the-art methods. © 2023 IEEE.

关键词： pattern recognition

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Feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection 4th

Feature Extraction and Dimensionality Reduction to Evaluate ...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Lahmar, Chaymaa Idri, Ali Mohammed V Univ Rabat SPM ENSIAS Rabat Morocco Mohammed VI Polytech Univ Fac Med Sci Ben Guerir Morocco

ISBN: (纸本)9783031821554;9783031821561

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. this paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. the hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. the results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.

关键词： Computer-Aided Diagnosis Diabetic Retinopathy machine learning Deep learning Ensemble learning Image Processing

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A data-Driven machine learning Approach for Human Gender Classification Using Explainable AI 4

A Data-Driven Machine Learning Approach for Human Gender Cla...

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4th international conference on Computer, Communication, Control and Information Technology, C3IT 2024

作者： Dipto, Shakib Mahmud Habib, Adria Binte Mim, Nadia Tasnim Eunus, Salman Ibne Orchi, Nabiha Tasnim University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350389128

Since gender recognition contains a wealth of information about the differences between male and female characteristics, it is crucial and essential for many applications in commercial domains, such as human-computer interaction applications and computer-aided physiological or psychological analysis. there are lots of scientific studies that contain different approaches to classify gender. Apart from those, we have included Explainable Artificial Intelligence to interpret the usability of the model. In this paper, we study a gender classification dataset to predict gender from tabular data of specific human body features such as hair length, forehead length, height, nose width, and length. We have used several machine learning (ML) algorithms such as - Gaussian Naive Bayes (GNB), Support Vector machine (SVM), Decision Tree (DT), and Stochastic Gradient Descent (SGD) to predict gender. After that, we performed a comparison study and implemented Lime on the best performing ML model to interpret the prediction of the model. the Gaussian Naive Bayes architecture with the dataset shows a reasonable accuracy of 98%, proving the usability of the model. © 2024 IEEE.

关键词： Contrastive learning

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