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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6841 条记录，以下是151-160 订阅

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Analysis of the Effectiveness of Large Language Model Feature in Source Code Defect Detection 3

Analysis of the Effectiveness of Large Language Model Featur...

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3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： He, Tao Yang, Meini Hu, Wei Chen, Yun Naval University of Engineering Department of Information Security Hubei Wuhan China

ISBN: (纸本)9798350365443

The defects in software source code threaten the usage of software. With the increase in the quantity and complexity of software, traditional methods of source code defect detection are unable to meet the needs of software security testing, leading to the emergence of machine learning-based source code defect detection methods. The effectiveness of machine learning-based method is closely related to the input features. This paper investigates the impact of using outputs from large language model as input features for machine learning models on the task of source code defect detection. Experimental results demonstrate that adding feature from large language model to machine learning models can enhance the effectiveness of source code defect detection. Furthermore, among different strategies for constructing prompt words, few-shot prompting shows relatively better performance. © 2024 IEEE.

关键词： Adversarial machine learning

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data-Driven Model for Improving MEG Epileptic Spike Detection 3

Data-Driven Model for Improving MEG Epileptic Spike Detectio...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Dev, Antora Fouda, Mostafa M. Fadlullah, Zubair Md Idaho State Univ Elect & Comp Engn Pocatello ID 83209 USA Western Univ Comp Sci London ON Canada

ISBN: (纸本)9798350372977;9798350372984

Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures, affecting over 50 million people worldwide. The prompt and accurate detection of epileptic events is crucial for effective treatment and management. Traditional methods such as Electroencephalogram (EEG) have been complemented by Magnetoencephalography (MEG), which offers superior spatial resolution due to its insensitivity to the distortive effects of the skull and scalp. This study advances the analysis of MEG data using Complex Continuous Morlet Wavelet Transform (CCMWT) for feature extraction, coupled with innovative machine learning architectures for classifying epileptic versus non-epileptic signal segments. We developed and compared a 3D Convolutional Neural Network (3D-CNN) and a VGG16 model with the Transfer learning strategy, in terms of accuracy, computational efficiency, and error rates. Our results demonstrate the VGG16 model with the Transfer learning strategy's superior performance in all cases except the computation times.

关键词： Epileptic Seizures Complex Continuous Wavelet Transform (CCMWT) Scalogram Deep learning MEG Magnetoencephalography CNN VGG16

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Perioperative Immune-Inflammatory Analysis Method for Colorectal Cancer Based on Explainable machine learning 24

Perioperative Immune-Inflammatory Analysis Method for Colore...

引用

3rd international conference on Public Health and data Science, ICPHDS 2024

作者： Tian, Naiyuan Cancer Hospital Chinese Academy of Medical Sciences Beijing China

ISBN: (纸本)9798400711671

The single-controlled experimental method plays an important role in medical research, while it has a lot of limitations. With the rapid development of artificial intelligence, machine learning, and game theory are playing increasingly important roles. The research is about what factors infect the immune inflammatory response of colorectal cancer surgery. We propose an innovative model to predict immunity inflammatory response, and the model has more accuracy and more interpretability. Ensemble learning improves accuracy and reduces errors. The shap value explains which features are important and how the features explain the result. Many factors are considered, and the result shows that the anesthesia method has a significant effect. The time of the surgery has no significant effect on the immune inflammatory response, which means that the surgical schedule can be more flexible. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Development of a Flexible Student Training System Using the Moodle LMS 3

Development of a Flexible Student Training System Using the ...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Yusupova, Shohida B. Davletboyev, Sardorbek Z. Ishmetov, Bahrom Y. Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Information Technology Urgench Uzbekistan

ISBN: (纸本)9798331516321

In today's era of extensive information flow and the rise of distance education, there is a growing need for innovative educational technologies that can support personalized learning paths. This article explores the development of an adaptive learning system within the Moodle LMS, focusing on the implementation of individual educational trajectories based on automated testing and neural network models. The system enables the delivery of educational materials at varying levels of complexity, combined with adaptive tests that adjust based on student performance. Through machine learning, neural networks evaluate students' knowledge and generate customized training courses tailored to their needs. The proposed system addresses the limitations of traditional assessment methods, such as subjectivity in grading, by offering a more objective, data-driven approach. The use of adaptive testing and neural networks enhances the accuracy of knowledge assessment and fosters independent learning, ultimately contributing to improved educational outcomes in a distance learning environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Convolutional Neural Network driven Electroencephalogram Characterization for Robust and Efficient Schizophrenia Diagnosis 3

Convolutional Neural Network driven Electroencephalogram Cha...

引用

3rd international conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Iqbal, Haseeb Khan, Salaar Tahir, Anoosha Ramzan, Hamza Biomedical Engineering Department University of Engineering & Technology Pakistan School of Science & Engineering Lahore University of Management Sciences Pakistan Buch International Hospital Multan Pakistan Computer Science Department University of Engineering & Technology Pakistan

ISBN: (纸本)9798331504861

Complex psychiatric disorders such as Schizophrenia show symptoms like disorganized thinking and hallucinations, typically rooted in abnormal brain activity. Early detection of such abnormal brain activity significantly improves diagnosis and subsequent treatment. To that end, Electroencephalogram (EEG), a non-invasive technique that records electrical activity in the brain is often used to record information about these cerebral anomalies. EEG data can be analyzed using various techniques to facilitate Schizophrenia diagnosis. In that respect, traditional machine learning techniques have been extensively used. However, traditional machine learning techniques necessitate pre-processing and extensive feature extraction, which can often considerably vary from one dataset to another. In contrast, deep learning techniques are better suited to classify between normal and schizophrenic cases, owing to their sophisticated network architecture and their ability to resolve complex data. In this work, we leverage a convolutional neural network (CNN) to characterize EEG data from two cohorts to facilitate schizophrenia diagnosis. We report a 96.4% accuracy of classification with fewer network parameters which beats the current gold standard and shows promise for effective and robust detection and subsequent Schizophrenia diagnosis based on EEG data. ©2024 IEEE.

关键词： Electroencephalography

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machine learning modeling and predictive control of nonlinear boiler-turbine unit 3

Machine learning modeling and predictive control of nonlinea...

引用

3rd international conference on Smart Grids and Energy Systems, SGES 2024

作者： Chen, Zhuo Hao, Yongsheng Xiao, Ming Wu, Guoquan Wu, Zhe School of Energy and Environment Southeast University Nanjing China Department of Chemical and Biomolecular Engineering National University of Singapore Singapore

ISBN: (纸本)9798331539634

The boiler-turbine unit (BTU) is extensively utilize-ed in power generation owing to its high reliability and operation flexibility. However, the control design of BTU system remains challenging due to its intricate dynamic properties, such as wide range of nonlinearities, large inertia, and strong coupling in multivariable systems. More challengingly, it is generally difficult and time-consuming to derive an accurate nonlinear model to capture the BTU dynamics and facilitate controller design. In this regard, machine learning methods show great potential in classical engineering fields, such as modeling nonlinear dynamic systems. In this article, a machine learning based model predictive control (MPC) scheme is presented for the nonlinear BTU. The machine learning method is first adopted to model the BTU using the input and output data directly, which can also be used to model a large class of nonlinear dynamic systems. Then, this data-driven model is deployed as a predictive model in the MPC for optimal control of the nonlinear BTU. Finally, a series of simulations are performed to validate the proposed method, including RNN training and testing, open-loop prediction simulation and closed-loop control simulation. © 2024 IEEE.

关键词： Predictive control systems

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Sports Game Classification and Detection Using ResNet50 Model Through machine learning Techniques Using Artificial Intelligence 3

Sports Game Classification and Detection Using ResNet50 Mode...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Gill, Kanwarpartap Singh Anand, Vatsala Malhotra, Sonal Devliyal, Swati Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

The intention of artificial intelligence (AI) sports analysis is to use AI and machine learning to get useful insights and enhance sports performance. data from sporting events, players, and welfare equipment are processed and analysed using AI methods. machine learning, computer vision, natural language processing, and other forms of artificial intelligence analysis are used to improve every facet of sports, from player efficiency to audience participation. It's a fast-paced, ever-changing industry that's revolutionising the way athletes, coaches, and fans interact with the sporting world. The ResNet-50 model and other machine learning techniques are at the bleeding edge of research into sports analysis and performance enhancement. The deep learning model ResNet-50 is used to analyse video and stills from sporting events. Application areas include player monitoring, injury prediction, and game result predicting, all of which are made possible by the use of transfer learning to collect subtle details in sports data. This approach ushers in a new age of data-driven decision making in the sports industry while also revolutionising sports analytics, providing access to invaluable information for coaches, players, and spectators. The primary objective of this research was to categorise nine distinct sports-related picture classifications and predict their expected accuracy, which came in at 93 percent. Future researchers in this field of study will benefit from the high level of accuracy shown in this work. © 2024 IEEE.

关键词： Sports

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Myocardial Infarction Diagnosis: Pattern Analysis of ECG Report Images Using machine learning Techniques 3

Myocardial Infarction Diagnosis: Pattern Analysis of ECG Rep...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Raghukumar, B.S. Naveen, B. Adichunchnagiri University Electronics And Communication Engineering Bgsit BG Nagara Mandya India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

The ECG machine data is utilized to diagnose cardiac conditions, specifically focusing on identifying myocardial infarction rates by analyzing pattern variations within ECG report images. Variations in the output of electrodes 2 and 3 are noted as indicative of a heart attack. The authors employ various image processing techniques like thresholding, contrast enhancement and machine learning methods like SVM, GBC, k-neighbors to process these patterns, aiming to enhance accuracy. After extracting four features, the most effective classifiers are employed, with the Gradient Boosting Classifier (GBC) with set of features exhibiting the highest accuracy at 76.60%. This paper emphasizes effective preprocessing methods crucial for obtaining structured and refined data, facilitating better feature selection and extraction from ECG graph images. It underscores the extraction of distinctive features to aid in myocardial infarction rate prediction. The paper evaluates several machines learning classifiers, highlighting their efficiency in simplifying and expediting the diagnosis process. Furthermore, the research suggests that incorporating additional features could potentially improve accuracy. © 2024 IEEE.

关键词： Electrocardiograms

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Speaker Gender Recognition Based on Semi-Supervised learning 3

Speaker Gender Recognition Based on Semi-Supervised Learning

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3rd international conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Zhang, Zheyan Li, Renwei Chen, Kewei Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China

ISBN: (纸本)9798331528386

Determining a speaker's gender from speech signals is essential for intelligent human-machine interaction. Significant progress has been made in supervised learning for speaker gender recognition. However, effective performance relies heavily on large labeled datasets. To address this, a semi-supervised learning framework is introduced to fully leverage vast amounts of unlabeled speech data for gender recognition. First, contrastive learning is employed to enhance and augment a relatively small set of labeled audio data, training an encoder capable of distinguishing speech genders. Next, a fuzzy clustering algorithm is utilized to generate gender pseudo-labels from a large pool of unlabeled data. Finally, the speaker gender recognition model is iteratively trained by using these pseudo-labels. Experiments and analyses on the Common Voice English dataset demonstrate that the model attains a mean accuracy of 97.4% in gender recognition tasks. © 2024 IEEE.

关键词： Self-supervised learning

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Research on the Evaluation of Chinese Articulation Using machine learning and Algorithm Optimization 3

Research on the Evaluation of Chinese Articulation Using Mac...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Tang, Meng Sichuan Technology & Business College Chengdu China

ISBN: (纸本)9798350380989

This design uses SPCE061A, SPLC501 and keyboard module to realize speech recognition system. The system realizes the functions of adding, deleting and searching basic information. The data saved in SPR4096 was recognized by voice. A steady state interference noise for Chinese speech audibility detection based on pink noise is proposed, and its frequency is consistent with the speech features recorded in anechoic chamber. The corresponding training samples were obtained by convolution of the Chinese speech audibility test word list with the equivalent binaural chamber impulse response. It is then tuned to a specific signal strength. The objective evaluation of the intelligibility of Chinese speech is done by audiologists. It is found that factors such as reverberation time, signal-to-noise ratio and pronunciation speed play an important role in the intelligibility of Chinese speech. Compared with reverberation time and speech frequency, SNR plays a more significant role in the intelligibility of Chinese speech. Compared with conventional methods, this method has better recognition performance and speed. © 2024 IEEE.

关键词： algorithm optimization articulation rate Chinese language articulation Chinese pronunciation machine learning

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