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

共 6842 条记录，以下是421-430 订阅

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Intelligent Driving Safety Monitoring Based on machine learning - An Example of Students Research Training Program in the Context of New engineering Disciplines 24

Intelligent Driving Safety Monitoring Based on Machine Learn...

引用

3rd international conference on Computer, Artificial Intelligence and Control engineering, CAICE 2024

作者： Liu, Jiacheng Luo, Haoyue Gao, Yushu Liang, Ruiyu Zeng, Ruoyan Pan, Xing School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798400716942

The development of new engineering technologies such as artificial intelligence, machine learning, and big data analysis has prompted engineering colleges to keep up with the times in their courses and introduce relevant knowledge to cultivate professionals in different fields. Research training, as an important form of practical teaching in current colleges and universities, plays an important role in improving the innovation and entrepreneurship abilities of undergraduate students. This article takes a comprehensive research project conducted by the junior students at Beihang University as an example, and provides an example of cross disciplinary research training using emerging technologies such as machine learning and quality and reliability related professional knowledge in the context of new engineering. Quality and reliability are interdisciplinary fields involving aerospace, information, transportation, new energy, and other fields. Through scientific research training in new engineering, students can aim at these fields and comprehensively apply professional knowledge learned in the classroom and new engineering technologies mastered in practice to solve practical problems. This research training example focuses on comprehensive security monitoring in the field of intelligent transportation, using machine learning and other methods to process driver personnel data, environmental data, and vehicle data related to driving safety. Through training, mathematical models and methods that characterize the safety status of human-machine co driving are obtained, and corresponding software systems are developed. Although this training example is targeted at the emerging field of intelligent transportation, it also has certain reference significance for practical teaching in other professions and practical applications in other fields. © 2024 ACM.

关键词： Students

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Ensemble Based Soil Classification Using machine learning Techniques 3

Ensemble Based Soil Classification Using Machine Learning Te...

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Aurchana, P. Antony Joseph Rajan, D. Madonna Arieth, R. Revathy, G. Malla Reddy University School of Engineering Department of CSE Telangana Hyderabad India SIMATS Saveeths University Saveetha School of Engineering Department of CSE Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India Srinivasa Ramunjan Centre Sastra Deemed University Department of CSE Tamil Nadu Kumbakonam India

ISBN: (纸本)9798350391565

India's inhabitants rely heavily on agriculture for their livelihood. This research contributes significantly to the country's economy. Soil is essential for agriculture. An important component of agriculture is soil. There are different soil types. Different crops thrive on different types of soils, and different types of soil might have distinct properties. To know which crops grow best in certain types of soil, need to know the characteristics and characteristics of different typeso of soil. With in the domain domaagricultural data analysis, machine learning is a challenging research area that is still developing. In the suggested work, soil classification dataset are collected from kaggle are classified into black soil, cinder soil, Laterite soil, Peat soil and yellow soil. In the proposed work 70% were contemplated for training, 30% were used for testing. Extraction is done using Speeded Up Robust Features which ensembles exploiting Radial Basis Functional Neural Network and Gaussian Mixture Model by combining outcomes voting and boosting where voting gives the satisfactory results of 86% © 2024 IEEE.

关键词： Adversarial machine learning

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Visionary Cardioscan: Heart Risk Detection through Retinal Imaging 3

Visionary Cardioscan: Heart Risk Detection through Retinal I...

引用

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

作者： Arun Kumar, S. Dharshini, B. Hemanth, M. Vignesh, S. Saratha, M. Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Heart attacks and hypertension are two cardiovascular disease risk factors that have a major impact on the microvascular system's structure and function. Fundus camera images can detect abnormalities in retinal blood vessels that indicate vascular injury. Using machine learning techniques, preclinical signs of damage, which may not be visible to an observer, can be detected. It proposes a system that uses retinal images to predict heart attack risk. The model leverages vessel segmentation to extract morphological details of retinal blood vessels. The results can assist in identifying heart disease early, especially in young patients. © 2025 IEEE.

关键词： Ophthalmology

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Adaptive machine learning Model for Complex data Processing 3

Adaptive Machine Learning Model for Complex Data Processing

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Rui Wei, Xiuzhuo Yang, Yuzhu Wan, Ruoyan Zhao, Huinan Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

With the rapid development of information technology today, data is emerging at an unprecedented speed and scale, with diverse forms of existence and extremely wide sources. Especially in the medical field, the complexity of data is particularly prominent, including multi-source, incremental, high-dimensional, and significant noise interference. To address these challenges, this article delves into adaptive machine learning (ML) models for complex data processing and focuses on their application in the field of medical diagnosis. ML technology, with its excellent data processing and pattern recognition capabilities, has opened up new paths for the precision and intelligence of medical diagnosis. This paper proposes a medical diagnosis model based on multimodal data fusion and ML to address the complexity of medical data. This model integrates multiple types of data from different sources and utilizes advanced fusion techniques to achieve comprehensive information extraction and deep fusion. The experimental results show that the medical diagnosis model proposed in this paper exhibits excellent performance in various disease diagnosis tasks, significantly improving the accuracy and efficiency of diagnosis. © 2024 IEEE.

关键词： data fusion

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Research on the automatic crack detection system of asphalt pavement based on deep learning 24

Research on the automatic crack detection system of asphalt ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Chi, Fengxia Wang, Yangyang Wei, Jintao Sun, Yihan Road Engineering Research Institute Zhejiang Scientific Research Institute of Transport Hangzhou Zhejiang China Key Laboratory of Road and Bridge Detection and Maintenance Technology Research of Zhejiang Province Zhejiang Hangzhou China Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400718120

This study aims to develop a deep learning-based automatic asphalt pavement crack detection system to improve road maintenance efficiency and safety. Through detailed analysis of crack features, deep learning model is used for crack detection, and data processing, model training and optimization to ensure the efficient identification of the system in different scenarios. The experimental results show that the system has stability and reliability, providing strong technical support for road maintenance. © 2024 Copyright held by the owner/author(s).

关键词： Crack detection of asphalt pavement

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A Deep learning Approach for Semantic Similarity Prediction between Question Pairs using Siamese Network and Word Embedding Techniques 3

A Deep Learning Approach for Semantic Similarity Prediction ...

引用

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

作者： Iffath, Fariha Shopon, Md Ovens, Katie Gavrilova, Marina L. Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (纸本)9798350372977;9798350372984

Question-answering platforms, such as Quora, Reddit, and StackOverflow, have become immensely popular in the virtual community. Among these platforms, Quora stands out as a widely utilized and resourceful repository with over 300 million monthly visitors. However, the prevalence of similar questions with paraphrased content poses challenges for users in finding relevant answers. This paper proposes a deep learning approach for predicting the semantic similarity of question pairs to address the issue of duplicate questions. The proposed model employs a Siamese network-based Long Short-Term Memory (LSTM) architecture, utilizing word embedding vectors from various algorithms such as GloVe, Word2Vec, and FastText. Previous research on duplicate question identification is reviewed, with our approach overcoming drawbacks such as low-level semantic connections and text type incompatibility. After pre-processing the Siamese LSTM network compares the embeddings using L1 distance to determine question pair similarity. The model is trained and evaluated on the Quora Question Pair Classification dataset, and outperformed other state-of-the-art methods with an accuracy of 84.77% with the FastText word embedding.

关键词： Semantic similarity Natural language processing Sequence learning Siamese network Deep learning

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Creation of DALT-NET: Deep learning 3D lookup table generation approach to Daltonization for dichromatic vision 3

Creation of DALT-NET: Deep learning 3D lookup table generati...

引用

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

作者： Wang, Lucas (Zihe) Los Gatos High Sch Los Gatos CA 95030 USA

ISBN: (纸本)9798350372977;9798350372984

Color Vision Deficiency (CVD) is a condition which can inconvenience or even endanger 8% of men and 0.44% of women globally. To address this issue, this paper proposes the first deep learning and lookup-table based recoloring (Daltonization) method, termed Dalt-NET. Dalt-NET is composed of N learnable 3D Lookup Tables (LUT), one fixed failsafe 3D LUT, and a multi-task Convolutional Neural Network (CNN) to generate 3D LUT coefficients. This model employs an assembly architecture trained through enhanced data preprocessing. The advantages of the developed method are as follows: 1) Simulations show that Dalt-NET can achieve better CVD perceptual quality with more details, 2) it can transform an 8K image at 0.003s under a 0.3G FLOP MACs requirement, which is 3400 times faster and requires 30000 times less computations than previous methods, 3) it provides a novel framework for further development, such as model optimization, database augmentation, and specific spatiotemporal filtering. The proposed research opens a new door providing CVD individuals opportunities for the rapidly expanding media infrastructure and industry.

关键词： Color Vision Deficiency Daltonization 3d-lut image recoloring deep learning

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AI based Emotion Aware Music Composition using Spotify App 3

AI based Emotion Aware Music Composition using Spotify App

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Subbulakshmi, N. Begum, S. Ariffa Gari Archana, Bayapureddy Lakshmireddy Chandru, R. School of Computing Kalsalingam Academy of Research and Education Virudhunagar India Sri Ramakrishna Engineering College Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350396157

Users' emotions and moods can be inferred from their facial expressions. The live video from the system camera reveals these expressions. Many researches have been carried out in the fields of machine learning (ML) and computer vision to teach robots to identify various human emotions or moods. Numerous techniques for identifying human emotions are provided by machine learning. Our proposed method is a music player which works the human mood-based and it provides song recommendations in terms of the real-Time mood detection. This is now a capability that is linked with the standard music player application that come preinstalled on our phones. The objectives of this method are to study the user's look, forecast their facial expression, and suggest music based on mood. The discipline of music creation has changed dramatically as a result of technical improvements, providing new avenues for artists and composers to create captivating and emotionally stirring musical works. The concept of 'Emotional-Aware Music Composition,' a cutting-edge technique that employs artificial intelligence and machine learning to give compositions a profound emotional authenticity, is explored in this abstract. Emotionally aware music creation allows composers to produce music that not only transfers emotion but also deeply engages listeners by bridging the gap between human creativity and technological prowess. This multidisciplinary field uses components from psychology, music theory, and machine learning to achieve this. © 2024 IEEE.

关键词： machine learning

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Advanced Flood Prediction at Forest with Rainfall data Using Various machine learning Algorithms 3

Advanced Flood Prediction at Forest with Rainfall Data Using...

引用

3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Saravanan, M.S. Sivashankar, S. Rajesh, A. Ibrahim, Masrullizam Mat Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Engineering Tamilnadu Chennai India Gla University Mathura India Faculty of Electronics and Computer Technology and Engineering Melaka Malaysia

ISBN: (纸本)9798331518592

The aim is to classify and predict floods in advance with rain data patterns of India using spatio-temporal logic. Two Classification algorithms are used to achieve the maximum accuracy namely K-Nearest Neighbour with a sample size=5 and Logistic Regression with a sample size=5 for continues iterations. The work focused towards comparison of K-Nearest Neighbour and logistic regression, which has confidential and forecast the standards from the rainfall statistics to produce estimated accuracy with K-nearest neighbour has higher accuracy by comparing with Logistic Regression accuracy. It has a high accuracy of 50.35%, in comparison with the Logistic Regression algorithm 45.96%. The significant values have been statistically defined with the value of (p © 2024 IEEE.

关键词： Adversarial machine learning

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Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment 3

Wasserstein Distance-Weighted Adversarial Network for Cross-...

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3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Jiang, Mohan Lin, Jiating Ouyang, Hongju Pan, Jingming Han, Siyuan Liu, Bingyao New York University New York United States Brown University Providence United States Columbia University New York United States University of Pennsylvania Philadelphia United States University of California Irvine Irvine United States

ISBN: (纸本)9798331506582

This paper delves into the application of adversarial domain adaptation (ADA) for enhancing credit risk assessment in financial institutions. It addresses two critical challenges: the cold start problem, where historical lending data is scarce, and the data imbalance issue, where high-risk transactions are underrepresented. The paper introduces an improved ADA framework, the Wasserstein Distance Weighted Adversarial Domain Adaptation Network (WD-WADA), which leverages the Wasserstein distance to align source and target domains effectively. The proposed method includes an innovative weighted strategy to tackle data imbalance, adjusting for both the class distribution and the difficulty level of predictions. The paper demonstrates that WD-WADA not only mitigates the cold start problem but also provides a more accurate measure of domain differences, leading to improved cross-domain credit risk assessment. Extensive experiments on real-world credit datasets validate the model's effectiveness, showcasing superior performance in cross-domain learning, classification accuracy, and model stability compared to traditional methods. © 2024 IEEE.

关键词： Adversarial machine learning

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