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

共 6844 条记录，以下是591-600 订阅

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Port Cluster Analysis and Feature Importance in the Guangdong-Hong Kong-Macao Greater Bay Area: An Integrated machine learning Approach 13

Port Cluster Analysis and Feature Importance in the Guangdon...

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13th international conference on Transportation and Traffic engineering, ICTTE 2024

作者： Wu, Chengxi Guo, Yanru Department of Industrial Systems Engineering and Management National University of Singapore 21 Lower Kent Ridge Rd Singapore School of Shipping and Maritime Studies Guangzhou Maritime University 101 Hongshan 3rd Rd Guangdong Province Guangzhou City China

ISBN: (纸本)9781837242672

This study employs an integrated machine learning framework to analyze the cluster structure and feature importance of major ports in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Key methodologies include hierarchical clustering for uncovering port relationships, random forests and Lasso regression for feature selection, and PCA and t-SNE for dimensionality reduction and visualization. The results highlight container throughput, cargo throughput, and GDP as critical factors influencing port competitiveness, alongside the emerging importance of innovation and foreign trade. These findings provide actionable insights for enhancing regional supply chain integration and optimizing resource allocation. The study offers practical recommendations for port managers and policymakers while paving the way for future research on advanced machine learning applications in maritime logistics. © The Institution of engineering & Technology 2024.

关键词： Hierarchical clustering

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Prediction Of Autism Spectrum Disorder Using machine learning 3

Prediction Of Autism Spectrum Disorder Using Machine Learnin...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Arunprasath, T. Niranjana, M. Sharumathi, M. Buvanesh Pandian, V. Rajasekaran, M Pallikonda Ramaraj, Kottaimalai Kalasalingam Academy of Research and Education Department of Biomedical Engineering Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Eee Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Ece Krishnankoil621 216 India

ISBN: (纸本)9798350361186

This is to investigate the predictive potential of electroencephalogram (EEG) signals extracted from children diagnosed with autism spectrum disorder (ASD). The objective is to utilize machine learning techniques, specifically logistic regression, to discern patterns within EEG data that could aid in the early prediction of ASD. EEG signals collected from autism-affected children are analyzed and processed to extract relevant features. Subsequently, these features are used as inputs to train a logistic regression model. Thestudy evaluates the model's performance in distinguishing between ASD and typically developing individuals, aiming to provide a reliable predictive toolfor identifying ASD based on EEG signals. Results suggest promising prospects for leveraging EEG data and machine learning methods in the early detection and potential prediction of autism spectrum disorder in children. © 2024 IEEE.

关键词： Electroencephalography

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Unsupervised Vehicle Re-Identification with Clustering and Appearance Style Exploration 24

Unsupervised Vehicle Re-Identification with Clustering and A...

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

作者： Taleb, Hamdan Zhu, Xiaobo Wang, Chao College of Electronic and Information Engineering Tongji University Shanghai China School of Informatics and Engineering Suzhou University Jiangsu Suzhou China

ISBN: (纸本)9798400718120

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system’s ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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Breast Cancer Diagnosis using machine learning: Challenges and Opportunities 3

Breast Cancer Diagnosis using Machine Learning: Challenges a...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shobha, M. Kumar, N. Ashok Mohan Babu University School of Engineering Department of Ece Sree Sainath Nagar Andhra Pradesh Tirupati India

ISBN: (纸本)9798331509675

The purpose of this paper is to review breast cancer diagnosis methods. Cancer begins in cells, the basic building units of tissue. Sometimes the cell growth process fails, and new cells form when the body does not require them, while old or damaged cells do not die as they should. When this happens, a collection of cells frequently develops a mass of tissue known as a lump, growth, or tumor. One of the obstacles that current mammography image detection algorithms confront is the difficulty in analyzing dense tissues. This study provides a quick summary of various breast cancer diagnosis methods. This study also concludes that the features of the mammogram are the primary determinant of any method's accuracy. machine learning algorithms use these features as input for the predictions. Texture-Based Feature Extraction methods can be used to extract features from the mammogram images. To detect anomalies (tumors, masses, calcifications) and classify diseases (benign/malignant), we need a systematic approach that includes preprocessing, feature extraction, anomaly detection, and classification. In order to increase accuracy, machine learning algorithms need a lot of data. Obtaining sufficient data is one of the biggest obstacles to using machine learning algorithms. Therefore, an effective feature extraction method can be used to extract features from the mammography images before applying machine learning algorithms for the predictions. © 2025 IEEE.

关键词： Lung cancer

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machine learning-based development of cuproptosis-related lncRNA signature for prognosis and immunotherapy exploration in colon cancer 3

Machine learning-based development of cuproptosis-related ln...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Liu, Yuchao Zhu, Ruiyu School of Biological and Chemical Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9781510680449

This study aimed to construct a cuproptosis-related lncRNA signature through an integrated machine learningbased model to predict prognosis and response to immunotherapy in CC. Methods Raw RNA-seq data and corresponding clinicopathological information of patients with CC were retrieved from The Cancer Genome Atlas (TCGA) database. A gene-lncRNA co-expression analysis was applied to obtain cuproptosis-related lncRNAs. A prognostic risk model composed of cuproptosis-related lncRNAs was constructed using univariable Cox regression analysis and a machine learning-based integrative model. The clinicopathological samples were divided into high- and low-risk groups according to the median risk scores. Immunotherapy response was assessed across the groups using the Tumor Immune Dysfunction and Exclusion (TIDE) scores, while immune-related functions with single-sample gene set enrichment analysis (ssGSEA). Results The prognostic risk model was constructed based on 11 cuproptosis-related lncRNAs. The high-risk group exhibited higher immune-related function scores and a greater likelihood of immune evasion but a worse prognosis and immunotherapy outcomes. Conclusion The cuproptosis-related lncRNA signature could be utilized as an independent prognostic factor for CC to guide clinical treatment. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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A machine learning and Linear Regression Based Study of Momentum Changes and Impacts 3

A Machine Learning and Linear Regression Based Study of Mome...

引用

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

作者： Tang, Hanxuan Wu, Fangfang Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9798331519032

This paper explores the changes and effects of momentum in tennis matches, and introduces indicators such as "leverage", "turning point" and "leverage momentum" to construct a classification model. Three key models were developed: first, Logistic Regression, XGBoost and CatBoost models were used to analyze the effects of key features on momentum;second, hypothesis testing was used to verify the effects of momentum on match performance;and third, a turning point model was developed to reveal the key factors of momentum fluctuation moments. The results show the predictive accuracy of the model and a deep understanding of momentum changes, providing decision support for players and coaches. Through a data science approach, this study quantifies the impact of momentum, reveals its importance in the game, and provides new perspectives for controlling game momentum. © 2024 IEEE.

关键词： Logistic regression

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The Application of machine learning Techniques to the Diagnosis of Breast Cancer 3

The Application of Machine Learning Techniques to the Diagno...

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3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Prasad, Pratyush Manipal University Data Science and Engineering Jaipur India

ISBN: (纸本)9798350320749

Due to its high mortality rate and limited survival duration, early identification of breast cancer is vital. The primary application of machine learning models has been the prediction and early diagnosis of breast cancer. This article presents machine learning models and algorithms for automated breast cancer diagnosis using an exploratory approach. The technique makes use of eight distinct machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, XG-Boost, and Deep learning Model. Their precision is used to evaluate the performance of algorithms and models. Additionally, probabilistic predictions have been made using activation functions like Relu. In terms of test accuracy on the Kaggle data set, Deep learning Model 2 and the Support Vector Classifier fared the best. © 2023 IEEE.

关键词： Decision trees

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Blockchain Technology and data Privacy: A Comprehensive Review and Future Perspective 3

Blockchain Technology and Data Privacy: A Comprehensive Revi...

引用

3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ahirao, Purnima Shaikh, Bareerah Wahedna, Rahil Zubair K.J. Somaiya College of Engineering Department of Information Technology Mumbai India

ISBN: (纸本)9798350354348

Due to its decentralized and impervious to tampering nature, blockchain technology presents great opportunities for improving data privacy in a variety of contexts. This study offers a thorough analysis of the body of research on the subject of blockchain technology and data privacy. It explores privacy concerns in blockchain systems, clarifies privacy-preserving techniques, and examines their application in diverse contexts such as healthcare, IoT, crowdsensing, and federated learning. The paper highlights the challenges associated with blockchain-based privacy solutions, including data immutability, transparency, and scalability, and identifies emerging trends and opportunities for future research. This paper attempts to offer important insights into how blockchain technology can change the data privacy environment in our networked and data-driven society through a thorough analysis. Lastly, new developments and issues are addressed, including self-sovereign identification and integration with machine learning. Future research is encouraged to focus on issues like scalability, standardization, legal frameworks, and user education. © 2024 IEEE.

关键词： Federated learning

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An End-to-End Model for Chicken Detection in a Cluttered Environment 13

An End-to-End Model for Chicken Detection in a Cluttered Env...

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13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Chemme, Komeil Sadeghi Alitappeh, Reza Javanmard Univ Sci & Technol Mazandaran Elect & Comp Engn Dept Behshahr Mazandaran Iran

ISBN: (纸本)9798350350494;9798350350500

Animals in the livestock business need to be seen in real time and online in order for them to grow and develop. The growing global demand for meat and poultry, the development of the livestock and poultry business, and the expansion of their region have made it more difficult for humans to handle the task due to human mistake and the growing amount of work. But thanks to technological breakthroughs like artificial intelligence, learning models, and closed-circuit cameras, most tasks may now be left to robots, preventing human error. This system may be installed and connected worldwide, allowing it to count all animals at any one time and give us information on each animal's number and location. In this research, we created a model consisting of a segmentation module that readies input data for a detector model based on YOLOv8. In order to study chicken behavior, an integrated end-to-end approach is utilized to track and count the birds. The experimental results provide insight into the new proposal's accuracy performance compared to earlier methods, demonstrating an 8% improvement. The goal of this article is to teach a YOLOv8-based learning model that may be applied to the development of an automated, integrated system for counting and assessing animal welfare in the future.

关键词： Chicken detection Object detection Deep learning Segmentation

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An Attention-based Deep learning Model for Jasminum Quality Grading 3

An Attention-based Deep Learning Model for Jasminum Quality ...

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

作者： Karthikeyan, P. Uma Maheswari, P. Shivani Suvatheka, S. Pradeep Kumar, G. Al Mohamed Bilal, A. Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Jasmine flowers, which are much cultivated in the states of Tamil Nadu and Karnataka, contribute more than ?20 crores annually to the Indian economy through internal sales and exports. Traditional grading practices, however, suffer from human subjectivity, often resulting in inconsistencies. This paper presents a deep-learning-based grading system that utilizes the InceptionResNetV2 model with self-attention mechanisms to classify flowers based on bud tip size, freshness, color, and tubule presence. The system focuses on fine-grained details rather than the whole flower and achieves 92% accuracy, which ensures consistency and fairness in quality assessment. The automated approach is significantly more efficient, transparent, and scalable and likely to be compatible with global trade standards of jasmine flowers. © 2025 IEEE.

关键词： Adversarial machine learning

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