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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是11-20 订阅

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Privacy-Preserving Vertical Federated Logistic Regression without Trusted Third-Party Coordinator 2022

Privacy-Preserving Vertical Federated Logistic Regression wi...

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Sun, Huizhong Wang, Zhenya Huang, Yuejia Ye, Junda Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781450387477

Federated learning is a new distributed learning paradigm, which allows multiple parties to cooperatively train a centralized model without sharing their data. In this paper, a privacy-preserving logistic regression (LR) training algorithm for vertical federated learning (VFL) is proposed. First, this paper analyzes the related works and point out the privacy leakage risks. Then, based on the mini-batch SGD and parameter encryption method, a secure VFL model training scheme for LR without the assistance of a trusted third-party is designed. Next, to protect the privacy of model parameters, a differentially-private algorithm and comprehensive privacy analysis are provided. Finally, experiments show that the algorithm not only guarantees the security and privacy, but also ensures the model utility.

关键词： Federated learning Logistic Regression Differential Privacy Homomorphic Encryption

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COVID-19 Detection from CT Scan Images using Transfer learning Approach 24

COVID-19 Detection from CT Scan Images using Transfer Learni...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Bajaj, Navjot Singh Yadav, Pavinder Gupta, Nidhi National Institute of Technology Hamirpur Himachal Pradesh Hamirpur India National Institute of Technology Kurukshetra Haryana Kurukshetra India

ISBN: (纸本)9798400716546

In the past years, since 2020, the outbreak of COVID-19 has alarmed the world with the speed and its spread around the world. This raised the demand of early, accurate and automated detection system for the COVID-19 as there is a scarcity of manpower in medical field. This attracted many researches using deep learning to build COVID-19 detection model. For the diagnosis of COVID-19, computed tomography scanning are being used as more accurate, non-invasive and efficient method in real-time. In this work, we have proposed a model using six different image classification techniques of deep learning on CT scan images and compared the accuracy to find the most suitable and reliable model for transfer learning to achieve best result on ResNet50 as 97.19% training and 98.05% testing accuracy. The model will automate the process of detection of the COVID-19, leading to the advancement in the field of smart health-care. © 2024 ACM.

关键词： COVID-19

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Bi-objective lion swarm optimization based on teaching and learning algorithm 2022

Bi-objective lion swarm optimization based on teaching and l...

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6th international conference on machine learning and soft computing (icmlsc)

作者： Zhang, Qi Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

ISBN: (纸本)9781450387477

To address the problem that it is difficult to obtain a good quality and uniformly distributed Pareto optimal solution set for a complex biobjective system model, this paper proposes a Teaching-learning-based Bi-objective Lion Swarm Optimization algorithm (TLBLSO) for solving the bi-objective system uniform Pareto solution set problem. The idea and mechanism of teaching and learning optimization algorithm are introduced into the Lion Swarm algorithm. That is, the knowledge level of the whole group is improved by the way of individual teachers imparting knowledge and students exchanging knowledge, which effectively improves the spatial searching ability of the lion swarm. By comparing with other optimization algorithms, the experimental results show that the proposed TLBLSO can obtain well-distributed optimization solutions, which verifies the superiority of the proposed algorithm in this paper.

关键词： teaching and learning bi-objective lion swarm optimization algorithm uniform distribution

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Visualization Research on the Market Structure of China's SSE 50 Index Based on the Affinity Propagation machine learning Algorithm 24

Visualization Research on the Market Structure of China's SS...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Yu, Fuxiang Wang, Xiaowen Wang, Yakun Business School Hangzhou City University Hangzhou China College of Digital Economics Nanning University Nanning China

ISBN: (纸本)9798400716546

With a comprehensive review of relevant literature, data, and theories, this study collects post-pandemic data from the Shanghai Stock Exchange 50 Index. Utilizing the Sparse Inverse Covariance Estimation method (GraphicalLassoCV), the research computes the conditional correlation among stocks. Additionally, an unsupervised learning method, the Affinity Propagation algorithm from machine learning, is employed to further analyze and explore the market structure of the Shanghai Stock Exchange 50 Index in the post-pandemic period. The results reveal that the constituent stocks of SSE 50 are divided into 11 clusters, and specific clusters exhibit industry cohesion, reflecting similar intraday volatility patterns within sectors. Clusters 5 and 6 encompass various industries, implying shared price fluctuations across different sectors. Clusters 8 and 9, predominantly composed of financial listed companies, emerge as the largest clusters with the highest total market capitalization, signifying significant influence in the market. Furthermore, Cluster 5 exhibits noteworthy market capitalization, representing diverse manufacturing stocks. This underscores the potential risk of common fluctuations among stocks from the cluser. © 2024 ACM.

关键词： Inverse problems

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Severity estimation of Coffee leaf disease using U-Net and pixel counting mechanism 24

Severity estimation of Coffee leaf disease using U-Net and p...

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8th international conference on machine learning and soft computing, icmlsc 2024

作者： Deb, Sagar Deep Kashyap, Rachit Abhishek, Arjun Jha, Rajib Kumar Department of Electrical Engineering Indian Institute of Technology Patna India

ISBN: (纸本)9798400716546

Coffee leaf disease (CLD) is a major threat to coffee production worldwide, causing significant economic losses for farmers. Accurate and timely estimation of the severity of CLD is crucial for implementing effective control measures. In this paper, we propose a novel approach for severity estimation of CLD using a combination of the U-Net deep learning architecture and a pixel counting mechanism. The U-Net architecture is used to generate the segmented masks of the coffee leaf images. Finally to estimate the severity of CLD, we introduce a pixel counting mechanism that quantifies the proportion of affected pixels in the segmented regions. By summing the total number of diseased pixels and dividing it by the total number of pixels in the region, we obtain a severity score that reflects the extent of CLD infections. The algorithm proposed by us achieves a five class accuracy of 89.10%. © 2024 ACM.

关键词： Deep learning

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Early Fire Detection and Segmentation Using Frame Differencing and Deep learning Algorithms with an Indoor Dataset 24

Early Fire Detection and Segmentation Using Frame Differenci...

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8th international conference on machine learning and soft computing, icmlsc 2024

作者： Tomas, John Paul Q. Dava, Jean Isaiah Espejo, Tia Julienne Medina, Hanna Katherine M. Doma, Bonifacio T. Mapua University Philippines

ISBN: (纸本)9798400716546

Deep learning models, such as YOLOv5, well-known for object detection, and U-Net, used for segmentation, are known for their respective capabilities within computer vision tasks. In this study, the researchers introduced a novel framework that uses YOLOv5 and U-Net models, in combination with frame differencing techniques, to achieve early fire detection in an indoor setting. YOLOv5 was trained on a diverse dataset consisting of fire, smoke, and non-fire scenarios, while U-Net was exclusively trained on fire data. Motion detection was then implemented using frame differencing that allowed to identify fire movements effectively. The developed framework achieved an overall accuracy of 88%, outperforming the standalone YOLOv5 model with its 81% accuracy. This improvement of 7% in detection performance was influenced by the incorporation of fire motion analysis which effectively reduced false positive results. In summary, the study presents a robust framework that significantly improves fire detection in indoor environments with the help of motion analysis alongside the used deep learning models. © 2024 ACM.

关键词： Object detection

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FedCVD: Towards a Scalable, Privacy-Preserving Federated learning Model for Cardiovascular Diseases Prediction 24

FedCVD: Towards a Scalable, Privacy-Preserving Federated Lea...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Gaber, Abdelrhman Abdeltwab, Hassan Elbatt, Tamer American University in Cairo Cairo Egypt

ISBN: (纸本)9798400716546

This paper presents FedCVD, a federated learning model designed for predicting cardiovascular disease (CVD) by employing logistic regression and Support Vector machine (SVM) algorithms. FedCVD utilizes the privacy and scalability advantages offered by federated learning to facilitate collaborative model training using decentralized patient data, ensuring confidentiality. To evaluate the effectiveness of the proposed model, experiments were conducted using the 10-year risk of coronary heart disease Kaggle dataset. To address data imbalance challenges, three techniques - Random Over Sampling, Random Under Sampling, and Synthetic Minority Oversampling Technique (SMOTE) - were explored. The study demonstrates promising performance,For the federated logistic regression with SMOTE achieving an AUC value of 0.7048. Comparative analysis with a centralized logistic regression model shows competitive results, with an AUC value of 0.7081 using Random Over Sampling. For the federated SVM model, an AUC value of 0.7340 is achieved using Random Under Sampling. In comparison, a centralized machine learning approach utilizing SVM and Random Over Sampling achieves an AUC value of 0.6962. These findings highlight the effectiveness of the proposed federated learning approach, surpassing the performance of centralized machine learning models for CVD prediction. © 2024 ACM.

关键词： Support vector machines

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Knowledge Graph Entity Typing with Contrastive learning 2022

Knowledge Graph Entity Typing with Contrastive Learning

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Zhu, Guozhen Yao, Shunyu Beijing Univ Posts & Telecommun Beijing Beijing Peoples R China

ISBN: (纸本)9781450387477

Knowledge graph entity typing is an important way to complete knowledge graphs (KGs), aims at predicting the associating types of certain given entities. However, previous methods suppose that many (entity, entity type) pairs can be obtained for each entity type, performing poorly on entity types that only have a few associative entities. Besides, these methods cannot fully exploit the inherent correlation and complementarity information across different entities sharing the same entity type. To this end, we propose a novel model named Contrastive Entity Typing (CET) for KG entity tying. CET can better learn the mutual interactions among the entities with the same entity type and can fully utilize the hierarchical information in entity type trees by two contrastive learning modules. The main benefit of the proposed contrastive learning modules is that they can effectively encourage the consistency of the entity representations with the same type while improving the discriminability of the entity type classifiers. Empirically, our model achieves state-of-the-art results on KG entity typing benchmarks.

关键词： Knowledge graph entity typing Contrastive learning Graph convolutional network

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Improved Multilayer Perceptron Neural Networks Weights and Biases Based on The Grasshopper optimization Algorithm to Predict Student Performance on Ambient learning 23

Improved Multilayer Perceptron Neural Networks Weights and B...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Michira, Mercy K. Rimiru, Richard M. Mwangi, Waweru R. School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Kenya School of Computing and Information Jomo Kenyatta University of Agriculture and Technology Kenya

ISBN: (纸本)9781450398633

The classification accuracy of a multi-layer Perceptron Neural Networks depends on the selection of its parameters such the connection weights and biases. Generating an optimal value of these parameters requires a suitable algorithm to train the multilayer perceptron neural networks. This paper presents swam based Grasshopper optimization algorithm that optimizes the connection weights and biases of Multilayer Perceptron Neural Network. Grasshopper optimization algorithm is a swarm-based metaheuristic algorithm applied for accurate learning of Multilayer Perceptron Neural Networks. The proposed Multilayer Layer Perceptron Neural Networks based on the Grasshopper Optimization Algorithm was validated using a Genetic algorithm and Backpropagation algorithm this algorithm has proved to perform satisfactorily performance by escaping local optimal and its fast convergence. © 2023 Owner/Author.

关键词： Multilayer neural networks

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Deep-learning based method for breech face comparisons 2022

Deep-learning based method for breech face comparisons

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6th international conference on machine learning and soft computing (icmlsc)

作者： Zhu, Jialing Hong, Rongjing Robin, Ashraf Uz Zaman Zhang, Hao Nanjing Tech Univ Sch Mech & Power Engn Nanjing Peoples R China

ISBN: (纸本)9781450387477

When a bullet is fired from a barrel, micro impression marks caused by the breech face on cartridge cases are one of the most critical factors in ballistic identification. This paper focuses on breech face impression images and introduces a deep-learning based algorithm, superpoint to extract interest point features and offer the local descriptor for each keypoint. Superpoint is a self-supervised framework for interest point detectors and descriptors. The classical brute-force matching, distance ratio matching and RANSAC methods are used to find out the correct matches. Validation experiments were performed on an image set with a total of 40 breech face impression samples, giving 63 pairs of known matching (KM) and 717 pairs of known non-matching (KNM) image comparisons The proposed method can still figure out the matching points for breech face impressions with random biases. The results illustrate that the superpoint and the feature matching methods are feasible for breech face impression image comparisons. Moreover, compared with SIFT, the proposed method performs better.

关键词： breech face impression image interest point feature superpoint feature matching

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