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检索条件"任意字段=8th International Conference on Data Science and Machine Learning Applications, CDMA 2025"

共 804 条记录，以下是41-50 订阅

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Spatial and Temporal Analysis of Land Use and Land Cover (LU/LC) Analysis by Supervised Classification of Landsat data 5th

Spatial and Temporal Analysis of Land Use and Land Cover (LU...

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5th international conference on data science, machine learning and applications

作者： Suneetha, Yedla Reddy, M. Anji Vidya Jyothi Inst Technol Dept Humanities & Sci Hyderabad Telangana India Jawaharlal Nehru Technol Univ Environm Sci & Technol & Director Admiss Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the objective of the current work was to investigate LULC utilizing a Supervised classification method based on the MLC algorithm. Using images from Land sat 5 and 8, the quinquennial LULC for the previous 20 years (from 2000 to 2020) in the Hyderabad district of Telangana was determined. the LULC pattern in this area has been altered by recent significant development, which compelled the completion of this study using GIS (Geographical Information System) tools and RS (Remote Sensing) data. the rise in the built-up area (which expanded by 33%) contrasts with the change in the LULC every five years, vegetation (which reduced by 24%), and water body (which increased by 1%) all differed dramatically. As a result, it is determined that a possibility exists.

关键词： LULC Supervised classification Remote sensing Arc-GIS Landsat

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machine learning Induced Selective Identification of Dopamine Using Doped Carbon Quantum Dots 8

Machine Learning Induced Selective Identification of Dopamin...

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8th international conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Das, Ruma Paria, Abhirup Giri, P.K. Haldia721657 India Haldia Institute of Technology Department of Computer Science & Engineering Artificial Intelligence & Machine Learning Haldia721657 India Indian Institute of Technology Guwahati Department of Physics & Centre for Nanotechnology Guwahati781039 India

ISBN: (纸本)9798331520762

the present work highlights the selective identification of dopamine (DA) with machine learning (ML) classification. Dopamine is an essential organic neurotransmitter that acts a dynamic role. With the inhouse experimental da-ta, the ML models are trained with more than thirty classification algorithms for the efficient selection of DA among various metal ions and biomolecules that exist in human body fluids. the experimental data was collected by measuring the turn-off photoluminescence intensity of nitrogen-doped car-bon quantum dots in the presence of various metal ions and biomolecules. A promising agreement between predicted result and experimental consequences endorses the effectiveness of the proposed ML prediction. Additionally, confusion matrices and receiver operating characteristic (ROC) are introduced to validate the outcomes by visual analysis. © 2025 IEEE.

关键词： Glycoproteins

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AI for Healthcare: A Classification Model for Personalized Premenstrual Symptoms and Depressive Crisis Risk Tracking Using data Analytics and machine learning 10

AI for Healthcare: A Classification Model for Personalized P...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Nuankaew, Pratya Sorat, Jidapa Intajak, Jindaporn Inta, Jirapron Nuankaew, Wongpan S. University of Phayao School of Information and Communicatin Technology Phayao56000 Thailand

ISBN: (纸本)9798331543273

A significant obstacle to women's lives is menstruation, which, in some cases, leads to depression, anxiety, and stress. this research, therefore, aims to solve this problem with three objectives: to study the context of female adolescents who experience premenstrual syndrome (PMS) problems and depression crises, to develop a classification model for personalized premenstrual symptoms and depressive crisis risk tracking using data analytics and machine learning techniques, and to evaluate the performance of all developed models to select suitable models for future application. the research data included 282 volunteers who gave consent from the University of Phayao, with random sampling. the machine learning used in this research consists of six techniques: decision trees, k-nearest neighbors, logistic regression, naïve bayes, random forests, and support vector machines. the model performance is tested using 10-fold cross-validation and evaluated using five metrics: accuracy, precision, recall, f1-score, and time. the results of the construction and evaluation of all models are at a high level, which is encouraging. the researchers expect that this research can be further developed for future mobile applications. © 2025 IEEE.

关键词： Medical informatics

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An Efficient Federated learning Privacy Protection Algorithm based on Model Quantification 24

An Efficient Federated Learning Privacy Protection Algorithm...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Yufan Cao, Tianya Zhang, Yujing School of Computer Science and Engineering Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400710094

Federated learning, as a distributed machine learning method, enables data sets from different clients to train high-quality models without leaving the local area. However, in the process of federation learning to upload model parameters to the server, privacy leakage is easy to occur. At the same time, with the increase of training rounds, in order to achieve the expected effect, the client frequently uploads data to the server during each training, which will cause certain memory consumption. therefore, to solve the problem of privacy leakage and excessive memory consumption during client upload, this paper proposes an efficient federated learning privacy protection algorithm (FedEMQ) based on model quantification. the algorithm adds noise before the client uploads the model parameters to the server to protect the privacy of the client. the model parameters are then quantized to convert the floating-point data to a low-precision type to reduce its memory consumption. Finally, the experimental show that the system balances model performance and memory consumption well while protecting client privacy. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Poor Posture Detection Based on Behavioral and Environmental data Using machine learning 24

Poor Posture Detection Based on Behavioral and Environmental...

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8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Gu, Feng Li, Jianwei Deng, Shunrong Yang, Qin Department of Computer Science College of Staten Island Staten IslandNY United States School of Business Sichuan Normal University Sichuan Chengdu China

ISBN: (纸本)9798400718182

Poor spinal posture is a common problem among primary school students, which may lead to long-term health issues. It is essential to detect poor posture and identify the key behavioral and environmental factors for its preventions and intervention strategies, such as the sitting time, the backpack weight, the physical activities, and the sleep habits. We develop a machine learning method using random forests to predict the incorrect posture occurrences based on the collected behavioral and environmental data of primary school students and identify important influential factors. the data collected from 782 primary school students include six categories of behavioral factors and five categories of environmental factors. Experimental results show that the prediction accuracy reaches 75.16%, and some of the most important factors for poor posture include carrying backpacks on two shoulders, the mattress firmness, backpack weights, and the excessive use of the electronic devices, which are able to facilitate the early detection and prevention of scoliosis for school children and provide guidelines and insights for families and schools to develop intervention strategies, such as using the right backpack, cutting down on the screen time, increasing physical activities, and making the sleep surface more supportive. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Deep learning Based Pepper Maturity Detection 24

Deep Learning Based Pepper Maturity Detection

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Ma, Yiqun Peng, Ziyang Ruan, Yujie College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

China is one of the world’s important pepper production area, the existing picking method still rely on manual picking method for picking, and pepper is a kind of batch maturity of the herb, manual picking is not only time-consuming but also very laborious, and the pepper target detection technology is the key to realize the automatic picking of pepper, the existing research for pepper target detection is relatively small, this paper intends to choose the image processing and machine learning methods for pepper to carry out target detection. First of all, the processed pepper image data are passed into Yolov5s, Yolov5m, Faster R-CNN, SSD to compare the four common deep learning methods, the comprehensive comparison in the pepper recognition stage Yolov5s is the most appropriate deep learning algorithm in the deep learning algorithms mentioned above, and in the second stage, using the binary classification method, the pepper data are divided into immature pepper and mature pepper, and directly sent to Yolov5s. the second stage uses a binary classification method to classify pepper data into immature and mature pepper, which is directly fed into the Yolov5s model with an accuracy of 97.4% for pepper maturity discrimination, providing diversified detection and corresponding technical support for the intelligent pepper picking summary. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep learning Framework 10

Improved Cotton Leaf Disease Classification Using Parameter-...

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作者： Patra, Aswini Kumar Gajurel, Tejashwini NERIST Department of Computer Science and Engineering Itanagar India Neerja Modi School Rajasthan Jaipur India

ISBN: (纸本)9798331543273

Cotton crops, often called 'white gold', face significant production challenges, primarily due to various leaf-affecting diseases. As a major global source of fiber, timely and accurate disease identification is crucial to ensure optimal yields and maintain crop health. While deep learning and machine learning techniques have been explored to address this challenge, there remains a gap in developing lightweight models with fewer parameters which could be computationally effective for agricultural practitioners. To address this, we propose an innovative deep learning framework integrating a subset of trainable layers from MobileNet, transfer learning, data augmentation, a learning rate decay schedule, model checkpoints, and early stopping mechanisms. Our model demonstrates exceptional performance, accurately classifying seven cotton disease types with an overall accuracy of 98.42% and class-wise precision ranging from 96% to 100%. this results in significantly enhanced efficiency, surpassing recent approaches i n accuracy and model complexity. the existing models in the literature have yet to attain such high accuracy, even when tested on data sets with fewer disease types. the substantial performance improvement, combined with the lightweight nature of the model, makes it practically suitable for real-world applications in smart farming. By offering a high-performing and efficient solution, our framework can potentially address challenges in cotton cultivation, contributing to sustainable agricultural practices. © 2025 IEEE.

关键词： Precision agriculture

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Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the data Set 10

Classification Models and Variable Selection for SME Credit ...

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作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. this study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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PrivFED: Protecting User Privacy in Federated learning Systems through Differential Privacy 8

PrivFED: Protecting User Privacy in Federated Learning Syste...

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8th international conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： thumula, Karthik Holla, Hariprasad Gutti, Chandu Sasikumar, Arun Ambika Gogineni, Hemanth Santa Clara Valley and Denver Chapter United States University of the Cumberlands Williamsburg United States

ISBN: (纸本)9798331520762

this research examines the integration of Federated learning (FL) with differential privacy (DP) to enhance privacy protection in distributed machine learning systems. We propose and implement a comprehensive privacy-preserving framework PRIVFED that addresses the growing challenges of data privacy while maintaining model utility. through extensive experimentation using the MNIST dataset, we demonstrate that our approach achieves significant privacy improvements with minimal performance degradation. Our results show that moderate privacy protection (ϵ= 1.0) achieves 92.4% accuracy, while strong privacy protection (ϵ = 0.5) maintains 88.7% accuracy compared to the baseline of 98.2%. Furthermore, our system shows efficient scaling with increasing number of clients, introducing only 15.3% computational overhead. the proposed PRIVFED framework adapts dynamically to varying privacy requirements and data distributions, making it suitable for real-world applications across healthcare, financial services, and critical infrastructure sectors. this work establishes that machine learning systems can achieve both strong privacy guarantees and high model performance, providing practical guidelines for privacy-preserving distributed learning implementations in sensitive domains. © 2025 IEEE.

关键词： Federated learning

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learning-Based Traffic Classification for Software-Defined Networks 8th

Learning-Based Traffic Classification for Software-Defined N...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Panni, Priyanka Budihal, Suneeta V. KLE Technological University Hubli India

ISBN: (纸本)9789819784219

Recent advancements in linked devices and industrial automation have created a large demand for network resources. the amount of traffic created by these technologies is so great that traditional networks are becoming less capable of handling it. Network automation is made possible by machine learning (ML) applications using Software Defined Networking (SDN), which simultaneously presents a programmable, reconfigurable, and scalable networking solution. this SDN system can address problems with conventional approaches to categorize network traffic and assign resources. the SDN controller will collect network data that data analytic techniques may analyze and use ML models to tailor network management. the paper proposes to analyze network data, apply ML to classify network traffic, and integrate the model into LabVIEW. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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