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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1856 条记录，以下是1-10 订阅

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Research on the application of machine learning in corporate public opinion monitoring and management 24

Research on the application of machine learning in corporate...

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Ma, Xiaohui School of Management Shanghai University Shanghai China

ISBN: (纸本)9798400710834

This article makes a point of the overall application of corporate public opinion monitoring and management technology based on machine learning. This work proves that by means of case analysis, the basic theories and methods of machine learning, it elaborates on how to use effective machine learning for large-scale public opinion data collection, processing, and analysis, and prediction. Considerable attention is paid to the way natural language processing techniques and sentiment analysis are applied in processing text data and how the models of machine learning, among other support vector machines, decision trees, and neural networks, bear upon implementation in real enterprise environments. Research results show that the level of efficiency and prediction accuracy will be greatly improved if the machine learning technique is applied during public opinion data processing, therefore effectively optimizing the strategy of public opinion response and management of the company. The effectiveness and application scenarios of each prediction model in public opinion analysis were verified through comparative analysis with multiple prediction models, which provided the scientific data support for the decision-making reference of the company. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

来源：评论

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Synergistic Integration of Blockchain Technology and machine learning Algorithms for Optimization of Agriculture Supply Chain Management 3

Synergistic Integration of Blockchain Technology and Machine...

引用

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

作者： Asalkar, Gayatri Sahane, Prema Pedde, Atharv Hapse, Samarth Ghondage, Tejas Bhosale, Krishna Department Of Computer Engineering JSPM's Rajarshi Shahu College of Engineering Maharashtra Pune411033 India

ISBN: (纸本)9798331527549

This research study focuses on the discussion of blockchain and machine learning optimization in the conventional agricultural supply chain. Optimization of both rising technologies has the possibility of addressing some of the complex and entrenched issues that confront the agricultural industry while receiving calls for more openness, honesty, and productivity. Blockchain, being the technological application that is based on secure and transparent transactions and enables the assurance of immutability and machine learning as a data driven power in optimization, leads to a significantly better traceability, erased inefficiencies, less substances and time to market. It is however not entirely without some few challenges that will have to be overcome for all these innovations to come into their own. Among the obstacles that need to be overcome for realization of these innovations some of the critical ones are Technology and Data issues and Stakeholder issues. © 2025 IEEE.

关键词： Blockchain immutability machine learning optimization traceability

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Epilepsy Seizure Detection based on EEG Signals using machine learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Smartphone Price Prediction and Category Classification using machine learning 3

Smartphone Price Prediction and Category Classification usin...

引用

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

作者： Agalayil, Helen Terese Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and LightGBM was found to be the best regressor for price prediction. © 2025 IEEE.

关键词： Smartphones

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Hyperspectral Images for Crop Prediction using machine and Deep learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

引用

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

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. machine learning (ML) and Deep learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep learning(DL) Hyperspectral Images(HSI) machine learning(ML)

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Prediction of mechanical properties of Q345qE steel welded joints based on machine learning 24

Prediction of mechanical properties of Q345qE steel welded j...

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Liu, Chengqing Hou, Xiaolong Liu, Lijun Qiao, Zhu Qin, Zhiqi Wang, Shengbao Company Limited Heilongjiang Harbin China School of Architecture and Civil Engineering Harbin University of Science and Technology Heilongjiang Harbin China Heilongjiang Provincial Transportation Planning and Design Institute Group Co. Ltd. Heilongjiang Harbin China

ISBN: (纸本)9798400710834

The mechanical properties of welded joints are the key indicators to evaluate the welding quality, which are mainly subject to the rationality of the welding process and the technical level of the welders. Traditionally, enterprises screen out the optimal welding scheme by implementing a large number of welding procedure qualification tests, but this method is costly and inefficient. In this study, the prediction model of tensile strength and yield strength of Q345 qE steel welded joints was established based on the machine learning algorithm of BP neural network. The results show that the prediction accuracy of the model is high, the R2 value is more than 0.96, and the relative error is controlled within ± 3MPa (tensile strength) and ± 5MPa (yield strength). This prediction method is expected to reduce the dependence on laboratory tensile tests, reduce experimental costs, and has significant application value for actual production. © 2024 Copyright held by the owner/author(s).

关键词： Tensile strength

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using machine learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Comparative Analysis of machine learning Algorithms for Disease Prediction using Medical Datasets 3

Comparative Analysis of Machine Learning Algorithms for Dise...

引用

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

作者： Roopa, C. Latha, R.S. Dhavamani, M. Bommisrilekkhaa, G. Angel Shalini, P. Dhanyajothi, R. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Department of Artificial Intelligence Kongu Engineering College Tamilnadu Erode India Department of Mathematics Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798331527549

application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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A Hybrid Deep learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

引用

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

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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Applying machine learning Approaches in Fertilizer Optimization and Yield Prediction for Smart Agriculture 3

Applying Machine Learning Approaches in Fertilizer Optimizat...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Apuroop, Ithi Sandhu, Ramandeep Krishnamraju, Datla Eswar Sai Singh, Gurvinder Shikhar, Sanket Kumar, Sourabh Jakkampud, Saikumar School of Computer Science & Engineering Lovely Professional University 144411 India

ISBN: (纸本)9798331542375

In smart agriculture, the most revolutionary technological advancement is the application of machine learning to support better decisions making in developing augmented crop yields. This research looks at how predictive analysis can bring about the best choice of fertilizers can enhance the prediction of yields, to overcome problems such as excessive use of fertilizers, degradation of the environment and inconsistent yields. machine learning models were trained on various large datasets including soil health, the meteorological condition of the farming area, growth rate of crops to produce custom solutions that options of fertilizers to be used under certain situations. The findings affirm improved yield prediction and efficient and economical fertilizer application without compromising on environmental conservation. By focusing on increasing yields, this approach also adheres to sustainable or sustainable intensification that is a perfect fit for the modern world characterized by climatic volatility while providing a sound platform to feed the world in the future. The results thus indicate the convenience of machine learning over conventional farming lifts traditional farming to a new level of advancement hence the future of green farming. © 2025 IEEE.

关键词： Decision making

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