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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是751-760 订阅

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Stock trend prediction based on multi-factor screening machine 24

Stock trend prediction based on multi-factor screening machi...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Hu, Ruijie Faculty of Science and Technology Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400709999

With the rapid development of financial markets, stock price prediction, as a highly complex and challenging topic, has attracted more and more attention from academia and industry. This study aims to use multiple machine learning algorithms to predict stock market prices, especially exploring the advantages of deep learning models in capturing market nonlinear and time series characteristics. In this article, a machine learning model is used for contribution analysis, analyzing multiple features of stocks, performing a weighted combination analysis of features, and then using a convolutional neural network to perform feature extraction and analysis of stocks, and combining the attention mechanism for deep feature extraction, and finally use the long short-term memory network (LSTM) model to output the model to analyze and predict future stock trends. The experimental results show that combining deep learning analysis technology and factor screening machine can significantly improve the prediction accuracy of the model. At the same time, this article also compared the performance of different models in different market environments, and concluded that in markets with higher volatility, models based on deep learning are more stable. Ultimately, this research provides a new technical means for risk management and investment decisions in financial markets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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SAGEConv Graph Neural Network Model for Multivariate Regression using Google Quest dataset 2

SAGEConv Graph Neural Network Model for Multivariate Regress...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Ghadekar, Premanand Ingale, Varad Pungliya, Vithika Bhonsle, Roshita Raut, Ankur Purohit, Atharva Department of Information Technology Vishwakarma Institute of Technology Pune411037 India Department of Artificial Intelligence and Data Science Technology Vishwakarma Institute of Technology Pune411037 India

Natural Language Understanding (NLU) has become increasingly important in recent years, with the advent of various applications such as chatbots and sentiment analysis. In the domain of online question-answering platforms, the equitable visibility of valuable responses is paramount. The proposed automated solution employs SAGE Conv Graph Neural Network which uses the SAGEConv operator to extract features from the graph structure to comprehensively evaluate question- answer quality. By incorporating nuanced subjective labels from the Google Quest dataset, our methodology bridges the gap between human comprehension and machine analysis. In contrast to traditional Natural Language Processing (NLP) techniques that struggle with intricate language subtleties, GNNs capitalize on inherent graph structures within text, bolstering precision. Experimentation conducted on Google Quest dataset yields compelling quantitative outcomes. With an impressive R2 score of 0.644 and an exceptionally low MSE score of 0.00050. These results underscore the potency of GNN-driven Natural Language Understanding (NLU) models paves the way for improved NLU accuracy across various domains, marking a substantial advancement in the field. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Design of Intelligent Dustbin Based on machine learning 3

Design of Intelligent Dustbin Based on Machine Learning

引用

3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Feng, Xin College of Information and Electrical Engineering Hunan International Economics University Hunan Changsha410205 China

ISBN: (纸本)9781510664814

In recent years, with the development of economic society and the improvement of people's living standards, the number and types of household rubbish are increasing. However, on one hand, due to people's weak awareness of rubbish classification, rubbish in public places cannot be collected well. On the other hand, due to the lack of rubbish classification knowledge, people can not classify some rubbish correctly, which will result in environmental pollution and waste of resources. So we design a intelligent dustbin which can carry out rubbish classification management and delivery guidance, to ensure that rubbish can be correctly placed. For those who do not know the rubbish classification knowledge or cannot determine the category of rubbish, the rubbish characteristics can be identified by scanning the rubbish and compared with the information in the system database to identify the rubbish type and display it on the screen to promote rubbish classification and various rubbish types. © 2023 SPIE.

关键词： machine learning

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A New Approach to Road Incident Detection Leveraging Live Traffic data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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An Improved Federated learning Intrusion Detection with Collaborative Feature Selection on CICIDS 2017 30th

An Improved Federated Learning Intrusion Detection with Coll...

引用

30th international conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Cao, Yuan Chen, Chien-Ming Shandong University of Science and Technology No. 579 Qianwangang Road Shandong Qingdao China

ISBN: (纸本)9783031829062

This paper explores the application of feature selection in intrusion detection using federated learning, aimed at compressing training data features. It addresses the challenges posed by high-dimensional and redundant features in deep learning models, exacerbated in federated learning settings. Leveraging the gain-based importance of decision trees and entropy-based information gain, a collaborative selection algorithm is devised to reduce feature dimensionality. Contributions include a selection methodology, an intrusion detection system employing federated learning, process optimization, and achieving 97.81% accuracy on a subset of the CIDIDS2017 dataset, with training time reduced to one-third. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Deep Transfer learning Optimization Techniques for Medical Image Classification: A Review

Deep Transfer Learning Optimization Techniques for Medical I...

引用

2022 international conference on Intelligent Computing and machine learning, 2ICML 2022

作者： Kariuki, Paul Wahome Gikunda, Patrick Kinyua Wandeto, John Mwangi Dedan Kimathi University of Technology Department of Computer Science Nyeri Kenya

ISBN: (纸本)9798350346619

Medical image classification is a complex and challenging task due to the heterogeneous nature of medical data. Deep transfer learning has emerged as a promising technique for medical image classification, allowing the leveraging of knowledge from pre-Trained models learned from large-scale datasets, resulting in improved performance with minimal training and overcoming the disadvantage of small data sets. This paper concisely overviews cutting-edge deep transfer learning optimization approaches for medical image classification. The study covers convolutional neural networks and transfer learning techniques, including relation-based, feature-based, parameter-based, and instance-based transfer learning. Classical classifiers such as Resnet, VGG, Alexnet, Googlenet, and Inception are examined, and their performance on medical image classification tasks is compared. The paper also discusses optimization techniques, such as batch normalization, regularization, and weight initialization, as well as data augmentation and kernel mathematical formulations. The study concludes by identifying challenges when using deep transfer learning for medical image classification and proposing potential future approaches for this field. © 2023 IEEE.

关键词： Transfer learning

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Performance Analysis of machine learning on Homomorphically Encrypted data 16

Performance Analysis of Machine Learning on Homomorphically ...

引用

16th international conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Mahatho, Morogo Freshina Chen, Yueshan Zhang, Sihai School of Cyberspace Science and Technology University of Science and Technology of China China Cas Key Laboratory of Wireless-Optical Communications China School of Microelectronics University of Science & Technology of China Anhui Hefei China

ISBN: (纸本)9798350390643

The need for encrypted machine learning arises from the growing concern over data privacy and security in the era of big data and artificial intelligence. This study examines the performance of machine learning (ML) models on homomorphic encrypted data using the CKKS encryption scheme. We evaluated various models, including k-nearest neighbors (KNN), logistic regression (LR), and convolutional neural networks (CNN) on diverse datasets, including the Titanic, Pima Indians Diabetes, MNIST, and Fraud Detection datasets. Our experiments compare the performance of these models on encrypted and unencrypted data to assess the impact of encryption. The results demonstrate that while encryption introduces a slight performance degradation, the accuracy loss remains within acceptable limits. For instance, the logistic regression model's accuracy on the Titanic dataset was 82.72% for plain data, 81.36% for encrypted data with a polynomial degree of 4096, and 82.00% for a polynomial degree of 8192. The MNIST CNN model achieved 99.4% accuracy on plain data, 98.0% with 4096, and 99.0% with 8192. Our findings highlight the practical applicability of encrypted machine learning in preserving data privacy without significantly compromising performance, offering valuable insights for future research and development in secure machine learning systems. © 2024 IEEE.

关键词： data privacy Homomorphic encryption machine learning Performance analysis

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Copy–Move Forgery Detection Algorithm: A machine learning-Based Approach to Detect Image Forgery 4th

Copy–Move Forgery Detection Algorithm: A Machine Learning-B...

引用

4th international conference on data, engineering, and Applications, IDEA 2022

作者： Thakur, Abhishek Sharma, Shamneesh Sharma, Tushar upGrad Education Private Limited Mumbai India Lovely Professional University Punjab India

ISBN: (纸本)9789819700363

The development of technology and the widespread availability of editing software promote the forging of digital material. Anyone with a smartphone is able to edit photographs and modify their attributes. Copy and move forgery is the most prevalent sort of picture counterfeiting. An approach based on machine learning is proposed in this work to identify copy–move picture forgery. The suggested technique detects and localizes copy–move patches. The experiment is conducted with the CoMoFoD picture collection. This dataset contains both the original and ground truth pictures for each photograph. This collection includes photos with various manipulations, including scaling, JPEG compression, and rotation. The photos are scaled based on various scaling parameters. The suggested methodology utilized supervised learning to classify forgery patch pixels. The experiment obtains an accuracy of 94.37, a recall of 97.03, and an F1 value of 94.03. The performance of the suggested algorithms is compared to the state-of-the-art approach and found to be superior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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An integrated machine learning Techniques for Accurate Heart Disease Prediction

An integrated Machine Learning Techniques for Accurate Heart...

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2022 international Mobile and Embedded Technology conference, MECON 2022

作者： Ahdal, Ahmed Al Rakhra, Manik Badotra, Sumit Fadhaeel, Taha Lovely Professional University Department of Machine Learning and Arterial Intelligence Punjab Phagwara Jalandhar India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara Jalandhar India Lovely Professional University Department of Mechatronics Engineering Punjab Phagwara Jalandhar India

ISBN: (纸本)9781665420204

currently heart disease is considered among top major causes of deaths in the globe, prediction of heart disease is a serious complexity in medical data processing. machine learning (ML) has proven the beneficial in assisting with decision-making and prediction from the massive amounts of data provided by health care industry. We found machine learning approaches being employed in recent advancements in a long list of Internet Of Things (IOT) in a variety of industries. Different research suggests merely a glimmer of hope for using ML algorithms to predict cardiac disease. Several machine learning approaches are used in this paper to compare and analyze the outcomes of the UCI dataset using different machine learning algorithms the data was collected by researchers from the 'University of California Irvine' It contains 75 column and will use only 14 features. Calculating the accuracy and confusion matrix. some encouraging results are achieved and validated. the dataset consists various non - relevant attributes that were handled, and normalized for improved returns. © 2022 IEEE.

关键词： Internet of things

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10th international conference on Innovations in Computer Science and engineering, ICICSE 2022

10th International Conference on Innovations in Computer Sci...

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10th international conference on Innovations in Computer Science and engineering, ICICSE 2022

ISBN: (纸本)9789811974540

The proceedings contain 62 papers. The special focus in this conference is on Innovations in Computer Science and engineering. The topics include: machine learning-Based Indian Stock Market’s Price Movement Prediction and Trend Analysis;machine learning-Based Mortality Prediction of COVID-19 Patients;smart Computer Monitoring System Using Neural Networks;using Deep learning to Perform Payload Classification;malicious Domain Detection Using Memory Augmented Deep Autoencoder;graph Analysis Using Page Rank Algorithm to Find Influential Users;hate Speech and Offensive Language Detection in Twitter data Using machine learning Classifiers;secrecy Rate Optimization for Energy Efficient Cognitive Relay Networks;avian Influenza Prediction Using machine learning;An Explainable AI Approach for Diabetes Prediction;prediction and Comparison of Diabetes with Logistic Regression, Naïve Bayes, Random Forest, and Support Vector machine;bone Cancer Detection Using Deep learning;energy and Buffer Size-Based Routing Protocol for Internet of Things;v-Shaped Binary Version of Whale Optimization Algorithm for Feature Selection Problem;an Energy-Efficient Deep Neural Network Model for Photometric Redshift Estimation;deep learning-Based Diabetic Retinopathy Screening System;artificial Intelligence-Based data Analytics Techniques in Medical Imaging;ensuring data Protection Using machine learning Integrating with Blockchain Technology;evaluation and Language Training of Multinational Enterprises Employees by Deep learning in Cloud Manufacturing Resources;development of a Cognitive Question Answering System to Learn Concepts for Placement Assistance;classification of Cervical Cells Using Deep learning Feature Extraction;cervical Cancer Prediction Using Optimized Meta-learning;an Ensemble Deep Closest Count and Density Peak Clustering Technique for Intrusion Detection System for Cloud Computing;voice-Based Intelligent Virtual Assistant for Windows.

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