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

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Research on image classification algorithm of domestic garbage based on deep learning method 3

Research on image classification algorithm of domestic garba...

引用

3rd IEEE international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Pan, Youcai Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665472784

Garbage classification and treatment is a necessary measure to fully implement garbage reduction, recycling, harmlessness and the construction of ecological civilization. With the rapid development of artificial intelligence, the use of artificial intelligence technologies such as deep learning to automatically classify images has become possible, and the classification of domestic garbage based on images has also become a specific problem in the field of deep learning. Therefore, this paper focuses on the widely concerned domestic garbage image classification problem, adopts the open source domestic garbage image dataset and the self-made unbalanced domestic garbage image dataset as the research object, studies the image classification task based on deep learning, and builds a deep learning-based domestic garbage image classification system based on the research results of this paper for users to quickly and accurately classify domestic garbage. The research results of this paper will provide valuable reference and reference for the research and practice of garbage image classification method based on machine vision, as well as the application of Flask framework and My SQL database design framework in garbage classification tasks. © 2023 IEEE.

关键词： Image classification

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Detection and Classification of Brain Tumor Using machine learning 3

Detection and Classification of Brain Tumor Using Machine Le...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Kale, Prachi V. Gadicha, Ajay B. Dalvi, G.D. P R Pote College of Engineering & Management Department of Computer Science and Engineering Maharashtra Amravati India P R Pote College of Engineering & Management Department of AI Maharashtra Amravati India Allard College Department of EXTC Maharashtra Pune India

ISBN: (纸本)9798350391565

Diagnosing a brain tumor typically necessitates the expertise of a radiologist, whose skills and knowledge are essential to the process. Since the overall number of individuals has grown, so too has the quantity of medical data required to be managed, rendering outdated methods extremely costly and inefficient. Many investigators looked into different techniques that had been quick and effective in identifying and categorizing brain tumours. Currently, machine learning (ML) techniques have gained popularity in the development of computerized systems that can quickly and effectively diagnose or segment brain tumours. The deep learning (DL) enables the application of ML frameworks for brain tumor identification. This paper proposed various ML models to analyze the MRI images. First, preprocessing and augmentation algorithms were applied to the MRI brain images. We evaluated the performance of several models, including LR, SVC, kNN, NB, NN, RF, and K-means clustering. The analysis focused on accuracy, precision, recall, F1-score, and AUC. LR (96%), NN (95%), and RF (96%) showed the highest performance across all metrics, effectively distinguishing between tumor and no-tumor images and providing reliable results. © 2024 IEEE.

关键词： K-means clustering

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Age Based Hybrid Recommendation System using machine learning 3

Age Based Hybrid Recommendation System using Machine Learnin...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Karthikeya, Andhoju Kumar, Alle Aniesh Manicharan, Chennuri Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350303360

The recommendation system is one of the bigger developments brought about by machine learning in the machine-human interactions. In practically every area, including entertainment, e-commerce, farming, health, etc., recommendation systems are the primary players in delivering greater personalization. When it comes to content-based recommendation systems, there are many social platforms which uses recommendation system for providing improved personalization to their users and to access these social platforms the user need to have an account in that particular social platform. These platforms have different categories of content where not everyone is eligible to a particular content category. There are scenarios where different users may use the same account to access an entertainment application, or if a user inadvertently watched some inappropriate content, the recommendation system will make inappropriate recommendations, which can have an impact on the user's mental health-children. Therefore, after conducting research on the problem statement and reviewing various existing research works, machine learning model is proposed that offers recommendations to the user after determining the user's age and gender in order to deliver appropriate recommendations to the users. © 2023 IEEE.

关键词： Recommender systems

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machine learning and Data Mining for Predictive Modeling of Stroke Risk and Post-Stroke Care Management 3

Machine Learning and Data Mining for Predictive Modeling of ...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Gobi, N. Singh, B.P. Yadav, Surendra Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

The research investigates the efficacy of a comprehensive machine learning and data mining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integrating diverse datasets and advanced algorithms, including Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. Data preprocessing, feature extraction, and personalized care plan generation are key components. Performance evaluation using accuracy, sensitivity, specificity, precision, F1 Score, AUC, and MAE demonstrates the superior predictive capabilities and efficiency of the proposed method compared to traditional approaches. The study highlights the potential for revolutionizing stroke care, emphasizing personalized risk assessment and post-stroke management for improved patient outcomes and healthcare delivery. © 2023 IEEE.

关键词： Data mining

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Enhancing Convolutional Models for Indoor Radio Mapping via Ray Marching

Enhancing Convolutional Models for Indoor Radio Mapping via ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Mengfan Skocaj, Marco Boban, Mate Munich Research Center Huawei Technologies Germany School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (纸本)9798350368741

In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASSP 2025 Signal Processing Grand Challenge, specifically the First Indoor Pathloss Radio Map Prediction Challenge. Our method incorporates a ray-marching feature that, combined with a UNet architecture enhanced by dilated convolution layers, significantly improves indoor pathloss prediction accuracy. Our approach achieved a 3rd-place ranking in the challenge with a weighted RMSE of 10.33 on the test dataset. © 2025 IEEE.

关键词： feature engineering machine learning radio maps Wireless communications

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Research on Abstractive Summarization Technology Based on Deep learning 3

Research on Abstractive Summarization Technology Based on De...

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Chen, Yifei University of Arizona College of Science TucsonAZ85721 United States

ISBN: (纸本)9781665459112

Abstractive text summarization is an important research field in natural language learning. Compared with the extracted text summary, it is more in line with the preparation steps of the manual summary. In traditional machine learning, supervised learning is the main method. Text annotation, feature extraction, classification training, and weight extraction are also very different from those based on deep learning. Starting with the task of abstractive text summarization based on deep learning, this paper summarizes the current research status of abstractive text summarization, in-depth analysis of multiple designs of encoder-decoder for current deep learning methods, and points out the difficulties of current research and the hot direction of future development. © 2022 IEEE.

关键词： Extraction

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Design of learning Effect Prediction Algorithm for Primary School Chinese Online Teaching Based on Support Vector machine Model 3rd

Design of Learning Effect Prediction Algorithm for Primary S...

引用

3rd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Yang, Baolin Yan’an New District No. 1 Primary School Shaanxi Yan’an716000 China

ISBN: (纸本)9783031631382

The rapid development of networking technology and advanced and portable electronic devices provide technical and carrier support for online teaching. Online teaching breaks the limitations of traditional teaching area and time, and has the characteristics of real resource sharing, convenience and efficiency, which is the trend of future education development. Reasonable creation and application of distance education teaching mode is the basis for improving students’ online learning ability. Start with the current situation of distance education and students’ online learning. Most experts and scholars believe that the individual differences of online students are mainly reflected in learning motivation. In the context of big data, learning analysis has become a hot topic in academic research. For the study of learning analysis, the United States has proposed models to predict learners’ learning preferences, and adaptively guide learners according to the predicted results. The vector machine model algorithm maps the nonlinear regression problem in the low-dimensional space to the high-dimensional feature space through the kernel function, and then solves the convex optimization problem in the high-dimensional feature space. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Students

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Third international conference on Optics, computer applications, and Materials Science, CMSD 2023

Third International Conference on Optics, Computer Applicati...

引用

3rd international conference on Optics, computer applications, and Materials Science, CMSD 2023

ISBN: (纸本)9781510674486

The proceedings contain 57 papers. The topics discussed include: development of an information system of geological monitoring indicators of subsoil use;mathematical model of the formation of transport schedules for regular passenger traffic;social networks as a data source for real-time emergencies detection;breast cancer diagnosis using machine learning techniques;simulation modeling to improve the sustainability of the city's road transport system;information exchange in multi-agent systems;aspects of digital-based fashion design;on stochastic optimization for deep learning;solving the problem of classifying forest cover types based on soil characteristics;and application of a neural network approach for localization of problem areas of a centralized water supply system.

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A Review of Breast Cancer Detection Using the Internet of Things and machine learning 3

A Review of Breast Cancer Detection Using the Internet of Th...

引用

3rd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Sandhu, Jasjeet Kaur Kaur, Amandeep Kaushal, Chetna Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350338263

Using the Internet of Things (IoT) and machine learning (ML), it is possible to find breast cancer quickly and accurately, which is important for getting better and getting treatment right away. In recent years, the IoT has changed in a way that makes it possible to use artificial intelligence (AI) and ML to look at both real-Time and historical data. Many imaging methods, such as mammography, thermography, CT, ultrasonography, histology, etc., could find breast cancers in their earliest stages. Medical imaging studies are now heavily reliant on machine learning (ML). With the advancement of machine learning, computer-Aided diagnostic (CAD) systems are becoming increasingly smarter and more capable of operating independently. The Internet of Medical Things (IoMT) is a novel method of connecting systems and apps to healthcare services and data sources for monitoring and tracking patients. It can completely change how hospitals work to find and predict cancers with higher care and customer satisfaction. The analysis found that the SVM method had the highest accuracy (98.1%) compared to the other methods. The purpose of this study is to explore the application of imaging and machine learning in conjunction with internet-of-Things systems in the detection of breast cancer at an early stage. © 2023 IEEE.

关键词： Internet of things

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An Investigation into machine learning-Based Techniques for Detecting Malicious Cryptomining Activities 3rd

An Investigation into Machine Learning-Based Techniques for ...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Xie, Renchao Huang, Xuange Tang, Qinqin Jiangsu Future Network Innovation Institute Nanjing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819616978

With the popularity of blockchain concepts and the appreciation of cryptocurrencies, an increasing number of hackers and illegal elements have started to focus on stealing users’ hardware resources for mining activities. In this context, web mining has emerged, which can occupy the computing resources of website visitors to mine cryptocurrencies. However, this method can affect user experience, increase energy consumption, and reduce computer performance, making web mining detection and defense increasingly important. Due to the high degree of obfuscation and complex behavior in web mining code, this paper proposes a mining detection model based on machine learning, which extracts features from multiple dimensions and combines machine learning algorithms to identify web pages with mining behavior. We used four classification algorithms for model training, and through extensive experiments and performance comparison, the Random Forest algorithm was proven to be the most effective [1]. This indicates its high accuracy and suitability for detecting malicious mining activities. Our research not only provides a robust method for identifying malicious web mining but also lays a foundation for future cybersecurity research, offering insights into the application of machine learning techniques in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： blockchain cryptojacking cryptomining detection machine learning

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