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检索条件"主题词=CLASSIFICATION algorithms"

共 26481 条记录，以下是4921-4930 订阅

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YOLO-CD: YOLOv5-Based Neural Network for Empty Position Recognition of Dispatch Center

YOLO-CD: YOLOv5-Based Neural Network for Empty Position Reco...

引用

Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on

作者： Bin Liu Zengrong Zheng Peng Wang Lichun Shu Jun Wang Yunyang Wu Shengqiang Liu Xiaobo Zhang Hongfei Zhao Shenmu Energy Administration Shaanxi China Information Research Institute Ministry of Emergency Management Beijing China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Shenmu Information Industry Development Group Co. Ltd Shaanxi China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The mine dispatch room plays a critical role in ensuring workplace safety by enabling security personnel to monitor and respond swiftly to emergencies. Detecting whether the dispatch room is unoccupied is essential to maintain continuous monitoring and prevent potential safety risks. This study addresses the challenge of automated vacancy detection in dispatch rooms, which is complicated by unique environmental characteristics and the issue of imbalanced sample categories. To overcome these challenges, we utilized screenshots extracted from a mine's safety analysis and warning system as experimental data. An improved YOLOv5 model was proposed, integrating the Convolutional Block Attention Module (CBAM) to enhance its ability to focus on critical features of the human body while reducing the influence of background noise. Furthermore, a decoupling strategy was applied to mitigate recognition errors caused by low-sample categories, improving model robustness in detecting rare but critical scenarios. Experimental results validate the enhanced performance of the proposed model, showcasing its capability to accurately identify whether the dispatch room is vacant in real-world conditions. By enabling real-time monitoring and alerting, this research contributes to the automation of safety protocols in mining operations, reducing the reliance on manual supervision and improving overall safety management efficiency. This methodology offers a scalable solution for intelligent safety monitoring systems across similar industrial settings.

关键词： YOLO Automation Protocols Service robots Safety management Feature extraction Real-time systems Robustness classification algorithms Monitoring

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Study on Automatic Recording and Health Promotion System of University Sports Results

Study on Automatic Recording and Health Promotion System of ...

引用

Computers, Information Processing and Advanced Education (CIPAE), 2020 International Conference on

作者： Ting Cao Zhen Liu Huanggang Normal University School of Physical Education Huanggang Hubei China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The purpose of this study is to develop a set of automatic recording and health promotion system for college sports. The system uses the classification model based on support vector machine (SVM) to evaluate the health status of college students, and combines the evaluation results with the physical performance records to realize the dynamic monitoring and management of students' physical activity and health status. By integrating smart wearable devices, the system collects physiological data of students in real time, and uses SVM algorithm for data analysis to predict and classify the health risk level of students. At the same time, the system has designed a user-friendly interface for teachers to record and manage students' physical education results, ensuring the accuracy and timeliness of data. In order to verify the effectiveness of the system, detailed algorithm design and simulation experiments are carried out in this study. The experimental results show that the proposed SVM-based health assessment model has high accuracy and reliability in identifying potential health problems. In addition, the automated function of the sports performance recording system significantly improves the efficiency of data management and reduces the occurrence of human error.

关键词： Support vector machines Performance evaluation Accuracy Prediction algorithms Real-time systems Recording classification algorithms Wearable devices Monitoring Sports

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Compositional Study of Ancient Glassware Based on K-Mean Cluster Analysis and Logistic Regression Algorithm

Compositional Study of Ancient Glassware Based on K-Mean Clu...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Zhiting Zhao Minghao Li Junhao Deng Jinan University Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper focuses on the in-depth research on the chemical composition analysis and identification of ancient Chinese glass artifacts, and statistically analyzes and classifies and predicts the compositions of ancient glass artifacts using K-means cluster analysis and logistic regression algorithm. Firstly, some invalid data were excluded through data preprocessing, and the missing values were appropriately processed. In the analysis of the relationship between chemical composition and glass properties, the chi-square test was applied to find that there were significant differences between glass type and color and surface weathering. Secondly, by eliminating the SiO2 composition, which had a considerable influence on the clustering results, K-means cluster analysis was used to classify the subclasses of high-potassium and lead-barium glasses, and the reasonableness and sensitivity of the classification results were verified. Finally, the logistic regression algorithm was used to predict the type of unclassified glass artifacts, while the correlation between important chemical components and other components was explored by gray correlation analysis, which provided a new method and perspective for the compositional analysis and identification of ancient glass artifacts.

关键词： Logistic regression Analytical models Correlation Image color analysis Clustering algorithms Glass Prediction algorithms classification algorithms Chemicals Optimization

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Multi-Instance Learning for Parkinson’s Tremor Level Detection with Learnable Discriminative Pool

Multi-Instance Learning for Parkinson’s Tremor Level Detect...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haoyu Wu Yifan Guan Alexei Lisitsa Xiaohui Zhu Po Yang Jun Qi Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China Department of Computer Science The University of Liverpool Liverpool UK School of Computer Science The University of Sheffield Sheffield UK

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremors as its most typical symptom. Wearable accelerometer sensors, along with corresponding machine learning algorithms, can effectively assist in the diagnosis of PD tremors. However, due to the variations in disease progression and symptoms caused by individual differences among PD patients, it is challenging for existing algorithms to eliminate label noise and accurately identify and extract disease-related features across diverse patient data. In this study, we propose a Learnable Discriminative Instance Pool (LDIP) algorithm based on multi-instance learning, which integrates the concept of learnable shapelets. This method transforms the traditional DIP algorithm into a learnable instance pool that can be adaptively adjusted according to discriminative criteria, thereby enhancing the separability between different classes after bag mapping. We evaluated the proposed method on two clinical datasets using three different machine learning classifiers, achieving a maximum 73% accuracy for 5-class classification. The experimental results demonstrate that our proposed method consistently outperforms current baselines across various settings.

关键词： Machine learning algorithms Accuracy Machine learning Transforms Feature extraction classification algorithms Data mining Wearable sensors Diseases Electronics packaging

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Identifying Coordinated Activities on Online Social Networks Using Contrast Pattern Mining

Identifying Coordinated Activities on Online Social Networks...

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International Joint Conference on Neural Networks (IJCNN)

作者： Isura Manchanayaka Zainab Zaidi Shanika Karunasekera Christopher Leckie School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The proliferation of misinformation and disinformation on social media networks has become increasingly concerning. With a significant portion of the population using social media on a regular basis, there are growing efforts by malicious organizations to manipulate public opinion through coordinated campaigns. Current methods for identifying coordinated user accounts typically rely on either similarities in user behaviour, latent coordination in activity traces, or classification techniques. In our study, we propose a framework based on the hypothesis that coordinated users will demonstrate abnormal growth in their behavioural patterns over time relative to the wider population. Specifically, we utilize the EPClose algorithm to extract contrasting patterns of user behaviour during a time window of malicious activity, which we then compare to a historical time window. We evaluated the effectiveness of our approach using real-world data, and our results show a minimum increase of 10% in the F1 score compared to existing approaches.

关键词： Social networking (online) Organizations Artificial neural networks Real-time systems classification algorithms Data mining Fake news

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AI-Driven Insights: A Survey on Innovative Approach for Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods : Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods

AI-Driven Insights: A Survey on Innovative Approach for Lung...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： A. Lakshmi Bhargav C. Ashokkumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science & Technology Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The lungs are vital for exchanging oxygen and carbon dioxide, providing oxygen to cells for energy production, and removing waste carbon dioxide from the *** also help filter and protect the body from harmful particles and pathogens in the *** lung function is essential for overall health and *** cancer ranks as the second most common cause of cancer-related fatalities *** emphasizes the challenge of early detection due to the lack of symptoms in the initial *** contributes to a high mortality rate compared to other lung *** identification and precise prediction of lung cancer serves as vital for improving patient outcomes. Machine learning (ML) techniques have shown great promise in revolutionizing lung cancer prediction by leveraging various data sources such as medical images, clinical data, and genomic information. This paper presents a review of lung cancer prediction using ML, DL, and AI *** classification of lung cancer through medical imaging is essential for physicians to determine appropriate treatment options, ultimately aiming to reduce cancer *** goal is to compare the performance and accuracy of convolutional neural networks (CNNs), Random forest algorithm, ensemble extreme boosting (XGBoost) algorithm,Support Vector Machine (SVM), AdaBoost classification model (ADB-C), Long Short-Term Memory Networks (LSTM), incremental multiple resolution residual network (iMRRN) models while also providing explanations for their predictions.

关键词： Deep learning Machine learning algorithms Lungs Lung cancer Carbon dioxide Predictive models Prediction algorithms classification algorithms Prognostics and health management Long short term memory

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Prediction of Benign and Malignant Breast Cancer using Machine Learning Techniques: A Review and Comparative Analysis

Prediction of Benign and Malignant Breast Cancer using Machi...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Amit Manjhwar Deepak Batham Mahesh Parmar Vikas Sejwar Dept. of CSE MITS-DU Gwalior India Dept. of Electronics MITS-DU Gwalior India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This Breast cancer (BC) is a predominant illness majorly found in women which may cause death if predicted in an uncontrolled stage. In BC, breast cell rises uncontrolled and infects the surrounding part of the breast. To detect BC in premature stages using simple, rapid, and correctly detected methods, machine learning (ML) techniques played a vital role. This paper mainly emphasizes different ML algorithms such as classification and Regression Tree (CART), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB) to detect benign and malignant BC then a comparative analysis of these algorithms has been performed and tested on the standard BC datasets. These datasets were pre-processed first to minimize error after that ML models were trained and tested. The investigated experimental results of ML algorithms are evaluated on accuracy, recall, precision, and F1 score metrics. The SVM technique shows 98.83% accuracy and significantly improved performances on other metrics. These discoveries could help to the early prediction of BC and save life.

关键词： Support vector machines Measurement Accuracy Machine learning algorithms Reviews Nearest neighbor methods Prediction algorithms Breast cancer classification algorithms Random forests

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Binary Gannet Optimization Algorithm for Feature Selection Problem

Binary Gannet Optimization Algorithm for Feature Selection P...

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Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Jeng-Shyang Pan Longkang Yue Shu-Chuan Chu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

The Gannet Optimization Algorithm (GOA) has been proven to exhibit excellent performance in resolving issues in a range of fields. However, it is ineffective, nevertheless, when used for binary optimization issues. Therefore, this paper, aiming to further explore its outstanding capabilities, proposes for the first time using the V-shaped transfer function family to implement the binary version of the Gannet Optimization Algorithm (BGOA). Eleven datasets from the UCI Machine Learning Repository are used for feature selection applications. The transfer functions employed in BGWO-A, BPSO-T, and BQUATRE are compared with these datasets. The outcomes attest to BGOA’s exceptional performance in feature selection. It helps identify feature subsets, thereby maximizing classification accuracy while reducing feature count. This highlights the potential of the binary BGOA algorithm in exploring attributes and choosing the attributes that are most pertinent to classification issues.

关键词： Machine learning algorithms Accuracy Transfer functions Machine learning Feature extraction classification algorithms Optimization Machine intelligence

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Enhancing Precision in Gastric Cancer Detection: A Synergistic Approach with Convolutional Neural Networks and Metrological Integration

Enhancing Precision in Gastric Cancer Detection: A Synergist...

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URSI Atlantic Radio Science Conference (URSI AT-RASC)

作者： Paramita Guha Surinder P. Singh Sangeeta Choudhury Ashish Kumar CSIR-National Physical Laboratory Delhi India Dept. of Biotechnology & Research Sir Ganga Ram Hospital Delhi India Institute of Liver Gastroentrology & Pancreatico Sir Ganga Ram Hospital Delhi India

ISBN: (数字)9789463968102

ISBN: (纸本)9798350360257

Gastric cancer (GC) is one of the major causes of cancerrelated mortality. It requires major attention in terms of early detection and proper diagnosis. Endoscopy and analysis of endoscopic images is the most common detection method of GC in the early stage. However, specialised professionals are required for endoscopy. This becomes a bottleneck for patients in remote areas and is a major cause of late diagnosis. Hence, there is a need for an efficient, cost-effective user-friendly tool that can identify GC with optimal accuracy and less effort. Recently, Artificial intelligence (AI) has emerged as a promising tool that can enhance precision and accuracy in diagnosis, treatment and patient care. In this paper, a deep learning-based strategy has been presented as a possibility of identifying GC at early stage. For this purpose, clinical parameters, like, complete blood count (CBC) and endoscopic images were correlated to delineate cancerous and non-cancerous ulceration in GI tract. For this preliminary study, GC related kaggle-based open-source data bank has been used to develop various pilot algorithms. A PyCharm based interface has been developed to identify the changes in CBC parameters. If these parameters are beyond a pre-defined cut-off, then the corresponding endoscopic images are analysed. A couple of Convolutional Neural Networks (CNNs) based pre-trained models, like, VGG16 and NasNetMobile have been developed on these images. A comparative analysis has been performed to determine the accuracy, precision, recall, F1 score, sensitivity, specificity and run-time. The algorithms developed provide proof of evidences that deep learning networks can be used to identify GC accurately and sensitively and further can distinguish GC from non-malignant tissues. Thus, there is a potential role of deep learning algorithms in GC diagnosis, segmentation as well as classifications. Integrating CNNs into gastric cancer detection leverages advanced image analysis. Metrology e

关键词： Deep learning Accuracy Endoscopes Metrology Cancer detection classification algorithms Convolutional neural networks

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Hybrid Method for Monitoring Sundarbans Mangrove Forest Using Satellite Images

Hybrid Method for Monitoring Sundarbans Mangrove Forest Usin...

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Information and Communication Technology (ICICT), International Conference on

作者： MD. Abdullah Al Naim Shihab Sarar Mahdi Hasan Md Maruf Hossain Tasin Omar Farrok Department of Civil Engineering Ahsanullah University of Science and Technology Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

A hybrid method for monitoring Sundarbans mangrove forest using satellite images with machine learning techniques is presented in this paper. The Random Forest algorithm, data scaling, cloud, and shadow masking are used to enhance the precision of land cover classification over the Sundarbans. The study utilizes Landsat 8 imagery to classify land into deep forest, light forest, barren land, and water bodies. Significant fluctuations in land cover types over the analyzed period, with notable variations in forest density, barren land, and water bodies are identified. Normalized Difference Vegetation Index and Normalized Difference Water Index are two popular methods for observing vegetation health and water bodies in remote sensing. But it fails to classify different objects. This paper focuses on solving this issue using machine learning technique by combining the two methods along with the other band's reflectance as a feature to improve the classification and show a great f1 and recall. This study uses a hybrid model to monitor land cover changes in the Sundarbans mangrove forest from 2013 to 2022, achieving an overall accuracy of 0.98. Significant findings include a deep forest cover increase from 52.82% to 55.735%, fluctuations in barren land from 2.04% to a peak of 8.38% in 2021, and a decrease in water bodies from 32.18% to 28.82%, indicating possible changes in river dynamics and hydrological conditions.

关键词： Fluctuations Accuracy Machine learning algorithms Land surface Forestry Regression analysis Satellite images classification algorithms Random forests Monitoring

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