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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4166 条记录，以下是61-70 订阅

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FRPoint: learning spatial correlations in the frequency domain for point cloud classification 4

FRPoint: Learning spatial correlations in the frequency doma...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Jiao, Yang School of Information Science and Engineering Yunnan University Kunming650500 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the use of neural networks for identifying irregular 3D point cloud data is gaining increasing attention from researchers. Similar to CNN, in order to extract local features of point clouds, point cloud models typically construct neighborhoods, extract features from each neighborhood point, and finally use symmetric aggregation functions to capture local information. However, how to accurately utilizing spatial correlation in 3D space remains a challenging problem. To address this issue, we propose FRPoint, which transforms neighborhoods into frequency domain space and capture long and short distance dependencies in the frequency domain using a learnable weight matrices. Benefit to the frequency domain's ability to well describe the spatial relationships, our method efficiently extracts accurate spatial correlations. More importantly, our method only requires the introduction of a simple learnable matrix, without the need for any complex Attention operations or stacking of intricate feature modules. the effectiveness of our method has been demonstrated through experiments on the classification datasets ScanObjectNN and ModelNet40. © 2025 SPIE.

关键词： Deep learning

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Intelligent identification of hydropower engineering safety hazards: Research on text classification method based on deep learning 4

Intelligent identification of hydropower engineering safety ...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Shen, Binghua College of Intelligent Manufacturing and Non Destructive Testing Wuhan College of Arts and Science Wuhan430345 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

To achieve timely identification and prevention of potential construction risks, this paper presents an intelligent classification approach based on deep learning. Given that such texts abound with technical terms and have a complex engineering background, traditional classification techniques struggle to effectively capture their profound features, thereby leading to limited classification accuracy. First, the ALBERT model is employed to extract the dynamic semantic representation of the text, and subsequently, the complex semantic associations between the text are deeply captured through the multi-layer Long Short-Term Memory network (LSTM) coding layer. In the decoding stage, multi-layer LSTM integrated with the attention mechanism is introduced to enhance the inter-label dependency and optimize the multi-label sequence prediction. the experiment was carried out based on a disaster text dataset of a hydropower station, and the results indicated that the F1 score was as high as 90.64%, significantly enhancing the classification efficiency and safety management efficiency, and offering robust support for the formulation of precise preventive measures. © 2025 SPIE.

关键词： Hazards

来源：评论

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Overview of deep learning YOLO algorithm 4

Overview of deep learning YOLO algorithm

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Pan, Yaohui Wang, Gang Yu, Jun Jilin Institute of Chemical Technology Jilin China Jilin Communications Polytechnic Jilin China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

At present, the YOLO algorithm has become an indispensable core real-time object detection technology in aspects such as unmanned driving, face detection, and robot applications, and its versions are constantly being updated and upgraded. Herein, we deeply analyze the evolution process of the YOLO algorithm and carefully investigate the innovations and contributions arising from the algorithm iterations from YOLOv1 to YOLOv5. We make vivid and inspiring prospects for the future development direction and point out the feasibility and necessity of the research on the YOLO algorithm. © 2025 SPIE.

关键词： Robot applications

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MLCDT: Fine-Grained Multi-Task learning for enhanced cognitive assessment in the clock drawing test 4

MLCDT: Fine-Grained Multi-Task Learning for enhanced cogniti...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Yao, Lei Ma, Haomiao Sun, Haoran Zhang, Yingwei Liang, Shili Zhang, Lei Shang, Pan Sun, Shiwen The School of Physics Northeast Normal University Changchun130000 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institue of Computing Technology Chinese Academy of Sciences Beijing100190 China First People’s Hospital of Foshan Guangdong Foshan528010 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Artificial Intelligence (AI) is increasingly used in cognitive health assessments, with the Clock Drawing Test (CDT) being an effective cognitive evaluation tool. However, the complexity of CDT image structures, high subjectivity, and the lack of specialized cognitive health assessment datasets for specific populations pose significant challenges for feature learning and model construction using this method. To address these issues, we propose a fine-grained multi-task learning approach (MLCDT) for AI-assisted diagnosis of cognitive health using CDT. MLCDT integrates image pre-training models with a multi-task learning framework to capture fine-grained features of CDT images and constructs a final diagnostic support model through scientifically designed tasks. Experiments using real data from cognitive health assessments in a neurology department at a hospital validate the effectiveness of MLCDT in handling fine-grained tasks and aiding cognitive disorder assessments. © 2025 SPIE.

关键词： Electronic health record

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Fire location detection method based on deep learning 4

Fire location detection method based on deep learning

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Tang, Junjie Yang, Yushu Jiang, Tingting School of Information Science and Engineering Chongqing Jiaotong University Chongqing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the enclosed environment of tunnels poses significant challenges to the effective implementation of firefighting strategies in the event of a fire. Due to the complex internal structure and narrow space of tunnels, traditional emergency response methods are often inefficient under such conditions. therefore, it is particularly crucial to quickly and accurately determine the location of a fire, develop the optimal firefighting plan, and continuously monitor the effectiveness of firefighting. Currently, many methods rely on simple deep learning models to locate fire sources, but in complex fire scenarios, these models often exhibit insufficient lightweight and flexibility. To address this issue, we propose a deep learning based fire localization model that can achieve automated fire detection in complex and ever-changing tunnel fire environments. Compared with traditional deep learning methods, this model not only improves accuracy, but also significantly accelerates response speed. To verify the effectiveness of the model, we compared seven existing fire detection models, including backpropagation neural network (BPNN), convolutional neural network (CNN), long short-term memory network (LSTM), CNN-LSTM, bidirectional long short-term memory network (BiLSTM), CNN BiLSTM, and CNN BiGRU. the final results indicate that our proposed method based on CNN BiGRU performs well in fire location detection tasks, with excellent detection performance, significant robustness, and lightweight advantages. © 2025 SPIE.

关键词： Convolutional neural networks

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Identifying key indicators words of suicidal ideation through ensemble voting classifier learning methods 4th

Identifying key indicators words of suicidal ideation throug...

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad National Institute of Technology Patna Bihar Patna India

ISBN: (纸本)9781032911571

Suicide prevention is a pressing global concern, with a significant number of annual deaths related to suicide and numerous attempts reported each year. the rise of social media as a platform for emotional expression underscores the need for early detection of suicidal thoughts. this study introduces an innovative approach utilizing Natural Language Processing (NLP) techniques to analyze behavioral patterns and linguistic cues in social media interactions. the primary objective is to identify key indicator words in social media post i.e. Reddit posts, that may suggest the presence of suicidal ideation, through the application of advanced NLP techniques and variety of machine learning models, including Decision Tree, Random Forest, Bernoulli Naïve Bayes, Gradient Boosting, XGBoost, and an Ensemble voting classifier method that combines Random Forest, Gradient Boosting, and XGBoost. the models demonstrate impressive accuracy in the detection of posts related to suicidal ideation, providing a potential means for early intervention. On the Reddit dataset, these models achieve accuracy scores of 81%, 88%, 84%, 89%, 89%, and 92%. this research aims to identify keywords associated directly or inversely linked to suicidal ideation. the study offers a potent keywords indicator for detecting signs of suicidal thoughts in social media posts, potentially facilitating timely interventions and, ultimately, saving lives to support suicide prevention efforts. © 2025 the Author(s).

关键词： Decision trees

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Construction of Financial Statement Analysis and Prediction Model Based on Deep learning Algorithm 24

Construction of Financial Statement Analysis and Prediction ...

引用

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

作者： Liu, Jingjing Li, Yanwei Yang, Degang Weifang Engineering Vocational College Shandong Qingzhou China Shandong Lei Ao New Energy Co. Ltd. Shandong Weifang China

ISBN: (纸本)9798400709999

In order to understand the construction of financial statement analysis and prediction models based on deep learning algorithms, a research on the construction of financial statement analysis and prediction models based on deep learning algorithms is proposed. this paper first proposes a model for the intelligence of the financial information of enterprises based on deep learning. the next generation method transforms non-time indicators into real-time indicators and divides and evaluates financial data using neural networks with 4 hidden layers. Real financial data was used to evaluate the data and analysis, and the experimental results showed that the intelligent method focused on abnormal financial data based on deep learning is feasible. best and correct. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Deep learning approach for weather classification using pre-trained convolutional neural networks 4

Deep learning approach for weather classification using pre-...

引用

4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Tarwani, Harit Patel, Shivang Goel, Parth Anand Changa388421 India Anand Changa388421 India

machine learning has had significant uses in weather prediction, and is especially useful in areas of agriculture and livestock, and outdoor vision. While various studies have been done on weather forecasting, limited work has yet been undertaken on its classification. Automating weather classification can free and save human resources. While simultaneously enhancing decision-making in the fields discussed. As such, the researchers have implemented transfer learning on different convolutional neural network (CNN) models, like ResNet50V2, EfficientNetB5, and EfficientNetV2S, for classification across 11 fields. the models were fine-tuned to improve performance and trained on a dataset that included diverse conditions like snow, lightning, and haze. Among the considered models, EfficientNetV2S showed the best accuracy (92.35%). this outcome indicates the effective application of machine learning, mainly transfer learning, in the future for weather classification and thus, its subsequent uses. ©2025 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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An eve teasing prohibitor application with SOS services: A systematic review 4th

An eve teasing prohibitor application with SOS services: A s...

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Tomar, Harsh Srivastav, Ishan Fahad, Mohammed Lamba, Sonia Department of CSE ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9781032911571

Although, the constitution of India and IPC special laws pledge to provide gender equality, safety and dignity at all workplaces, educational institutions and other places for everyone in India. But still crimes like Eve –teasing is one of the profound and silent forms of crime, majorly in colleges as it is not reported in the capacity it happening now a days. this indicates a need of an emergency system for providing them help. this SOS application for android and iOS users provides support to students and help in situations of eve-teasing. It signals with the Morse code, short and long sounds, allowing students to transmit alert signals to higher authorities, thus responding swiftly to the location of the incident. It is a model based on SOS Emergency where a student can inform the officials by just giving some signs (Morse code, Short and long sounds). Hence, the officials get alert of eve-teasing along with the location of concerned student so they can reach out to help the student as soon as possible. therefore, we are developing this software that makes use Artificial Intelligence and machine learning to assist students and restrict eve teasing. We are adding features of location tracking, voice recognition and individual login features. Personalized and efficient emergency alert notification system is one of the most efficient tools for personal security and safety. Now a days, the most used and effective emergency alert notification system. © 2025 the Author(s).

关键词： Students

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Enhancing Sustainable Supply Chain Forecasting Using machine learning for Sales Prediction 4

Enhancing Sustainable Supply Chain Forecasting Using Machine...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Rahman Mahin, Md. Parvezur Shahriar, Munem Das, Ritu Rani Roy, Anuradha Reza, Ahmed Wasif Department of Computer Science East West University Dhaka1219 Bangladesh

Managing the supply chain is crucial to success in the competitive business sector. Demand forecasting using sales data is one of the major things in supply chain management because it is directly connected to profit margins, inventory levels, sales, and customer satisfaction. this research tried to provide an innovative approach to sales prediction using advanced machine learning methods to enhance supply chain operations and boost the predictive accuracy of supply chain models after analyzing historical sales data and considering different factors like seasonality, trends, and stock. Various machine learning algorithms were applied, including Linear Regression, Elastic Net Regression, KNN, Random Forest, and the ensemble Voting Regressor. the performance of Random Forest and KNN is very well but the Voting Regressor is better than other models for its strength of multiple algorithms. the Voting Regressor provides the lowest RMSE of 1.54 and the highest R2 of 0.9999. this ensemble method improves sales forecasting accuracy by reducing errors and ensuring computational efficiency. It also provides more reliable tools to manage inventory, prevent overstocks, and minimize holding costs. this research presents the importance of machine learning integration in supply chain management. It shows the Voting Regressor as the most effective approach for demand forecast. Future research could explore the model's application in broader markets, integrating other key factors and deep learning algorithms to refine predictive capabilities later. © 2025 the Authors.

关键词： Adversarial machine learning

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