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Research on end-to-end binocular stereo matching algorithm based on image segmentation 3

Research on end-to-end binocular stereo matching algorithm b...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Tian, Yajuan Yin, Lei Chang, Wenwen Qiang, Xinjian Xi'an Shiyou University School of Electronic Engineering Xi'an China Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9781665459112

Binocular stereo matching will lead to low texture and repeated texture due to uneven illumination and occlusion, and the matching accuracy will be reduced. To solve this problem, the image preprocessing methods of Gaussian filter preprocessing and super-pixel segmentation are adopted, and the processed image area is used as the input of the real-time end-to-end stereo matching algorithm stereonet network to assist the stereo matching algorithm to estimate the parallax. This method can expand the stereo disparity estimation under the low resolution features, effectively reduce the memory consumption, improve the running speed, and improve the recognition of the edge region of the disparity map. The algorithm in this paper is verified by the standard data of the Middlebury test platform. The results show that the algorithm in this paper can achieve a relatively accurate result in a fast time in the ill-conditioned region, which has certain engineering significance for the realization of real-time stereo matching algorithm. © 2022 IEEE.

关键词： Image segmentation

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Rnbformer: A High-Performance Roadside Noise Barriers Recognition Algorithm 24

Rnbformer: A High-Performance Roadside Noise Barriers Recogn...

引用

3rd Asia conference on Algorithms, Computing and Machine learning, CACML 2024

作者： Zhang, Zirui Wu, Jiaheng Huang, Heqiang Chen, Junzhou Hu, Hongyu Zhang, Ronghui Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen Uniyersity Guangdong Guangzhou510275 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun Jilin Changchun130022 China

ISBN: (纸本)9798400716416

Traffic noise pollution is a significant source of urban pollution. Roadside noise barriers (RNBs) serve as a primary solution to urban traffic noise pollution, but achieving precise positioning of RNBs requires on-site inspections and manual labeling, which is time-consuming and labor-intensive. Deploying an RNB recognition algorithm on inspection vehicles equipped with cameras and positioning devices is an efficient approach to establishing a high-precision RNB positioning database. We propose a high-accuracy RNB recognition algorithm, RNBFormer. In the model design, we integrate both convolutional and transformer structures to enhance the model's global feature extraction capability while optimizing the representation of local features. Additionally, we incorporate a channel attention mechanism to provide more discriminative features at the channel level. We trained and tested our model on a publicly available dataset, achieving a classification accuracy of 98.93%. © 2024 ACM.

关键词： Convolution

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Design of multi-function parrot cage control system based on MCU 3

Design of multi-function parrot cage control system based on...

引用

3rd international conference on Control and intelligent Robotics, ICCIR 2023

作者： Zhang, Tingting Xu, Zhichao Zhang, Liying School of Traffic and Electrical Engineering Dalian University of Science and Technology Liaoning Dalian116052 China

ISBN: (纸本)9781510671867

This paper provides a better living and learning environment for training parrots. During the training period, the parrots were trained on a daily basis, provided with food when needed, and regularly disinfected and cleaned up their habitat. Reduce the workload of trainers and improve the efficiency of parrot training. Through the single-chip microcomputer controller, the use of temperature detectors and humidity detectors to monitor the environment, and according to the actual environmental conditions to adjust the temperature and humidity, displays the temperature and humidity of the current environment and time on the OLED. To arrive at feeding time, control the motor for feeding, or use an LED lamp to simulate the expression. Arrive disinfect clean time, control motor to undertake disinfect and clean excrement, or use an LED lamp to simulate to express. To reach the preset time, play voice to teach the parrot. The app monitors the environment on the phone and can directly control feeding, garbage removal and voice teaching functions. Finally, the correctness and reliability of the design are verified by simulation. © 2023 SPIE.

关键词： Control systems

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Deep Convolutional Networks for Flower Species Recognition: A ResNet Study 2

Deep Convolutional Networks for Flower Species Recognition: ...

引用

2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Verma, Goldy Prasad, Chandan Mahajan, Shubham Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Pimpri-Chinchwad University Computer Science & Engineering Plot No. 44 49 50 Mohitewadi Rd Mohitewadi Sate Tal- Maval Maharashtra Pune412106 India Deemed to Be University Department Of Computer Science Christ Delhi-NCR India

ISBN: (纸本)9798350364699

Research on deep learning approaches of flower classification has been mostly focused on key implications for environmental monitoring, botany, and horticulture. Usually labour-intensive and prone to inaccuracies, conventional methods of floral identification rely largely on professional knowledge and manual judgment. Aiming high accuracy in identifying between five different flower classes: Lily, Lotus, Orchid, Sunflower, and Tulip, this work presents a solution using a revised fine-tuned ResNet model. Using a dataset obtained from Kaggle encompassing varied photos of these flowers, the model goes through a rigorous training procedure by means of data augmentation techniques including rotation, flipping, and scaling to boost its resilience and generalizing capacity. By splitting the dataset into training, validation, and test sets, stratified sampling ensures fair representation over all classes. Performance evaluation is done with accuracy, precision, recall, and F1-score;error analysis provides insight of misclassification tendencies. The improved fine-tuned ResNet model reveals the outstanding high-precision floral recognition ability of the model and achieves an incredible accuracy of 95%, thereby differentiating the flower classes. This work presents a significant response for exact and rapid floral classification and shows how well deep learning changes automated plant identification. © 2024 IEEE.

关键词： Deep learning

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ICU Mortality Prediction Using Long Short-Term Memory Networks 1

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23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Mili, Manel Kerkeni, Asma Ben Abdallah, Asma Bedoui, Mohamed Hedi Univ Monastir Fac Med Monastir Tunisia Univ Monastir Higher Inst Comp Sci & Math Monastir Tunisia Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia

ISBN: (数字)9783031217531

ISBN: (纸本)9783031217524;9783031217531

Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.

关键词： Electronic health record Multivariate time-series data MIMIC-III

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Comparative Study of Machine learning Techniques on Chronic Kidney Disease Prediction 3

Comparative Study of Machine Learning Techniques on Chronic ...

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

作者： Panigrahi, Soumyarashmi Adhikary, Dibya Ranjan Das Pattanayak, Binod Kumar Mohanty, Bibhuprasad Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Siksha 'o' Anusandhan Deemed to Be University Department of Computer Science & Engineering Bhubaneswar India Siksha 'o' Anusandhan Deemed to Be University Department of Electronics & Communication Engineering Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

A malfunctioning kidney can cause the increase of waste in the blood, adversely affecting various bodily processes and systems. ML techniques can assist in classifying patients as having chronic kidney disease (CKD) or not. Although previous researchers have conducted several studies, they have often neglected important steps like data preprocessing and feature engineering. In this paper, we have addressed these gaps by implementing various data preprocessing and feature engineering techniques such as Principal Component Analysis (PCA) on the CKD dataset. By carefully selecting features from our dataset, we identified the top-performing classification models which are RFC, SVC (Polynomial Kernel), XG Boost Classifier (XGBC) and Gradient Boost Classifier (GBC) gave the best performance in terms of accuracy. To evaluate the effectiveness of these models, we have utilized various performance metrics. The results obtained have significant contribution to the understanding and management of CKD, ultimately helping in the early detection and treatment of this widespread and critical health issue. © 2023 IEEE.

关键词： Principal component analysis

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Posture Correction Device Based on IMU Sensors and CNN 3rd

Posture Correction Device Based on IMU Sensors and CNN

引用

3rd international conference on Innovation in engineering, ICIE 2024

作者： Ionica, Darius Ioan, Gota Dan Stan, Ovidiu Technical University of Cluj-Napoca Cluj-Napoca400027 Romania

ISBN: (纸本)9783031615740

In our daily lives, good posture is important to prevent complications and promote good health. This paper introduces an innovative approach that integrates wearable technology with Convolutional Neural Networks (CNNs) to address the general issue of body posture. The proposed system comprises a lightweight wearable device embedded with sensors, continuously monitoring and capturing real-time posture data. Leveraging deep learning algorithms, a powerful CNN processes this data to accurately classify and identify various posture patterns. Unlike traditional methods, our approach offers personalized and immediate feedback through an intuitive mobile app. Users receive real-time posture assessments, visualizations, and corrective recommendations, empowering them to proactively improve their posture habits. This not only enhances posture correction but also fosters a sustainable approach to musculoskeletal health. The proposed method contributes to a more proactive approach to preventive healthcare, significantly improving musculoskeletal health and enhancing the quality of life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Prediction of Irregular Bangladesh-EU Migration Trends Using Machine learning Techniques 3

Prediction of Irregular Bangladesh-EU Migration Trends Using...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md. Shariful Rahim, Md Abdur Podder, Nitun Kumar Hossain, Md. Najmul Hossain, Md Imran Pabna University of Scence and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Electrical Electronic and Communication Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Inormation and Communication Engineering Pabna Bangladesh

ISBN: (纸本)9798350388282

Remittances are important economic contributors to a countrya's economy. It has increased national savings, lowered foreign exchange limits, and helped balance payments and development budgets. Migrant workers are the main source of these remittance revenues. Many workers worldwide leave their homes and move to different parts of the world for overseas employment. These workers are authorized and have documentation of their country of origin and final destination. Besides, many workers have migrated illegally globally, particularly to European Union (EU) countries via marine routes. However, illegal immigration leads to a loss of national revenue due to non-payment of taxes and social security contributions and problems with living circumstances and legal issues. Therefore, this paper aims to present a time series forecast of irregular migrants in EU countries from Bangladesh over the next five years. We used five methods to analyze this forecasting: ARIMA (an autoregressive integrated moving average), XGBoost Series, Decision Tree, Feed Forward Neural Network, and CatBoost Regressor. In addition, we used data from the European Border and Coast Guard Agency to perform the time series forecast. A field intervention is carried out to assess the gender, age, education, socioeconomic level, and skill profile of migrant workers. Moreover, a few case studies of migrant workers and a survey were conducted to investigate the underlying causes of irregular migration. Finally, this study explains the underlying cause of irregular migration and its repercussions. It can provide some ideas for decreasing the loss of human life and property by reducing irregular migration to help take essential actions in the home and destination countries. © 2024 IEEE.

关键词： Forecasting

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A Novel Text Resemblance Index Method for Reference-based Fact-checking 3

A Novel Text Resemblance Index Method for Reference-based Fa...

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3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Barve, Yashoda Saini, Jatinderkumar R. Suryadatta College of Management Information Research and Technology Pune India Pune India

ISBN: (纸本)9781665462006

As in electronic era, data is transmitted via social network and web, people are harmed by false and deceptive content, such as misinformation. A fact-checker will examine specific indicators of falsity to determine the veracity of a piece of content. To perform automated fact-checking, retrieving information from the web, cleaning the contents, and using machine learning techniques are required. Prior research has focused on linguistic and literary characteristics as well as approaches based on similarity measurements. All the details and intricacies of the problem are not adequately covered by any of the papers referenced in the resource. We advanced a Text Resemblance Index (TRI) algorithm with a veracity-scanning model for the purpose of automating fact-checking URLs in the healthcare industry. To execute fact-checking in a journalistic manner, a set of innovative content, domain-specific, and sentimental scores based on polarity are applied. The Text Resemblance Score (TRS), a newly generated feature, outscored comparable properties with an accuracy of 88.27%, according to a thorough analysis of the proposed approach in contrast to traditional distance measures. In comparison to the Jaccard distance-based algorithm accuracy of 75.56% when leveraging an algorithm, TRI showed enhanced accuracy of 90%. Compared to the prior study, the TRS feature-based technique produced an accuracy improvement of 2.40%. © 2022 IEEE.

关键词： learning systems

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Deep learning Methods For COVID-19 Mitigation: Applications, Challenges and Future Implications 3

Deep Learning Methods For COVID-19 Mitigation: Applications,...

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3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Shah, Siddhant Bikram Bhandari, Aashish Shambharkar, Prashant Giridhar Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665462006

The breakout of the COVID-19 infection caused a worldwide pandemic in recent years. Traditional healthcare measures and infrastructure are unable to properly manage the detection, prevention and treatment of the infection. Since the onset of the pandemic, researchers have tried to implement various deep learning approaches to counter COVID-19 with much success. Novel architectures and new ideas are being developed to this day. Motivated by this, we have reviewed the different ways deep learning can be applied in real-world COVID-19 problems. We present the challenges these implementations face. Finally, we discuss the future directions that can be taken to improve upon these DL methods to control the COVID-19 pandemic as well as future pandemics, which will result in a healthier and safer environment. © 2022 IEEE.

关键词： COVID-19

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