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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"

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Atrial Repolarization Wave Features for Enhanced Heart Rhythm Classification Using Extra-Trees Model 4

Atrial Repolarization Wave Features for Enhanced Heart Rhyth...

引用

4th international conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Bhardwaj, Arya Bala Chakravarthy, N. Kumar, R. Pradeep Pal, Kunal Sivaraman, J. National Institute of Technology Rourkela Department of Biotechnology and Medical Engineering Odisha India Institute of Cardiac Care Miot International Chennai India

ISBN: (纸本)9798350389128

Atrial repolarization (Ta wave) characteristics has not been well studied for atrial arrhythmia diagnosis unlike other ECG waves, as it is obscured by QRS complex. Hence, this study proposes a new method to interpolate the Ta wave and aims to introduce Ta wave features as a predictor of atrial arrhythmias. ECGs from ten Sinus Rhythm (SR), ten Sinus Tachycardia (ST) were recorded using EDAN SE-1010 ECG system, and ten Atrial Tachycardia (AT) ECGs were extracted from the public database. Lead-II 10-second ECGs were pre-processed in MATLAB R2021a version, and spline interpolation model was employed to visualize the complete Ta wave within QRS complex, which was otherwise hidden. Further, Ta wave feature sets were prepared separately and analyzed for all three rhythms. through statistically significant features, machine learning (ML) classifiers were implemented for optimal model selection. Ta peak duration and amplitude, Ta duration/amplitude and Ta area are the most potent feature in heart rhythm classification. the extra-trees model possessed the highest accuracy values of 94.31 % with the computational time of 0.64 seconds using Ta wave features. the findings of this study indicate that Ta wave features have the potential to assist clinicians in improved detection of atrial arrhythmias. © 2024 IEEE.

关键词： Diseases

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application and Prospect of Artificial Intelligence Image Analysis Technology in Natural Resources Survey 1

引用

4th international conference on Artificial Intelligence in China, AIC 2022

作者： Wang, Yuehong Luo, Hao Tianjin Natural Resources Investigation and Registration Center Tianjin300201 China

ISBN: (数字)9789819912568

ISBN: (纸本)9789819912551

With the rapid development of artificial intelligence, the application of computer vision and machine learning in the field of photogrammetry and remote sensing continues to enrich. Cognitive reasoning based on spatiotemporal big data has gradually deepened, which greatly promotes the development of remote sensing and surveying and mapping geographic information technology. the natural resource survey and monitoring technology system presents the characteristics of intelligence, spatialization, ubiquity and multi-source, which promotes the development of natural resource survey and monitoring business. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural resources

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Self-Supervised learning for Point Clouds through Multi-crop Mutual Prediction 4

Self-Supervised Learning for Point Clouds through Multi-crop...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Zeng, Changyu Wang, Wei Xiao, Jimin Nguyen, Anh Yue, Yutao Institute of Deep Perception Technology JITRI Wuxi China Xi'An Jiaotong-Liverpool University School of Advanced Technology Suzhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350324303

As the demand for high-level autonomous driving continues to grow, efficient processing of large amount of point cloud data from LiDAR sensors has become a severe issue. Despite its great success, it is extremely difficult, if not impossible, to apply supervised learning in many real-world applications with natural scenes lacking annotations. In addition, removing label dependency and improving algorithm generalization ability are the current key research challenges in 3D deep learning. We propose a crop-based self-supervised learning framework for point clouds called Multi-Crop Mutual Prediction (MCMP). By cropping the original point clouds into different number of multiscale patches and making patches from the same object as close as possible through dynamic updates of online prototypes, the encoder is well able to maintain consistency between both local and global features. MCMP framework is model-agnostic so it is convenient to integrate it into encoders of any architectures. We pre-train three commonly used point cloud feature extraction networks on a single object-level dataset MondelNet40 and evaluate MCMP on three downstream tasks, namely, object classification, partial segmentation and semantic segmentation. Experimental results show that MCMP outperforms the previous crop-based self-supervised approaches in the object classification and semantic segmentation tasks and produces comparable results with the supervised benchmark methods. © 2023 IEEE.

关键词： Semantic Segmentation

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An Efficient Real-Time NIDS Using machine learning Methods 4th

An Efficient Real-Time NIDS Using Machine Learning Methods

引用

4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Goud, Konda Srikar Shivani, M. Reddy, B. V. S. Selvi Shravyasree, Ch. Reddy, J. Shreeya Department of Information Technology BVRIT Hyderabad College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9783031488870

Recent developments in network technology and related services have caused a significant rise in data traffic. However, there has also been a massive rise in the negative consequences of cyber-attacks. Many new types of network attacks are emerging. As a result, designing a robust Intrusion detection system (IDS) has become essential. this paper presents a framework for designing an efficient IDS to enhance detection accuracy and reduce false positives on real-time data. this research used the CIC-IDS 2017 dataset to train machine learning models such as Logistic Regression, K Nearest Neighbor, Gaussian Naive Bayes, and Random Forest. machine learning models often perform well on benchmark datasets but may encounter challenges when applied to real-time traffic scenarios. So, we created a Real-time dataset and tested it on the trained models. In the evaluation, the Random Forest classifier outperformed all other models and achieved an accuracy of 99.99%. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Botnet

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Automatic Subjective Answer Grading Software Using machine learning 7

Automatic Subjective Answer Grading Software Using Machine L...

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7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Kothari, Rishabh Rangwala, Burhanuddin Patel, Kush K. J. Somaiya College of Engineering Computer Engineering Mumbai India

ISBN: (纸本)9798350397284

One of the major challenges during online examinations is the assessment of answers, particularly of the subjective type. Subjective answers test a student's ability to retain information and express it in natural language. While objective questions have a correct fixed answer, subjective questions can have multiple correct answers. these answers can convey the same information while using a completely different language and grammatical syntax. this makes it difficult to automate the process of grading subjective questions and requires a lot of manual work hours. this study intends to automate the process of grading subjective questions using machine learning (ML) and Natural Language Processing (NLP). the study has compared the subjective answer with an ideal answer that is provided by the authority that creates the question. Based on the similarity between the two answers, a score is generated which can be mapped to an appropriate grade. the authors have provided a web application made using the Django framework for people to give online examinations and be automatically graded in near real-time. No machine learning model can be 100% accurate, so there is a functionality for admins to edit the grades. © 2023 IEEE.

关键词： Grading

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Xception Based Transfer learning Combined With Residual Block for Facial Emotion Recognition 4

Xception Based Transfer Learning Combined With Residual Bloc...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Yoshino, Erio Hendry, Antoluis Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Human emotion plays a crucial role in communication, making its recognition an essential task. In recent years, transfer learning and residual blocks have emerged as effective methods for improving image classification performance. this paper aims to develop an emotion recognition model by leveraging the Xception architecture and incorporating residual blocks from ResNet. To achieve this, we made modifications to both the model architecture and the residual block architecture. Additionally, we utilized two different validation datasets. the second validation dataset involved image augmentation techniques, introducing additional variations and noises. Our study demonstrates the benefits of integrating residual blocks into the Xception-based model, achieving a validation accuracy of 65.78% on the first dataset and 60.56% on the second, surpassing the accuracy of the standard Xception model. However, during experimentation, we observed that all tested models exhibited overfitting, with validation accuracy significantly lower than training accuracy, partly attributed to the limited number of images in the datasets. As for the future works, we intend to address dataset imbalance through image augmentation and implement dropout layers to prevent overfitting. © 2023 IEEE.

关键词： Emotion Recognition

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An Intelligent Method for Predicting Skin Disease Using OM-Extreme learning machine 4

An Intelligent Method for Predicting Skin Disease Using OM-E...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Suja, G.P. Akshaya, M. Salim, Abu Sarfaraz, Mohd Kathiravan, Mathur Nadarajan Singh Pundir, Aditya Kumar Muslim Arts College Research Centre of Computer Science Tamilnadu Kanyakumari India Department of Cse Tamilnadu Coimbatore India College of Computer Science and Information Technology Jazan University Department of Computer Science Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Department of Information Technology and Security Jazan Saudi Arabia Dr N. G. P Arts and Science College Department of Biotechnology Tamil Nadu Coimbatore India Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350300888

In terms of prevalence, skin disorders rank towards the top. Various organisms, including fungi, bacteria, allergens, viruses, and so on, can trigger skin illnesses. Improved speed and accuracy in the diagnosis of skin diseases has resulted from the development of lasers and Photonics-based medical technology. However, there is now a severe lack of affordable options for such diagnostics. therefore, at the outset, image processing techniques aid in constructing an automated screening system for dermatology. To aid in the categorization of skin disorders, feature extraction is essential. Several methods exist where computer vision can aid in the diagnosis of skin disorders. Skin infections are frequent in Saudi Arabia because of the country's scorching, desert climate. this proposed method adds to the literature on the early diagnosis of skin diseases. To help diagnose skin conditions, to recommended using image processing. this technique uses digital images of the affected skin to determine the disease kind through the application of image analysis. the method to offer straightforward, quick, and can be done with just a camera and a ***, feature extraction for segmentation, and model performance evaluation make up the suggested method. Preprocessing makes use of a histogram and an RGB to Grayscale conversion. Segmentation is performed with the help of adaptive thresholding. the GLCM, IQA, and complete reference methods are all utilized for feature extraction. Finally, OM-ELM is utilized to assess the model's performance. Comparing the suggested method to ELM and AE-ELM, it fares quite well. © 2023 IEEE.

关键词： Mean square error

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2023 IEEE 17th international conference on the Experience of Designing and application of CAD Systems, CADSM 2023 - proceedings

2023 IEEE 17th International Conference on the Experience of...

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17th IEEE international conference on the Experience of Designing and application of CAD Systems, CADSM 2023

ISBN: (纸本)9798350310856

the proceedings contain 45 papers. the topics discussed include: designing the topology of microelectromechanical systems by machine learning methods;development of a remote-control system for a mobile vibration-driven robot;models of recurrent distributions statistical averaging for electronic components with fluctuations;inverse-dynamic neural controller simulation;a method of creating virtual pixels in matrix;the computer modeling of the thermal agent hydrodynamics through the alcohol distillery stillage stationary layer;method for estimating the topological structure of self-organized networks;a new hybrid method for predicting recommendations for collaborative recommender systems;software-defined transmitter to support automatic dependent surveillance-broadcast;deductive matrix synthesis for fault simulation;investigation of response from the micro objects of complex shape irradiated by acoustic wave;outdoor positioning for industrial workplace;a software complex for researching algorithms for working with graphs;and hydrogen adsorption in porous silicon: simulation and control method.

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An Autonomous Player Agent for Game Balance Insight on an Educational Video Game 4

An Autonomous Player Agent for Game Balance Insight on an Ed...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Istamar, Ahsan Imam Terence Philip, Samuel Hidayaturrahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

this paper explores the use of Reinforcement learning (RL) to address the challenge of game balance in Educational Video Games (EVG). We adapt Eco City, a city management simulator EVG where the player enacts city policies to maintain the city for as long as possible, as the environment for study. Our RL agent, based on a Deep Q-Network (DQN), is trained to maximize its duration of survival. We analyze the behavior patterns of the agent during both training and evaluation to gain insights into the game dynamics and player strategies. the results show that the agent outperforms random play and exhibits a dominant strategy that effectively ignores half of the possible actions in the game. the analysis of the agent's behavior provides valuable information for game balancing efforts, such as adjusting policy effects and introducing new policies. We demonstrate the potential and the implications of our RL approach to augment the game balance process for developers, particularly in the educational gaming domain. © 2023 IEEE.

关键词： Reinforcement learning

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A Load-Balancing Strategy Based on Multi-Task learning in a Distributed Training Environment 4

A Load-Balancing Strategy Based on Multi-Task Learning in a ...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Huang, Yinqi Zhou, Wenjiang Li, Liang Lyu, Ting Xu, Haitao Univerisity of Science and Technology Beijing School of Computer and Communication Engineering Beijing China China Academy of Information and Communications Technology Beijing China Shunde Innovation School Univerisity of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350308082

With the development of machine learning and big data technologies, distributed training has become an important way to improve computational efficiency. However, in the distributed training environment, the performance difference between workers and the interference of unrelated tasks may lead to the uneven load of the system and cause the 'straggler phenomenon'. therefore, how to solve the straggler phenomenon and achieve load balancing of the system is a pressing problem of distributed training. To address the shortcomings of the existing parallel computing model DSSP, this paper proposes a load-balancing strategy for DDP, which effectively reduces the load difference by adjusting the batch size and data size of workers in the training process. then, to explore the correlation between data size in DDP and synchronization threshold in DSSP in dynamic adjustment, we perform multi-task learning for the two dynamic adjustment strategies and integrate the proposed Joint Multi-Task Prediction scheme on DSSP to implement a new parallel computing model ESP. extensive experiments the results show that ESP can not only guarantee the model accuracy but also effectively improve the training speed in distributed training. © 2023 IEEE.

关键词： Computational efficiency

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