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检索条件"任意字段=International Conference on Machine Learning and Computing"

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Optimized Doctor Recommendation System using Supervised machine learning 23

Optimized Doctor Recommendation System using Supervised Mach...

引用

24th international conference on Distributed computing and Networking (ICDCN)

作者： Singh, Himanshu Singh, Moirangthem Biken Sharma, Ranju Gat, Jayesh Agrawal, Ayush Kumar Pratap, Ajay Banaras Hindu Univ Indian Inst Technol Dept Comp Sci & Engn Varanasi Uttar Pradesh India

ISBN: (纸本)9781450397964

In the past decade, we have seen many patients and healthcare problems. Due to this, patients find difficulty choosing doctors according to their disease. Several machine learning (ML) based techniques already exist to predict doctors based on patient's health conditions. However, it is essential to accurately recommend doctors to patients with low errors based on patients' health conditions. Therefore, we propose a method that assigns quantitative importance (weight) to each feature using an ML technique. Moreover, we offer a framework to recommend doctors based on the similarity score and doctor's skill score, which utilizes weight prediction to enhance operational efficiency. Additionally, on real-world datasets, the effectiveness of the proposed framework is demonstrated empirically by lowering the average loss by roughly 34% and 3% as compared to Convolutional Neural Network (CNN) and Support Vector machine (SVM), respectively. The outcome demonstrates that the algorithm can efficiently recommend doctors to patients compared to state-of-the-art techniques. This analysis technique aid patients in opting for the right doctor.

关键词： Healthcare machine learning Doctor Prediction error Weights

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Exoplanet Detection Using Feature Engineering with Ensemble learning 3

Exoplanet Detection Using Feature Engineering with Ensemble ...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Sai Rakesh, G. Venkata Bhuvana Chandrika, M. Jahnavi Rami Reddy, Ch. Venkata Suneetha, Muvva School of Cse VIT-AP University Amaravati India Amaravati India

ISBN: (纸本)9798350322842

In this work, a novel technique for detecting extra-terrestrial bodies using the transit method, with the aim of improving traditional algorithmic strategies in astronomy through machine learning algorithms. The potential for this approach to revolutionize the field of exoplanet detection and unlock new insights into the universe is highlighted. An ensemble learning approach using maj ority voting technique is employed to detect Exoplanets. In this work, we have used six machine learning models Random Forest, Decision Tree, Support Vector Classifier, K-Nearest Neighbor and Multi- Layer Perceptron, for Exoplanet Detection. Based on accuracy, we combined Support vector Classifier, K-Nearest Neighbor, Random Forest and Multi-Layer Perceptron using Majority Voting Technique to predict whether the planet is exoplanet or not. Majority Voting Technique (MVT) of machine learning models has shown higher significance when compared to the individual machine learning model in terms of accuracy. The accuracy of different individual models is ranging from 92.59% to 99.88% and MVT produced 99.97% accuracy. © 2023 IEEE.

关键词： Decision trees

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Ensemble learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

引用

5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. Therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. This method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. This research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： machine learning feature fusion kill well overflow

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Economic Denial of Sustainability Attack Detection Using machine learning 26

Economic Denial of Sustainability Attack Detection Using Mac...

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26th international conference on Computer and Information Technology, ICCIT 2023

作者： Hossain, Md. Sharafat Islam, Md. Saiful Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka Bangladesh

ISBN: (纸本)9798350359015

Cloud computing stands as one of the most pervasive technological innovations, providing computing services, including IT infrastructure, over the internet. This technology alone saves many small and medium-sized IT companies from the significant expense of building their IT infrastructure at the beginning of their business journey. Cloud computing offers various user-friendly features, including scalability and flexibility, with a pay-as-you-go model. However, these features come with some drawbacks along with their benefits. Among the challenges posed by the flexibility and pay-as-you-go attributes of cloud computing, one of the most detrimental threats is Economic Denial of Sustainability (EDoS), leading to significant financial losses for consumers of cloud services. Consequently, an effective and efficient solution is urgently needed to fully realize the benefits of cloud computing. In this research, we aim to address the EDoS attack using machine learning-based methods. Our research is conducted on the UNSW-NB15 dataset, and the result demonstrates that our optimized LightGBM model with chosen hyperparameters surpasses existing models with significant improvements in attack detection, achieving a recall score of 99.21%. © 2023 IEEE.

关键词： Cloud computing Deep learning Economic Denial of Sustainability (EDoS) machine learning

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Research on Digital Transmission Line Path Planning Based on Hierarchical Reinforcement learning 3

Research on Digital Transmission Line Path Planning Based on...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Li, Hongjiang Liu, Zaochen Wang, Dijin Yunnan Power Grid Co. Ltd. Kunming650100 China Technology Co. Ltd Kunming650032 China

ISBN: (纸本)9798350319125

With the development of smart grid, the path planning of digital transmission lines has become an important research direction. The path planning of transmission line is to determine the direction of transmission line reasonably according to the starting point and the end point of transmission line and various constraints along the way (such as terrain, environment, etc.). Hierarchical reinforcement learning is a new machine learning method, which divides the learning process into several levels, and each level can learn independently, thus improving the learning efficiency and effect. This paper will introduce the application of hierarchical reinforcement learning method in digital transmission line path planning. © 2023 IEEE.

关键词： digital transmission lines hierarchical reinforcement learning hierarchical reinforcement learning theory Path

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Reliable ML-Based Image Processing and Compression for an Onboard Imaging Pipeline 15

Reliable ML-Based Image Processing and Compression for an On...

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15th Space computing conference

作者： Garrett, Tyler Wildenstein, Diego Wang, Eileen Gretok, Evan George, Alan Univ Pittsburgh NSF Ctr Space High Performance & Resilient Comp S Pittsburgh PA 15260 USA

ISBN: (纸本)9798350384543;9798350384536

Sensing and onboard-processing capabilities of next-generation spacecraft continue to evolve. Enabled by advances in avionic systems, large amounts of data can be collected and stored on orbit. Nevertheless, loss of signal, communication delays, and limited downlink rates remain a bottleneck for delivering data to ground stations or between satellites. This research investigates a multistage image-processing pipeline and demonstrates rapid collection, detection, and transmission of data using the Space Test Program - Houston 7 - Configurable and Autonomous Sensor Processing Research (STP-H7-CASPR) experiment aboard the international Space Station as a case study. machine-learning (ML) models are leveraged to perform intelligent processing and compression of data prior to downlink to maximize available bandwidth. Moreover, to ensure accuracy and preserve data integrity, a fault-tolerant ML framework is employed to increase pipeline reliability. This pipeline fuses the fault-tolerant Resilient TensorFlow framework with ML-based tile classification and the CNNJPEG compression algorithm. This research shows that the imaging pipeline is able to alleviate the impact of limited communication bandwidth by using reliable, autonomous data processing and compression techniques to achieve reduced transfer sizes of essential data. The results highlight the benefits provided by resilient classification and compression including minimized storage use and reduced downlink time. Furthermore, the findings of this research are used to assess the feasibility of such a system for future space missions. The combination of these approaches enables the system to achieve up to a 98.67% reduction in data size and downlink time as well as the capacity to capture imagery up to 75.19x longer time period for a given storage size, respectively, while maintaining reconstruction quality and data integrity.

关键词： Data Compression Fault-Tolerant computing Resilient computing Image Processing Image Classification machine learning Deep learning Onboard Processing

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GraphGPT: A Self Supervised-learning for Intuitive, Logical, and Visual Education

GraphGPT: A Self Supervised-Learning for Intuitive, Logical,...

引用

2025 international conference on computing, Networking and Communications, ICNC 2025

作者： Wang, Nicholas X. Basis Independent Silicon Valley San Jose United States

ISBN: (纸本)9798331520960

In this paper, we introduce GraphGPT, a novel machine-learning approach designed for intuitive, logical, and visual education. GraphGPT is inspired by the observation that scientific graphs help learners visualize concepts, while causal graphs enhance the logic of responses. By combining the masked prediction capabilities of GPT with transform prediction using causal graphs, we can improve explanation accuracy. GraphGPT generates logical steps alongside diagrams, significantly enhancing intuition and visual presentation compared to ChatGPT. Experimental results show that GraphGPT outperforms ChatGPT by 25% in accuracy for multiple-choice questions. Furthermore, a vast majority of participants in the subjective tests confirmed that GraphGPT surpasses ChatGPT in all four evaluation categories. Many highlighted the diagrams and graphical explanations as key factors, giving GraphGPT a notable intuitive advantage. Our work is available at https: //***/nicholasxwang/GraphGPT. © 2025 IEEE.

关键词： Self-supervised learning

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Emotion Detection in Multimodal Communication through Audio-Visual Gesture Analysis 10

Emotion Detection in Multimodal Communication through Audio-...

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Minu, R., I Aamuktha, Divya Sai P. Ishita, B. Anubhav, P. Kumar, Tanishq Jayaram, Ramaprabha Karjee, Jyotirmoy SRM Inst Sci & Technol Dept Comp Technol Kattankulathur India Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

Emotions play a pivotal role in human communication, influencing our interactions and relationships profoundly. Understanding and accurately detecting emotions in multimodal communication is essential for various applications, from human-computer interaction to mental health assessment. This research paper explores a novel approach to emotion detection by leveraging both audio and video modalities in the context of gesture recognition. The requirement for a multimodal approach in our work stems from the limitations of unimodal emotion detection systems. While individual modalities like audio or video have shown promise in emotion recognition, they often fail to capture the full spectrum of emotional cues present in human communication. By integrating these modalities, we aim to achieve more robust and comprehensive emotion recognition results. We present a comprehensive analysis of our proposed system's architecture, experimental methodology, and results, demonstrating its potential to significantly enhance the accuracy and reliability of emotion detection in real-world scenarios. The proposed framework achieves an overall accuracy of 87%, showcasing its effectiveness in addressing the challenge of improving emotion recognition accuracy in multimodal communication.

关键词： machine learning Multimodal fusion NLP Computer Vision

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Analysis of the Mechanism of Generative Adversarial Networks learning Based on Gray Image 3

Analysis of the Mechanism of Generative Adversarial Networks...

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3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Geng, Chaozhi 21st Century International School Beijing100080 China

ISBN: (纸本)9781510661301

machine learning has a crucial role in people's lives. machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results. © 2023 SPIE.

关键词： Generative adversarial networks

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Dual Part Siamese Attention Convolution Network for Change detection in Bi-temporal High Resolution Remote Sensing Images 24

Dual Part Siamese Attention Convolution Network for Change d...

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16th international conference on machine learning and computing (ICMLC)

作者： Li Zongpu Bao Tengfei Xiao Zhiyun Inner Mongolia Univ Technol Hohhot Peoples R China Shanghai Jiao Tong Univ Inner Mongolia Univ Technol Shanghai Peoples R China

ISBN: (纸本)9798400709234

The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. However, due to the noise interference and redundancy in remote sensing multispectral images, it is valuable to transform the available spectral channels into a suitable feature space to alleviate noise and reduce redundancy. In this paper, we propose a supervised change detection method for optical aerial images based on deep Siam convolution network, which is mainly composed of two branch network, heuristic feature aggregation module and feature attention module. This method achieves better performance in experiments on remote sensing aerial public datasets. We show that our network can extract feature from two images at a same time, and depend on feature fusion strategy together with attention mechanism to further enrich the difference information. Subsequently, a decoder structure of semantic segmentation is developed to harvest binary change detection result. Experimental results show that our algorithm achieves a comprehensive F1 score of 83.9% on different data sets, which is superior to the traditional algorithm and other deep learning networks, and has a certain degree of effectiveness and robustness.

关键词： Change detection deep learning Siamese network feature fusion attention module

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