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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Xorbits: Automating Operator Tiling for Distributed data science 40

Xorbits: Automating Operator Tiling for Distributed Data Sci...

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40th IEEE international conference on data Engineering, ICDE 2024

作者： Lu, Weizheng He, Kaisheng Qin, Xuye Li, Chengjie Wang, Zhong Yuan, Tao Liao, Xia Zhang, Feng Chen, Yueguo Du, Xiaoyong Renmin University of China China Xorbits Inc China Communications Information Technology Group China Tsinghua University China

ISBN: (纸本)9798350317152

data science pipelines commonly utilize dataframe and array operations for tasks such as data preprocessing, analysis, and machine learning. The most popular tools for these tasks are pandas and NumPy. However, these tools are limited to executing on a single node, making them unsuitable for processing large-scale data. Several systems have attempted to distribute data science applications to clusters while maintaining interfaces similar to single-node libraries, enabling data scientists to scale their workloads without significant effort. However, existing systems often struggle with processing large datasets due to Out-of-Memory (OOM) problems caused by poor data partitioning. To overcome these challenges, we develop Xorbits, a high-performance, scalable data science framework specifically designed to distribute data science workloads across clusters while retaining familiar APIs. The key differentiator of Xorbits is its ability to dynamically switch between graph construction and graph execution. Xorbits has been successfully deployed in production environments with up to 5k CPU cores. Its applications span various domains, including user behavior analysis and recommendation systems in the e-commerce sector, as well as credit assessment and risk management in the finance industry. Users can easily scale their data science workloads by simply changing the import line of their pandas and NumPy code. Our experiments demonstrate that Xorbits can effectively process very large datasets without encountering OOM or data-skewing problems. Over the fastest state-of-the-art solutions, Xorbits achieves an impressive 2.66 × speedup on average. In terms of API coverage, Xorbits attains a compatibility rate of 96.7%, surpassing the fastest framework by an impressive margin of 60 percentage points. Xorbits is available at https://***/xorbitsai/xorbits. © 2024 IEEE.

关键词： Large datasets

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Advanced Pedestrian Detection in Low Visibility Scenarios Using UltraLytics YOLOv8 and Kalman Filtering 5

Advanced Pedestrian Detection in Low Visibility Scenarios Us...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pattem, Sanjay Kumar Reddy Rajagopal, Manjula Nirujogi, Nandan SRM Institute of Science and Technology Computing Technologies Chennai India SRM Institute of Science and Technology Computing Technologies Faculty of Engineering and Technology Chennai India

ISBN: (纸本)9798331530013

Nighttime detection of pedestrians brings important problems such as low light conditions, limitations of sensors, and environmental noise. To tackle this problem, in this study, we propose a novel visual-radar data fusion to enhance a pedestrian detection system using the UltraLytics YOLOv8 deep learning model together with Kalman Filtering technique. The system is based on a Low-Light Vision Infrared Paired (LLVIP) dataset that includes infrared vision data to attenuate the performance decrement of a pedestrian detector in the dark. The combination of infrared cameras and millimeter-wave radar achieves a detection rate of 97.6%, in which UltraLytics YOLOv8 exhibits excellent object recognition performance by generating accurate spatial & category information from the infrared image;complementary distance and velocity data are obtained by millimeter-wave radar. It brings Kalman Filtering to track the radar-detected pedestrian targets, making the system more robust by predicting target and noise reduction. It was capable of fusing infrared and radar data at decision-level to make the system provide very precise position accurately in even nighttime situations. The experimental results show that the proposed model has improved capability of detection accuracy, robustness, and response time in comparison with the single-sensor system, which is suitable for practical autonomous driving scenarios. © 2024 IEEE.

关键词： Kalman filters

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Occluded Image Reconstruction under Single Sample Condition Using Diffusion Models Aiming Facial Recognition 9

Occluded Image Reconstruction under Single Sample Condition ...

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9th international conference on Image, Vision and computing, ICIVC 2024

作者： da Silva Alvez, Uendel Diego Leher, Quefren Oliveira Bezerra, Emili Silva Mafalda, Salomão Paixão, Thuanne Alvarez, Ana Beatriz University of Acre UFAC Rio Branco Brazil

ISBN: (纸本)9798350385991

In view of the indisputable power of the inpainting technique for filling out visual information in images, this paper presents an approach for reconstructing occluded images from a single sample using the inpainting technique based on a diffusion model for facial recognition. The experiments were carried out in five different scenarios, each with an assigned occlusion percentage ranging from 35% to 50%. The face recognition is based on the VGG Face Descriptor and its behavior is evaluated using cosine similarity and Euclidean distance values. The experimental results indicate that the approach can effectively reconstruct the information in a facial image for subsequent recognition. For all the scenarios tested, the cosine similarity and Euclidean distance values of the reconstructed images are below the established thresholds, 0.40 and 95, respectively, indicating effectiveness in the process. © 2024 IEEE.

关键词： Contrastive learning

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An Action Recognition Algorithm Based on Two-Stream Deep learning for Metaverse Applications 20

An Action Recognition Algorithm Based on Two-Stream Deep Lea...

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20th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Liu, Jiayue Mao, Tianqi Huang, Yicheng He, Dongxuan Beijing Inst Technol MIIT Key Lab Complex Field Intelligent Sensing Beijing 100081 Peoples R China Beijing Inst Technol Jiaxing Yangtze Delta Reg Acad Jiaxing 314019 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

ISBN: (纸本)9798350361261;9798350361278

Action recognition algorithms have gained significant attention in recent years, which can be indispensable for a plethora of cutting-edge applications like extended reality or Metaverse. These services often pose stringent requirement on immediate sensing and cognition of the surroundings, which necessitates immediate classifications of the captured actions (e.g., video data) that classical signal processing methods can hardly attain. In this paper, we introduced a residual artificial neural network with two-stream structure to further improve the accuracy of action recognition algorithm. Specifically, two residual networks (ResNet101) are trained separately, one by spatial RGB image streams, and another by optical flow streams. The two-strem network outputs are then fed into a fusion classifier, in which information extracted by spatial network and temporal network jointly determines the classification result. Moreover, in the training process, hyper-parameters setting and optimizer selection are performed numerically to achieve optimal performance. Finally, the recognition accuracy of the proposed algorithm has been compared to other existing widely-employed counterparts, where UCF101 data set is utilized for training and testing. Simulations validates aiming that the network can achieve higher recognition accuracy than traditional algorithms, and the two-stream method shows its superiority over the single-network counterpart.

关键词： Action recognition deep learning residual network ResNet101 two-stream method

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Robust Activity Recognition for Adaptive Worker-Robot Interaction Using Transfer learning

Robust Activity Recognition for Adaptive Worker-Robot Intera...

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ASCE international conference on computing in Civil Engineering (I3CE)

作者： Shahnavaz, Farid Tavassoli, Riley Akhavian, Reza San Diego State Univ Computat Sci Res Grp San Diego CA 92182 USA San Diego State Univ Dept Civil Construct & Environm Engn San Diego CA USA San Diego State Univ Dept Civil Engn San Diego CA 92182 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers' activities. HAR has many applications in human-robot interaction research to enable robots' understanding of human counterparts' activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner, which is crucial for the widespread deployment of collaborative robots in construction.

关键词： Materials handling

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Non-Destructive Judgment of Watermelon Ripeness Based on Multi-Feature Fusion of Image 20

Non-Destructive Judgment of Watermelon Ripeness Based on Mul...

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20th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Shi, Hongbao Liu, Jun Huang, Yimei Li, Jinping School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350356328

The nondestructive judgment of watermelon ripeness is a key issue in everyday life. Whether watermelons are ripe or not affects the interests of watermelon farmers and consumers. So far, acoustic, electrical, machine vision and other methods have been applied to the nondestructive judgment of agricultural product ripeness, but the equipment used in the lab, such as spectrometers and laser Doppler vibrometers, is expensive, difficult to carry, and has limited practical value in daily life. Based on practical experience, the ripeness of watermelons can be judged by observing specific physical characteristics, such as the rind's glossiness, texture, fuzz on the fruit stalk and navel. In this study, images of the watermelon's texture, fuzz and navel were captured using a smartphone. We utilized ResN et with an embedded Coordinate Attention mechanism and Swin-Transformer networks to independently analyze the ripeness of each feature. Then we combined the output prediction scores using post-fusion technology, with the highest weighted prediction score judging the final ripeness assessment. Our method achieved a 90.2% accuracy rate on the test set. The experiments demonstrate that a deep learning model utilizing multi-feature fusion of image data can reliably and accurately identify watermelon ripeness, even under varying image conditions. © 2024 IEEE.

关键词： Laser Doppler velocimeters

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A Customer Churn Prediction Using CSL-Based Analysis for ML Algorithms: The Case of Telecom Sector 6th

A Customer Churn Prediction Using CSL-Based Analysis for ML ...

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6th international conference on Innovative computing and Communication, ICICC 2023

作者： Lavanya, Kampa Aasritha, Juluru Jahnavi Sai Garnepudi, Mohan Krishna Chellu, Vamsi Krishna Department of Computer Science and Engineering University College of Sciences Acharya Nagarjuna University Andhra Pradesh Guntur India Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819940707

The loss of customers is a serious issue that needs to be addressed by all major businesses. Companies, especially in the telecommunications industry, are trying to find ways to predict customer churn because of the direct impact on revenue. Therefore, it is important to identify the causes of customer churn to take measures to decrease it. Customer churn occurs when a company loses customers because of factors such as the introduction of new offerings by rivals or disruptions in service. Under these circumstances, customers often decide to end their subscription. Predicting the likelihood of a customer defecting by analyzing their past actions, current circumstances, and demographic data is the focus of customer churn predictive modeling. Predicting customer churn is a well-studied problem in the fields of data mining and machine learning. A common method for dealing with this issue is to employ classification algorithms to study the behaviors of both churners and non-churners. However, the current state-of-the-art classification algorithms are not well aligned with commercial goals because the training and evaluation phases of the models do not account for the actual financial costs and benefits. Different types of misclassification errors have different costs, so cost-sensitive learning (CSL) methods for learning on data have been proposed over the years. In this work, we present the CSL version of various machine learning methods for Telecom Customer Churn Predictive Model. Furthermore, also adopted feature selection strategies along with CSL in real-time telecom dataset from the UCI repository. The proposed combination of CSL with ML, the results outperforms the state-of-the-art machine learning techniques in terms of prediction accuracy, precision, sensitivity, area under the ROC curve, and F1-score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： data mining

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The Role of Ensemble learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques 1

The Role of Ensemble Learning in Advancing Child Fetal Healt...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Swain, Satyaprakash Patra, Kumar Janardan Jayasingh, Suvendra Kumar Mohanty, Mihir Narayan Pattanayak, Binod Kumar Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Electronics and Communication Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350361780

The field of healthcare has witnessed significant advancements in leveraging technology for in-depth analysis of fetal health data, offering profound insights into child development. We meticulously gathered an extensive dataset comprising 1500 instances of child fetal health data sourced from New Born and Pediatric Health Centre, Cuttack, Odisha. We proposed a robust analytical pipeline for effectively classifying and predicting fetal health outcomes. The process initiated with meticulous data preprocessing and refinement, ensuring data integrity and quality. We use clustering techniques unveiled inherent patterns and groupings within the dataset, setting the stage for in-depth analysis and feature extraction. Further enriching the analysis, Principal Component Analysis (PCA) is deployed to streamline data dimensionality while retaining essential information. For prediction we use ensemble learning techniques. To predict the condition, we used voting ensemble method encompassing Decision Trees (DT), Support Vector machines (SVM) and Naïve Bayes (NB), after predictive models are successfully constructed, showcasing notable accuracies of 92.28%, 83.89% and 82.22%. These results underscore the effectiveness of these models in predicting fetal health outcomes based on the comprehensive dataset. To further refine model performance, a meticulous GridSearchCV approach exhaustively explored optimal parameter values within predefined parameter grids, significantly enhancing predictive capabilities. Additionally, leveraging a bagging method, particularly Random Forest (RF), Logistic Regression (LR), and Gradient Boosting, further bolstered predictive accuracies. These bagging models demonstrated exceptional achieving accuracy of 94.02%, 93.22% and 92.28%. The systematic approach involving clustering, dimensionality reduction, ensemble learning, and parameter optimization resulted in highly accurate predictive models for fetal health assessment. Our research not only highligh

关键词： Decision trees

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Exploring Convolution Neural Networks for Image Classification in Medical Imaging 2

Exploring Convolution Neural Networks for Image Classificati...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Vishwa Priya, V. Chattu, Padmini Sivasankari, K. Pisal, Dnyaneshwar Tukaram Renuka Sai, Bantupalli Suganthi, D. Vels Institute Science Technology and Advanced Studies Department of Cs Tamilnadu Chennai India Ace Engineering College India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India SVPMs Institute of Management Baramati Pune Maharashtra Malegaon India Raghu Engineering College Computer Science and Engineering Visakhapatnam India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350306415

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy;however, this shows that CNNs can diagnose diseases from microscopic images. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Classification Medical data Analysis Medical Imaging

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Tomo-NeRL: Tomographic neural representation learning for implicit CT image reconstruction

Tomo-NeRL: Tomographic neural representation learning for im...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Xiaoqin Tan, Boheng Guo, Yuanhao Yu, Xianping Kendrick, Jake Reynolds, Mark Southwest University College of Computer and Information Science Chongqing China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Research Institute of Highway Ministry of Transport Beijing China The University of Western Australia School of Physics Mathematics and Computing Perth Australia Perth Australia

ISBN: (纸本)9798350386226

CT image reconstruction is a challenging inverse problem of estimating image intensities from sensor signals. While deep learning-based methods hold promise, they are typically classified into fully-supervised, self-supervised, and case-by-case approaches, each offering distinct advantages and limitations. Among these, case-by-case reconstruction techniques, exemplified by models like NeRP, provide the advantage of flexible data-sampling and image sizes, making them well-suited for the complex CT inverse problem. However, existing case-by-case models encounter difficulties in achieving precise reconstructions, particularly in sparse-view scenarios. To address this limitation, we propose tomo-NeRL, a novel implicit reconstruction model that incorporates tomographic information of individual pixels within the reconstruction network. By harnessing this tomo-graphic data for implicit inverse learning, tomo-NeRL enhances reconstruction quality while effectively mitigating artifacts. The core innovation of tomo-NeRL lies in defining and extracting tomographic insights, such as visual characteristics and spatial variations specific to tomographic imaging, from the sinogram data for individual pixels. Moreover, our work introduces an adaptive inversion framework that amalgamates tomographic information from diverse projection angles, constructing a latent feature space that enhances the reconstruction process. Extensive experiments unequivocally showcase tomo-NeRL's outstanding reconstruction performance, excelling in artifact suppression and detail preservation, especially in sparse-view scenarios. © 2024 IEEE.

关键词： Image reconstruction

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