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Simulation Of Bone Fracture Healing Process Using Ultrasound And BMD data 21

Simulation Of Bone Fracture Healing Process Using Ultrasound...

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21st international conference on Machine learning and Cybernetics, ICMLC 2022

作者： Ueyama, Takumi Kumabe, Yohei Oe, Keisuke Fukui, Tomoaki Niikura, Takahiro Kuroda, Ryosuke Morimoto, Masakazu Yagi, Naomi Hata, Yutaka University of Hyogo Graduate School of Information Science Kobe Japan Kobe University Graduate School of Medicine Department of Orthopaedic Surgery Kobe Japan University of Hyogo Graduate School of Engineering Himeji Japan University of Hyogo Advanced Medical Engineering Research Institute Himeji Japan

ISBN: (数字)9781665488327

ISBN: (纸本)9781665488327

In this paper, we simulate the fracture healing process using ultrasound and Bone Mineral Density (BMD). The frequency component of the reflected wave from the rat's bone is used. A hole was drilled in the center of the rat's femur to simulate a fracture. Firstly, the frequency response is obtained by adapting a Fast Fourier Transform to the resulting reflected wave, which is then cross spectrum to highlight characteristic frequencies. Next, we use the frequency and BMD healthy bone data to construct a pseudo-individual without considering overlap. Finally, we determine the degree of healing process for each individual. In our previous studies, it has the lack of reliability since there was only one data set that was set to be a bone hole was a problem, so the objective was to increase the number of data and improve reliability. The reliability of bone hole selection is demonstrated by comparing data increased frequencies data for pseudo-individuals with increased data frequencies to the healing process of pseudo-individuals used BMD from previous studies. © 2022 IEEE.

关键词： Reliability

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Improving automated Code Reviews: learning from Experience

Improving Automated Code Reviews: Learning from Experience

引用

international Mining Software Repositories, MSR

作者： Hong Yi Lin Patanamon Thongtanunam Christoph Treude Wachiraphan Charoenwet The University of Melbourne Melbourne Australia Singapore Management University Singapore Singapore

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from the feedback of experienced reviewers; however, it often brings a large workload and stress to reviewers. To alleviate this burden, the field of automated code reviews aims to automate the process, teaching large language models to provide reviews on submitted code, just as a human would. A recent approach pre-trained and fine-tuned the code intelligent language model on a large-scale code review corpus. However, such techniques did not fully utilise quality reviews amongst the training data. Indeed, reviewers with a higher level of experience or familiarity with the code will likely provide deeper insights than the others. In this study, we set out to investigate whether higher-quality reviews can be generated from automated code review models that are trained based on an experience-aware oversampling technique. Through our quantitative and qualitative evaluation, we find that experience-aware oversampling can increase the correctness, level of information, and meaningfulness of reviews generated by the current state-of-the-art model without introducing new data. The results suggest that a vast amount of high-quality reviews are underutilised with current training strategies. This work sheds light on resource-eﬃcient ways to boost automated code review *** CONCEPTS• Software creation and management; • Machine translation;

关键词： Training Industries Codes Quality assurance Reviews Training data data models

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Dynamic Fuzzy Evaluation learning Network for the Cooperative Vehicle-Infrastructure Roadside System of Highway 23

Dynamic Fuzzy Evaluation Learning Network for the Cooperativ...

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23rd COTA international conference of Transportation Professionals: Emerging data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Liu, Siyuan Wang, Bo Zhang, Jian Chen, Xinhai Jiangsu Key Laboratory of Urban Its Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies School of Transportation Southeast Univ. Jiangsu Nanjing China Southeast Univ. Nanjing China China Merchants Testing Vehicle Technology Research Institute Co. Ltd. Chongqing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the wide development of intelligent highways, cooperative vehicle-infrastructure roadside systems of highways have been rapidly adopted. It is the key to building an evaluation model of the cooperative vehicle-infrastructure roadside systems of highways for reasonably judging the quality and achievement level of the roadside systems. However, most of the evaluation models are susceptible to subjective factors and do not achieve dynamic optimization. In this paper, a dynamic fuzzy evaluation learning network (DFELN) is proposed to improve the accuracy of the evaluation model for cooperative vehicle-infrastructure roadside systems of highways. First, the evaluation index system and initial membership function are studied according to the actual business requirements. Then, based on the current stage of the questionnaire numerical distribution, the weight coefficient between the indicators and the evaluation model of the input set are research as a simulate path. Finally, the multi-stage dynamic adjustment of fuzzy set and membership are introduced to meet the changing needs of intelligent evaluation models of highway roadside systems in different stages. The proposed DFELN method is compared by using the fuzzy comprehensive evaluation method, fuzzy BP neural network, and DFELN-DN method. The analyzed results demonstrate that the proposed approach outperforms the state-of-the-art work, and it can accurately evaluate the performance of cooperative vehicle-infrastructure roadside systems in different typical scenarios of highway. © ASCE.

关键词： Membership functions

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AGNE: Attentional Graph Convolutional Network Embedding for Knowledge Concept Recommendation in MOOCs 21st

AGNE: Attentional Graph Convolutional Network Embedding for ...

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21st international conference on Web Information Systems and Applications (WISA)

作者： Chen, Jiahui Meng, Dan Gao, Xiangyun Zhang, Liping Kong, Chao Anhui Polytech Univ Sch Comp & Informat Wuhu Peoples R China OPPO Res Inst Shenzhen Peoples R China

ISBN: (纸本)9789819777068;9789819777075

The knowledge concept recommendation aims to identify and suggest specific knowledge concepts that a student needs to master on Massive Open Online Courses (MOOCs) platforms, addressing the information overload issue and offering a tailored educational experience. Existing studies have constructed heterogeneous information networks to capture accurate representations of both learners and concepts, thereby mitigating the challenges posed by data sparsity. However, these approaches have limits in their ability to adequately synthesize and portray node-related data and nearby connections within the graph structure. Furthermore, they underutilize the potential of heterogeneous network data and fail to adapt or customize it in response to the dynamic nature of learning processes. To address these issues, we propose a new method named AGNE, short for Attentional Graph Convolutional Network Embedding, consists of three components. Firstly, we construct a heterogeneous information network that includes knowledge concepts, students, and other entities like videos, courses, and instructors. We employ meta-path embedding techniques to generate node embeddings enriched with semantic content and utilize Graph Convolutional Networks to integrate contextual information of these nodes. Secondly, we utilize a graph attention network to augment the efficacy of information dissemination among the entities. Lastly, we implement matrix factorization to refine and enhance the recommendation algorithm. These components are systematically amalgamated to produce more precise recommendation outcomes. Extensive experiments have been conducted on the large public MOOCCubeX dataset, demonstrating the superiority of AGNE against several state-of-the-art methods.

关键词： Online learning Knowledge Concept Recommendation Graph Convolutional Network Heterogeneous Information Network

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learning-Based Activation of Energy Harvesting Sensors for Fresh data Acquisition 1st

Learning-Based Activation of Energy Harvesting Sensors for F...

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21st international conference on Web engineering (ICWE)

作者： Yun, Sinwoong Kim, Dongsun Lee, Jemin Daegu Gyeongbuk Inst Sci & Technol Daegu 42988 South Korea Sungkyunkwan Univ Suwon 16419 South Korea

ISBN: (纸本)9783030922313;9783030922306

We consider an energy harvesting wireless sensor network (EH-WSN), where each sensor, equipped with the battery, senses its surrounding area. We first define the estimation error of the sensing data at a measuring point, which increases as the distance to the sensor increases and the age of information (AoI) of the data increases. The AoI is the elapsed time since the latest status is generated. We also define the network coverage, which is defined as the area having the estimation errors lower than a target value. As a performance metric, we use the acoverage probability, which is the probability that the network coverage is larger than a threshold a. Finally, in order to deal with dynamic and complex environments, we propose a reinforcement learning (RL) based algorithm which determines the activation of the sensors. In simulation results, we show the proposed algorithm achieves higher performance than baselines. In addition, we show the impact of the transmission power and the number of sensors on the a-coverage probability.

关键词： Age of information center dot Energy harvesting wireless sensor network Reinforcement learning

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TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection 21

TRIO: Task-agnostic dataset representation optimized for aut...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Cohen-Shapira, Noy Rokach, Lior Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781665423984

With the growing number of machine learning (ML) algorithms, the selection of the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automatic solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. In this study, we first propose a novel learning-based task-agnostic method for dataset representation. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which is capable of accurately recommending top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation on 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.

关键词： AutoML algorithm selection meta-learning task-agnostic dataset representation

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Convolutional Variational Autoencoders for Image Clustering 21

Convolutional Variational Autoencoders for Image Clustering

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Nellas, Ioannis A. Tasoulis, Sotiris K. Plagianakos, Vassilis P. Univ Thessaly Dept Comp Sci & Biomed Informat Volos Greece

ISBN: (纸本)9781665424271

The problem of data clustering is one of the most fundamental and well studied problems of unsupervised learning. Image clustering, refers to one of the most challenging specifications of clustering, concerning image data. Thankfully, the emerging Deep Neural Networks, and in particular Deep Autoencoders lead to the automation of image clustering, which until recently, was time consuming and labor intensive. However, the effect of the consideration of local structure during feature extraction from a Variational Autoencoder on clustering, is still an unstudied subject in the literature, while simultaneously constitute a baseline approach for supervised learning (Convolutional Neural Networks). For this reason, the methodology proposed in this paper, is composed from a Variational Autoencoder (VAE) surrounded by a convolutional network in a symmetric way. The resulting embedded image data are fed to various established clustering algorithms to examine clustering performance. In addition, we propose a modification of this approach, able to reduce complexity while achieving similar or even better clustering performance. Finally, we investigate the combination of VAE's produced embedding and manifold learning for image clustering. The extensive experimental analysis, verified the importance of the proposed methodology, exposing the potential for further developments.

关键词： Deep learning Image Clustering Convolutional Neural Networks Autoencoders Variational Autoencoders

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Machine learning KNIME Based Diseases Prediction for Healthcare Analytics 21

Machine Learning KNIME Based Diseases Prediction for Healthc...

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21st IEEE India Council international conference, INDICON 2024

作者： Devi, M. Akila Bapuram, Apoorva Kumar, Abhishek Banoth, Yakub Dwivedi, Santosh K. Kumar, Amarjit National Institute of Technology Warangal India Gnitc Hyderabad India

ISBN: (纸本)9798350391282

This study explores an innovative application of the KNIME Analytics Platform to create a predictive model for various diseases, including diabetes, heart conditions, asthma, kidney disorders, and skin cancer. The dataset, obtained from Kaggle, provides a comprehensive collection of patient data essential for accurate predictions. The research focused on designing a robust predictive framework utilizing machine learning techniques such as Random Forest, Naive Bayes, and Logistic Regression. To improve model effectiveness, advanced data preprocessing and feature engineering methods were implemented. The primary aim was to develop a reliable system for predicting multiple diseases based on patient attributes, supporting the development of tools for better medical decision-making and patient management. This work underscores the potential of KNIME as a flexible analytics tool, demonstrating its effectiveness in advancing disease prediction and enhancing healthcare strategies. © 2024 IEEE.

关键词： Prediction models

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A Framework for intelligent Fitness Guiding System 21st

A Framework for Intelligent Fitness Guiding System

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21st international conference on Man-Machine-Environment System engineering (MMESE)

作者： Yang, Haohao Chen, Jin Shen, Lian Liang, Yuwei Lee, Yu-Chi South China Univ Technol Sch Design Guangzhou 510641 Peoples R China

ISBN: (纸本)9789811659638;9789811659621

Using fitness equipment for workout is becoming increasingly popular. High expertise and skills were required for the user to manipulate the equipment for exercising. Injuries were commonly generated by applying an awkward posture to exertion due to lack of professional guidance. This study therefore proposed an intelligent guiding system for instructing users for fitness. The system used 3D computer vision technology to identify the body contour of the user and established a video-base of virtual fitness posture model. This model digitized the user's actions to accurately obtain fitness posture data in real time, and comparison with the standard motion model through the in-depth algorithm calculation to identify whether the fitness posture is standard. Providing feedbacks to the bodybuilder in time, correct the bodybuilder's motions, and effectively ensure that people are efficient and safe in weight training.

关键词： Fitness Posture Guiding system Computer vision

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Towards Open Zero-Shot learning 21st

Towards Open Zero-Shot Learning

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Marmoreo, Federico Carrazco, Julio Ivan Davila Cavazza, Jacopo Murino, Vittorio Italian Inst Technol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Genoa Dept Marine Elect Elect & Telecommun Engn Genoa Italy Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9783031064302;9783031064296

In Generalized Zero-Shot learning (GZSL), unseen categories (for which no visual data are available at training time) can be predicted by leveraging their class embeddings (e.g., a list of attributes describing them) together with a complementary pool of seen classes (paired with both visual data and class embeddings). Despite GZSL is arguably challenging, we posit that knowing in advance the class embeddings, especially for unseen categories, is an actual limit of the applicability of GZSL towards real-world scenarios. To relax this assumption, we propose Open Zero-Shot learning (OZSL) as the problem of recognizing seen and unseen classes (as in GZSL) while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided. We formalize the OZSL problem introducing evaluation protocols, error metrics and benchmark datasets. We also tackle the OZSL problem by proposing and evaluating the idea of performing unknown feature generation.

关键词： Zero-Shot learning Open-Set

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