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Human Action Recognition Based on Vision Transformer and L2 Regularization 11

Human Action Recognition Based on Vision Transformer and L2 ...

引用

11th international conference on Computing and Pattern Recognition, ICCPR 2022

作者： Chen, Qiliang Tang, Hasiqidalatu Cai, Jiaxin School of Computer and Information Engineering Xiamen University of Technology China School of Mathematics and Statistics Xiamen University of Technology China

ISBN: (纸本)9781450397056

In recent years, the field of human action recognition has been the focus of computer vision, and human action recognition has a good prospect in many fields, such as security state monitoring, behavior characteristics analysis and network video image restoration. In this paper, based on attention mechanism of human action recognition method is studied, in order to improve the model accuracy and efficiency in VIT network structure as the framework of feature extraction, because video data includes characteristics of time and space, so choose the space and time attention mechanism instead of the traditional convolution network for feature extraction, In addition, L2 weight attenuation regularization is introduced in model training to prevent the model from overfitting the training data. through the test on the human action related dataset UCF101, it is found that the proposed model can effectively improve the recognition accuracy compared with other models. © 2022 ACM.

关键词： Feature extraction

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the Development of A Digital Transformation Model for Higher Education Using the Object-Process Methodology 11

The Development of A Digital Transformation Model for Higher...

引用

11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Bisri, Aria Nurtantyana, Rio Prasetyadi, Abdurrakhman Putri, Atina Koesoema, Allya Paramita Rosmansyah, Yusep Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Center of Excellence Artificial Intelligence for Learning and Optimization Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331542313

Digital transformation (DT) is an essential need for Higher Education Institution (HEI) in the era of modern technology to improve the quality of education and operational efficiency. this research develops a DT model using Object-Process Methodology (OPM), which integrates objects and processes into a single structured framework. this research utilized the results of a systematic literature review that had been carried out in previous studies to identify key components and challenges in DT. In detail, the OPM model was developed to map the interactions between technology infrastructure, business processes, human resources, and change management. Further, the model validation was conducted through three experts’ judgments. therefore, the results show that our OPM model helps HEI to plan and implement DT strategies more effectively, improving the efficiency and quality of education. Hence, the author encourages HEI to use OPM model like in this study as a strategy to implement DT. © 2024 IEEE.

关键词： Contourlet transform

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theory and Implementation of intelligent System for Auxiliary Diagnosis of Chronic Disease

Theory and Implementation of Intelligent System for Auxiliar...

引用

IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Chengwei Feng Jian Zhang He Cui Bei Liu Beijing University Of Technology Beijing China

With the accelerated development of internet and information technology, the healthcare industry has undergone tremendous changes. Big data, cloud computing and intelligent algorithms are playing an important role in the medical field. Due to unbalanced economic development, there is a large gap between medical care in urban and rural areas. However, most patients with chronic disease live in rural and remote areas. In order to enable low-income groups to have access to advanced medical resources, this paper designs an intelligent healthcare system for chronic disease auxiliary diagnosis. Taking heart disease, the most common disease in daily life, as the research object, an ensemble learning algorithm based on stacking is explored to predict the early diagnosis of heart disease for users. the results show that the ensemble learning algorithm is better than a single machine learning algorithm, and the accuracy of the prediction can be greatly improved. It is expected that this can be used to improve diagnosis in rural and remote areas.

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Design and Implementation of an intelligent Monitoring System for Internet of things Aquaculture Based on ZigBee Technology

Design and Implementation of an Intelligent Monitoring Syste...

引用

IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Yuanfang Fu Yingying Liu Danping Wang Kaiwen Liu Xinyu Li Jiaxin Xu Zilong Wu School of Information Engineering Shenyang University Shenyang China School of Mechanical Engineering Shenyang University Shenyang China

According to the impact of marine pollution on mariculture, an intelligent monitoring system for mariculture water quality based on ZigBee is designed in this study. the system includes a data acquisition part, a data transmission part and a user-side early warning part. It is composed of data terminals and multiple data acquisition points, which detect the temperature, PH value, oxygen solubility and other related parameters of water quality. the data of each collection point is transmitted to the ZigBee coordinator node through the ZigBee network. the coordinator node monitors the collected data value and makes a risk warning according to the early warning value. GPRS is used to upload the data to the server in real time, so that the staff can find and handle the abnormalities in time, and an intelligent monitoring system based on the Internet of things is utilized.

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Flamingo Diet and Health Detection Based on Colour Classification 11

Flamingo Diet and Health Detection Based on Colour Classific...

引用

11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Halwani, Said Elbatanouny, Hagar Turky, Ayad Khan, Wasiq Tawfik, Hissam Hussain, Abir University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Department of Computer Science Sharjah United Arab Emirates Liverpool John Moores University School of Computer Science and Mathematics Liverpool United Kingdom

ISBN: (纸本)9798350367300

Flamingos are known for their vibrant pink and reddish hues, which are not merely aesthetic but indicative of their overall health and diet. these colors are derived from carotenoid pigments in their food sources, making coloration a vital marker for monitoring their well-being and environmental conditions. this study introduces a two-stage classification methodology designed to safeguard flamingo populations by leveraging deep learning techniques. Convolutional Neural Networks (CNNs) are used for both shape and color detection, ensuring accurate identification of flamingos and insights into their health status. Simulation results demonstrated the CNNs model's effectiveness, making it a valuable resource for wildlife conservation efforts aimed at preserving flamingo habitats. the first stage employs digital classification filters to distinguish flamingo images from other species, achieving an accuracy of 97.52%, while the second stage refines these detections through color analysis with an accuracy of 86.27%. this approach promises to mark a significant advancement in wildlife conservation, offering reliable methods for assessing and managing flamingo populations in their natural environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

引用

11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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data Integration for Big data analytics to identify the gaps in Rheumatoid Arthritis Genomics in a Post-GWAS era 2

Data Integration for Big Data analytics to identify the gaps...

引用

2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Jahangir, Sidrah John, Peter Bhatti, Attya Aslam, Muhammad Muaaz Peffers, Mandy J Islamabad Pakistan University of Pittsburgh Department of Human Genetics Pittsburgh15216 United States University of Liverpool Department of Musculoskeletal and Ageing Science L78TX United Kingdom

ISBN: (纸本)9781665437899

Genome-wide association studies (GWASs) enable simultaneous detection of several genetic variations. the variants that do not match GW ASs' significance threshold can be missed. We aimed at the identification of novel candidate single nucleotide polymorphisms (SNPs) for rheumatoid arthritis (RA) using data integration based bioinformatics for analysis of publically available big data that employ computational processing and machine learning. 340 GW AS significant RA SNPs were searched for the proxy SNPs using SNipA proxy search tool that were then scored and annotated. Of 9194 proxy SNPs, 492 SNPs returned significant RegulomeDB scores and were linked to 94 expression quantitative trait loci (eQTLs). Protein interaction analysis identified 13 'hub proteins' expressed by these eQTLs linked to 54 SNPs. Of these, nine were already GW AS significant RA SNPs. Remaining 45 novel SNPs, mapped to 11 genomic loci, are novel candidate SNPs for RA. the proposed pipeline can be integrated in an automated machine learning algorithm to automate the identification of novel SNPs for other disease with underlying complex genomics. © 2022 IEEE.

关键词： Big data

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RJoules: An Energy Measurement Tool for R

RJoules: An Energy Measurement Tool for R

引用

IEEE international conference on automated Software engineering (ASE)

作者： Rajrupa Chattaraj Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Systems and Human Analytics Lab Indian Institute of Technology Tirupati India

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. the demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

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Comparison of the Performance of Statistical and Spectral Feature Based Models for Embedded Cough Detection Using Accelerometry data 11th

Comparison of the Performance of Statistical and Spectral Fe...

引用

11th international conference on E-Health and Bioengineering (EHB)

作者： Diab, Maha S. Rodriguez-Villegas, Esther Imperial Coll London Dept Elect & Elect Engn Wearable Technol Lab London England

ISBN: (纸本)9783031625015;9783031625022

the work presented in this paper compares the performance of different machine learning approaches, based on spectral and statistical features, in identifying coughs from accelerometry data sensed via a wearable attached to a shirt's collar. the extracted features are separately used to train and evaluate neural network models for cough detection - first as a multi-class problem, second as a binary problem. the models' performance was compared in terms of overall accuracy, cough sensitivity, specificity, and F1-score. It is concluded that the model using statistical features for a multi-class cough detection achieved the best cough sensitivity of 100% and F1-score of 0.95 with cough specificity of 97.3%. the four classification models were further evaluated for on-board performance as a TinyML cough system. they were all successfully deployed on Nordic thing:53 using Edge impulse, and their memory requirements and estimated time per inference are reported. In terms of memory and time, the statistical- multi-class model was the smallest model occupying 13.7 KB of Flash memory and 1.1 KB of RAM;it was also the fastest model, requiring an estimated 1 ms per inference.

关键词： Accelerometer Cough detection Machine learning TinyML Wearable device

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On Geometric Properties of Adversarial Examples

On Geometric Properties of Adversarial Examples

引用

IEEE international Workshop on intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Andrii Ivaniuk Galyna Kriukova National University of Kyiv-Mohyla Academy Kyiv Ukraine

ISBN: (纸本)9781665426060

Majority of widely used machine learning models are vulnerable to adversarial attacks, which are slight modifications of inputs that change the model's prediction with high confidence. the detection of such attacks and adversarial samples is still an open problem. In this work, we continue exploration of a novel method for adversarial image detection by means of linear algebra approach. the method is built on a comparison of distances to the centroids for a given point and its neighbours. the method of adversarial examples detection is considered theoretically, and the numerical experiments are done to illustrate the approach. A number of methods of adversarial examples generation are considered. It is shown, that the method is prone to error in case of certain adversarial attacks and training sets.

关键词： Training Dimensionality reduction conferences Neural networks data acquisition Linear algebra Predictive models

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