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Team-Tamil@LT-EDI-RANLP2023: Automatic Detection of Hope Speech in Bulgarian Language using Embedding Techniques 3

Team-Tamil@LT-EDI-RANLP2023: Automatic Detection of Hope Spe...

引用

3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Ponnusamy, Rahul Subramaniam, Malliga Thavareesan, Sajeetha Priyadharshini, Ruba Insight SFI Research Centre for Data Analytics University of Galway Ireland Kongu Engineering College Tamil Nadu India Eastern University Sri Lanka Department of Mathematics Gandhigram Rural Institute-Deemed to be University Tamil Nadu India

ISBN: (纸本)9789544520847

Many people may find motivation in their lives by spreading content on social media that is encouraging or hopeful. Creating an effective model which helps in accurately predicting the target class is a challenging task. The problem of Hope speech identification is dealt with in this work using machine learning and deep learning methods. In this paper, we present the description of the system submitted by our team(Team-Tamil) to the Hope Speech Detection for Equality, Diversity, and Inclusion(HSD-EDI) LT-EDI-RANLP 2023 shared task for the Bulgarian language. The main goal of this shared task is to identify the given text into the Hope speech or Non-Hope speech category. The proposed method using H2O deep learning model with MPNet embeddings and achieved the second rank for the Bulgarian language with the Macro F1 score of 0.69. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Speech recognition

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dataHarbour: Enabling Decentalized AI data Marketplace using Blockchain 3

DataHarbour: Enabling Decentalized AI Data Marketplace using...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Dave, Maheak Saraf, Awantika Kumar, Rishabh Bhattacharya, Rituparna Techno India University Department of Computer Science and Engineering-Artificial Intelligence West Bengal 700091 India Techno India University Department of Computer Science and Engineering India

ISBN: (纸本)9798350354348

Artificial Intelligence revolutionizes digital applications through simulation of human intelligence in machines, driving unprecedented progress in technology and decision-making. By adeptly processing large volumes of data, AI systems excel in pattern recognition, learning from experience, and autonomously making decisions. However, the present AI ecosystem suffers from data inequity, with major corporations dominating the market while smaller businesses still struggling. Access to AI data is the privilege of tech giants. In this paper, we propose dataHarbour, a blockchain-based marketplace that addresses issues with AI data access. dataHarbour's decentralized platform seeks to democratize data access and promote cooperation. Built on blockchain- a disruptive emerging technology that buttresses decentralized systems, it leverages smart contracts, incorporates data verification, and a reputation system. This paper also delineates the design and operation of AI data market place and portrays the system's ability to foster a more inclusive AI environment. dataHarbour aims to accelerate AI innovation and close the data divide by facilitating safe data interchange and fostering integrity. © 2024 IEEE.

关键词： Smart contract

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Sports Training Strategies Based on data mining Technology 3

Sports Training Strategies Based on Data Mining Technology

引用

3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Niu, Mengran China Sichuan Chengdu Chengdu Polytechnic Chengdu610041 China

According to the demand of sports athletes' scientific training, combined with data mining technology, a set of sports training model which is suitable for China's national conditions and has a higher level of scientific research is established. Starting from the data mining technology, this paper expounds its connotation and several common methods in detail. Based on the demand of sports training, this paper studies the quality control of sports training and the statistical system of tactical combat. On this basis, an improved output method of Aprilio algorithm is introduced to enhance the correctness of systematic evaluation. On this basis, three algorithms, classic, DC-Apriori and modified Apriori, are simulated and compared. The results show that the improved Apriori algorithm studied in this project can provide better decision support for the training model. © 2023 Elsevier B.V.. All rights reserved.

关键词： Quality control

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mining novel glaucoma drug target using disease-Associated multiomics between ocular hypertension dependent and independent pathways 3

Mining novel glaucoma drug target using disease-Associated m...

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3rd international conference on Biological Engineering and Medical Science, ICBioMed 2023

作者： Cao, Yun Dana Hall School Wellesley United States

ISBN: (纸本)9781510671515

Glaucoma is a group of ocular disorders that will affect 111.8 million people globally by 2040 and can lead to permanent vision loss and blindness. However, current treatments are insufficient to modify the progression of the disease. Elevated intraocular pressure (IOP) is known as the leading risk factor of glaucoma, while some cases also occur without elevation in IOP. Therefore, it is crucial to analyze the molecular similarity and differences between IOP-dependent and IOP-independent glaucoma, which is a largely understudied topic. Using disease-Associated genomic and transcriptomic sequencing datasets, this study aims to characterize the gene expression pattern and cellular composition in different pathogenic factors related to glaucoma. To achieve this research goal, I utilized multiple machine learning based bioinformatic tools to analyze genome wide association study (GWAS) data and a cross-species single-cell RNAsequencing (transcriptomics) dataset. The findings of this study revealed specific cell types and gene targets in different pathways in humans. Furthermore, new drug targets were identified based on the identified culprit disease-Associated genes. The results of this study provided us a better understanding of the global and cell type specific mechanisms for different types of glaucoma. It can also facilitate future research and therapeutic options with a novel framework of sequencing data-based drug mining. © 2023 SPIE. All rights reserved.

关键词： machine learning

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Utilizing machine learning for Metallographic Compression Algorithm in Power Grid Metal Materials Research 3

Utilizing Machine Learning for Metallographic Compression Al...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Zou, Runze Tian, Jiahao Materials Science and Engineering Shanghai University Baoshan Shanghai201900 China Sino-European School of Technology Shanghai University Baoshan Shanghai201900 China

With the advancement of science and technology, metallographic research on metal materials has made remarkable progress. In this paper, the authors processed metallographic images using machine learning algorithms to classify the different elements in the images. The classification results divide the elements into three categories, which are metal elements, non-metal elements and metal-nonmetal combination elements. On the basis of this research, a metallographic compression algorithm based on adaptive threshold is also explored, and grayscale experiments are carried out on carbon steel samples. The recognition rate is 98.3%, and the compression rate is 1.06. Experimental results verify the effectiveness of the algorithm in processing metallographic compressed images of metal materials in power grids. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning

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Architecture Design of Intelligent Education and Teaching System Based on machine learning Algorithm 3rd

Architecture Design of Intelligent Education and Teaching Sy...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Zhu, Libo Ma, Xin Liang, Xinlong Zhang, Jun Zhou, Yufei Zibo Normal College Shandong Zibo255100 China Yunnan University of Business Management Yunnan Anning650300 China

ISBN: (纸本)9783031631382

With the great success of machine learning (ML) algorithms represented by deep learning in vision, speech recognition and other fields, while looking forward to and promoting the large-scale educational application of artificial intelligence (AI), data center network is an important infrastructure supporting big data and cloud computing platforms, which is widely used in data-intensive and large-scale modular parallel computing tasks. AI has entered an unprecedented period of rapid adoption and stability. At the beginning of architecture design, the traditional distributed big data processing system didn't optimize the ML task, but ensured the training convergence efficiency, improved the iterative computing speed and improved the model quality. The application of AI education is limited by the common sense and symbolic grounding and transfer of AI technology. Based on the above analysis, this paper aims to solve the efficiency bottleneck of distributed system in dealing with ML tasks, and design and implement a high-performance distributed ML system for heterogeneous environment in data centers, so as to promote the intelligent development of education and teaching by applying heuristic AI teaching ideas. Overall, the degree of automation that AI technology can achieve in education is still very limited. It is more realistic to strengthen teacher design than to replace it. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Efficiency

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Ensemble Feature Selection based on Multiple Metrics and Improved Aggregation Strategies 24

Ensemble Feature Selection based on Multiple Metrics and Imp...

引用

3rd international Symposium on Control Engineering and Robotics, ISCER 2024

作者： Lin, Yumeng Ren, Xiaoying Wang, Shuqin College of Computer and Information Engineering Tianjin Normal University Tianjin300382 China

ISBN: (纸本)9798400709951

Feature selection plays a crucial role in the field of machine learning and data mining. Some feature selection methods use a single evaluation measure to evaluate the classification ability of features, but different measures have their own characteristics. Ensemble learning can be applicable to feature selection for improve the performance by combining the output of several feature selectors. Therefore, we present an ensemble feature selection based on multiple metrics and improved aggregation strategies (EFSMMIAS). Three evaluation metrics, Symmetric Uncertainty (SU), Fisher Discriminant Ratio (FDR) and ReliefF are employed. Each of them is combined with the discriminant structure vector complementarity to construct a base feature selector for selecting multiple subsets of features. Subsequently, we employ an enhanced aggregation strategy to derive the desired feature subset from the feature subset generated by the base feature selectors, which can improve classification capabilities. In order to evaluate the effectiveness of our proposed method, we compare it with other methods on 11 standard datasets through a series of experiments. The results validate that our method is capable of improving classification performance while simultaneously reducing the dimensionality of features. © 2024 ACM.

关键词： Contrastive learning

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machine learning Approach for Discovering Successful Books Based on the Achievement of Award-Winning 3

Machine Learning Approach for Discovering Successful Books B...

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3rd international conference on Advanced Research in Computing, ICARC 2023

作者： Wijekoon, S.M.S.T. Rupasingha, R.A.H.M. Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Economics and Statistics Belihuloya Sri Lanka

ISBN: (纸本)9798350347371

Many people are very interested in reading books. Some of them like to read books for various reasons like as a hobby or to learn something new. At present, with modern technology, people have become accustomed to reading books online in addition to physically reading books. The most important factor in identifying which books are the most successful is the award-winning. The goal of this research is to forecast how the most successful books such as award/winning books are identified based on 10 distinct attributes including publishing date, average ratings, author, genre, language, publisher, number of pages, ratings, title, and reviews. A data set was gathered for this purpose through the online community platform Kaggle. The dataset provides information about books gathered from the website 'Goodread' for the period between 2000 and 2021. Firstly, the data set has been pre-processed. For that, duplicate data, unnecessary special characters, and missing values have been removed. To rank the preprocessed data, the Waikato Environment for Knowledge Analysis (WEKA) data mining tool was utilized, and results are improved by hyperparameter tuning. The predictive model is generated using six machine learning approaches at the classification level: Random Forest, Support Vector machine (SVM), Decision Trees, Multilayer Perceptron (MLP), Logistic Regression, and Naive Bayes. A high accuracy of 86.32% was gained with the lowest error rates in addition to high precision, recall, and f-measure values using a Naive Bayes algorithm. Based on the results, the Naive Bayes algorithm makes a reliable prediction of how to find the most successful books using the above features. © 2023 IEEE.

关键词： Forecasting

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Non-contact sensor-based human fall detection method 3

Non-contact sensor-based human fall detection method

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3rd international conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Ruan, Tingting Yan, Tianxing Song, Yongkun Zhang, Ke Liu, Xian Changsha Health Vlocational College ChangSha41000 China Changsha University of Science & Technology Changsha410000 China

ISBN: (纸本)9781510681279

With the exacerbation of global population aging, the issue of falls among the elderly is increasingly drawing attention from various sectors of society. Consequently, the development of an effective human fall detection system is crucial for timely identification of fall incidents and provision of emergency assistance. This paper proposes a fall detection method based on non-contact sensors, aiming to achieve real-time monitoring and accurate identification of fall events through advanced sensor technology and data processing algorithms. Initially, this study employs low-frequency ultra-wideband radar (UWB) as the primary tool for data acquisition, followed by the utilization of deep learning techniques to extract key dynamic features from radar data, thus providing a reliable foundation for fall event identification. Furthermore, this paper designs and implements a fall recognition model based on Convolutional Neural Networks (CNN), which, through training on a large amount of radar image data, learns to distinguish patterns between normal activities and falling behavior. The results demonstrate that the proposed method can effectively detect falls under various common daily activities, with a recognition rate as high as 99.338%. © 2024 SPIE.

关键词： data acquisition

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Research on metadata management of data lake 3

Research on metadata management of data lake

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3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Guo, Zhixia Pan, Chenshuo Chang, Ke Li, Xinchi Cheng, Shasha Zhang, XinRui Research Institute of China Telecom Corporation Limited Beijing100000 China University of Wisconsin-Madison MadisonWI999039 United States Beijing Institute of Technology Beijing100000 China Beijing Normal University Beijing100000 China

ISBN: (纸本)9781510661363

Metadata management plays an important role in enterprise information management. The complete metadata management system has directly affected the flexibility and high scalability of the platform. This paper summarizes several important links in metadata management technology in data warehouses and large-scale distributed file systems. This paper organizes the metadata management standards, compares various metadata management architecture technologies,and summarizes the metadata management characteristics and metadata management strategies. At the same time, this paper introduces the applicability of the current mainstream open source metadata management tools in detail, focuses on the research of cognitive metadata directory based on machine learning, and prospects the future research direction. © COPYRIGHT SPIE.

关键词： Information management

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