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Machine learning tool for exploring sentiment analysis on twitter data

Materials Today: Proceedings 2022年 56卷 1927-1934页

作者： Biradar, Shanta H. Gorabal, J.V. Gupta, Gaurav Department of Information Science & Engineering Sir M Visvesvaraya Institute of Technology Bengaluru562157 India Department of Computer Science & Engineering ATME College of Engineering Mysore570028 India Yogananda School of AI Computing and Data Science Shoolini University Himachal Pradesh Solan India

In social media, micro-blogging is a common routine of most people around the world. Social media analytics gathering structure and unstructured Big-data from various social sites and analyzing to make business decisions using Apache Hadoop and Apache Hive respectively. The objective of the research work was to develop big data technology used for gathering and handling large unstructured data from real time social media for sentiment analysis for identifying the brand and services. The methodology devised an algorithm based on sentimental analysis using customers review classification, which dealt with prepossessing the datasets, clustering of the data based on the specific domains, feature vector using n-gram models and tf-idf vectors extracts synonyms and classification sentiment analysis. The result shows that applied sentimental analysis with unsupervised clustering of data into specific domains and supervised machine learning techniques handle large amounts of twitter data in an efficient way. The developed tool 1.5 times faster than that of traditional database to Hadoop cluster and also the accuracy is nearly 80 %, which helps the user in computing, analyzing and interpreting interaction and associations between people, topics and ideas. © 2021

关键词： Sentiment analysis

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Analysis of Factors Influencing Early Detection of Breast Cancer

Analysis of Factors Influencing Early Detection of Breast Ca...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Hassal Malik Sonia Umaira Ahmad Sial Faisal Bukhari Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer.

关键词： Hospitals Statistical analysis Psychology Machine learning Forestry Market research Breast cancer Timing Decision trees Socioeconomics

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Predictive Stress Management for Working Women in Educational Institutions using Federated Learning and Wearable Technology

Predictive Stress Management for Working Women in Educationa...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： D. Sudha Devi M. Sujithra P. Velvadivu V. V. Kavya S. Jhunandhini Department of Computing Data Science Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The proposed methodology uses Artificial Neural Networks (ANN) to predict stress levels of women working in educational institutions by analyzing physiological and demographic data collected through wearable technology. TensorFlow Federated framework is used here to ensure the data privacy by enabling secure and decentralized training of the proposed model. The approach integrates various steps, including data preprocessing, feature selection, and model training with federated learning. Based on predicted stress levels, the platform provides personalized recommendations to manage stress and improve mental well-being, while keeping user data private. The process involves collecting data from surveys and wearable devices, optimizing the model through techniques like hyperparameter tuning and regularization, and deploying the system via a web interface for real-time stress management.

关键词： Training Surveys data privacy Federated learning Human factors Artificial neural networks data models Wearable devices Tuning Synthetic data

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Towards understanding evolution of science through language model series

arXiv

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arXiv 2024年

作者： Dong, Junjie Lyu, Zhuoqi Ke, Qing Department of Data Science College of Computing City University of Hong Kong Hong Kong

We introduce AnnualBERT, a series of language models designed specifically to capture the temporal evolution of scientific text. Deviating from the prevailing paradigms of subword tokenizations and "one model to rule them all", AnnualBERT adopts whole words as tokens and is composed of a base RoBERTa model pretrained from scratch on the full-text of 1.7 million arXiv papers published until 2008 and a collection of progressively trained models on arXiv papers at an annual basis. We demonstrate the effectiveness of AnnualBERT models by showing that they not only have comparable performances in standard tasks but also achieve state-of-the-art performances on domain-specific NLP tasks as well as link prediction tasks in the arXiv citation network. We then utilize probing tasks to quantify the models' behavior in terms of representation learning and forgetting as time progresses. Our approach enables the pretrained models to not only improve performances on scientific text processing tasks but also to provide insights into the development of scientific discourse over time. The series of the models is available at https://***/jd445/AnnualBERTs. Copyright © 2024, The Authors. All rights reserved.

关键词： Text processing

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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data-Driven Design for Anomaly Detection in Network Access Control Systems 2

Data-Driven Design for Anomaly Detection in Network Access C...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Muhammad, Musa Abubakar Caraffini, Fabio Fasanmade, Adebamigbe Ishola, Olabayo Mohammed, Kabiru Morden, Jarrad Coventry University School of Computing Mathematics and Data Science Coventry United Kingdom Swansea University Department of Computer Science Swansea United Kingdom De Montfort University School of Computer Science and Informatics Leicester United Kingdom

ISBN: (纸本)9798350335644

Current network access control systems can contain unpredictable interactions between multiple device models, multiple network protocol layers (e.g. TCP, UDP and ICMP), hardware, and clock-skew-specific influences, and cannot detect or identify abnormal behaviours based on the type of device. To complicate things further, the 'bring your own device' policy is increasing security threats, vulnerabilities, and risks to enterprise network environments, making intrusion detection and prevention systems unable to detect illegal and unauthorised access to devices in the enterprise network. The consequences can be disastrous. In this light, this work propose a simple but effective clustering approach capable of separating normal and abnormal network traffic patterns to detect such challenges (anomalies). The work in this paper applies this approach to single devices and aggregations of data per device type. Additionally, this work propose plotting the notched box for each cluster to acquire a better understanding of their data distributions and measuring the clusters' performance using the Adjusted Rand Index. The results show that the proposed method is valid, can be used in several contexts, and features a 95% confidence that most single device and device type distributions overlap, which makes them equivalently usable for anomaly detection purposes. © 2023 IEEE.

关键词： K-means clustering

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

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Convergence Analysis of Hierarchical Split Federated Learning

Convergence Analysis of Hierarchical Split Federated Learnin...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Hualei Du, Jun Hou, Xiangwang Jiang, Chunxiao Wang, Jintao Niyato, Dusit Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Nanyang Technological University College of Computing and Data Science Nanyang Avenue 639798 Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time. © 2024 IEEE.

关键词： Federated learning

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