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Dynamic Hub Labeling for Road Networks 37

Dynamic Hub Labeling for Road Networks

引用

37th IEEE international conference on data Engineering (IEEE ICDE)

作者： Zhang, Mengxuan Li, Lei Hua, Wen Mao, Rui Chao, Pingfu Zhou, Xiaofang Univ Queensland Brisbane Qld Australia Shenzhen Univ Shenzhen Inst Comp Sci Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781728191843

Shortest path finding is the building block of various applications in road networks and the index-based algorithms, especially hub labeling, can boost the query performance dramatically. However, the traffic condition keeps changing in real life, making the pre-computed index unable to answer the query correctly. In this work, we adopt the state-of-the-art tree decomposition-based hub labeling as the underlying index, and design efficient algorithms to incrementally maintain the index. Specifically, we first analyze the structural stability of the index in dynamic road networks which enables us to concentrate on label value maintenance. We then introduce the minimum weight property and minimum distance property to guarantee the index correctness without graph traversal. Moreover, we propose the star-centric paradigm for tracing index change and design various pruning techniques to further accelerate the index maintenance. Finally, we extend our algorithms to batch mode for shared computation, extend to structural maintenance for full types of update, and generalize to all kinds of TDHL. Our experimental results validate the superiority of our proposals over existing solutions on both index maintenance and query processing.

关键词： Roads Heuristic algorithms Query processing conferences Maintenance engineering data engineering Structural engineering

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Unsupervised Deeply Embedded Online Multi-Product Recommendation of Commercial Banks Based on Behavior data Trajectory

Unsupervised Deeply Embedded Online Multi-Product Recommenda...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Xiaopeng Liu Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study presents an innovative approach to online multi-product recommendation for commercial banks based on unsupervised deep learning and user behavior data trajectories. The model first captures and analyzes user behavior data trajectories, using filtering techniques and Markov modeling to understand and predict user behavior patterns. Then, the model proposes an unsupervised deep learning algorithm, DSRET, which integrates expert trust and similarity computations to provide real-time product recommendations. The proposed algorithm is evaluated using real data from ZA Bank, which demonstrates its superior performance in terms of recommendation accuracy and computational efficiency compared to existing methods. The results show that the designed approach achieves an average RMSE value of 0.768, highlighting its effectiveness in providing accurate recommendations while ensuring fast computation. This study contributes to the advancement of online recommendation systems for commercial banks and lays the foundation for future research aimed at further optimizing and extending the proposed algorithm.

关键词： Deep learning Analytical models Accuracy Computational modeling Predictive models Prediction algorithms data models Real-time systems Trajectory Recommender systems

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Machine Learning Driven Analysis of Mental Health Indicators in Social Media Posts

Machine Learning Driven Analysis of Mental Health Indicators...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Kamal Garg Srishti Garg Himanshu Dixit Kavita Pandey Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Mental stress has been a major issue among all age groups today. This is more like a stigma in the society where people don't even want to talk about it openly leading to severe health issues. Nowadays, people are deeply engaged with social media platforms for connection and self-expression of their emotions. They express all these through posts, photos, and comments. Thus, social media platforms can help in predicting the mental state and well-being of an individual. Prediction of a person's mental health can help to undertake some preventive measures. Various researchers have worked around this topic but there is no evidence of utilizing the most active web of people for obtaining the most accurate results. Machine Learning (ML) algorithms are used in this research study to identify whether a person is mentally stressed or not based on the social media data obtained from the user's post or by using a text input of around hundred words from users related to their day-to-day activities. This research study has used a decision tree, random forest, and Bidirectional Encoder Representations from Transformers (referred as BERT onwards) model classifier to train models. This study has used a dataset of Reddit (a social media platform) posts text data for training the models. The primary objective is to compare the efficacy of various machine learning models in predicting mental health based on social media data.

关键词： Visualization Accuracy Machine learning algorithms Social networking (online) Mental health Bidirectional control Predictive models Encoding data models Numerical models

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Multi-dimensional data Quick Query for Blockchain-Based Federated Learning 17th

Multi-dimensional Data Quick Query for Blockchain-Based Fede...

引用

17th international conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Yang, Jiaxi Cao, Sheng Peng Xiangli Li, Xiong Zhang, Xiaosong Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen Peoples R China Univ Elect Sci & Technol China Sch Elect & Comp Engn Chengdu Peoples R China MIIT Elect Res Inst 5 Guangzhou Peoples R China

ISBN: (纸本)9783031192104;9783031192111

Due to the drawbacks of Federated Learning (FL) such as vulnerability of a single central server, centralized federated learning is shifting to decentralized federated learning, a paradigm which takes the advantages of blockchain. A key enabler for adoption of blockchain-based federated learning is how to select suitable participants to train models collaboratively. Selecting participants by storing and querying the metadata of data owners on blockchain could ensure the reliability of selected data owners, which is helpful to obtain high-quality models in FL. However, querying multi-dimensional metadata on blockchain needs to traverse every transaction in each block, making the query time-consuming. An efficient query method for multi-dimensional metadata in the blockchain for selecting participants in FL is absent and challenging. In this paper, we propose a novel data structure to improve the query efficiency within each block named MerkleRB-Tree. In detail, we leverage Minimal Bounding Rectangle (MBR) and bloom-filters for the query process of multi-dimensional continuous-valued attributes and discrete-valued attributes respectively. Furthermore, we migrate the idea of the skip list along with an MBR and a bloom filter at the head of each block to enhance the query efficiency for inter-blocks. The performance analysis and extensive evaluation results on the benchmark dataset demonstrate the superiority of our method in blockchain-based FL.

关键词： Federated learning Blockchain Multi-dimensional query

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Application of the Random Forest Algorithm for Breast Cancer Analysis with data Balancing: Case Study at Al-Ihsan Hospital

Application of the Random Forest Algorithm for Breast Cancer...

引用

data Science and Its Applications (ICoDSA), international conference on

作者： Risma Intan Wulandari Riska Yanu Fa'rifah Nur Ichsan Utama School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350365351

ISBN: (纸本)9798350365368

This study explores the application of the Random Forest Algorithm for predicting breast cancer using patient medical records from Al-Ihsan Hospital, West Java Province, during 2020–2023. The research addresses the challenges posed by imbalanced data by utilizing the Synthetic Minority Over-sampling Technique (SMOTE) to enhance the prediction accuracy of breast cancer cases. The data, categorized into “ca mammae” and “ca mammae + associate,” were split into training and testing sets with proportions of 90:10 to comprehensively evaluate the model's performance. Breast cancer, a leading type of cancer affecting millions globally, necessitates effective early detection techniques to significantly improve patient outcomes. This research underscores the importance of leveraging machine learning algorithms, like Random Forest, to analyze complex and non-linear relationships within medical data for improved diagnostic predictions. The Random Forest algorithm, known for its high accuracy, noise handling, speed, and overfitting control, was selected for its suitability in analyzing the imbalanced dataset of breast cancer records. The findings revealed that the Random Forest model, combined with SMOTE for data balancing, achieved an accuracy of 89%, as indicated by the confusion matrix and classification report. Cross Validation was employed to validate the model's performance, yielding an average accuracy of 90,35% with a standard deviation of 3%. This highlights the model's precision and recall capabilities in predicting breast cancer from the test data. This research contributes to the field by demonstrating the effectiveness of integrating data mining techniques, specifically the Random Forest algorithm and SMOTE, in enhancing the classification performance for early detection and treatment of breast cancer.

关键词： Accuracy Machine learning algorithms Hospitals Predictive models Prediction algorithms Breast cancer data models

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Enhanced Prediction of Extreme Monthly Precipitation in Yinchuan City Using CEEMDAN-Decomposed LSTM and BP Neural Networks

Enhanced Prediction of Extreme Monthly Precipitation in Yinc...

引用

Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE international conference on

作者： Siqi Dong College of Humanities and Law Southwest Forestry University Kunming China

ISBN: (纸本)9798350355260

To improve the accuracy of extreme precipitation prediction and based on CEEMDAN-decomposed LSTM and BP neural networks, this paper proposes a hybrid model. Traditional models like ARIMA, LSTM, and BP neural networks can capture precipitation trends but often exhibit large errors when predicting extreme precipitation events due to high data volatility and uneven distribution. Applying the Adaptive Noise Complementary Ensemble Empirical Mode Decomposition (CEEMDAN) algorithm for precipitating data in Yinchuan can solve this problem, reducing volatility and achieving a more balanced distribution, which simplified the modeling process. The BP and LSTM models were then used to predict each decomposed component individually, and the results were aggregated for overall precipitation forecasts. Experimental results show that the CEEMDAN-BP and CEEMDAN-LSTM models outperform single models in predicting extreme precipitation, with predictions closely matching actual observed values. This study offers new insights and methods for precipitation forecasting, and future work may expand the algorithm’s application to more datasets, incorporating additional meteorological factors for model optimization.

关键词： Adaptation models Precipitation Meteorological factors Atmospheric modeling Neural networks Urban areas Predictive models Prediction algorithms data models Long short term memory

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Adaptive Alternating Stochastic Gradient Descent algorithms for Large-Scale Latent Factor Analysis

Adaptive Alternating Stochastic Gradient Descent Algorithms ...

引用

IEEE international conference on Services Computing (SCC)

作者： Qin, Wen Luo, Xin Zhou, MengChu Chinese Acad Sci Chongqing Sch Univ Chinese Acad SciSch Comp Sci & Technol Chongqing Inst Green & Intelligent TechnolChongq Chongqing 400065 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Hengrui Chongqing Artificial Intelligence Res Ctr Dept Big Data Anal Tech Chongqing 400714 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781665416832

Latent factor analysis (LFA) is highly efficient in knowledge discovery from high-dimensional and sparse (HiDS) matrices frequently encountered in big data and web service related applications. A stochastic gradient descent (SGD) algorithm is commonly adopted as a learning algorithm for LFA owing to its high efficiency. However, its sequential nature makes it less scalable when processing large-scale data. Although an alternating SGD algorithm decouples an LFA process to achieve parallelization, its performance relies on its hyper-parameter selection that is highly expensive to tune. To address it, this paper presents three adaptive alternating SGD algorithms, thus leading to three Parallel Adaptive LFA (PAL) models for LFA on large-scale HiDS matrices. Experimental studies on HiDS matrices from industrial service applications show that the proposed PAL models perform significantly better than existing ones in terms of both convergence rate and computational efficiency, as well as achieve competitive prediction accuracy for missing data.

关键词： Latent Factor Analysis High-Dimensional and Sparse Matrix Quality of Service Stochastic gradient descent

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AI data Analytics for Air Quality Modeling

AI Data Analytics for Air Quality Modeling

引用

international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Annandip Barua Md Abu Ahammed Babu Department of Computer Science & Engineering Cox's Bazar International University Bangladesh Volvo Car Corporation Sweden

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Air quality is a critical concern globally, with implications for public health and environmental sustainability. While improvements have been made since the implementation of the Clean Air Act of 1970, the adverse effects of air pollution persist, leading to significant health burdens and mortality rates worldwide. In this context, this study focuses on analyzing air quality in Leeds, UK, during the year 2020, leveraging data from monitoring stations administered by the Department for Environment, Food, and Rural Affairs (DEFRA). Given the unique circumstances of 2020, which included lockdowns due to the COVID-19 pandemic, this period offers valuable insights into the interplay between human activity and air pollution. The research employs a multi-phase approach, encompassing descriptive, inferential, machine learning, and deep learning analyses to comprehensively understand air quality patterns and predict pollutant concentrations. While existing literature predominantly focuses on other regions, particularly China and India, this study addresses the scarcity of research specific to the UK context. Findings reveal variations in pollutant levels, with particulate matter (PM10 and PM2.5), nitrogen oxides (NO2 and NO) and carbon monoxide (CO) being the primary pollutants of concern. Inferential analyses highlight significant differences in pollutant levels and correlations, potentially influenced by lock-down measures. Machine learning algorithms, notably random forests, demonstrate efficacy in predicting PM2.5 concentrations, mirroring findings from deep learning approaches. However, the study underscores the need for larger and more diverse datasets to enhance predictive accuracy and generalize findings. Future research directions include exploring hybrid deep learning algorithms, refining machine learning models, and expanding data collection efforts across various locations and time periods to better understand the dynamics of air quality. Overall, this research

关键词： Deep learning Machine learning algorithms Atmospheric modeling Heuristic algorithms Prediction algorithms Air pollution Market research

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Towards First Urban data Space in Bulgaria 8

Towards First Urban Data Space in Bulgaria

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8th IEEE international Smart Cities conference, ISC2 2022

作者： Vassilev, Vassil Petrova-Antonova, Dessislava Bali, Tarun Radu, Sorin Sowinski-Mydlarz, Viktor Mariyanayagam, Dion Marinov, Evgeny Hristov, Petar O. Nakarmi, Sabin Rabka, Monika London Metropolitan University United Kingdom Gate Institute Sofia University Bg Bulgaria

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Smart city is no longer fiction, which targets the future - it is now around us and requires immediate action in different directions: environment, transport, energy, social and cultural life, healthcare, etc. This article presents the current efforts of GATE Institute at Sofia University for building an urban data space based on data from a variety of sources in the Bulgarian capital. It has an open architecture, based on a private cloud, which allows the integration of diverse data and provides different data processing capabilities and services necessary to build integral data spaces. The pilot implementation currently performs monitoring and analysis of the environmental factors in Sofia using a set of bespoke components, which implement data management and data analysis algorithms from simple filtering and correlation to data analytics and prediction using historical data, static modelled data and dynamic data from environmental sensors in real-time. It serves as a basis for data enrichment based on different sources and cross-domain analysis using a variety of methods. This new opportunity has a huge potential and will have a significant impact on urban life - from planning the infrastructure and managing the communal services to the personalization of social services for the citizens. © 2022 IEEE.

关键词： Information management

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An Efficient Approach for Cross-Silo Federated Learning to Rank 37

An Efficient Approach for Cross-Silo Federated Learning to R...

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37th IEEE international conference on data Engineering (IEEE ICDE)

作者： Wang, Yansheng Tong, Yongxin Shi, Dingyuan Xu, Ke Beihang Univ BDBC SKLSDE Lab Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ IRI Beijing Peoples R China

ISBN: (纸本)9781728191843

Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data. However, with the increasing awareness of data privacy, it is harder to collect data from multiple owners as before, and the resultant data isolation problem makes the performance of learned LTR models severely compromised. Inspired by the recent progress in federated learning, we propose a novel framework named Cross-Silo Federated Learning-to-Rank (CS-F-LTR), where the efficiency issue becomes the major bottleneck. To deal with the challenge, we first devise a privacy-preserving cross-party term frequency querying scheme based on sketching algorithms and differential privacy. To further improve the overall efficiency, we propose a new structure named reverse top-K sketch (RTK-Sketch) which significantly accelerates the feature generation process while holding theoretical guarantees on accuracy loss. Extensive experiments conducted on public datasets verify the effectiveness and efficiency of the proposed approach.

关键词： Differential privacy Privacy conferences Collaborative work data engineering data models

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